Part 1 of this series found that the left side attack is the biggest driver of winning in NCAA Division I women's volleyball, worth roughly double any other attack family. Today I'm asking the same question of the two US professional women's leagues, MLV and LOVB.

My working assumption going in was that the right side attack, particularly the D-ball, would be the most important at the women's pro level. And the data didn't disappoint!

One caveat up front: both leagues are small. For the 2026 season LOVB had 6 teams and MLV had 8. So there isn't the same quantity of data backing this analysis as some of the other studies I've blogged about. That makes it harder to have super high confidence in the data I'm sharing here. Treat everything here as directional, not definitive.

The data and the method

I used the data for every match from the 2026 seasons for LOVB and MLV, giving me a total of 14 team-seasons and about 63,500 attacks.

I used the same attack families as Part 1, with one change: I split back-row attacks into the BIC/pipe (back-row attacks in the middle third of the court) and the D-ball (a back-row attack in the right third of the court).

As in Part 1, I calculated each team's attack efficiency by family, then ran a relative-importance analysis (Shapley/LMG) for the share of Set Win% variation each family explains, alongside each family's own correlation (r).

The attack that separates winners from everyone else

Pooling both leagues for more statistical power, one attack stands out.

The D-ball leads the way in these two leagues. It is the attack that most differentiates winning and losing teams. It's also the only attack with a statistically significant correlation to winning in this data set (r = +0.68, p = 0.007). The slide is the runner-up with the left side attack coming in a close 3rd.

Interestingly, women's pro teams run the D-ball on 6.8% of their attacks, more than double the frequency of NCAA women's teams (3.0%). It’s just a much bigger deal in the women’s pro game.

The D-ball vs. the BIC

While I was expecting the D-ball to be the most important attack in these two leagues, I was surprised that the BIC attack didn't come out much closer to it. After all, nearly every coach I know is enamored with BIC attacks!

I was really curious to dig in a bit and see what's going on here. My perception from watching LOVB matches is that the best teams hit a lot of BICs.

Unfortunately, this data set is too small to do a meaningful Shapley analysis for each league, but we can do a more traditional correlation analysis like I have for the majority of my posts. When I did this, I found the results pretty interesting:

In LOVB, the D-ball (r = +0.71) and BIC/pipe (r = +0.65) are both highly correlated with winning (though both fall short of statistical significance). In MLV, the gap is much wider: the D-ball clears significance on its own (r = +0.78, p = 0.023) while BIC/pipe is just not a thing (r = +0.19, p = 0.645).

Why is MLV so different? Talking with Lauren Bertolacci, the head coach for the Indy Ignite, her take is that there just weren't many teams in MLV who had good BIC attackers last year, but she expects that to change next season.

Coaching takeaway: Don't treat the BIC / Pipe as interchangeable with the D-ball just because they're all back-row attacks. If you're choosing which weapon to develop first, the data across both pro leagues points to the D-ball, with the BIC and pipe a credible second option, especially in LOVB.

Wrapping up

The D-ball, not the left side, and not the BIC, is the attack most associated with winning across LOVB and MLV, and it replicates in each league on its own. The left side, Part 1's college standout, still correlates meaningfully at the pro level.

And just to repeat my warning from the beginning: These two leagues are small and rapidly evolving, and had only 6 and 8 teams in 2026. As a result, every correlation except one here is directional. As both leagues add teams and matches, I wouldn't be surprised to see the data shift.

Next up: the international game, using 2026 Volleyball Nations League women's data.

Send comments, feedback, and suggestions to alex_simons@hotmail.com, and stay tuned for Part 3.

See you in the gym!

Alex

We spent a chunk of practice this week here at Texas working on covering our attackers. If you have coached it, you know the drill: get your setter, middle and some combination of back-row athletes to turn and face or run to the right spots around the attacker so they can dig up an attack rebounding off the block. It's one of those parts of the game that is mostly about hard work and discipline.

(One of my favorite photos of Washington athletes doing a great job of covering when I coached there in 2022.)

Biking home from practice Thursday, I found myself wondering "Does any of this actually show up in the win column?" Most of us coach covering because it seems important and that's what everyone else does. But seeming important and actually mattering are two different things, and I had never seen any analysis of this specific part of the game.

So, I booted up my PC, launched Claude Code and dove into 2,439 match files to find out for myself.

Measuring covering

Figuring out how to measure covering took me down a few dead-end alleys until I finally settled on a simple model. When a ball comes off the block and lands back on your side of the net, you either dig it up or you do not. Cover % is the share of those you dig up.

The nice thing is that I did not have to reconstruct any of this from the play sequence. The scout already grades every block at the moment it happens, and two of those grades say exactly what I needed: one means the block sent the ball back to the attacking team and they got a chance to play it, and one means the block put the ball down on their floor. I checked that against the play sequence across 46,534 blocks and the grades held up every time.

So I've defined a new metric in my standard analysis model: Cover % = blocks dug up / (blocks dug up + block stuffs). With only a few exceptions, everything else went off the block out of bounds or went over to the opponent's side.

What the numbers say

Across five seasons of NCAA Division I Women's volleyball (2019, 2022, 2023, 2024 and 2025), looking only at matches between top-50 season-end RPI teams, the average top-50 team covers 44.8% of the blocks that come back at them.

And the correlation to winning is real, but it's modest: r = 0.28, p < 0.001, across 203 team-seasons. (I have to admit, that is stronger than I was expecting!)

On the scale I use for these, anything under 0.30 is a weak correlation, 0.30 to 0.70 is moderate, and above 0.70 is strong. For context, the metrics that really drive winning sit far higher. Measured across this same set of teams, attack efficiency comes in at 0.81 for the women and 0.88 for the men. Covering is not close to that neighborhood.

Here is what that looks like. Each logo is one of the RPI top 25 from 2025, and the dashed line is the trend across all 203 team-seasons in the study.

Nebraska is where you would expect them. Texas, Kentucky, Creighton and Stanford are all on the high-covering side with winning set records. Utah State is the outlier down at the bottom, and Indiana and USC are the two teams covering well but not having much to show for it.

The somewhat random spread around that line is the point: covering helps, but it is just not nearly as important as other parts of the game.

Another way to view it: the best third of teams at covering won 55.6% of their sets. The worst third won 45.4%. That is a ten-point gap in set win percentage between the teams that cover well and the teams that do not.

Coaching takeaway: Covering is worth the practice time, but it is the kind of thing you want to get in place as quickly as possible so you can invest in other higher-leverage areas. If you are choosing between investing an hour on covering or serve receive, you really want to invest it in serve receive.

What good actually looks like

The whole league fits inside about ten points, which surprised me. There is not a team out there covering at 70%.

The top 10% of teams cover 50.5% or better. A typical team is right around 44.9%. The bottom 10% are at 38.3% or worse.

Moving from the bottom third of the country to the top third works out to digging up roughly one extra blocked ball in every nine. Over a season that is real, and it is achievable, which is the part I like. This is not a metric where you need a different roster to move it.

Coaching takeaway: If you want to be great at covering, 50% is a good target for Cover %. That is roughly the number that separates the top 10% from everyone else. Roughly one in two blocked balls that come back at you should stay off the floor.

It's slightly more important in the men's game

I ran the same calculation on the men's side.

The first pass used only the current men's season, which gave me 29 teams. The result was nothing at all, r = 0.003, p = 0.99. That is the kind of number that tempts you to write "covering does not matter for men" and move on.

But 29 teams is a very small sample, and a sample that small simply cannot detect an effect the size of the one I had found in the women's data. So instead of calling it a null result, I went and pulled three more seasons of men's matches.

With four seasons and 103 team-seasons, the answer is yes, and it is a bit stronger than the women's: r = 0.35, p < 0.001. Men cover at 43.4% on average, which is within about a point of the women's average, so the skill itself looks much the same in both games. The best third of men's teams at covering won 52.4% of their sets against 41.4% for the worst third.

Coaching takeaway: Covering travels across both games at about the same size, so if you coach both, you can use the same standard for either group.

Some caveats

A few things to be aware of when interpreting this data.

First, there's no way in the data to tell the difference between a straight-down "monster block" (thank you VNL for the annoying chant which now lives rent-free in my brain) and a ball that was intentionally rebounded off the block for a recycle. Cover % includes every ball that came back off the block onto the attacker's side, regardless.

Second, it's also important to keep in mind that teams that are good overall tend to cover well, so the ten-point difference in sets won between the best-covering and worst-covering teams is due to a variety of factors, not just the ability to cover.

Where I landed

I went into this expecting to find no correlation or a tiny correlation. I was more right than wrong, but not enough right to make covering unimportant.

What I found is a real, repeatable, modest effect that shows up in both the men's and women's games, at a size that says covering is worth coaching and worth measuring but is not going to decide your season. That is a perfectly good answer, and it is the kind of thing I would rather know than guess at.

We will keep doing the covering reps here, though I'm hoping we'll have it down well enough to focus our time on other higher-impact areas soon!

As always, if you have any suggestions, comments or feedback, feel free to reach out or comment below. My email is open: alex_simons@hotmail.com

See you in the gym!

Alex

P.S. Some of you who have read a lot of my blogs may notice that the correlation values for attack efficiency I cited here for both the women's and men's games are lower and different than in some of my previous posts. That is correct, and it's because we're looking at different cohorts of teams. Whenever I use a bigger or differently defined cohort so I can get more data or focus on higher level competition, as expected, the numbers come out slightly different.

As I get ready to start my new role as the performance analyst for Texas, I'm spending a lot of time thinking about team offense and specifically, which attacks matter most to winning at a high level. We have a limited number of practice hours a week and every minute we spend perfecting a less important attack (i.e. learning to run the slide) is a minute we don't spend on an attack that might have a higher payoff (i.e. maybe a BIC). So I want to know, in a data-driven way, where the reps are likely to pay off the most so we can maximize the value of our practice time.

To figure it out, I booted up my PC (actually right now, it's my laptop as my main dev PC is in transit) to see what the data could tell me. In the process I've learned a lot. It turns out the answer is more varied than you might expect. It differs depending on the league and on whether we are talking about men's or women's volleyball.

Given this variability, it's going to take more than one blog post to do this topic justice. I'll start today with NCAA Division I women's volleyball (where I have the largest data sets), and then in subsequent posts I'll work my way through US pro women, women's international volleyball (using 2026 VNL data), and NCAA DI men's volleyball.

Let's start digging into the NCAA DI Women's data!

The data and the method

For this post I used match data for the final top 50 RPI teams in NCAA Division I women's volleyball from the 2022 to the 2025 seasons. To avoid having teams that play in weaker conferences skew the results, I'm only using data for matches between top 50 RPI teams. So we are only analyzing data from matches where top teams are playing each other. Each team-season is its own data point (Texas 2025 and Texas 2024 count separately), which gives us 180 team-seasons and a little over 300,000 individual attacks to work with.

I sorted every attack into one of six families, grouped by where and how it was hit rather than by the exact play call:

• Left side: attacks by outside-hitters on the left pin, both in-system and out-of-system (A "Go" and a "Hut"/High Ball L).

• Right side: attacks by an opposite on the right pin, both in-system and out-of-system (A "Red" and a "Mac"/High Ball R).

• Quick attacks: In system quick attacks by a middle blocker in front of and behind the setter.

• Back row: BIC, pipe, and D-ball attacks out of the back court.

• Gap: A quick attack by a middle blocker in the gap between the middle and the left side blockers.

• Slide: An attack by a middle taking off on one-foot behind the setter (both far and close slides).

For each team-season, I calculated their attack efficiency for each attack family and I did a multi-variate analysis correlating those attack efficiencies with each team's set win percentage. Then, I used the results to answer three questions, each with its own analysis. Which families actually predict winning? How much does breadth of attack matter? And in what order should a program invest in its offense?

Which attacks are most correlated with winning?

This is a trickier question to answer than you might think. Good teams tend to be good at a variety of attacks, so the six families are all correlated with each other. If you just run a regression and read the coefficients, the shared credit gets split in misleading ways.

To handle that, I used a relative-importance analysis (a Shapley-value decomposition) that fairly divides up the explained variance across the six correlated families, no matter what order you consider them in.

Together, how efficiently a team hits these six families explains about 55% of the variation in Set Win% (R² = 0.55, p < 0.001). That is a lot for a measurement that only takes one skill into account. And when you split that credit up, one family towers over the rest.

The left side attack accounts for roughly 46% (nearly half!) of all the variation in Set Win% that attacking creates, double the next closest attack family. On its own, left-side efficiency correlates with Set Win% at r = 0.61 (p < 0.001), comfortably the strongest single attack family. The right side (r = 0.47, p < 0.001) and the middle quick (r = 0.45, p < 0.001) come next, then the back row (r = 0.36, p < 0.001). The gap barely registers (r = 0.17, p = 0.025), and the slide, at this level, shows essentially no relationship with winning (r = 0.07, p = 0.33).

Most experienced college coaches intuitively know this, but it's great to have the concrete data to back it up. As we've seen in previous posts, attacking is the most important individual skill in terms of winning. And now we know that, in the US women's college game, how well you terminate the ball on the left side is the single most important attack, by a significant margin.

Coaching takeaway: If you have to rank your attacking priorities, invest time in improving your outside hitters' ability to terminate on the left side. It is the biggest single lever in the women's college game, and no other attack comes close to it in isolation.

A great left side isn't enough by itself

Here is where it gets interesting. A dominant left-side attack is necessary, but it is not enough by itself. To test that, I looked only at teams that already had an above-average left side for this elite field, and then asked a follow-up question: how many other attacks are they using effectively and how does that number relate to their Set Win%?

I decided to define "using effectively" relatively conservatively. An attack family only counts as being effective if the team runs it on at least 8% of their attacks and hits it with a higher than average attack efficiency among the top-50 RPI teams. Additionally, I required that there were at least 100 attempts to avoid small sample sizes skewing the results. Then I grouped the teams with a good left side by how many effective attacking options they had in addition to their left side attack.

The results give you a pretty nice staircase effect. If your only effective attack is your left side and nothing else, among the top 50 teams, you are very likely to end up with a losing record at 48%. Add one more effective attack option and you move up to 52%. Two more and you jump to 59%. Three or more and you are up at 66% and if you can add one more, you move up to 67%. (Across all these teams, the number of additional effective attacking options correlates with Set Win% at r = 0.54 [p < 0.001]).

Boiling it all down, a great left side earns you a winning record only once you pair it with one or more additional effective options. Teams with a good left side and three or more other effective attack options reached the top 20 more than half the time. Those with fewer than three got there just 12% of the time.

Coaching takeaway: A great outside attack gets you to respectable. Making it into the top 20 (or competing for a championship) takes more. If your left side is already a strength, the highest-value next step is developing a genuine second and third option, not squeezing a little more out of the one attack you already trust.

The order to invest in your attack

So a program should have a great left side and then add options. But in what order? Which attacks should a team invest time in first to get the biggest return on investment? To answer that, I added each attack family to the model, one attack family at a time, to figure out which family increased the predictive power the most at that step. That gives a priority order and shows how much each addition actually contributes.

The left side alone explains 37% of the variation in Set Win%. Adding the right side is by far the most valuable next move, lifting that to 49%, a bigger jump than everything after it combined. The middle quick attacks come next (up to 53%), then the back row attacks (up to 54%). The gap adds 1 more percentage point (up to 55%) and then you've hit a plateau. The slide adds essentially nothing else.

Coaching takeaway: If your left side is solid and you are deciding where to invest next, the data points to the right side first, then the middle, then the back row. Developing a real opposite threat is the single most valuable addition to a women's college offense that already has its outside attack.

Wrapping up

Pulling it together, three things stand out for NCAA Division I women.

How effectively a team hits these six attack families explains about 55% of who wins. The left side is the dominant single driver, worth roughly double any other attack. And winning at the top takes a great left side paired with a real second and third option, developed in the order of right side, then middle, then back row.

That is the picture in the women's college game. That said, the picture changes, quite a bit, when we move to US pro women, to the international game, and to men's volleyball. The attacks that carry those levels are not the same ones that carry college women, and I cannot wait to walk through why.

I would genuinely love your reactions to this. Send me your comments, feedback, and suggestions at alex_simons@hotmail.com, and stay tuned for Part 2, where we head to the US pro women's game.

Howdy folks!

A few weeks ago I published "Serve or Receive in Set 5?" and found that the receive-first team wins 51.8% of deciding sets across NCAA DI Women, a +3.7 percentage point edge. A few of you asked the natural next question: does that hold outside of women's NCAA DI? I've recently acquired data for a few more leagues, so I checked.

The same question, six leagues

Four of the six leagues show the receive-first team winning more often in set 5. The two that don't? MLV (30 matches) and LOVB (42 matches). With an effect this size, you need hundreds of matches before you have a statistically significant result, and those sample sizes just aren't large enough to tell us much. (The 95% confidence interval on LOVB's receive-first edge runs from -35pp to +25pp. MLV's is even wider: -36pp to +36pp. There just isn't enough data to draw any conclusion from those two leagues.)

The more interesting data is highlighted at the top of the table.

The receive-first edge appears to grow as side-out% grows: Women's VNL (+12.7pp), Men's NCAA DI (+9.1pp), and P4 conference play (+5.7pp) all show a larger edge than the +3.7pp NCAA DI Women's average.

This makes sense. Better teams side-out at higher rates, which means the structural +1 real point from receiving first is more likely to actually matter. In my original post, elite teams (60%+ Side-Out%) showed an 8.6pp edge in set 5 compared to just +0.3pp for above-average teams. The VNL and Men's DI results fit that same pattern: higher side-out quality translates into a bigger receive-first payoff.

Coaching takeaway: The receive-first advantage in the deciding set is not a quirk of one league or one gender. It shows up in NCAA DI Women, NCAA DI Men, international women's play, and P4 conference matchups. If your team sides out well, that coin toss matters. Choose to receive.

Same conclusion as last time, now with a wider lens. Win the coin toss? Take the ball.

See you on the court.

— Alex

Methodology Notes

• NCAA DI Women 2025: 5,048 match files, 1,034 five-set matches (non-DI opponents excluded)

• P4 vs P4 2025: Subset of NCAA DI where both teams are Power 4 (Big Ten, SEC, Big 12, ACC), 157 five-set matches

• Women's VNL 2026: 189 senior national team match files (weeks 1 - 3), 55 five-set matches

• Men's NCAA DI 2026: 939 match files, 154 five-set matches

• LOVB 2026: 136 match files, 42 five-set matches

• MLV 2026: 115 match files, 30 five-set matches

• Served-first identification: First "Serve" skill action in each set's play-by-play data

• Set winner determination: From match file header metadata, with fallback to points-won calculation

• Tool: Data pipeline built with R, Claude Code, and the openvolley/datavolley package

Howdy folks!

Back in March I blogged about my analysis of a question every coach asks at some point: How much do service errors matter? Across 5,107 match files from the 2025 NCAA Division I women's season, the answer was pretty clear, "Not much."

In Women’s DI, Serve Error% correlated with set win% at just r = -0.12 (p = 0.02), explaining only about 1.5% of the variation in who wins a set. It finished 31st out of the 36 metrics I track, miles below the things that actually move sets like Attack Eff%, Kill%, and Block Impact%.

A few of you asked a natural next question: does that hold true outside of women's NCAA DI volleyball? I've recently acquired data for a few more leagues, so I pulled the same correlation for the men's game, two pro leagues, and one international competition.

The same question, five leagues

Here is Serve Error% against set win%, one correlation per league:

• NCAA DI Women (349 teams): r = -0.12, p = 0.02

• NCAA DI Men (30 teams): r = +0.10, p = 0.61

• LOVB (6 teams): r = -0.12, p = 0.82

• 2026 Women's VNL, weeks 1 & 2 (18 teams): r = -0.10, p = 0.69

• MLV (8 teams): r = +0.76, p = 0.03

For four of the five, the answer is pretty clear. The correlation is weak at best, and in the men's game it is slightly positive (more errors = more wins). And in each of those four leagues, service errors explain under 2% of the variability in who wins.

The men's number is worth a second look for a different reason: men miss a lot more serves than women do (the average error rate in Men’s DI is 18.8%), and even with that huge number, the errors still tell you almost nothing about who wins.

Serving tougher, and missing more because of it, simply did not show up as a penalty.

That Interesting MLV number

The league that does stand out is the MLV, where service errors actually correlate positively and strongly with winning (r = +0.76, p = 0.03). The two best teams in the league, Indy Ignite and Dallas Pulse, led the league in service errors, and as you can see from the graph, the relationship between service errors and set win% is pretty linear.

I love this data, and it fits the "serve tough" idea nicely. That said, it's important to keep in mind that this is a league of eight teams, so the data set is pretty small. I would not be surprised if, as the MLV grows over the next few years, the correlation flattens.

Coaching takeaway: Across five different leagues, service errors barely register in who wins. If your team serves aggressively and misses a little more than you would like, the data says that is a fine trade. Keep the pressure on, and worry about the metrics that actually decide sets.

Same conclusion as last time, now with a wider lens. Serve tough, and do not sweat the errors.

See you in the gym,

Alex

I took every 2025 match between two top-50 RPI women's college teams, 324 matches and more than 54,000 rallies, and counted possessions. A possession is a change in ball control. Possession 1 is the serve and side-out phase, possession 2 is the serving team's first transition swing, and so on.

Here is what jumped out. Just over half of all points, 51%, are won in the very first possession, on the serve or the side-out swing. Another quarter are won in possession 2, the first transition. Put those together and about 76% of points are decided in the first two possessions. By possession 3 you are already at nearly 90%.

The long, scrappy rallies we all love to watch are the exception, not the rule. Only about 12% of points reach a third possession, and true scrambles of five or more possessions are just 6%.

I wondered whether this was just a women's college thing, so I ran the same numbers for men's Division I, the LOVB pro league, and Major League Volleyball.

The pattern held in all four leagues. In every one, more than seven in ten points were over by the second possession. Men's Division I finished the earliest, around 62% in the first possession alone. That is no surprise. The men hit the ball harder, and first-ball side-out in men's Division I runs about ten points higher than the women's, so more rallies end on the first swing. Major League Volleyball rallied the longest, though even there 72% of points were decided inside two possessions, and LOVB landed right on the top-50 college number.

Coaching takeaway: You score early or your chance of scoring gets pretty small. Win your side-out, or win the very next swing. Serve-receive, first-ball attacking, and your first transition decide most points, so that is where the practice hours should go.

Say Team A wins the first two sets and goes up 2-0. Team B finds a way to turn the tide and ties the match at 2-2 and force a deciding fifth set. Who is more likely to win that fifth set? Alessandro's hunch was Team A. He feels that in his personal experience, he sees that happening a lot more than 50% of the time.

To check it, I went back to four seasons of NCAA Division I women's volleyball, from 2022 to 2025, and pulled every five-set match in which one team won the first two sets before the other tied it at two sets apiece. That left 1,431 matches. For each one I simply asked whether the team that had led 2-0 went on to win set five, or whether the comeback team did.

Surprisingly, the data shows a slight tilt in the opposite direction from the one Alessandro (and I) were expecting. The team that came back from 0-2 down won the deciding set 53.6% of the time, while the team that had led 2-0 won just 46.4% (p = 0.007, so the gap is real and not noise). It's pretty close, but the comeback team does have a statistically significant edge.

Expanding that same analysis to the other leagues I have data for, I found pretty similar results.

In every instance, the team that came back from down 0-2 has a higher likelihood of winning the fifth set. (The LOVB result stands out obviously, but with a sample size of only 6 matches, it's very likely a statistical anomaly.)

Of course, the numbers tell us who wins, not why. Maybe it's momentum, maybe it's an adjustment from Team B that keeps paying off, or maybe it's just regression to the mean. But if you ever find yourself down 0-2 and manage to even it up, the data says you are the team to bet on in the fifth!

Thanks for the question, Alessandro. Keep them coming. 🤘

I spent the past four days in the gym coaching my U15 girls team at the USA Volleyball Girls National Championships. The team played pretty well, but things didn't turn out quite how we had hoped. We were very competitive, but we ended up losing the deciding third set in four of our matches. And in three of those matches, we had taken the second set by five points or more. So we went into the third feeling pretty good, and still came up short.

Then, to pile on a little, when I got back to the hotel Thursday night, I watched the USA Men's Team lose to Japan in the deciding fifth set. Quite a bummer of a day!

By the end of the week, I could not stop wondering: when a team wins the set right before the deciding set, do they actually carry momentum into it? Does winning that set hand you a real edge, or does it just feel that way in the moment?

So I did what I always do when a question will not leave me alone. I opened up my trusty laptop, launched Claude Code, and started "typey coding," as my team likes to tease me. 😅

The data I used

Unfortunately, I don't have a great data source for club volleyball (I'm looking at you, HUDL!).

So, the closest I can get to an answer is to pull together match data from a few different places where I do have good data. The bulk of it is four seasons of NCAA Division I women's volleyball, from 2022 to 2025 (21,147 total matches). Filtering gave me 4,258 matches that went the full five sets, which is a big enough sample to make some strong, statistically valid statements. I also looked at the 2026 NCAA Division I men's season, the 2026 LOVB season, and the 2026 Major League Volleyball season, though those samples are much smaller.

The question itself is simple to measure. In a five-set match, the deciding set is set five and the set right before it is set four. So for every five-set match, I just asked one thing: did the team that won set four go on to win set five?

It's a really small edge

I have to admit that I was really surprised by these results. I have always assumed that winning the fourth set (or the second set in my team's case) gave you a notable advantage in the deciding set. It does give you an edge, but it's a really small one. In women's Division I, the team that won set four went on to win set five 51.8% of the time (p = 0.02). That is a real edge in the statistical sense, but it is pretty close to a coin flip.

The men's and pro leagues leaned a bit higher, with men's Division I at 57.8% (p = 0.06), LOVB at 57.1%, and Major League Volleyball at an even 50%. The catch is that those samples run from 30 to 154 matches, and none of them is a big enough sample to be statistically significant (p = 0.44 for LOVB and p = 1.00 for Major League Volleyball).

So the NCAA DI women's number is the one I would lean on. It's a pretty reasonable bet that if those other leagues had similarly sized data sets, their numbers would land close to the women's Division I figure.

Coaching takeaway: Winning the set before the decider is worth something, but it is closer to a small nudge than a momentum wave. If you drop that set, you are still very much in the match. As a coach, it's pretty clear that even if your team lost set 4, they can still walk into the deciding set with their heads up.

The size of the win is what matters

Here is where it got interesting, and where my own week started to make a little more sense (though I didn't feel much better about it). When I split the matches by how big the set-four win was, a clear pattern emerged: the bigger your winning margin, the better your odds in the deciding set.

Surprisingly, if you win set four by only two or three points, you actually lose the deciding set slightly more often than you win it, at 47.4%. Win it by eight or more, and your chances climb to 58.5%. The edge grows steadily as the margin grows, which tells me the momentum people talk about is mostly the momentum of having clearly outplayed the other team in that prior set.

My team won three of those second sets by five or more and still lost the deciding third in all three. It was nice to see the NCAA data show that this probably wasn't as big an outlier as I thought. Sometimes you land on the wrong side of a coin that is only barely weighted in your favor.

Coaching takeaway: If you take the set before the decider by a big margin, you have earned a small but real advantage, so keep doing what built that lead. If you only edged it out, treat the deciding set as a fresh start, because the numbers say it basically is one.

Deciding sets are not always nail-biters

The last finding surprised me as well. I picture deciding sets as tight, back-and-forth battles to the final point (much like the 5th set in the USA vs Japan match was). After all, the teams have already battled back and forth through four sets.

Fifth sets are often close, but not nearly as often as I would have thought.

In women's Division I, 30.1% of deciding sets were won by exactly two points, and roughly six in ten were decided by four points or fewer. But 40% were won by five points or more, and nearly one in five were won by seven or more. That is a lot of deciding sets that were not especially close. A short set to fifteen can quickly turn into a runaway, which matched my week perfectly. A couple of those third sets definitely got away from us.

Coaching takeaway: Get your team ready for the deciding set to go either way, a dogfight or a quick run. Stringing a few points together in a row matters even more in the deciding set, because there are far fewer points available to win it.

Leading in the deciding set is a different story

Winning the set before the decider barely moves the needle. But a lead inside the deciding set itself is a very different picture.

In women's Division I, once a team builds a lead in the deciding set, they hold on to win it the large majority of the time, and the bigger the lead, the more likely the win. A two-point lead in a set to fifteen was good for a 67.1% win rate, a four-point lead reached 88.7%, and once a team led by seven or more they closed it out 98.6% of the time. The full climb is in the chart.

The most valuable ground is the early ground. Going from a two-point lead to a three-point lead raises your win rate by 12.2pp. From there, getting to a four-point lead adds another 9pp. Once you have a four-point lead, you are in a very good position.

Looking back at my week, that reframed the whole thing for me. The lesson was not really about the second set we had already won. It was about how we started out early in the third.

Coaching takeaway: In the deciding set, the early points carry extra weight. Stretching a two-point lead out to four is where your odds climb the fastest, so it is worth a real push to build and protect a small cushion early.

One more thing

I have some pretty exciting news to share (at least it's exciting for me!). I have accepted an offer to join the University of Texas Women's Volleyball Program as a Performance Analyst for the 2026 season!

I am thrilled, a little stunned, and very much looking forward to getting going. I'm excited to work with (and learn from) Jerritt, David, Ben, Reilly, and the rest of the staff as we try to help the team win a national championship.

The one downside is that my time is about to get a lot tighter. I hope to keep blogging, but I expect to post quite a bit less frequently over the next six months as the season gets underway.

Thank you all for reading along, and for the questions and ideas you keep sending my way. Please keep them coming, whether by email (alex_simons@hotmail.com), or in the comments below. Your comments and feedback are a big part of why I keep digging into this stuff. Until the next one, take care of your deciding sets.

Hook 'em Horns 🤘,

Alex

If you've been watching VNL matches this month like I am, you know that the USA Women's team has been on fire. After the first two weeks of competition they are ranked #1 overall and sit at 7-1 atop the standings. They even have a 3-set sweep of world No. 1 Italy to add to their list of accomplishments. After watching all that great volleyball, the stats geek in me was curious to see if I could pinpoint what they are doing that's leading to this level of success.

As I dug in I was excited to see that some of the things I thought made their performance special when I watched (amazing blocking by the middles and strong attacking by Jordan Thompson) really are special but that there's more going on than I realized. I definitely learned a few things doing this analysis and I hope you'll learn a few things reading it!

One data set, two comparisons

All of the data in this analysis is based on all the match files from pool 1 during week 1 of the Women's VNL, every match file from pool 5 from week 2 (these were the two pools the US Women played in) plus the files for every match played by the other top 8 teams in the competition.

To help understand why I picked that set of teams, it helps to look at the standings from the end of week 2:

If you do a quick scan down the set ratio column, you can see there is a pretty natural break between the Netherlands (#9, 1.250 set ratio) and Czechia (#10, .857 set ratio). I've picked that break point to make sure I'm basing my analysis on how the strongest teams are performing, a group which I'm referring to in this blog as the "Elite 9".

In this blog, I'm doing two types of comparisons.

First, I looked at how the US Women's teams stats in VNL competition stack up compared to the other Elite 9 teams (Brazil, Italy, Poland, Japan, Türkiye, Canada, China, and the Netherlands). This helps us understand how the US compares to the other top teams, many of whom they haven't played yet.

Second, I look at how the US Women performed in direct comparison in head to head VNL competition against the teams they've played so far (Ukraine, Canada, France, Germany, Dominican Republic, Czech Republic, Italy, and Serbia).

Top level metrics

To start off, let's take a look at how the US is doing in the normal high-level "phase of the game" stats:

The US is clearly playing well in both the Side-Out and Point Scoring phase of the game. However, the USA gap in both SO% and FBSO% is so small that it's not even statistically significant. But the 5.3pp gap in Point Scoring %? That's huge and yes, it's definitely statistically significant.

So now we're onto something pretty interesting! It appears the US's strong performance is based on an unusually strong performance in the point scoring phase of the game compared to the rest of the Elite 9 teams:

At 44.5%, the USA wins a higher share of its serving rallies than any other elite team, more than five points clear of the field average. The top of the table is tight, with Brazil and Italy close behind, but no one in the field wins the serving phase more often than the USA. As a result, the US Women are holding the majority of their competitors to some of the lowest SO%'s I've ever seen in high-level volleyball:

This is my favorite chart from this entire analysis. Here you can see what each of the US's direct opponents average SO% in VNL competition has been compared to their SO% when they played the US Women.

Look at how far their competitors SO% falls. Germany dropped from 70% to 46%. Czech Republic fell from 71% to 48%. Even Italy, the best side-out team in the world, dropped from 74% to 59%. Across the eight matches, opponents sided out almost seven points below their own norm.

Only two teams escaped that trend, Canada and France, both in Week 1 when the US's performance was a bit uneven.

Coaching tip: Comparing an opponent's Side-Out% when they played you to their season average is a nice way to differentiate between "we beat up on a weak team", "we made a good team look weak" and "we made a weak team look good".

What is Team USA doing in the Point Scoring phase that makes their performance so good? Well, I'm glad you asked!

It Starts with Tough Serving

Let's start with the USA Women's serving. One of my favorite serving metrics is knockout rate, or KO%, the share of serves that are either an ace or which knock the opponent out-of-system. It's a simple metric and of the common serving metrics teams use, it's the most strongly correlated with winning sets (In the VNL so far, KO% is correlated at r = 0.65, p = 0.004 with Set Win%).

The USA leads the elite field at 44.6%, and it shows up match to match: their KO% topped 60% against Czech Republic and 55% against Ukraine and served above the VNL average in six of their eight matches.

Breaking this down further let's take a look at the subcomponents of KO%:

This gives you a clear view of what's going on. The US is creating a gap from the rest of the field by forcing their competitors out of system more frequently than anyone else in the VNL. It's interesting to see that both Italy and Canada are acing their opponents more than the US, but not enough to overcome the US's significant lead knocking teams out of system.

Who is causing all this chaos when they serve?

Two of the most effective servers in the entire Elite 9 wear USA jerseys: Jordan Thompson at a 62.5% knockout rate and Logan Eggleston at 59.3%. Notably, both are jump-float servers, and they are achieving KO% that rival the world's best topspin jump servers.

Interesting side note: Melissa Vargas is the top server by KO% in the VNL. She's a topspin jump server and her serving is incredibly effective despite having a 20%+ error rate!

But it's more than a two-woman show for Team USA. Six US servers are all contributing strong serving with results well above the VNL average.

Coaching takeaway: If you have an athlete with a cannon for an arm like Melissa Vargas, a topspin jump serve is hard to beat. But as Team USA is demonstrating, in women's volleyball you don't need top-spin cannons to serve tough. A jump-float that consistently takes the opponent out of system, at a low error rate, can move the needle just as much.

A Block Built for Transition

Let's dive into blocking next. As I've written about in previous blogs, block impact rate is one of the best ways to measure how effectively your team is blocking. It measures how many stuff blocks or positive block touches your team is getting per opponent attack.

The US sits near the bottom of the elite field. I was pretty surprised at this. As I've watched the USA matches, my eye was telling me that the USA middle blockers were having a bigger impact in the game with their blocking than this metric seemed to indicate. So, I kept digging until I found this:

The USA's block isn't doing much on first-ball attacks. But it's having a significant impact in transition. When an opponent digs a ball and tries to counterattack, the USA stuff blocks those attacks at the 2nd highest rate in the elite 9.

And what my eye was telling me was definitely correct, the middle blockers (especially the middle blockers who played for Texas!) make up the majority of those transition blocks.

Asjia O'Neal, Chiaka Ogbogu, Molly McCage, Dana Rettke, and Anna Hall account for roughly 65% of those blocks in transition.

I also checked to see if the USA back-row defense was having a similar level of success digging up balls in transition, and surprisingly, they are not. Team USA is digging at a 44.9% rate on transition attacks, 7th out of the Elite 9. (Japan is #1 at 57.4%).

Given what we know now, let's look at the impact USA is having on opponent transition attacking:

The US is having a big impact on their opponents' transition attacking effectiveness. Given the data we've looked at, I think we can feel pretty comfortable saying the big differentiator for Team USA that's making this happen is the USA middle blockers, who are doing an awesome job stuffing transition attacks.

Coaching takeaway: When you evaluate your blocking, it's important to look at how effectively you're blocking on first-ball side-out separately from transition. You might be surprised at how different the numbers look!

Killing It in Transition

For the last part of our deep dive into why Team USA is doing so well in the Point Scoring phase, let's take a look at their transition attacking:

Their transition attack efficiency of 25.7 trails only Italy and sits well above the field average. Diving into the transition attacking numbers, two Team USA athletes really stand out.

First, let's take a look at Jordan Thompson. Jordan's overall performance at the VNLs has been inspiring to watch:

Jordan Thompson is the second most effective right-side attacker in transition in the VNL. That's pretty awesome, but what's even more impressive is that she is just as effective attacking in first ball and transition, which is unusual. The other two "super star" right-side attackers (Dambrink & Vargas) are strong in one phase or the other, but not both. Similar to Thompson, Xin Tang from China is equally effective in first ball and transition, but her effectiveness is 10 points less.

Next, let's take a look at Simone Lee-Wank:

Lee-Wank's efficiency on first-ball attacks is about middle of the pack for the VNL, but in transition on the left pin she is 15 points better than any other team's left-side attacker. Pretty remarkable!

But once again, it's not just a two-woman show. All six of the US pin hitters with >15 transition swings are hitting above the VNL average in transition:

What's Next

Two weeks is a small sample, the VNL is a long grind, and all the top Italian players are re-joining their team for week 3. So anything can happen. But so far, Team USA has made a strong showing.

Their all-around performance has been solid, but as we've now seen, they are distinguishing themselves in three key areas:

1. Tough serving

2. Strong blocking against transition attacks

3. Highly effective transition attacking

Week 3 starts in only a few days! I'll be watching, spreadsheet open. If there's a team or a matchup you want me to dig into, drop it in the comments and I'll see what the data says.

See you in the gym!

Alex

Today's post is something a little different. I'm going to walk you through a new metric I've recently started using that I'm quite excited about.

This past December at the AVCA Convention, I caught a talk by Lauren Bertolacci, the head coach of Indy Ignite and the Swiss Women's team. She said something in that talk that has been rattling around in my head ever since. She made a wonderfully simple claim: the most important thing when it comes to winning sets is the number of points you score yourself per hundred rallies. I love this kind of thinking. One clear idea, stripped of jargon, that a whole gym can rally around.

It sent me on a hunt. I wanted to find something similar to what Lauren had said that I could use with my team. To do that, it needed to be:

1. Simple to track, so we could track it in match or at practice on a white board.

2. Simple to explain to the team.

3. Something every player could contribute to improving.

4. Something that was largely under our control.

5. Something that had a large/significant impact on our chances of winning

After a lot of experimenting, I think I've found a metric that meets all these criteria and works really well: Good Attacks Per Possession.

Introducing Good Attacks Per Possession

The idea is about as simple as it gets. When the ball comes over the net to your team, how often do they turn it into a good attack? That's it. It's kind of volleyball boiled down to its essence.

I'm defining "Good Attack" broadly here: any kind of attack (a spike, a roll shot, a tip, a toss, etc.) that doesn't result in an unforced error. Then I'm dividing it by the team's total number of possessions. In essence, what percentage of the time that your team received the ball did they find a way to successfully pass/dig the ball up, set it and get an attack off that made the other team play defense?

The formula is easy enough to track on a white board during practice. Count your possessions. Count how many you turned into an attack of any kind that didn't go in the net or out of bounds. Divide. That is your GAPP%.

I love this metric because it's a powerful predictor of winning (as we'll see below), it's simple to explain and everyone on the team can easily understand how they can contribute to improving it:

• Passers, by passing a few more settable balls

• Defenders, by digging up a few more balls that lead to transition attacks

• Blockers, by getting a few more block touches that make the ball easier to dig

• Setters, by delivering a few more hittable sets

• Non-setters, by being ready to set a few hittable balls when the setter can't

• Attackers, by attacking aggressively & avoiding a few more unforced errors

Nobody is a bystander.

Coaching takeaway: GAPP% gives you one simple, easy to track metric that sets the frame for every possession: "Can we turn this into a good attack?"

Does it actually predict winning?

While the simplicity of this metric is appealing, it's really only worth using if it actually correlates strongly with winning, and the data shows it definitely is!

I ran the correlation between GAPP% and the percentage of sets won (i.e. Set_Win%) for every NCAA DI Women's team in the 2025 season. The relationship is strong and statistically significant. Across 349 teams, GAPP% and set win rate move together with a correlation of r = 0.775 (p < 0.001).

And unlike a few other metrics which don't hold up well when the level of play goes up, this one is very stable. When you consider only matches between Power 4 teams, the correlation holds up really well (r = 0.733, p < 0.001) and when you look at only matches between teams in the top 50 RPI, the correlation is still strong (r = .661, p < 0.001).

Even when the talent gap narrows, GAPP% keeps sorting the contenders from the pack.

And the relationship here is very straightforward. There is no threshold where the metric suddenly stops mattering, and no point where it flattens out. From the bottom of Division I to the very top, more good attacks per possession keep turning into more sets won.

Coaching takeaway: Because the relationship holds across the entire range, any team can benefit from improving their GAPP%. Wherever your team sits today, improving GAPP% is highly likely to help you win more sets.

The men's game shows it even more clearly

And this isn't just true in the women's game. In fact, it's an even stronger predictor in DI Men's Volleyball.

Across the 30 NCAA Division I men's programs, GAPP% and set win rate move together at r = 0.832 (p < 0.001), the strongest correlation of any group I looked at. With only 30 teams on the chart, you can read it like a leaderboard, from UCLA up in the top right to the programs at the bottom of the standings in the bottom left.

What is a good number?

Here is where it gets practical. GAPP% lives in a surprisingly narrow band. Across all of Division I the average is about 67, and every DI women's and men's team falls somewhere between 63 and 73. That tight range is exactly why small gains matter so much.

To make it concrete, here is where all 349 Division I women's teams landed last season, with four landmarks marked.

A few landmarks worth knowing. The break-even point, where a team wins about half its sets, sits right at the Division I median of roughly 67. To win 60% of your sets you are generally looking at about 70. The 2025 Final Four teams all had an outstanding GAPP%:

• Texas A&M: 70.0%

• Wisconsin: 71.1%

• Pitt: 71.2%

• Kentucky: 73.8%

If you are a regular reader of my blogs, this fits in nicely with what we've learned from previous blogs. To win the NCAA Tournament, you have to be really good at everything (GAPP% included) and you have to have an elite kill% (which Texas A&M did).

For the men's side, the numbers are remarkably similar though the very top of the scale is compressed:

• Struggling/bottom 11%: 65%

• 50/50 (median): 67%

• Top tier (top 13%): 70%

• Elite (top 2%): 70.5% (not shown above)

Coaching takeaway: Pick the tier just above where you are now and make it the season target. Moving from a 67 to a 70 does not sound dramatic, but on this scale, it is the difference between break-even and a winning record.

The payoff: one point is worth about four

If you coach a Division I women's team, you could reasonably expect that each one-point gain in Good Attacks Per Possession brings about a four-point gain in your set win rate. And if you coach a Division I men's team, the payoff might be even bigger, as high as five points of set win rate for every point of GAPP%.

Let that sink in. Making a relatively small improvement in GAPP% (1pp) leads to an outsized gain in Set_Win% (4pp). All you have to do is find a way to trim a handful of hitting errors per set. Or turn a few free balls into real swings. Pass a touch cleaner so your hitters get a few more hittable sets. None of that requires a recruiting miracle. It just requires a team that decides to focus on getting a good attack off as frequently as possible.

That is the whole pitch. One number. Easy to track on the bench, easy to explain in a timeout, and every kid on your roster owns a piece of it.

I used it with my U15 club team at practice on Tuesday. Over three sets to 25, playing pretty clean volleyball (for U15), our average GAPP% was 63.6%, which is right about what you'd expect for this level. I tracked it on the white board (photo below). I definitely find it’s a useful tool! As a coach it helped me focus my attention and feedback, plus it gave the players an easy way to quickly assess how they were doing between sets.

And it was a fun reality check. A good night of U15 volleyball landed right around the bottom of DI. 🙂 I'll keep you posted as I learn more!

Hopefully you’ll give this new metric a try. Let me know if you do and if you find it as useful (or not!). And of course, as always, if you have any suggestions or feedback, hit me up in the comments or email me at alex_simons@hotmail.com

See you in the gym!

Alex

One of the most frequent questions I get from US Women's coaches is "Should we run a 5-1 or a 6-2?" and “Is there any statistical advantage to one over the other?”

And those questions usually come with a bunch of additional questions - is it better to have 3 hitters in every rotation or to have one setter so set location and tempo are consistent? If I run a 6-2, will my blocking be better than if I run a 5-1? If I run a 5-1 and my setter is front row, I can use a DS which will make my passing and digging better right? I know that as a club coach I'm asking myself some version of these questions every year.

Coach Swing recently blogged about the topic as well which also motivated me to dig in and see what the statistics could tell us.

This blog post is my best attempt to use data to answer these questions and explore some of the points Coach Swing made. Hopefully you'll find it useful!

The Question the Data Can't Answer

Let's start off with the big question - is a 5-1 or a 6-2 statistically better?

Unfortunately, there is nothing in the data that is going to answer that question.

Why? Because elite setters get recruited to play in the top programs. And if your program has one of those elite setters, of course you run a 5-1! You certainly don't want to take your elite setter off the floor.

Regular readers may remember the Charlie Fuerbringer example from my NCAA Tournament post. Wisconsin's SO% went up by 9% when she rejoined the team. If you've got an elite setter like that, you'd be insane to keep them on the bench.

That means the system a team runs isn't an independent tactical choice we can evaluate. It's a reflection of the roster. Comparing 5-1 teams to 6-2 teams and declaring a winner would really just be telling you that teams with elite setters win more. You already knew that.

So instead of asking "which system wins more," this post asks a question the data can actually answer: which of the things coaches believe about these two systems can we demonstrate statistically, and which ones don't hold up?

I'm going to do this by comparing each system to itself. In a 5-1, the setter is in the back row for three rotations and the front row for the other three. How big are the deltas that result? In a 6-2, you really have two sets of rotations 1,2 & 3, each with a different setter + opposite combination. I'll compare them as well. By comparing each system's two halves, every team serves as its own control, and the recruiting problem disappears.

Digging into the data

For this analysis, I pulled every match for every team that finished in the top 50 end-of-year RPI from 2022 to 2025. I classified every set each team played as a 5-1 or a 6-2. I did this by identifying every athlete (excluding liberos) who set >20% of a team's sets and where they were in the rotation in each rally they set in.

Here's what surprised me when I started digging: the 6-2 is used much more widely than I realized, and it's being used more and more each year. In 2022, about 37% of all NCAA DI women's sets were played in a 6-2. By 2025 that number had climbed to nearly 47%.

Digging a bit more, in 2025, 97 of the 348 DI programs were primarily 6-2 teams (i.e. ran a 6-2 >=80% of the time), 120 were hybrids that split their sets between the two systems and 131 were primarily 5-1 teams (i.e. ran a 5-1 >=80% of the time). Add the hybrid team's 6-2 sets to the primarily 6-2 teams 6-2 sets and you arrive at nearly half of all sets played.

As you would expect the mix shifts as you climb the rankings since the top programs have the best setters. Among teams that finished in the RPI top 50, only seven to eleven programs per season are primarily 6-2 teams.

When we get to the top-50 data set below, you will see a lot more 5-1 team-seasons than 6-2 team-seasons. The deeper into DI you go, the more 6-2 you find. But that doesn't mean there aren't some great teams running a 6-2! Arizona State, TCU, and Michigan all ran 6-2's in 2025.

Quick data notes: All the comparisons in this post were tested two ways: a z-test on the pooled rally counts and a paired test across team-seasons. I only call something demonstrated when both agree.

Claim 1: Three Attackers Beat Two

The most common argument for the 6-2 is that you always have three front-row attackers, while the 5-1 spends three rotations attacking with two. If that matters, we should see the 5-1 team's offense is less effective when the setter is front row. That's what the data shows, and the decrease is significant.

When the setter on a 5-1 team rotates to the front row, Side-Out% decreases 2.1%, from 63.8% to 61.7% (p < 0.001), and their first-ball side-out rate drops 2.9%, from 37.5% to 34.6% (p < 0.001). Most of this drop is because their ability to score in-system on first ball serve receive drops by 4.2%, a big change.

This makes sense to me. When a 5-1 team is in-system on serve receive, they don't have a 3rd attack option to use so they are easier to defend and as a result their conversion rates drops from 49.1% to 44.9% (p < 0.001). Across 141 team-seasons, 87% showed the in-system conversion drop. This is about as robust as a volleyball finding gets.

Interestingly, 5-1 teams out of system attacking on serve receive also drops when the setter is front row, though not nearly as much. Again this makes sense to me as we've seen in my previous posts, that when NCAA DI Women's teams are out of system, they tend to set their left side outside hitter a lot, so even when they do have a 3rd attacking option, they don't use that option very much in transition, limiting its value.

That said, as you might have noted from the bar chart up above, the 6-2 doesn't escape having a weaker half either. Most teams have a stronger and weaker setter + opposite pairing and here you can see that in the numbers. When the second setter and opposite are in (which is usually the weaker pairing), 6-2 teams lose about a point of Side-Out% (62.6% to 61.6%, p = 0.009).

The main difference here then is in the size of that SO% drop: the 6-2 gives back 0.9 points of SO% where the 5-1 gives back 2.1 points. Going from three attack options down to two costs the 5-1 roughly twice what the 6-2 pays for turning the offense over to its second unit.

Coaching takeaway: The three-attacker advantage is real. The 6-2's structural attacking advantage is in first-ball serve receive. Having 3 attack options available is a real advantage and in this data set, resulted in a 1.2% advantage in SO% made up almost entirely of gains on first-ball Side-Out%.

Claim 2: The 6-2 Enables Better Blocking

The blocking version of the same argument says the 6-2 pays off because you can have an opposite (usually a bigger blocker) blocking in all six rotations rather than relying on your setter (who is usually a smaller blocker) blocking for three rotations. This one doesn't survive contact with the data.

When 5-1 teams move their setter to the front row, Block Impact% (stuff blocks plus positive block touches per opponent attack) goes from 19.7% to 19.5%. That's a tiny drop, it's not statistically significant (p = 0.07), and only 57% of team-seasons declined at all. So at least at this level, the 6-2 shows no blocking advantage.

Whatever blocking cost the front-row setter imposes, it's too small to detect across four seasons of high-level volleyball. I have to say, this one really surprised me. There are some DI setters doing a much better job of blocking than I would have guessed!

Coaching takeaway: If you're considering a 6-2 as a way to improve your blocking, the data offers very little support for that choice.

Claims 3 & 4: A DS Makes Your Back Row Better

In NCAA women's volleyball, the 5-1 has a quiet luxury: unused substitutions. A 6-2 uses a lot of subs on setter and opposite platooning. A 5-1 doesn't, so when the setter rotates to the front row and the opposite drops to the back row, the coach has a lot of options including replacing the opposite with a defensive specialist.

I checked the lineups for every 5-1 set in the sample, and swapping a DS for an Opp is what the majority of teams do: in 64% of setter-front-row serve-receive rallies, a DS was subbed in for the opposite.

At the team level, 82 of the 141 5-1 team-seasons swap systematically (a DS in for the opposite in at least 70% of those rallies), 38 leave the opposite in to play all the way around, and 21 mix the two approaches. And the substitutes really are specialists: they attack 0.014 times per rally, which is to say, they never attack.

Does this approach work? Yes!

When the 5-1's setter goes front row, Good Pass% rises by 2.1 points (p < 0.001) and Dig% rises by 2.6 points (p < 0.001). The dig improvement is the single most consistent finding in this entire study. Only 14% of 5-1 team-seasons failed to dig better in the setter-front-row half.

Meanwhile the 6-2, with no sub budget to spend, gets no such boost. Its passing actually declines by 1.6 points in its second half (p < 0.001). (Why? I have no idea.)

All that said, this improvement isn't enough to overcome the negative impact to the 6-2s Side-Out% advantage. Even with better passing and better digging, the 5-1's setter-front-row rotations still side out 2.1 points worse. Ball control helps; it doesn't make up for having only two attackers.

Coaching takeaway: Subbing in a DS for your Opp is a great use of the 5-1's "extra" subs, and 2/3s of elite NCAA Women’s programs do it. Just be realistic about what it buys - Improved Passing and Digging, but not enough to overcome having only two attack options.

Claim 5: It's easier to Attack with Only One Setter

This is the last of the classic arguments and the one I hear talked about the most. Many coaches feel strongly that it's just a lot easier for hitters to learn to hit off one setter than hitting off two: one tempo, one delivery, and one relationship.

I don't really have a perfect way to test this claim, because the head-to-head comparison is exactly where the recruiting problem bites hardest: Since the top teams get the elite setters (and thus run 5-1s), their hitters get to hit sets from better setters on average, so any raw gap between the systems is tangled up with talent.

That said, we can check to see whether 6-2 hitters show a measurable drop in Atk Eff% and Kill% when the second setter is in the match:

This data makes it pretty clear. Yes, teams running a 6-2 see a statistically significant drop in Atk Eff% and Kill% when the second setter + opposite enter the match. The cause of this decline is split roughly in half: the second opposite hits 3.3 points less efficiently than the first, and the rest of the attackers hit about 1.6 points worse off the second setter.

So here we're seeing some support for this claim in the actual data, though there is no way to know if it's because having two setters to learn to hit off is harder, or if it's just that the second setter is less skilled.

Interestingly, the 5-1 teams have a similar drop in Ro4-Ro6. I think it's safe to assume this is the result of having fewer attacking options.

Coaching takeaway: While there's no way to be sure, the data does seem to support the idea that hitting off two setters is harder than hitting off of only one, but the effect is modest.

The 6-2 Plays Its Worst Rotation Twice

Next, let's dive into some things I learned as I went through this analysis that I have not heard other coaches mention before.

As experienced coaches know, most teams are less effective in Ro1, the rotation where most teams have their setter is pulled back in zone 1, than in the rest of their rotations. It jumps right out of the data. For 5-1 teams, there is a -1.9% delta between Ro1 and Ro2 + Ro3, the other two setter-back-row rotations (p < 0.001).

And for the 6-2 teams, the delta between Ro1 and Ro2 + Ro3 is also pretty big at -1.6% , but they face the same challenge again in Ro4 which is similarly -1.6% less effective than Ro5 + Ro6. In a 6-2 you essentially end up running your worst rotation (Ro1) twice!

The table below makes it even easier to spot. Clearly the 5-1 team has an advantage here as Ro4 is essentially the same as Ro5 & Ro6.

The 6-2 sides out 1.6 points worse in Ro1 than Ro2 + Ro3, and 1.6 points worse in Ro5 + Ro6.

Coaching takeaway: If you run a 6-2, you probably need to spend even more time working to improve on Ro1 than normal as it's going to bite you twice!

The 5-1's Area of Unexpected Strength

The second area coaches don't talk about is something I was surprised to find. While doing the analysis work to test all the claims above, I noticed a pattern kept showing up that I wasn't expecting. Whenever I looked at the Point Scoring phase of the game in Ro4-Ro6, the 5-1 was surprisingly strong.

For teams running a 5-1, the key Point Scoring phase metrics are very consistent regardless of whether the setter is front or back row. Point Scoring% actually improves by 0.6 points (p < 0.001) when the setter is front row which is a small but statistically significant change. And Dig-Set-Kill rate and Knockout% (aces plus serves that force an out-of-system pass, per serve) are flat (p = 0.19 and p = 0.10).

Looking through the numbers, my theory is that this happens due to the positive impact of having a DS backrow on defense and because teams are out-of-system more frequently in transition, so the impact of having fewer attackers is not as big in the point scoring phase as it is in first ball serve receive.

The 6-2 is the mirror image. Its second unit declines in every point-scoring-phase metric: Point Scoring% drops 0.9 points (p = 0.007), dig-set-kill rate drops 1.5 points (p < 0.001), and Knockout% drops 1.0 points (p = 0.003). The weaker setter and opposite pairing serves worse and converts fewer transition opportunities.

Coaching takeaway: A 5-1 has a systematic advantage in this data set in the Point Scoring phase. If your team identity is built on serving and defense, a 5-1 might be a better option for you.

So What Does It All Mean?

Let's tally the scorecard. Three front-row attackers: real, but only for first-ball offense, and the 6-2 quietly pays a similar hitting-efficiency cost through its second unit. Better blocking: not demonstrated. The DS swap: real, popular, and helpful for ball control, but it doesn't cancel the two-attacker tax, and teams that skip it do just as well in the back half. One setter for the hitters: untestable.

And the hidden items most people don't puts on the list: the 6-2 plays volleyball's worst rotation twice per wheel and the 5-1 has a systematic Point Scoring Advantage.

And the scoreboard? Surprisingly, top-50 5-1 teams and top-50 6-2 teams win sets at statistically identical rates (68.5% vs 68.9%, p = 0.66). Two very different sets of tradeoffs, arriving at the same place.

My net net: The structural differences between these two systems are real but small. A 6-2 gives you a structural advantage Siding Out and a 5-1 gives you a structural advantage Point Scoring. However these advantages are pretty small - less than 2%.

Given how small these advantages are, the decision here should be based largely on your personnel. If you've got a great setter who improves your Side Out% by 5-10% ala Charlie Fuerbringer, you'd have to be crazy not to run a 5-1. If you're setters are pretty similar and you've got a strong second opposite and your pin hitters can pass, a 6-2 is probably the way to go!

Hopefully this blog post was interesting and helpful. If you've got any feedback, questions or suggestions for future posts, please hit me up in the comments or send me an email at alex_simons@hotmail.com

See you in the gym!

Alex

Today's post is a quick one diving into an area I've been thinking a lot about, the BIC attack. One of the things that really surprised me in my last blog was that the top Power 4 women's teams run the BIC just about as frequently as the top men's programs. This is certainly NOT the conventional wisdom in the volleyball community.

So I decided to drill in and see what all I can learn about the BIC from a statistical point of view. Who runs it, who runs it well, where are the best locations to run it from, and which locations on the opponent side are the most effective to attack? Let's dig in!

A quick word on the data. For the men I used the full 2026 NCAA DI men's season. For the women I used 2025 Power 4 teams, filtered to matches where both teams are Power 4. This mirrors the approach I took in my last post.

The Top Line Numbers

Let's start with the headline. As we learned last time, the average men's DI BIC hitter has a higher attack efficiency than the average Women's Power 4 BIC hitter.

That 10.8-point efficiency gap (30.8% for men, 20.0% for women) is one of the larger efficiency gaps. Interestingly, most of the gap comes from the kill %. Men and women have pretty similar error rates (19.7% for men, 17.5% for women) with the women a little better. But the kill rate gap is a 13-point spread (50.5% vs 37.5%).

My assumption is that this is because the men just hit the ball harder making it more difficult to block or dig.

The men also run it more. The BIC accounts for 4.9% of all men's attacks versus 3.1% for the women, so the men use it 58% more relative to their total offense, but as we'll see, the difference there almost disappears when you look at the top teams.

Who Runs It, and Who Runs It Well

Here are the top 10 programs by BIC%, the share of all their attacks that go to the BIC.

A fun thing jumps out right away: running the BIC frequently is not the same as running it well. Merrimack devotes a bigger share of its offense to the BIC than any men's team (8.4% of all attacks) but converts at just 18.5%. UCF tops the women at 7.3% and a matching 17.7%.

But then look at UCLA! Second highest BIC% in Men's DI and crushing it at 46.4% attack effectiveness. On the women's side, Miami pairs a hefty 6.3% share with a strong 30.8% and Nebraska at 5.7% and 40.7%. I suspect lots of men's teams would love to run the BIC that well.

Now the efficiency leaders, with a 50-attempt minimum (to avoid getting mislead by small sample sizes).

Ball State leads the men at 49.7% (😮)! But scan over to the women's column and notice SMU (41.9%) and again, Nebraska (40.7%). Those two numbers would slot into the men's top five, ahead of every men's program outside the top four. The very best women's BIC teams are right there with elite men's offenses.

Coaching takeaway: If you want a model for the BIC in the women's game, study SMU and Nebraska. They prove the play can be a primary, efficient weapon rather than a niche play.

When Do Teams Run It?

We know who runs the BIC and who runs it well. The next question I had is which rotations they run it in, and this turned out to be my favorite finding of the whole exercise.

Look at the women's bars on the left side of the chart. When the setter is in the back row, Power 4 women's teams rarely run the BIC, right around 1.9 to 2.0% of their attacks. But when the setter rotates to the front row, the rate more than doubles to roughly 4.5%. This would seem to say that most women's teams are using the BIC to add a third attacking option when their setter is front row.

But the men's data tell a different story. Their BIC% is relatively similar in every rotation, between 4.0 and 5.9% across the board. Men's teams run the BIC whether the setter is in the front row or the back row.

I think this is because of the D-ball and how it’s used in men’s volleyball.

As we learned in my last blog post, men's teams love to run the D-ball, so when their setter is front row, they already have a third attacking option. What we're seeing here is that in the men's game, the BIC is used to create a 4th attacking option in every rotation by having both outsides hit BICs.

Coaching takeaway: I think the men have it right here - If you aren't using the BIC in all six rotations, you are taking a really effective weapon off the table. For my U15 girls’ teams, we run a BIC in every rotation and since we usually run a 6-2, we always have 4 attacking option. The next time your setter is in the back row, take a look to see if you can find ways to get your backrow OH available to attack!

Where to Hit It From

And now for the geeky part. I was talking with Jonathan Winter, the head coach for the Pepperdine Men's team this week. He asked if I had any data about which locations/route were the most effective for hitting a BIC.

To answer that question, I took the top 10 BIC programs by efficiency in each pool (we are looking at how the best of the best do it) and split each map into three efficiency tiers. Gold marks the most efficient third, silver the middle, and bronze the least. The legend on each map lists the actual attack efficiency range for every tier.

First, here's the attack from location heatmap for the top 10 men’s teams:

And here's the same for the women's side:

Remarkably similar! The gold zone is in the front left of zone 8 for both. This area is where you would attack if you were running what many of us call a "flare" route. The BIC is most efficient when it is contacted left of center, and it drops to bronze as the contact point slides to the right.

Coaching takeaway: Shade the BIC slightly left of dead center as often as possible and have your hitters run a flare route.

Where to Attack to

Finally, which locations on the opponent’s side are the best to attack based on attack efficiency? These maps show efficiency by landing spot on the opponent's court, with the net at the top.

Important Note about these heatmaps. This data shows, when BIC attack ends up in this location, the likelihood of the result being a kill. And importantly, this does not include any attacks that were blocked or attacking errors (they didn't make it to this spot) or had block touches (since we don't know where those were meant to go).

First the men's heatmap:

The interesting thing here is that clearly the best place to hit a BIC in men's volleyball is the last half of the court, in the middle. And tipping over the block? Not nearly as effective.

And now the women's heatmap:

This one is notably different! The middle of the court is not a great target here - the biggest gold areas are clustered deeper in the court to the left and right of middle back.

David Hunt, the Associate Head Coach for the Women's team at Texas and I were discussing BICs this week. His take (which this data seems to support) is that in the men's game, the BIC hitters hit it hard enough to overpower the backrow defender whereas in the women's game, it's about the speed of attack and getting the ball on the ground in the corners faster than the defender can get there.

Coaching takeaway: If you are coaching men to hit BICs, they need to focus on hitting it hard to the middle/middle back third of the court. If you are coaching women to hit BICs, they need to focus on hitting more for the deep corners/seams of the court to be effective.

Wrapping Up

The BIC has become one of my favorite parts of the men's and women's game and as someone who has always coached women's teams, I was pretty excited to see that in many cases, the women are hitting BICs just as frequently and just as effectively as the men's teams. That's very cool to see!

Hopefully you will find these new heatmaps as useful as I did. I reviewed these and a few other new ones with my team on Tuesday before practice and they were already adjusting their attacking routes and locations by the time we finished up that night.

Wishing you all similar success with your teams!

As always, if you have any feedback, suggestions or questions, please hit me up in the comments or send me an email at: alex_simons@hotmail.com

See you on the court!

Alex

Howdy folks!

The 2026 NCAA Men's season just wrapped up (congratulations to Hawaii on their championship even though I was rooting for UCLA). I thought I might learn a thing or two if I ran my full statistical analysis suite for the NCAA DI men's season to see how it compares to what we've learned about the women's game over the last nine posts in this series.

The short version: the men's and women's games are remarkably similar in what predicts winning, more so than discussions with coaches might lead you to believe. The same core metrics are the most strongly correlated, and the same general hierarchy of importance/impact holds. But there are a few areas where the two games diverge in meaningful ways, and those differences tell us something interesting about how the men's game is played at the highest level in the U.S. They might also point the way to some interesting opportunities in the women's game.

Let's dig into it...

(This blog was updated 5/26/26 at 7:53 Pacific to fix a small data discrepancy in SO%’s)

The Dataset

NCAA Men's DI volleyball is a much smaller world than women's. There are only 31 programs classified as DI by the NCAA (source: AVCA, see Appendix), and one of those (University of the Pacific) isn't competing in DI until 2027. That leaves us with 30 DI programs, spread across five conferences (Big West, MPSF, MIVA, EIVA, and NEC) plus a few independents. One quirk of men's college volleyball: these DI programs share their conferences with DII programs. But I've filtered to only DI teams for this analysis.

For this analysis, I used 939 match files covering 30 DI men's teams. For the women's comparison, I've elected to use data from the 66 Power 4 conference teams (ACC, Big Ten, Big 12, SEC) from the 2025 season. I further filtered to only matches where both teams are P4, which gives us 764 match files.

I chose to use Women's NCAA DI Power 4 teams for this comparison as that is the data set I used in my original post for this series. In addition, when I compared the Men's DI data with both Women's DI and Women's P4, P4 was more similar to the men's results compared to the much wider diversity of the DI dataset.

One important note - this data set contains every match between two P4 teams both in and out of conference, so some of the numbers are slightly different from those in my first blog post. I made the switch to all P4 vs P4 matches a few blog posts back in order to increase the size of the data set giving me better statistical significance scores.

That said, having only 30 teams on the men's side means I'm working with a relatively small dataset. Luckily, all of the correlations I'm sharing turned out to be statistically significant and the top 35 metrics all have p-values well below 0.001. Even then, I'm being more careful than usual about noting p-values and statistical significance throughout this post so you can judge the confidence level for yourself.

Quick Methodology Refresher

If you've been following this series, you know the drill: for each metric, I calculated team-level aggregated values (summing raw counts before calculating percentages, never averaging percentages) and computed the Pearson correlation coefficient (r) against Set Win%. For a refresher on what r values mean in practice, check out Part 1 of this series.

The Phase Metrics: Familiar Territory

Let's start with the big picture, the big four metrics representing the main phases of the game (Earned Side-Out%, Side-Out%, FBSO%, and Point Scoring%).

The first thing that jumps out is that men's teams have a notably higher Side-Out% at 66.1% compared to the P4 women at 58.8%, a 7.3-point gap. The average men's team sides out at the same or higher rate than all but one of the P4 women's teams (SMU at 68%).

Similarly men's teams have a 4.1% higher Earned Side-Out% (which removes service errors from the equation) and FBSO% (the percentage of receptions that result in a first-ball kill) is also higher for men at 32.7% vs. 29.7% (which I will note is not nearly as big a gap as you might expect from talking with the coaches of men's teams).

I find many of these Side-Out numbers pretty astonishing. Ball State at 72.8% makes my brain hurt. And because the math is mathing, Point Scoring% flips in the other direction. P4 women average 40.9% on PS% while men average only 36.3%.

(Note, you might very reasonably expect the Men’s PS% to be 33.9% if the SO% is 66.1%. That isn’t happening here because I’ve included data for Men’s DI teams when they play Men’s DII teams, but the DII team data is not being included in the analysis)

Net net - it's notably more challenging to score a point in the Point Scoring phase in the men's game (what some people refer to as "real points") than in the women's game.

Interestingly, you can have a VERY good Side-Out% or PS% in men's volleyball and still have challenges. USC is a great example - their 71.2% Side-Out rate is remarkable, but because their PS% was only 37% (13th out of 30 teams), their Set_Win% was only 8th.

You have to be very good in both phases to make it into the top 5 of Set Win%.

That said, the correlations to Set_Win% are nearly identical between the two games. Side-Out% leads for both (men r = 0.916, P4 r = 0.932, both p < .001), and all four metrics have r values above 0.83.

Coaching takeaway: The game phase-level blueprint for winning is very similar for men and women. You have to be able to side out and point score.

The Skill Metrics: The first set of differences.

When we drill down from phase metrics into the individual skill metrics, the overall order of importance is strikingly similar especially in the top 10. Here are the top 15 metrics by men's correlation rank, with their P4 equivalents.

The top 10 is nearly identical between the two games. Kill%, Attack Efficiency%, FBSO Atk Kill%, In-System Atk Eff%, MTRP%, and the transition metrics all appear in both lists with strong correlations. If you asked me to guess whether a correlation table came from the men's or P4 women's game based on the top 10 alone, I honestly couldn't tell you without a cheat sheet.

Kill% is the #1 individual skill metric for men (r = 0.874, p < .001) vs. #3 for P4 women (r = 0.847, p < .001). Both are strong, but in the men's game, the ability to put a ball on the floor is a sharper differentiator. The scatter plot below shows the relationship clearly: with 30 teams, there's very little noise. UCLA sits at the top right corner with a Kill% above 51%.

Toward the bottom of the metrics table up above, two of the interesting differences between the men's and women's games first emerge. I’ve highlighted them in yellow to make them easier to see.

Backrow Atk Eff% is at #12 for men (r = 0.752, p < .001) vs. #19 for P4 women (r = 0.590, p < .001). Both are significant, but back-row attacking explains substantially more of the variance in winning for men. And the actual Atk Eff% is significantly different at 23.4% back-row attack efficiency while P4 women average 12.8% and the difference is statistically significant (Z = 10.67). Men are converting back row attacks at nearly twice the rate that women are.

Ace:Error Ratio sits at #14 for men (r = 0.725, p < .001) but drops to #40 for P4 women (r = 0.200, p = .107). That's not just a difference in magnitude. For P4 women, Ace:Error Ratio has NO statistically significant relationship with winning. For men, it's one of the strongest predictors in the dataset.

We'll dive deep into both back-row attacking and serving in the following sections.

Coaching takeaway: The core winning formula is gender-neutral: attack efficiently, convert in system, finish in transition. But there are two significant differences and those are how much serving and back-row attacking matter. In the men's game, you have to have a potent backrow attack and you have to be able to serve tough and get aces while keeping your error rate proportional. These elements of the game are currently nowhere near as important in the women's game.

Deep Dive: Attacking

This is where the comparison really gets fun.

Men hit more efficiently across every attack type, but the size of the gap varies dramatically, and those gaps tell a story about how the two games are structured differently.

The quick attack is highly efficient for both men (42.5%) and women (30.5%) but the gap there is big - 12.0-points! Interestingly, Gap attacks are also very efficient for both as well (39.0% and 30.5%), but the gap is much smaller (only 8.3%).

The back-row attacks (row 4 & 6 above) are the big story here.

The BIC runs at 30.8% efficiency for men vs. 20.0% for P4 women, a 10.8-point gap. If you scan the table, you'll recognize that the average men's BIC attack has a higher attack efficiency than any Women's DI attack option.

The in-system D-ball has an attack efficiency of 27.2% vs. 17.5%, a 9.7-point gap. These attack types are fundamental to the modern men's game.

As you would hope, since these attacks are so effective, men's teams run them a lot more frequently, the D-ball being the most notable difference at 7.2%. And even though the BIC gap is only 1.9%, if you do the quick math, you realize that this means the men run it 55% more frequently than women do.

If you look back up at the hitting efficiency table, you’ll see that out-of-system pin attacks (Hut and Five) show the smallest effectiveness gaps: 1.3 and 2.9 points. These gaps are so small they aren't statistically meaningful.

When both genders are out of system and sending a high ball to the pin, the efficiency gap vanishes. Neither men nor women have a ton of success out of system on the pins.

Coaching takeaway: Men hit more efficiently across the board, but the gap is largest on middle attacks (quicks, gaps) and back-row attacks (BICs, D-balls). Not surprisingly, men run these attacks quite a bit more. Women's teams rely more heavily on the pin attackers and slide hitters. I can't help but wonder if that will change over the coming years as we see even bigger and more powerful women playing in NCAA DI volleyball.

Where Do Attacks Happen?

Before we drill into specific attack types, let's look at where attacks actually come from on the court. The heatmaps below show distribution of attack by court location.

This men’s heatmap gives you a pretty good sense of the game. A lot of attacks in Zone 4, but not the majority. Now let's take a look at the equivalent for the P4 women:

The distribution of attacks is notably different. P4 women's offenses are more concentrated on the left side: Zone 4 accounts for more than half of all attacks (101k of 197k). If you're P4 OH, you're pretty much guaranteed to get a lot of swings! The other really notable difference here is in zone 9 (middle of the court on the right) - 10.8% of attacks on the men’s side, 2.2% on the women’s. Men's Zone 4 volume is high too (about 20,000 of 46,000), but the range in the men’s game is definitely more widely distributed.

This leads to a similarly different distribution of where kills come from. Kills in the men’s game are more concentrated in the middle of the court than in the women's game:

Men run attacks in the middle of the court more frequently than women and those attacks have higher attack efficiencies leading to the 6.1 percentage point difference you see in the table above.

The back-row attack story as a whole is striking. Men hit back-row attacks more than twice as often with much higher efficiency (23.4% vs. 12.8% overall). This is one of the more glaring structural differences between the men’s and women’s game.

The correlation data confirms this: Backrow Atk Eff% is the #12 predictor of winning for men (r = 0.752, p < .001) vs. #19 for P4 women (r = 0.590, p < .001). Back-row efficiency is meaningful in both games, but it matters significantly more in the men's game.

Coaching takeaway: If you're looking for where the men's game diverges from the women's most visually, these heatmaps and graphics do a pretty good job of illustrating the key differences. Men spread the offense across the court more than the P4 women who concentrate attacks more in the left front. I feel like the men are on to something here. My experience coaching has always been that when my teams use a more evenly spread set of attack options, they put more pressure on the block, making us harder to defend.

The BIC: How big a deal is it?

When I talk to men's coaches, they are almost universally excited about the BIC. Most see it as a major part of their offense and something that is critical to their success. Last week a group of coaches at our club had the privilege to spend time with Joel Jansen from Trinity Western University (They are the 8-time Canadian Men's national champions) and he was telling us that they feel the BIC is their number one attack option.

So I was curious to see what the data would say about the BIC in the men's game. And when I first saw the results, I was surprised with the story the data reveals.

In men's DI, the BIC makes up only 5.3% of all attacks. Aside from UCLA (8.1%), men’s programs don’t run the BIC all that often: Hawaii (4.3%), Long Beach State (3.4%) and Pepperdine (4.9%). A number of top women's teams like Miami (6.3%), Wisconsin (6.0%), Kentucky (5.8%) and Nebraska (5.7%) run the BIC more than every men’s team except UCLA.

That said, BIC attack efficiency in the men's game is 30.8%, which is impressive. But as we discussed above, it only ranks 4th in Men's DI behind Quick attacks, Gaps and Reds (RS in system).

My take based on the data? The BIC is a great option (I'm teaching my U15 girls to hit BICs) and adding a fourth attacker puts more stress on the defense. But from purely statistical analysis point of view, it isn’t quite as big a deal in the men's game as it's being made out to be and the use of the BIC in the women’s game is not all that different.

Who Hits the BIC the Best?

All that said, there are some DI men who have sky high attack efficiencies hitting BICs!

Patrick Rogers of Ball State is in a class of his own: 69.9% kill rate and 58.4% efficiency on 113 attempts. Those are staggering numbers. Sean Kelly (UCLA, 49.0%), Skyler Varga (Long Beach State, 48.1%), and Ethan Saint (UC Santa Barbara, 47.6%) round out the elite tier.

On the P4 women's side, Malaya Jones of SMU leads at 46.7% efficiency on 75 attempts, followed by Harper Murray of Nebraska at 44.8% on 116 attempts. Those are elite numbers that hold up well even on the men's leaderboard.

Coaching takeaway: The BIC is an important weapon in men's volleyball, but don't over-index on it at the expense of quicks and gaps, which are substantially more efficient.

The D-ball: The Men's Game's Signature Attack

If there's one attack type where the men's and women's games diverge most sharply, it's the in-system D-ball.

For the average DI Men's teams, D-balls make up 8.7% of all attacks, a significantly higher proportion than the BIC. And many top programs like Hawaii (9.8%), Pepperdine (11.4%), and Ball State (9.3%) run it even more. P4 women's teams run D-balls just 1.5% of the time.

Overall the men hit 5 times more in-system D-balls per match than P4 women. That is an enormous difference in offensive structure. For Men's teams the D-ball is a significant part of the offense, whereas women's teams in the U.S. generally prefer to run slides.

And the men are pretty good at it. Men's in-system D-ball efficiency is 27.2% with a kill rate of 46.9%. That's higher than their out-of-system pin attack efficiency.

I wonder if women's teams are missing out here. Talking with Courtney Thompson last week, she pointed out that as a setter, you can set a good D-ball even when you get pushed quite far off the net, whereas the slide geometry starts to break down beyond 10 feet. I had never considered this, but after talking with Courtney, I pulled the data, and it supports what she was saying.

The numbers tell a pretty clear story. The kill% of the D-ball holds up when the setter is 12-14 feet off the net while the slide attack is starting to die at 11 feet. You’ll notice the huge jump in attempts at 13-14 feet. Many men’s teams use the D-ball as a preferred out of system set because even when the setter is 16-18 feet off the net the D-ball still has a 36.5% kill%. It gives you an effective right side attack option in a lot more situations than a slide attack does.

Which Teams Run the Best D-ball Attacks?

Hawaii leads the men with a jaw-dropping 48.3% D-ball efficiency on 319 attempts. Pepperdine (42.1% on 323 attempts) and Ohio State (36.9% on 252 attempts) also show that high-volume, high-efficiency D-balls are very much achievable.

Coaching takeaway: If you coach men's volleyball, your D-ball attacking is potentially one of your biggest competitive levers. Hawaii's 48.3% efficiency on 319 attempts shows what's possible when D-balls are a first-class weapon.

The Quick Attack: Peak Attack Efficiency

Quick attacks (front quicks, center quicks, and back quicks combined) deserve their own spotlight because they're the most efficient attack type in both the men’s and women’s game4, and the efficiency gap between men and P4 women is the largest of any in-system attack.

Men's quick attack efficiency is 42.5% with a kill rate of 55.3%. P4 women's is 30.5% with a kill rate of 42.7%. As someone who played middle blocker, these numbers make me happy. Men's teams run quick attacks on 13.2% of their total attacks. P4 women run them on 8.9%. So men are running quicks about 50% more frequently.

Which Teams Run the Best Quick Attacks?

On the men's side, Lindenwood (54.4%), Long Beach State (54.1%), and Ball State (54.1%) are all running quicks above 54% efficiency. The top 5 teams all exceed 50% attack efficiency %, which is remarkable.

On the women's side the numbers are also incredible. Nebraska leads the P4 women at 48.9% on 264 attempts, a number that would place comfortably on the men's top 10 list. Texas A&M (43.9% on 342 attempts) stands out for combining high volume with high efficiency. Tennessee (42.9%), Stanford (42.6%), and Indiana (42.5%) are all tightly grouped in the next tier.

Coaching takeaway: The quick attack is the most efficient weapon in both games, it's just incrementally more efficient in the men’s game. If you aren't working on middle attacks, you're missing out on the most effective attack options available in men’s and women’s volleyball.

Deep Dive: Serving

Serving is the area where the men's and women's games look the most different statistically speaking.

Let's start with the headline number highlighted in yellow above. Men's teams average an 18.9% serve error rate, nearly double the P4 women's average of 10.4%. Nearly one in five serves in men's DI volleyball is an error. There are times watching men's volleyball that I want to pull my hair out over how many service errors they make!

But as you scan across that row, you'll see that the R values and P values for both men and women are not even close to being statistically significant. So they tell the same story I discussed in my first and second posts in this series. Serving Error % is not correlated with winning (or losing) sets.

The Men's Ace:Error Ratio is r = 0.725 (p < .001) which is almost as important as transition attacking, while for P4 women it's not even statistically significant [r = 0.200 (p = .107)]. Honestly, the huge difference here boggles my mind. Incredible!

Getting aces in men’s volleyball is really important - the Men's Ace% correlates at r = 0.655 (p < .001), which is a pretty strong correlation; In the P4 women's game it's irrelevant with an r = 0.089 (p = .476). Knockout% (the percentage of serves that result in an ace, error pass, or overpass) tells the same story: r = 0.609 for men (p < .001) vs. r = 0.165 for P4 women (p = .184).

The scatter plot tells the story visually. There's a clear, tight relationship between Ace:Error Ratio and winning in men's DI. The teams in the upper right (higher ratio, more winning) include Pepperdine (0.44), Long Beach State (0.43), Hawaii (0.39), and Lindenwood (0.39).

And for comparison, here's the same graph for P4 women. There's no visible correlation at all here - just a cloud of dots spread all over.

Why the difference? I think it comes down to the men's top spin jump serve. Top spin jump serves in men's DI volleyball are going 60-80 MPH and are much more difficult to control. This creates a higher-variance outcome: more aces and more errors. But teams that can manage that variance (keeping their error rate from exploding while still generating aces) gain a substantial edge.

Coaching takeaway: If you coach men's volleyball, serving is a genuine competitive differentiator in a way that it simply isn't in the women's game. Invest in it. Train your servers to maximize aces relative to errors. The data is clear: teams that serve with controlled aggression win more sets.

Deep Dive: Passing

Passing tells a subtler story than serving. Men's teams actually pass slightly better than P4 women on average: Good Pass% of 50.7% vs. 48.9%, and Perfect Pass% of 13.9% vs. 11.8%. The gap is smaller than you might expect given how much harder men serve. Men also get aced less often: their Aced% (the percentage of serve receptions that result in an ace, as opposed to Ace%, which measures how often a team aces when serving) is 4.8% compared to 6.3% for P4 women.

But here's the key finding: pass quality correlates with winning less strongly in the men's game. Good Pass% has an r of 0.429 (p = .018) for men vs. 0.605 (p < .001) for P4 women. Perfect Pass% is r = 0.201 (p = .287) for men, which is not even statistically significant, while for P4 women it's r = 0.472 (p < .001).

Why would passing matter less in a game where serving matters more? I think the answer is that men's teams are better at converting out-of-system opportunities. Men's overall Out-of-System Conv% averages 36.7% vs. 30.7% for P4 women (a gap of 6.0 points), and the quality gap between in-system and out-of-system attacking is smaller.

Coaching takeaway: For men's coaches, this doesn't mean passing doesn't matter. It absolutely does, and Good Pass% is still statistically significant. But the data suggests you may get more marginal value from investing in your out-of-system attacking than from chasing perfect passes. Make sure your hitters can terminate from a wider range of pass locations.

Deep Dive: Defense

Defense is another area where you might expect the men's and women's game to differ significantly, but based on the data, they are pretty similar in terms of the importance of blocking and the relative unimportance of digging up a lot of attacks.

Block Stuff% and Block Impact% are significant predictors of winning for both genders.

Block Impact% is my preferred blocking metric. It measures how often a team gets a stuff block or a positive block touch (one that stays on the defensive side), divided by the total number of opponent attacks. Block Impact% is similarly correlated with set win% for both men (r = 0.612, P < .001) and women (P4 r = 0.671, p < .001).

The percentage of attacks dug up on the other hand is not correlated with winning in the men’s game and only weakly correlated in the women's game. And as you might expect given the higher attack velocities efficiencies, men only dig 35.3% of attacks compared to 43.9% for women.

Coaching takeaway: The block is a big part of your defense in both games. For men's coaches, floor defense matters but won't give you a competitive differentiator. Focus your block training on stuff blocks and meaningful touches that make it easier to get a dig and successfully attack in transition.

Spotlight: UCLA

UCLA finished #3 in the NCAA RPI rankings (behind Hawaii and Long Beach State) but was ranked #1 most of the season and was the most statistically dominant team in the 30-team DI field by set win percentage.

UCLA was #1 in four of the seven most important metrics and #2 in the other three. Their Kill% of 51.8% is staggering: more than half of their attacks resulted in kills. Their attack efficiency of 37.0% was the best in the country by nearly 2 points. And their Backrow Atk Eff% of 36.0% means they were attacking from the back row at a level that most teams can't achieve from the front row.

If you read Part 1 of this series, you'll see the parallel to Nebraska in the women's game. Both teams were statistical outliers that sat in the upper-right corner of nearly every scatter plot, dominating their respective competition. Nebraska was #1 in 5 of 8 top metrics for P4 women; UCLA was #1 in 4 of 7 for men.

And of course, both of them came up just a little short in the playoffs (or maybe there wasn't actually a touch?🤔)

Spotlight: Hawaii

Hawaii won the 2026 NCAA Men's Volleyball Championship. They finished #1 in the NCAA RPI rankings with a 27 - 5 record, and like Texas A&M on the women's side, their regular-season statistical profile tells an interesting story about what it takes to win a title.

Hawaii wasn't the most dominant regular-season team statistically (that was UCLA), but they were elite in one important area, Point Scoring%. They were #1 in the entire country at scoring when they served, 2% ahead of UCLA and 3% ahead of UC Irvine (the NCAA tournament runner up). And Hawaii's Ace:Error Ratio of 0.39 was #3 nationally, meaning they could serve aggressively without giving away too many free points.

In some ways, this reminds me of the Texas A&M championship on the women's side. Texas A&M was #1 in SR In-System Conv% by a notable margin. Hawaii was #1 in PS%. Both champions were elite in one specific phase of the game and very good across the board in everything else.

Coaching takeaway: Championships aren't necessarily won by the team that leads every statistical category. They're won by teams that are very good at everything and elite in one phase. If your team's profile shows balanced excellence with one clear area of dominance, that's a championship contour, at least in 2025/2026.

Wrapping Up

After analyzing 939 men's match files and comparing them to 764 P4 women's match files across 66 teams, here's what I've learned:

The core winning formula is the same for both games. Side-Out%, Kill%, Attack Efficiency%, and Point Scoring% are the dominant predictors regardless of gender. The top 10 correlation rankings are nearly interchangeable. If you're a coach who works with both men's and women's programs, the strategic framework doesn't change.

But there are four areas where the men's game is meaningfully different:

1. Serving is a weapon, not just a start to the rally. Ace:Error Ratio, Ace%, and Knockout% are all strong predictors of winning in the men's game (r = 0.61 - 0.73, all p < .001) while they have essentially zero predictive value in the P4 women's game. Men serve nearly twice as many errors (18.9% vs. 10.4%) but the teams that manage the risk/reward equation come out ahead.

2. Back-row attacking is central to the men's game. Men hit back-row attacks with dramatically higher efficiency (23.4% vs. 12.8%) and higher frequency (19.8% vs 9.0%). In-system D-balls in particular are roughly 5 times more frequent per team in the men's game. Backrow Atk Eff% is the #12 predictor for men (r = 0.752) vs. #19 for P4 women (r = 0.590).

3. This BIC might not be as big a deal as men's coaches make it out to be, but the D-ball certainly is. And there's definitely something to like about having an attack option where the effectiveness stays constant even when the setter is pushed 16-18 feet off the net.

4. Good passing matters less for men. Good Pass% and Perfect Pass% are weaker predictors for men, likely because men are better at converting out-of-system opportunities (36.7% OOS conversion vs. 30.7% for P4 women).

My bottom line? Volleyball is volleyball and the men’s game and women’s game are more similar than they are different. The same principles drive winning in both games.

But the men's game is faster, the athletes are more powerful, and the game is more serve-dependent, which shifts the tactical emphasis toward serving very aggressively, and back-row attacking. Conversely, passing and digging are less important/differentiating in the men’s game than they are in the women’s game.

If you coach men's volleyball, I'd love to hear if these observations mirror your experience or not. Hit me up in the comment section or email me at alex_simons@hotmail.com

See you on the court!

— Alex

Appendix: NCAA DI Men's Volleyball Programs (2025 - 2026)

Source: American Volleyball Coaches Association (AVCA), 2026 Men's DI/DII Programs List.

The following 30 programs competed at the DI level during the 2025 - 2026 season (University of the Pacific, the 31st DI program, was not yet competing at the DI level):

1. Ball State University

2. Brigham Young University

3. California State University, Long Beach

4. California State University, Northridge

5. Fairleigh Dickinson University

6. George Mason University

7. Harvard University

8. Lindenwood University

9. Long Island University

10. Loyola University Chicago

11. Manhattan University

12. Merrimack College

13. New Jersey Institute of Technology

14. Northern Kentucky University

15. Ohio State University

16. Pennsylvania State University

17. Pepperdine University

18. Princeton University

19. Purdue University Fort Wayne

20. Queens University of Charlotte

21. Sacred Heart University

22. Saint Francis University

23. Stanford University

24. University of California, Irvine

25. University of California, Los Angeles

26. University of California, San Diego

27. University of California, Santa Barbara

28. University of Hawaii

29. University of Maryland Eastern Shore

30. University of Southern California

Methodology Notes

• Unit of analysis: Team-level aggregated statistics (not set-level or match-level)

• Correlation measure: Pearson r, each metric vs. Set Win%

• Aggregation method: Raw counts summed across all sets, then percentages calculated (never averaging percentages)

• Men's data: 30 DI teams (939 match files), 2025 - 2026 season

• Women's data: 66 Power 4 conference teams (764 P4 vs. P4 match files), 2025 season

• Attack type efficiencies and FBSO heatmaps computed from P4 vs. P4 match data only (not P4 vs. non-P4 matches)

• Attack efficiency comparisons use minimum thresholds to ensure statistical reliability

• BIC hitter leaderboard: minimum 50 attempts; team leaderboard: minimum 20 attempts

• FBSO attack heatmaps use attack start coordinates from match data, with coordinate flipping applied to standardize court sides

• Data pipeline built with R and the openvolley/datavolley package

• All match data sourced from scouting match files

Howdy folks!

Welcome back to my "What Really Wins Sets" series. After five posts focused on the Point Scoring phase, today I'm shifting my focus to the Side Out phase. To kick that off, I'm starting by trying to answer a fundamental question that I know many coaches have strong points of view on; Where should our passing target be in serve receive?

Nearly every coach I've met has a point of view on this question, some of them quite strongly held! Many coaches believe in trying to pass the ball to a target about ~3 feet off the net as that's the distance where their setters have the most options and can easily run a highly effective offense (which the data I'm going to share with you confirms is true).

I've met other coaches who are advocates for a passing target quite a bit farther off the net, maybe even 5 or 6 feet off. They are focused on minimizing overpasses and giving themselves more opportunities to attack. And of course I've seen coaches advocate for everything in between.

But I've never seen much in the way of data to back up these arguments. My goal today is to change that and walk you through a data driven approach to setting passing targets that hopefully you can use in your gym.

I want to start the discussion by proposing a simple idea:

1. Every team has some variability in their passing.

2. Some passes go to their target, some go deeper in the court, some go tight to the net and some are overpasses.

3. Depending on how skilled a team is at passing, the range of these outcomes can be smaller (highly skilled) or larger (less skilled).

I suspect that idea isn't controversial. Given this skill based range of passing outcomes exists, I’m proposing that we shouldn't ask the question "Where is the best place to pass to maximize my teams attacking efficiency?". Instead, we should ask the question "Given my teams passing skill level and range of passing outcomes, where should our passing target be to maximize the number of points we score while minimizing the number of points our opponent scores?"

As we dive into the details, you'll see that the answer, for most teams, is very likely "farther from the net than your current target".

Important note: This blog post relies heavily on the location data I've extracted from 5,107 DVW files. As many of you know, this location data definitely isn't perfect, and usually has an error rate of 3-4%. That can be pretty annoying when reviewing data for individual matches, but when used in aggregate across thousands and thousands of touches, it's reasonably safe to assume that the errors cancel each other out. In this analysis I'm keeping that in mind and making sure to only report numbers that have hundreds and, in most cases, thousands of data points backing them up.

The Expected Outcome Approach

To answer this question, I built an expected outcome based model that combines three datasets derived from the passing results from every 2025 NCAA DI Women's Volleyball match (682,712 total pass attempts). Those three data sets are:

1. An attack efficiency surface: how effective on average are the attacks that result from passes to a given location?

2. An overpass penalty: how frequently does the opponent score a point when a team overpasses?

3. Each team's passing distribution: how tightly (or loosely) their passes cluster around their passing centroid (the calculated average center point of all their passes)

I used these three datasets to build a model that places a team's passing distribution (their measured 2025 scatter pattern) at every possible point on the court. At each passing target point, my model computes the expected attack efficiency based on their distribution (including the penalty for overpasses), then finds the passing target point that maximizes the overall expected outcome (i.e. expected number of kills - expected number of opponent kills).

Let's walk through each dataset and see how its used.

Step 1: Where Passes Land and Expected Attack Efficiency

For every serve reception in the 2025 NCAA DI season, I traced the resulting pass, set, attack sequence. The pass destination is measured by where the setter contacts the ball (the set's starting coordinate in the match data), and the attack outcome is recorded as a kill (+1), error or blocked (-1), or kept in play (0). This gives us 621,119 pass-to-attack sequences across all of DI.

I used that data to build a a 36-cell attack efficiency heatmap. The value (and color) in each cell represents the average attack efficiency % of all the passes to that cell. Every cell has > 700 observations, and most have thousands. So the 95% confidence intervals are very tight.

The pattern is what you would expect: passes closer to the net and just to the right of center produce higher attack efficiency (up to 29% in the cells nearest the net), while passes deep in the backcourt produce attack efficiencies near or below zero. No surprises here.

Here's the same heatmap filtered to only P4 vs P4 matches (games where both teams are from a Power 4 conference), covering 96,758 pass-to-attack pairs across 67 teams:

The pattern is essentially the same, with overall efficiency numbers a little higher across the board. P4 teams convert more efficiently at every location.

Both heatmaps temptingly shout the same thing: pass close to the net! But there's a huge cost hiding in this picture that the heatmaps don't show. We'll get to that next.

Coaching takeaway: Attack efficiency drops off steadily as passes land further from the net. The data from 621,119 pass-to-attack sequences confirms what every coach intuitively knows: passes that are about 3 feet off the net produce better attacks. But as we'll see, "aim closer" actually isn't the right advice.

Step 2: The Overpass Penalty

When a team overpasses, sending the ball back to their opponent, they don't just loose a chance to score, they also give their opponent an opportunity to attack. I calculated the average outcome of overpasses in 2025 Women's DI season:

Overpasses result in very high opponent attack efficiencies. 55.2% of overpasses result in an opponent kill with a 10.2% error rate bringing overall attack effectiveness on overpasses to 45%. For comparison, Women's DI attack efficiency is 20%. So when you overpass, your opponents gets 2.25X better than normal chance to score a point.

Now you might look at a 4.7% overpass rate and think it's not a big deal. But the math tells a different story. Each overpass flips an expected outcome from something positive (your attk eff%, typically around +20%) to something deeply negative (opponent attk eff%, which is -45% from your perspective). That's a swing of 65 points in expected outcome value per overpass.

An easy way to conceptualize this is to recognize that from an expected outcome point of view, it takes slightly more than three good or perfect passes to get back to "even" after an overpass!

This penalty is what creates the core tradeoff here. Passing closer to the net improves your attack efficiency on the passes that land on your side of the net, but increases the chance of an overpass that hands the opponent a high probability opportunity. The question is where those two forces balance out.

Coaching takeaway: Overpasses are one of the most costly outcomes in serve receive, second only to getting aced. An overpass rate that seems modest (around 5%) creates a significant drag on your side-out offense because of how efficiently opponents convert those free attacks. A 2% decrease in overpasses has a positive impact on expected outcome equal to a 6% increase in pass quality! Any tactical choice that reduces overpass rate by even 1 - 2 percentage points can meaningfully improve your team's success.

Step 3: How Accurate Is Your Passing?

This is where the analysis becomes team-specific. For each of the 348 DI teams for which I have pass location data, I computed the standard deviation (SD) of where their passes went using a set of X & Y coordinates:

• Lateral SD (the X coordinate): the left-to-right spread of passes

• Depth SD (the Y coordinate): the close-to-far spread (distance from the net)

These two numbers define each team's passing "fingerprint." A team with small SDs has a narrow, consistent passing distribution. A team with large SDs spreads their passing distribution across a wider area. Across all of DI:

• Median lateral SD: 5.3 feet

• Median depth SD: 4.4 feet

There's a wide range in these numbers, and the range matters. A team with a depth SD of 3.5 feet has a fundamentally different risk profile than a team at 5.0 feet, even if both aim at the same spot on the court.

To illustrate what this looks like on the court, here's Nebraska (the best passing teams in P4 last year) side by side with Penn State (a middle of the P4 pack passing team):

And here are some visuals to illustrate what's going on here.

First up is a diagram representing Nebraska's passing results. The inner ellipse with the solid boarder represents where every Nebraska pass within one standard deviation (SD) of their actual passing center point went (68% of their passes). Look at how tight that ellipse is! Nebraska's setter only has to take two or three steps to get to the pass 68% of the time.

Oh, to be Bergen Reilly!

The ellipse with the dashed border shows where every Nebraska pass within two standard deviations of their actual passing center point went (95% of their passes). Nebraska's SD was the lowest of any P4 team last year, so these ellipses are pretty small.

Note: Obviously I don't know where Nebraska's actual passing target was. All I can tell you from the stats is where the actual center point of all their passes was.

For comparison, here's the same diagram for Penn State. Penn State distribution is notably larger than Nebraska's, particularly in terms of how far off the net their passes landed.

Here's the interesting part: despite the visible difference in spread, both teams have very similar overpass rates (Nebraska 3.9%, Penn State 3.7%).

I don't know if Coach Schumacher-Cawley made a choice to have a passing target 8 feet off the net or if it just happened that way, but Penn State's passing centroid is notably farther off the net than Nebraska's. Whether intentional or not, Penn States deeper passing center point enabled them to keep their overpassing at a manageable level.

To illustrate how important that deeper passing center point is, I created a quick diagram and modeled what would have happened if Penn State had tried to pass as close to the net as Nebraska did:

Here you can see that if Penn State had moved their passing target to 6.2 feet from the net (Nebraska's distance), their overpass rate would most likely have jumped from 3.7% to 8.6%! Penn State's wider spread would result in way too many overpasses at that distance. Nebraska can have a passing target that close because their tighter spread keeps the overpasses in check.

This is the key insight: there is no single "right distance" from the net to pass. The right distance depends on your team's passing accuracy.

Coaching takeaway: Two teams can have nearly identical overpass rates while passing to targets at very different distances from the net. A really good passing team (like Nebraska, with a 3.5 foot depth SD) can aim close and benefit from the higher attack efficiencies closer to the net. A less skilled passing team needs to give itself more cushion. Before setting a pass target for your team, consider how much natural scatter your passers have.

Step 4: Finding the Sweet Spot

Now we have reviewed all three of the data sets we need to build the model: the efficiency surface (closer is better), the overpass penalty (-45% in DI), and each team's passing distribution (how much their passes scatter). The model combines them by asking: "For each possible passing target on the court, what's the expected attack efficiency based on their team passing range, including overpasses?"

For every potential passing target, the model considers every location a pass would likely go (weighted by the team's calibrated 2D passing spread), looks up the expected outcome at each landing spot (attack efficiency for on-court passes, negative 45% for overpasses), and sums the weighted results. The location with the highest expected outcome is the team's optimal pass target.

Here are the DI-wide results across 348 teams:

• Median optimal depth target: 8.5 feet from the net

• Median optimal lateral target: 1.6 feet right of center

• Median actual depth: 7.7 feet from the net

• Median depth gap: about 0.8 feet

The typical DI team passes about a foot closer to the net than the model says they should. As we saw in the heatmaps, the raw efficiency surface peaks near the net (3 to 5 feet), so why doesn't the model say to aim there? Because the overpass penalty pulls the optimal target back. Even though passes at 3 to 5 feet produce better individual attack efficiency than passes at 8 to 9 feet, the cost of the extra overpasses outweighs the gains.

When we narrow to P4 vs P4 (67 teams), things don't change much:

• Median optimal depth target: 8.7 feet from the net

• Median optimal lateral target: 1.8 feet right of center

• Median actual depth: 7.5 feet from the net

• Median depth gap: about 1.2 feet

P4 teams pass a little closer to the net on average than the DI median (7.5 vs 7.7 feet). But their optimal distance is further back (8.7 vs 8.5 feet) because the calibrated passing spread for P4 teams reflects facing tougher servers. The gap between where they pass and where they should aim is about 1.2 feet.

This finding holds true for every one of the P4 teams. They all have an optimal target farther from the net than where they actually passed.

The 3% Rule of Thumb

Here's the part that makes this practical for any coach, without needing to compute your team's passing ranges and standard deviations. The key insight here is that the expected outcome model doesn't try to eliminate all overpasses - it finds the point with the mix of settable passes and overpasses that maximizes the expected outcome. It looks for a balance: close enough to maintain good attacking efficiency, but far enough back that the overpass penalty doesn't drag you under.

When I checked each team's predicted overpass rate if they used their optimal passing target, the numbers cluster super tightly: a median overpass rate of 3.3% for all of DI and 2.7% for P4 teams. That's pretty cool! Across NCAA DI Women's teams, the expected outcome value peaks when the overpass rate is around 3%. We can use that as a good rule of thumb.

Coaching takeaway: You don't need to calculate your team's passing range and standard deviation to use this finding. Just track your overpass rate. If your team overpasses more than 3% of the time, it's very likely (95%+ odds) that you should move your passing target further from the net.

SEC Deep Dive

To make this more concrete (and more fun as well), let's zoom into one conference, the SEC, to see how this applies to what was arguably the top conference in the country last year. Here are all 16 SEC teams from the 2025 P4 vs P4 analysis, sorted by how big the delta between their actual and optimal passing target was in 2025:

A few things jump out from this table.

First, you'll notice that the teams with the highest overpass %'s in 2025 tend to also have the largest gaps from their optimal passing target. This gives us some confidence that this expected outcome approach is working. South Carolina, Vanderbilt and LSU would all very likely benefit significantly from moving their passing targets deeper into the court.

The bottom of the table also tells a clear story about passing targets.

Teams like Auburn, Texas, Arkansas and Georgia, are doing a pretty good job of using a passing target that is a good fit for their teams and maximizes their expected outcomes. I know Coach Black at Georgia, Coach Crouch at Auburn and Coach Watson at Arkansas and they are all advocates for passing targets that are well off the net. It's nice to see the data reflects and support their philosophies.

That said, for most of these teams, they could move their passing targets a little farther off the net and get better results since the expected value optimizing overpass rate for P4 teams is 2.7%.

I love this graph as it makes it super easy to quickly see that every single 2025 SEC team's passing center was too close to the net with the exception of Georgia. All but one of them would very likely have better outcomes with a passing target deeper in the court.

Georgia is a fascinating case. They already pass the furthest from the net in the SEC (8.7 feet), and the model says they should be only slightly further back (8.8 feet). Given the quality of this location data, that isn't a meaningful difference. From my perspective, they are the only team I've found in the P4 data passing to their optimal target.

Their depth SD of 4.6 feet is one of the widest in the conference, but because they already pass so deep, Georgia's overpass rate is only 2.9%, one of the lowest in the SEC. They've already compensated for their spread with distance. As I said, I'm sure Coach Black understands their passing reality, and in this case they're basically right at optimal.

Coaching takeaway: If you're an SEC coach (or anyone coaching at the DI level, since these patterns hold across DI), find your team's depth standard deviation and overpass rate. If your depth SD is above 4.0 feet or your overpass rate is above 3% (2.7% in the P4), you'd likely benefit from pulling your passing target off the net more.

Addressing the Pushback

I know for some of you, this finding will be controversial, so let me address a few objections head on.

"You're telling passers to pass worse?"

Not at all. I'm saying that given a team's current accuracy, they may be aiming at the wrong spot. If a team improves their passing precision (reduces their SD), their optimal target moves closer to the net. If your passing improves during the season, definitely move your target closer!

"Overpass rate is only 4.7%, how much can that really matter?"

At negative 45% expected value per overpass, even a small overpass rate creates a large drag on your offense. Reducing your overpass rate from 5% to 3% is equivalent to improving your good pass% by > 6%. That's a huge improvement, one that might take an entire season of practices to realize through another means.

"This ignores what kind of sets the setter can run from different locations."

The efficiency surface captures this implicitly. Passes to zone 3 near the net produce high attack efficiency partly because they enable quicks, slides, and combinations that keep the block from loading up. The model doesn't explicitly model set selection, but the attack outcomes at each location already reflect the full range of sets that teams actually run from that spot. If you could run a full offense from 10 feet off the net, the efficiency surface would show it. It doesn't, and the model accounts for that.

Wrapping Up

This is my first post focusing on the Side Out phase of the game, and I wanted to start by establishing a framework for thinking about passing optimization. The model isn't just an academic exercise. It provides a concrete, team-specific recommendation for where to aim passes, based on three measurable inputs: the efficiency surface, the overpass penalty, and your team's passing accuracy.

The headline finding: the median P4 team passes over a foot closer to the net than the model says they should. That doesn't mean everyone is passing wrong. It means the conventional wisdom of "get it close to the net" is incomplete. It should be "get it close to the net, but not so close that you end up overpassing a lot."

If you have any thoughts or feedback on this post, I’d love to hear about them in the comments, or feel free to email me at alex_simons@hotmail.com

Thanks for reading!

Methodology note: This analysis uses 5,107 match files from the full 2025 NCAA DI season: 682,712 serve receptions and 621,119 pass-to-attack pairs (receptions that led to a set and attack attempt). Pass destination is defined as the setter's ball-contact coordinate (the set's starting location in the match data). Attack efficiency = (kills minus errors) divided by attempts, where errors include both unforced hitting errors and attacks blocked back for a kill, consistent with the standard NCAA definition. The 36-cell heatmap uses the standard scouting zone/subzone grid with a minimum of 100 observations per cell. The 2D efficiency surface uses Gaussian kernel smoothing (bandwidth = 0.20) on a 0.05-unit resolution grid, with the grid extended past the net into an "overpass zone" where every cell is assigned the overpass penalty value rather than a smoothed efficiency. Each team's passing distribution is modeled as a 2D Gaussian with team-level SD_x (lateral) and SD_y (depth). SD_x is computed directly from serve reception coordinates. SD_y is calibrated so that the Gaussian tail past the net matches each team's actual observed overpass rate, correcting for the fact that raw SD computed from kept passes alone underestimates the depth spread. The overpass penalty is the actual opponent attack efficiency on overpass-initiated attacks (45.0% across DI, 44.7% in P4 vs P4), expressed as a negative value from the passing team's perspective. Optimal targets are found by convolving each team's 2D Gaussian kernel with the penalty-augmented efficiency surface across all candidate on-court target locations; the candidate with the highest expected value is the team's optimal target. Because the overpass zone is embedded directly in the efficiency surface, passes that would scatter past the net at a given candidate target automatically incur the overpass penalty in proportion to the team's Gaussian tail beyond the net. P4 vs P4 analysis includes 67 teams and 105,498 receptions, filtered to matches where both teams are from a Power 4 conference. Actual pass center ("actual distance from net") is the mean of all pass landing coordinates for each team. All distances are computed from court coordinates using the standard conversion (1 court unit = 9.84 feet).

Howdy folks,

I'm back! After 4+ weeks coaching at national qualifiers (and some recovering from it), I'm back in Seattle and blogging again. Welcome to the next entry in my "What Really Wins Sets" series. In my last two posts, I looked at where setters should set to in transition and how those options change as they get pushed off the net. Today, I'm tackling a different question: when someone other than your setter sets in transition, where and when should they set? And how important is it to your odds of winning to have them do it well?

Spoiler: It matters more than I expected.

For this analysis, I pulled every transition Dig-Set-Attack sequence from the 2025 NCAA DI season. That's 521,139 transition sets across all of DI, and the patterns are remarkably consistent regardless of how you slice the data. For every transition set, I classified the player who set the ball as one of three types: the team's setter, a libero, or a non-setter/non-libero.

A lot of what I'm going to cover in this blog will not be super new for experienced coaches, but if you are like me, you'll like it anyway because it's always cool when new data supports something you kind of already "knew" but didn't have the data to back it up.

The Drop-Off Is Real

Let's start with the big picture. How does transition attack effectiveness change based on who sets the ball?

All NCAA DI 2025 (521,139 transition sets)

The drop-off from setter to libero is about 8 percentage points of attack efficiency. The drop from libero to non-setter is another 7. That's a 14.7 point gap!

What's interesting is where the gap comes from. The error rate only creeps up a few points (15.1% to 18.6%) as you go from setter to non-setter. The majority of the problem is on the kill side: kill rate drops from 32.8% to 21.7%. When a non-setter sets the ball, the attackers aren't making a lot more errors. They're just getting a lot fewer kills.

This pattern holds when you filter to only P4 vs P4 matches, though the numbers are higher across the board:

P4 vs P4 matches only (75,944 transition sets)

The gap between setter and non-setter is 14 points in P4 vs P4, basically the same as all the DI dropoff. And the same pattern holds: error rates are similar, but the kill rate drops off a cliff. No matter how you slice it, who sets the ball in transition has a massive impact on your attackers' success.

Given these numbers, you might think the best strategy is simply to get your setter to take as many transition sets as possible. I tested that and was surprised to see that the correlation between a team's setter share of transition sets and set win% is basically non-existent (r = 0.08, p = 0.15) across DI and P4 (r = 0.19, p = 0.12). These correlations are super small and neither is statistically significant. Winning teams don't have their setter setting a meaningfully higher proportion of transition balls.

This makes sense when you remember that the opposing team gets to decide where to attack, and many teams are consciously trying to take the setter out of the play. 59% of the time when someone who is not the setter sets in transition, it's because the setter just dug the ball. Your opponent is deliberately working to get your team out of system, and they have a lot of say in whether they succeed. So you can't control who ends up setting in transition a lot of the time. What you can influence is how well each of the non-setters sets and how well your hitters attack those sets.

Does better transition non-setter setting actually improve the odds of winning?

Next I wanted to check to see if the ability to dig, set, and get a kill when a non-setter is setting is important to winning.

DSK% (Dig-Set-Kill%) correlation with set win %

Reminder: DSK% is the (kills after a dig) / (total number of digs)

These correlations are all pretty meaningful and they are all statistically significant. Setter DSK% is the strongest predictor (r = 0.74 across DI, r = 0.79 in P4), which makes sense since setters handle two thirds of all transition sets. But even libero DSK% (r = 0.53, p < 0.001) and non-setter DSK% (r = 0.38, p < 0.001) are meaningful predictors of winning. Your team's ability to convert transition opportunities into kills matters no matter who is doing the setting.

Coaching takeaway: It's important for your team to get good at setting and attacking in transition when your libero is setting and when other non-setters are setting. For comparison, getting good at transition attacking when your libero sets (r = .52, p < 0.001) is only slightly less important than being able to get a good (or better) pass on serve receive (r = .59, p < 0.001).

Libero Setting: The Hidden Range

Liberos set 17% of all transition attacks, and the outcomes vary enormously from team to team. Across DI, the best team's libero-set attacks produce a 24.9% attack efficiency while the worst produce a -10.3%. That's a 35-point spread.

This is the libero transition setting heatmap for all of NCAA DI. Compare the to the setter heatmap from my earlier post and you'll see that liberos set much more frequently to the left side (zone 4) which is what most teams coach their liberos to do. But based on these attack efficiency numbers, it's not clear that is the right call. It would make just as much sense for liberos to set the right-side attacker in zone 2B and 2A as it does to set the left-side attacker in 4C and 4D. In fact, surprisingly, the overall attack effectiveness when liberos set to Zone 2 is slightly higher (12.1%) than when they set to Zone 4 (11.4%). And while that difference is small, it is statistically significant (p = .005).

In P4 vs P4 matches, the attack efficiency goes up across the board but liberos send an even higher percentage of their sets to zone 4 (65.8%) than the overall DI rate (57.8%).

One cell that jumped out to me in the P4 heatmap is 8C, with an 11% attack efficiency on 115 attempts. That's surprisingly strong for an out-of-system attack off a libero set in the back row. When I drilled into those 115 attacks, 96 of them were pipe attacks (VP), and those pipes produced 14.6% attack efficiency, nearly as high as the 15% in the left and right pin cells (4B and 2A).

Even more interesting, one team, Miami, accounted for 23 of those pipe attacks at 34.8% attack efficiency. Maybe Coach Keno at Miami knows something the rest of us don't!

Just how wide is the range?

The range of libero transition setting effectiveness across DI is surprisingly large. Among 342 teams with 100+ libero transition sets, the team-level attack efficiency on libero sets ranges from -10.3% to 24.9%, a 35-point spread!

That is an amazing difference. Quite a few DI teams can't really terminate when their libero sets and others are getting attack efficiency %'s that are nearly as good as their attack effectiveness % when their setters are setting!

The best dig-libero-set-attack% teams in DI

Which programs are generating the best attack efficiency when their liberos set in transition? Here are the top 10 across all of DI (minimum 100 transition sets):

Pittsburgh at 24.9% is more than 2.5x the DI median (9.5%). That's a staggering gap. San Diego and Texas are right behind.

What's interesting is the variety on this list. You've got elite programs like Pitt and Texas alongside mid-majors like American, Hofstra, and Fresno State. Good dig-libero-set-attacking isn't limited to the P4 programs with the biggest budgets. It's a skill that can be developed.

Coaching takeaway: The ability to get a kill when your libero sets is important to winning and effectiveness varies enormously across DI making it an opportunity for real differentiation. If your team isn't achieving an attack eff% >10% in this specific scenario, it's probably worth investing practice time on it.

When Should the Libero Set Instead of the Setter?

Of course, when the setter digs the ball, the setter can't also be the one to set it. But if the setter and the libero are both available to set, are there court locations where having the libero set produces comparable or even better results than the setter? Well, I'm glad you asked!

This heatmap shows the difference in attack efficiency between setter-set and libero-set transition attacks at each court location across all of DI. Green cells mean the setter produces better results from that location, red means the libero does. The farther from the seam between zone 2 and 3 the dig goes, the more effective having the libero set becomes, and in a number of cells the libero is a better (6B, 6C) or statistically indistinguishable choice (7A, 7B, and 1C).

And here's the same view for P4 vs P4 matches. The patterns are pretty similar to DI all up, in almost every location the setter produces better attack efficiency than the libero.

One interesting detail here is zone 9D where the setters are SO much more effective (17%). Don't read too much into this though because the sample size of libero sets is so small. I did watch a bunch of these libero sets just to see what was going on. The majority are liberos working very hard to set a ball they can hardly even get to, so it's not surprising the resulting sets weren't very good.

Coaching takeaway: As a general rule, you want your setter setting whenever possible in transition. But in situations where the setter is deep in the court or having to sprint toward zone 7 to get their hands on the ball, it makes a lot more sense for your libero to take the second ball. The setter's advantage disappears in those locations, and a libero is in a good position to put up a hittable ball.

The Non-Setter Wild Card

About 16% of all transition sets come from players who are neither the setter nor the libero: outside hitters, middles, defensive specialists, and occasionally even opposites. That's a bigger share than I expected, and given that it happens nearly as often as libero setting, it's worth understanding what happens when these players set.

The non-setter heatmap tells a pretty clear story. Attack efficiency is low across almost every location, and the volume is concentrated heavily on the left side. When a non-setter sets in transition, the team is usually in reaction mode, setting the ball high to the outside and hoping for the best.

In P4 vs P4 matches, non-setter attack efficiency improves from 3.1% to 5.1%, but it's still dramatically lower than both setter (19.1%) and libero (12.2%) setting. Even the best P4 teams struggle to generate efficient attacks when a non-setter is delivering the ball.

The range of non-setter effectiveness

Among 346 teams with 100+ non-setter transition sets, the team-level attack efficiency ranges from -13.2% to 16.8%. That's a 30 point spread.

Again, the range here is surprisingly large. A small number of teams are pretty effective at scoring when a non-setter/non-libero is setting for them:

The best non-setter-setting teams in DI

Baylor and Texas at the top are generating non-setter attack efficiencies that are higher than the DI median for libero setting (9.5%). And again, the list is a mix of elite P4 programs and mid-majors, suggesting that non-setter setting is a trainable skill, not just a function of having better athletes.

Coaching takeaway: Non-setter setting happens about 16% of the time in transition, which is too often to ignore. While the attack efficiency numbers are low, they don't have to be terrible. Consider identifying which of your non-setters are the best emergency setters and make sure to get some reps with hitters attacking their sets in practice. The gap between the median team (2.8%) and a top-10 team (12.6%+) is nearly 10 points of attack efficiency. Over the course of a season, that adds up!

Wrapping Up

The Dig-Set-Kill chain in transition is highly correlated with winning sets in NCAA DI Women's volleyball. And the data is clear: Getting good at all three of the possible variations (setter sets, libero sets, someone else sets) is pretty important to winning. In fact, these three variations are all more strongly correlated with winning sets than tough serving is!

The range of libero setting effectiveness across DI is massive, and that range represents a real opportunity for programs willing to invest the practice time. Your libero touches the ball on 17% of transition sets. Making sure they can deliver quality sets and that your team can effectively attack in that situation is one of the higher-leverage and more differentiating investments you can make.

Thanks for reading! This post ends my deep dive into the Transition Setting. In my next post, I'll be shifting gears a little bit and looking at something completely different. Stay tuned!

Methodology note: This analysis uses every transition Dig-Set-Attack sequence from the 2025 NCAA DI season (521,139 total). "Setter" means any player detected as one of the team's active setters (someone who set 20%+ of team sets, excluding liberos). "Libero" comes from the match metadata. "Non-setter" is any player not identified as either a setter or a libero. Players who were listed as a libero in some matches and a DS in others had all their transition sets combined and counted in the libero category. All correlations use Pearson's r with team-level aggregation (minimum 50 transition sets per setter type per team). Attack efficiency is calculated as (Kills minus Errors) divided by Attempts, where Errors includes both unforced errors and stuff blocks, consistent with the standard NCAA box score definition.

Howdy folks,

Welcome back! In Part 6, I looked at where setters should set in transition and which attack types are most effective. The headline was that middle quicks are the most efficient transition play, slides are nearly as good, BICs are a real option for Power 4 teams, and D-balls are a niche specialty option for the small number of teams with an elite opposite.

This blog post is the follow-up where we'll dive into the next level of detail. My friend Ben Josephson at the University of Texas, gave me the idea for this post: What locations and attacks work best when the setter is pushed off the net and how does that change as they get further off the net?

To answer Ben's question, I've divided every 2025 NCAA Women's DI transition set into three buckets based on how far off the net the setter was when she contacted the ball: 0 to 8 feet (in system), 8 to 12 feet (around the 10' line), and 12 to 16 feet (deep in the court). Then I broke each bucket down by attack type so you can see exactly how each play call changes as the setter gets pushed back.

A few notes:

1.) All of the sets in this blog are by setters. I have excluded all the sets by non-setters. I'll cover those in the next post.

2.) As you read this post you'll find lots of references to zones (i.e. zone 4) and subzones (i.e. zone 4C). For a quick refresher on those, head over to my last post.

3.) This post turned out much longer than I expected so your email provider (i.e. Gmail) may cut if off. If that’s the case - you can read the full version here

Methodology and Sample Size Challenges

If you've read my previous blogs, you might notice that in this new set of heatmaps I greyed out more cells than in previous posts. After looking at the confidence intervals closely, I've decided to set a higher exclusion bar just so I'm not sharing questionable data with you. So any cell with < 100 observations is now greyed out.

Additionally, as I get into the discussion about setting from farther off the net, especially in the P4 data set, the sample sizes get pretty small. In many cases there just isn't enough data to make a reliable heatmap. I've left those heatmaps out all together and have to rely on just the NCAA DI heatmaps.

0 to 8 Feet: All Options are on the Table

69% of all transition setter sets in NCAA DI happen with the setter inside 8 feet of the net. This is the ideal situation: the setter has time, every option is available, and you can run a fast offense. Overall attack efficiency in this bucket is 27.7% in DI and 29.1% in P4 vs P4 matches.

All attacks

The heatmap at 0 to 8 feet looks very similar to the combined transition heatmap from Part 6. Zone 3 cells have the highest attack efficiency (28 to 30%) and zone 4 is in the very high 20s. The left side of the court (zones 4 and 7) accounts for just over 45% of all transition attack volume at this distance, which is more than I would expect (and I suspect more than is optimal).

If you look closely, you'll also notice that the right-side cells (zone 2) are some of the most productive, at 26 to 31%. The efficiency numbers are slightly higher on the right side than the left at this distance and that difference is definitely statistically significant. As we dive in farther the opportunity there looks pretty interesting.

Left-side pins

Isolating just X5 (Go) and V5 (Hut) attacks to the left side, the picture is clear: this is the workhorse of transition offense in terms of volume. There are 89,260 left-side pin attacks in this bucket across DI, making it the highest-volume play call by a wide margin at 43% of all transition sets from this distance. Attack efficiency is 28.0% in DI and 29.4% in P4. These are impressive attack efficiency %’s, but they are not the highest available in this scenario.

Right-side pins

The right-side pins (X6/V6) are the second-highest volume play at 0 to 8 feet with 44,166 attacks in DI, about 1/2 the volume that gets set to the outside hitter. Their efficiency is 30.2% in DI and 32.0% in P4, a couple of points higher than the left-side pins.

Right side attacks being 50% of the volume of left side attacks might sound about right for a team running a 5-1, since they only have a right-side hitter available 50% the time. But I suspect there iare more opportunities to set the right side. Only 50% of DI teams run a 5-1 as their primary offense (26% run a 6-2 as their primary and 24% run a mix of the two) so it's easy to argue this number should be higher than 50%.

And even teams that primarily run a 5-1 (i.e. they run a 5-1 offense in 75% or more of their rallies) only set their right sides in transition 17% of the time when the setter is 0 - 8' off but they set their left-side hitters 44% of the time (2.5X more). And while teams that primarily run a 6-2 definitely use their right sides more in transition, right side sets still only make up 30% of their transition sets compared to 40% to the left side.

Given right-side attackers are 2.6% more effective, this looks like an opportunity for an upgrade to me!

Middle quicks

Similar to what I covered in the previous blog, setting middle attackers in transition is a great option at the DI level when the setter is 0 - 8' off the net. The overall attack effectiveness is high (30.6%) and sets to the gap get up into the 32% range, the highest available on the court. Yet DI teams only set their middles in transition 18.6% of the time. As a performance analyst, I find this puzzling. As a former middle blocker, I find it irritating 😉.

And as you would expect, in the P4, the middle quicks (X1, X2, XM, XL, X7) at 0 to 8 feet are even more efficient and high volume. P4 attack efficiency here is 32.3% on 4,507 attempts. But surprisingly, P4 teams actually set this even less frequently, only 14% of the sets from 0 - 8' go here. You definitely have to feel for P4 middles!

If you look closely at that P4 heatmap, again you'll see some tantalizingly high numbers in 3C, 4A and 4B. If you filter those to just gap sets (I had to watch quite a few clips to make sure that's what's really going on here) you get the following heat map:

P4 middle blockers are delivering on gap attacks in transition at a 34% attack efficiency overall and 36% in 4B, yet only 4.9% of transition sets from 0 - 8' off the net go here.

Slides

Slides (CF/CD) are another very effective attack option in this 0 - 8' range: 32.0% attack efficiency in DI on 6,362 attempts and 31.9% in P4 on 1,300 attempts. It's a great transition option, but as we'll see its effectiveness fades pretty quickly as the setter gets pushed further off the net.

As we discussed in the previous blog, there is a deep field of DI teams that run slides very effectively in transition. Last year 69 DI teams reached 30% or higher attack efficiency on transition slides and some teams were significantly higher. To help paint the picture, here’s the slide attack efficiency for the top 10 most effective programs with 50 or more transition slide attempts last year.

This is another area with a lot of opportunity. DI teams only set the transition slide when the setter is 0 - 8' off the net 2.8% of the time last season. The P4 number is slightly higher, 4.04%. But in both cases, just like gap attacks, this looks like a seriously under-utilized option.

BICs

As in my previous blog, BICs don't look great at the NCAA DI level when the setter is 0 - 8' off the net. There are a lot of much more effective options. Many teams are hesitant to run this attack in transition and the data helps explain why.

That said, as I also called out in my last blog post there is a reasonably deep bench of DI athletes who can hit the BIC in transition with high attack efficiency. For example, 103 DI athletes had attack efficiencies >27% hitting the BIC in transition last year.

And the story is definitely better in the P4. In P4 matches setting a BIC from 0 - 8 feet off in transition produced an attack efficiency of 25% when set left of center, a pretty reasonable option, though still not among the highest at this distance. Interestingly, we'll see that this actually improves when setter is 8 - 12' off the net.

D-balls

D-balls (X8/V8) are the lowest-efficiency play in this bucket at 16.3% in DI and 22.1% in P4 on smaller samples (3,485 and 768 attempts). As I covered in Part 6, the D-ball is a niche specialty option: a handful of elite opposites (Olivia Babcock at Pitt, Alanah Clemente at Oregon, Cari Spears at Texas) can make it work, but the average transition D-ball is not a high-percentage play.

You might be wondering what is going on in cell 2A in this heatmap. I looked through it and its mostly noise. Just ignore it.

The P4 gap is again large (nearly 6 points), reinforcing that this play lives and dies with the specific athlete running it.

Coaching takeaway: When the setter is within 8 feet of the net, every attack option is on the table. Unfortunately many DI setters are not taking advantage of the opportunity! Sets to the outside hitter on the left are the default option (43% of volume) but not the most efficient option. Gaps (34 - 36%), slides (32%), quicks in the middle (30%) and right-side pin hitter attacks (30%) all convert at higher rates than left-side pins (28%).

If your setters are regularly defaulting to the left side when they are this close to the net, there's room for improvement! I’d encourage you to actively coach them to move some of that volume toward the middle and right side. My U15 setters regularly send >30% of their transition sets to their right side attackers. If they can do it, your setters can do it!

8 to 12 Feet: Off the Net, Fewer Options on the Table

15% of transition setter sets were from 8 - 12 feet off the net last year. Overall efficiency drops 4 to 5 points compared to the 0 to 8 foot bucket: 23.2% in DI and 25.4% in P4. This is where the geometry of certain plays starts to break down, and the data reflects that.

All attacks

The all-attacks heatmap at 8 to 12 feet shows a noticeable color shift from green toward yellow compared to the 0 to 8 foot version. The left side (zones 4C and 4D) become the majority of attacks by volume (51.8%) as setters default to "give it to the outside hitter". Interestingly, attacks on the right-side are just slightly more efficient than left-side attacks.

The P4 heatmap is a little less helpful here as we start running into issues with sample sizes. But from what we can see, the left-side and right-side attacks are both still very efficient, and maybe something interesting is going on in 3D.

Left-side pins

The efficiency of attacks by pin hitters on the left holds up relatively well at 24.9% in DI and 26.6% in P4. That's about a 3-point fade from the in-system bucket. That's good compared to some of the other options.

Right-side pins

Right-side pins also hold up well at this distance: 25.4% in DI on 10,225 attempts and 26.4% in P4. The fade is about 5 points from the in-system bucket, steeper than the left side. With that decrease, right-side attacks are now statistically the same effectiveness as left-side pin attacks.

The proportional drop in volume of sets is a lot bigger on the right side (from 44K to 10K) than it is on the left side. Maybe this makes some sense as a backset from off the net is a bit more challenging to execute. And if your right-side hitter is right-handed, attacking a set coming from behind their left shoulder can be quite challenging as it's hard to see the set.

But again, I've had U15 setters and hitters who can do this quite proficiently, so it can be addressed with the right emphasis and training.

Middle quicks and gap attacks

The middle quick fades at 8 to 12 feet off the net in DI: efficiency drops from 30.6% to 25.5% and volume drops from 41,532 to 3,398. That's pretty much what you would expect: it's challenging to set quick attacks 8 - 12 feet off the net. That said these are some good options in here. For instance, quick attacks (X1, X2, XM) in 3C and 3D are still very effective: 26.1% and 28.7% respectively.

And again, something interesting is happening when you look at the attacks in the gap between zones 3 and 4. When I pulled out every transition attack at this distance that was either coded as a gap (X7) or coded as a quick (X1, XM) but actually landed in the gap cells (4A, 4B), and combined them into one "reclassified gap" group, this is what it looks like:

That's 1,223 attacks across DI at 24.1% efficiency, with the highest-volume cells (4A at 623 attempts, 3D at 189 attempts) both converting at 25 to 26%. Watching the clips, it is definitely important that your setter is keeping your middle off the net in this attack. The gap is still a good option and easier to run than the quick, but the quick is more efficient at this distance.

Slides

Here is where the geometry challenge shows up. It's just hard to run a good slide when the set is coming from this far off the net especially if your middle is transitioning from blocking to the left side of the net. Slide efficiency holds up surprisingly well (32% to 27.4% in DI, 31.9% to 32.1% in P4), but the volume falls off a cliff. DI goes from 6,362 slide attempts at 0 to 8 feet to just 435 at 8 to 12 feet. P4 goes from 1,300 to 81. Setters just don't set this because the geometry doesn't work.

BICs

BICs tell the opposite story from slides. The volume does drop significantly (10,872 to 2,515 in DI), but less dramatically than slides, and the efficiency stays flat and, in some cases, gets better.

In P4, the BIC is actually working better at this distance than it does from 0 - 8' off the net. I think this is another geometry story. As the setter is pushed out to the 10-foot line, it becomes easier to set the BIC rather than harder and thus the effectiveness of this attack at this distance is remarkably good.

D-balls

Is Olivia Babcock, Alanah Clemente, Kennedy Martin or Cari Spears on your roster? If so, a D-ball at 8 - 12 feet might be a great option. Otherwise keep scrolling! 🙂

Coaching takeaway: At 8 to 12 feet off the net, the most important decision is what things to not to set. The data says to drop the slide (the rest of the country already has) and certainly don't set the D. Obviously the default is to set to your left-side pin. But there's room to improve your attack effectiveness if your team can get good at setting/attacking on the right side in this situation. Running a quick in the middle or the gap is also still a more effective option than setting the left side.

But the most interesting and under-utilized option at this distance is a BIC. Running a BIC when your setter is 8 to 12 feet is a remarkably effective option, at least in the Power 4, and many DI athletes can hit this in transition with very high effectiveness (27%+).

12 to 16 Feet: Deep in the Court the Table is Pretty Bare

About 16% of these transition sets occur 12 - 16 feet of the net. Overall efficiency drops to 19.1% in DI and 21.7% in P4. As you'll see in these heatmaps, most options are off the table and the offense reduces to feeding your best pin hitters and hoping.

All attacks

Obviously, not a lot to choose from here.

The all-attacks heatmap at 12 to 16 feet is noticeably more orange and yellow than the previous two buckets. Almost everything that was green before has faded. The left side (zone 4D) has become the primary location in terms of volume, and P4 teams attack efficiency hold up about 2 to 3 points better than DI at every cell, the largest gap of the three buckets. That makes sense: when the play is hard, the gap between elite hitters and average ones matters more, not less.

Left-side pins

Sets to the Left-side pins are now the dominant play by an even wider margin: 33,378 attacks in DI, representing 64% of all transition sets at this distance. Efficiency fades to 21.2% in DI and 23.2% in P4. The left-side pin is the last play standing at every distance.

Right-side pins

Right-side pins are still alive at 12 to 16 feet (8,093 DI attacks) but the volume is way down from the in-system bucket. Efficiency fades to 21.6% in DI and 24.3% in P4. The back-set to the right side from 12+ feet off the net is a difficult set, but it's still being run and still converting at a slightly higher rate than the left side.

Middle quicks

At 12 to 16 feet, the middle quick is on life support. Volume drops to 1,095 (1.2% of transition sets in this range) in DI and just 79 in P4. Efficiency fades to 20.5% in DI and 22.8% in P4.

I watched a big sample of these to make sure this wasn't just a data quality issue. These are in fact middle attacks when the setter is behind the three-meter line, but none of them struck me as good attack options. In most the middle was forced to make some kind of awkward push/throw/power tip to the opposing team.

Slides

The slide at 12 to 16 feet is essentially gone. DI has just 106 attempts in the entire season, and P4 had only 17, too few to even generate a heatmap. The geometry problems that started at 8 feet become insurmountable at 12.

BICs and D-balls

At 12 to 16 feet, BICs disappear entirely (zero attempts in both DI and P4). The BIC geometry, while more resilient than slides, finally breaks down at extreme distances.

DI logs 942 D-ball attempts at 12 to 16 feet at 11.7% efficiency, and P4 has 179 at 18.4%. So not super effective but still being run from time to time.

Surprisingly, a tiny group of elite opposites are still very good D-ball options even at this distance. Penn State, Pittsburgh and Oregon run transition Ds at this distance a few times a match usually and their opposites deliver. Kennedy Martin hit 26.7%, Olivia Babcock hit 33.3%, and Alanah Clemente hit 30.4%. Pretty jaw-dropping performances! Cari Spears at Texas also delivered here (37.5%) but Texas rarely sets a D at this distance off the net.

Coaching takeaway: At 12 to 16 feet off the net, there just aren't a lot of good options. Your setter's only real decision is whether to set the right or left pin. The right-side attack is slightly more effective than the left-side attack but can be challenging for right-handed opposites to hit. If you have an elite opposite, D-balls are still a viable attack option. BICs and slides and quick attacks are pretty much toast.

The Geometry of Why

The pattern across all three distance buckets tells a clear story about geometry. As the setter moves off the net, plays break down in a specific order:

Slides go first. The slide requires the middle to run from left to right across the front court while the setter delivers the ball over her head / right shoulder. That geometry demands the setter be near the net. At 8 feet off, volume drops by 14x. At 12 feet, it's essentially extinct.

Middle quicks fade next, but more gracefully. The quick runs through the middle of the court and doesn't require the same lateral footwork as a slide, so it holds up better as the setter get pushed off the net.

BICs hold up well until they don't. Because the back-row pin attacks from behind the 3-meter line regardless of where the setter is, the setter being pushed off the net doesn't change the play's geometry as much and in some ways the attack gets easier to set. BICs convert at essentially the same rate at 0 to 8 feet and 8 to 12 feet in DI and in the P4 they are actually more effective. But at 12+ feet, even BICs disappear.

D-balls are the last back-row attack standing. For a small number of elite athletes, D-balls are a great option at all three of these set distances. It's the only back-row attack that survives all three distances.

Pin attacks are the constant. Left-side and right-side pin attacks are available at every distance, and they fade gradually rather than falling off a cliff. They are the backbone of transition offense at every distance, and they become an increasingly large share of the volume as other plays drop off the menu.

Coaching takeaway: The order in which plays disappear as the setter moves off the net is predictable. Teach your setters to think in these three buckets:

At the net: Run everything and find more opportunities to set the gap, the slide and the right-side.

8 to 12 feet off: Drop the slide, lean on the pins, BICs and quicks, try to increase the emphasis on BICs and right-side sets when possible

12+ feet off: Pins hitters on the right and left only.

And of course, you also want to teach the same things to your hitters, so they know what's expected of them depending on where the setter sets from.

What's Next

We've now covered where to set in transition and how the menu changes based on how far off the net the setter is. In Part 8, I'll look at the other side of the coin: what happens when the setter can't set at all? When the libero or another non-setter has to take the second ball, how much does the outcome change, and is there a way to predict which teams handle that situation better than others?

Methodology note: This analysis uses every transition Dig-Set-Attack sequence from the 2025 NCAA DI season (521,139 total transition sets, of which 322,990 were setter sets with valid coordinates). "Setter" means the team's active setter (any player who took 20% or more of the team's sets, excluding liberos). Setter distance from the net was computed from the set contact coordinates. The 36-cell subzone grid uses a minimum cell threshold of 0.5% of observations per distance bucket (or 100 observations, whichever is higher). Cells below the threshold are greyed out because the 95% confidence interval on attack efficiency at that sample size is too wide to trust. P4 vs P4 data includes only matches where both teams are Power 4 conference members (764 matches, 46,030 setter sets with valid coordinates). Attack codes: left-side pins = X5 (Go) and V5 (Hut); right-side pins = X6 (Red) and V6 (Mac); middle quicks = X1, X2, XM, XL, X7; slides = CF, CD; BICs = XP; D-balls = X8, V8.

Howdy folks! Welcome back to the "What Really Wins Sets" series. In my last post, I dug into the first link in the Dig-Set-Kill chain and showed that where your team digs the ball matters far more than how often you dig it. Today we're moving one link further in that chain and looking at setting in transition.

First, I want to warn you that this blog post turned out a LOT longer than I anticipated. In fact it's so long, I've split it into three separate posts on the topic, this being the first. So you might want to read this one in multiple sittings. It definitely took me many sittings to write it.

For this first post, my main focus is on answering one question:

*When the setter is setting in transition, which setting targets and attack types are most effective?*

In the subsequent posts I'll dig into a bunch of related questions:

• When the setter is pushed farther off the net in transition, how do those targets and attack types change?

• When the libero is setting in transition, what setting targets and attack types are the most effective?

• Are there locations on the court where it's more effective to have your libero set rather than your setter?

To answer these questions, I fired up my data pipeline and Claude Code and pulled every transition Dig-Set-Attack sequence from the 2025 NCAA DI. That's a lot of data, 521,139 transition sets to be precise. Many of the patterns are pretty clear. Some of them surprised me.

A quick refresher on zones and subzones

For those of you who aren't familiar with VolleyStation, DataVolley or other volleyball stats products, all the heatmaps in this post divide the court into 36 cells: 9 top level zones, each split into 4 subzones labeled A, B, C, and D. The labeling looks like this:

When I mention subzone "3C", I'm referring to the front-middle area of the court (zone 3), and the top-left quadrant within that zone (subzone C). "4D" means the front-left area of the court (zone 4), bottom-left quadrant (subzone D).

Where Should The Setter Set to in Transition?

Let's start with the easy one. When your setter is setting in transition, what are the most effective locations to set to?

This is the transition setting heatmap for NCAA Women's DI in 2025. Each subzone is colored based on the average attack efficiency % of the subsequent attacks of all sets that were set to that location in transition by a setter. Greener is better, redder is worse. Inside each cell you'll see two data points: resulting attack efficiency on top and the number of attacks in parentheses underneath it.

If you've been coaching for any amount of time, none of this surprises you. It's pretty much the accepted gospel. But it's nice to see the data confirm it so cleanly:

• Attacks in the middle (3C & 3B) are the highest-efficiency at 30% and 29%.

• Right-side attacks (2B & 2A) are right behind at 29% and 28%

• Left-side attacks (4C & 4B) round out the top tier at 27% and 26%.

• The far left-side (4C & 4D) is where setters set to the most in transition (45% of sets).

This next heatmap is using the same approach, but the data has been filtered to use only 2025 matches between P4 teams. As you would expect the attack efficiency numbers go up across the board and the patterns are pretty similar. Interestingly, the attack numbers in the middle of the court do not go up as much as those in the other zones.

Looking at these heatmaps I wondered which setters were making the best set location choices based on expected attack efficiency % (i.e. using the heatmap values) and which teams were exceeding those expected %s.

You can see the results of that analysis for the Power 4 matches below.

The list is pretty much what you might expect; the best setters on the best teams do both really well. I was excited to see #10 on the list though. Based on this data, Sienna Noordermeer is making some very effective choices in transition. I've never seen her play but now I definitely want to watch her during the 2026 season.

After reviewing this data and the heatmaps I wanted to understand which attack types these setters were setting. Which ones were the most efficient in each of these zones, particularly in 3C & 4B (quick attacks by middles or inside attacks by pins?),3D & 3A (quick attacks or BICs?) and 2A & 2B (right side attacks, slides or D-Balls?).

Setting middle quicks in transition

To start answering these questions I filtered the all DI heatmap to only the transition attacks that are tagged as being quick middle attacks.

This heatmap is showing the results for every quick attack set in transition in NCAA DI 2025, a total of 46,159 attempts with an attack efficiency of 30%. If you can set your middles in transition, you get some of the highest attack efficiencies available. The middle quick attacks are the most productive transition attacks in NCAA DI.

When I reviewed this data, I was actually a little surprised the attack efficiency numbers weren't even higher. Shouldn't an elite middle blocker have an attack efficiency better than 30%? So I added the P4 match filter again to see what the results were when Power 4 teams play each other.

These numbers were starting to look more like what I expected. P4 setters and middles run transition quicks at 32% efficiency overall, two points above the NCAA DI number.

But there are some pretty interesting things going on in this data. Look at subzones 4B & 4A. Those are sets to middles running gap attacks. Those attack efficiencies combined are just over 34%, meaning that in P4 competition, this is the single most effective transition attack option available.

Note: Gap % in the table above is the percentage of total transition sets that were set to the gap

As you can see from this table, some teams have figured this out and are taking full advantage of the opportunity. Some of these attack efficiency %'s are pretty impressive (way to go Florida and Virginia Tech!).

There's also something interesting going on in Zone 2. Those are middle-quick attacks (i.e. not slide attacks, I'll discuss those later) quite a ways over on the right side. And the attack efficiency is a combined 32% in front of the setter and 31% behind the setter. Watching a sample of clips of these attacks, what I learned is that most of these are quick attacks that are run relative to the setter's position rather than to a fixed point on the court. When the dig pushes the setter into zone 2, these middles go where the setter goes.

Coaching takeaway: Getting good at setting quicks in transition can significantly improve your attack efficiency, especially if your team can set the gap. The good news is that this is low hanging fruit even if you are not a Power 4 team. I've made setting the gap in transition a focus area for my U15 club team this year and it's become one of the most effective plays they run.

Setting BICs in transition

The BIC (Back-row Quick) is a mainstay of men's volleyball and is now being used in high level Women's volleyball as well. But as you'll see from this data, in NCAA Women's DI, at least in transition, the results are mixed. The DI attack efficiency of BICs in transition was only 18% on 13,387 attempts.

The volume (only 2.5% of all transition attacks) kind of tells the story. Many teams are hesitant to run this attack in transition and the attack efficiency numbers make it clear why. That said, some teams and some conferences run it more frequently and more effectively than others.

When you filter to only P4-vs-P4 matches BIC attacks in transition look a lot more promising. Transition BICs jump from 18% attack efficiency in all of DI to 24% in P4, and 26% when run them left of center. That is just about the same efficiency as the left side front row transition attacks (27%) in the Power 4, so for this group of teams, BICs are a good choice in transition.

But it's good to remember that these P4 athletes are pretty elite and play the game at a very high level. To give you an idea of just how elite, here are the top 10 BIC attackers in P4-vs-P4 matches in 2025 (minimum 20 attempts):

Luckily, the top 10 isn't the whole story. 35 P4 athletes hit 27% or better on BICs in transition against other P4 teams (with at least 10 attempts), so the volume of athletes who can execute an effective BIC in transition is meaningfully broader than just the volume leaders.

And the story is actually even broader than that. There are a lot of athletes in a lot of programs hitting BICs in transition with high effectiveness. The top 10 most effective non-P4 transition BIC attackers have attack efficiency numbers VERY similar to their P4 counterparts:

Encouragingly, 68 non-P4 athletes hit 27% or better on BICs in transition in 2025.

Net net: There are lots of teams and athletes that can run the BIC with high effectiveness.

Coaching takeaway: If you have a strong outside hitter on your team, you should consider training your setter and that outside to run BICs in transition. In many cases, they can be just as effective hitting the BIC attack in trans as they are hitting on the left. And if they get really good at it, they could be even more effective than your middles running quicks.

Setting the D-ball in transition

The D-ball is a right-side back-row attack, usually hit by an opposite. It is probably the single most important attack in international men's and women's volleyball but has never had much traction in NCAA Women's volleyball.

Across NCAA DI in 2025, transition D-balls result in a 15% attack efficiency on 5,678 attempts. That's the lowest attack efficiency and volume of these "non-standard" transition attacks (gap, BIC, D and slide). As evidenced by the very low volume, in the US at this level, D-balls are a specialty option for the small number of teams that have an unusually gifted right-side attacker.

While Women's DI attack efficiency on D-Balls is only 15%, P4-vs-P4 jumps to 20% on 1,182 attempts, but that still isn't great. Even in the P4, the D-ball is a low-volume specialty attack (P4 teams run roughly one D-ball for every four BICs and one for every twelve quicks). That said, if you are one of the lucky few who have a back-row right-side who can rip the ball, the data shows it works against the best defenses in the country.

One note: You might be wondering what's up with cell 2B in this heatmap. I pulled a sample of clips from the matches that produced the 2B-coded D-balls, and they are D-ball attacks. Watching the clips, many times the attack contact point is hard to see - it's somewhere between 2A and 2B in almost every case. So treat the 2B and 2A D-ball cells as essentially one combined area.

Just how much of the elite D-ball volume is concentrated in a few specific athletes? I pulled every P4-vs-P4 transition D-ball in 9B (where most of the attack volume is) and ranked the hitters by attempts. The list is short, and the names tell the story:

95 different DI athletes hit a D-Ball from this zone, but 62% of the volume comes from just the 10 athletes above. The number of teams using this option is tiny, and as you can see from the shading, the number of athletes who can hit this attack effectively is minuscule. The top four (Babcock, Clemente, Martin, Spears) are 34% of the entire cell between them.

Coaching takeaway: I have mixed feelings about this one. Clearly today, in the US, at the DI level, unless you have a truly elite opposite, your team should not run D-Balls in transition. That said, in women's professional volleyball around the world, this is a very normal attack that many teams run effectively. My take is that because the US women's volleyball community loves the slide attack, we don't train young athletes to hit a D very often. Thus there aren't very many athletes for college programs to recruit who are good at this specific attack. It may be up to my fellow club and high school coaches to decide if we want to change this going forward!

Setting slides in transition

Alternatively, in the US, slides attacks are very popular and as the data shows, a slide can be a very effective option in transition. A slide is a front-row middle blocker running behind the setter and hitting off one foot, usually finishing near the right antenna. Anyone who has watched women's college volleyball over the last decade has seen how dangerous a middle like Andie Jackson, Erika Sayer, Carter Booth or Dana Rettke can be running slides.

Across NCAA DI in 2025, transition slides are highly efficient with an average 32% attack efficiency but they are run surprisingly few times with only 6,916 attempts, only slightly more frequently than D-Ball attacks. I'm not sure why that is the case. According to these numbers, running a slide in transition is almost as effective as running a quick attack and it's significantly more efficient than any pin attack.

Interestingly, this is an area where when you filter to just Power 4 vs Power 4 matches, the efficiency number barely changes. P4 slide attackers hit at 31% on 1,399 attempts, essentially the same as the DI number. This is the only attack we've looked at in this section where the P4 efficiency is not higher than the overall DI baseline. My suspicion is that is because slides are an attack that many athletes in many programs can run effectively unlike D's.

Coaching takeaway: Slides are a great option in transition, nearly as effective as quick attacks in the middle and much more effective for a much larger group of teams and athletes than Ds. So if you aren't already teaching this, it's probably time to start training your setters and middles to run slides in transition!

What's Next

Now that we know what attacks to run and where they go, the next question is: how does the menu change when the setter gets pushed off the net? In my next post, I'll analyze all these locations and attacks separated out by how far the setter has been pushed off the net so we can see which locations and attacks remain effective and which should get taken off the menu.

Methodology note: This analysis uses every transition Dig-Set-Attack sequence from the 2025 NCAA DI season (521,139 total). "Setter" means the team's active setter (any player who took 20% or more of the team's sets, excluding liberos). "Libero" comes from the match metadata. The 36-cell subzone grid uses a minimum cell threshold of 0.25% or 0.50% of total observations to filter out scouting noise. The "Set Destination Quality" metric is computed by looking up the league-baseline attack efficiency at each subzone where a team's setter sets to, then averaging those values across all of the team's setter sets. Teams with fewer than 100 setter sets in transition were excluded from the correlation analysis.

Today I'm taking that a step further. If DSK% is the most important outcome for your defense, what makes it go up? Let's start at the beginning of the chain. Just digging more balls doesn't help more. But what if we dig the ball to better locations?

Note: If you’re an experienced coach, nothing in this blog is going to surprise you. But it sure is nice to have data that confirms what you already knew right?

A Heatmap of 579,000 Digs

I took every dig from the 2024-25 NCAA DI season, tracked where it landed on the court, and then measured the attack efficiency of the subsequent attack. The result is a heatmap showing which dig destinations produce the best and worst transition attack efficiency.

We're looking at 579,767 dig-to-attack pairs across 348 NCAA DI teams. The patterns are clear and just like you would expect.

The court is divided into 36 subzones and the color tells you the attack efficiency that followed digs landing in that subzone. A few things jump out immediately.

The center of the between 5 to 10 feet off the net is the place to target. Digs that land in zones 3 and 2D (the middle and right-center of the court near the net) produce the highest attack efficiency. The best single subzone is 3B at 27.4% (28,946 observations), followed by 3A at 26.6% (103,810 observations).

And no surprises, digs that land near the sidelines or deep in the back court produce the lowest attack efficiency.

Coaching takeaway: When training your defense, emphasize digging the ball toward the center of the court, ideally into zones 3 and 8 (the middle of the court between the attack line and the back row). A dig that lands near zone 3 gives your attackers roughly 27% attack efficiency.

Why the Middle Works

This obviously doesn't surprise experienced coaches, but it's nice to see the data confirm it so clearly. Digs to the middle of the court work better because:

The setter has options. A dig to the center of the court gives the setter access to at least two and sometimes three attacking positions. A dig pushed to the sideline limits options and often results in a predictable high ball to one hitter.

Transition tempo. When the dig lands near the center of the court where the setter can easily hand set, she can run plays at a faster tempo rather than having to run high ball sets.

I also computed a "sweet spot" for dig destination, a kill-weighted centroid across all 579K digs. Again, not surprising - it’s in the center of the court, about 1 foot inside the attack line.

Coaching takeaway: If you're charting digs in practice, consider tracking not just "did we dig it up" but "where did the dig land." Zones 3 and 8 are your targets. A dig that keeps the ball in the middle third of the court is significantly more valuable than one pushed to either sideline or deep into the corners.

But Does Digging to Good Locations Actually Predict Winning?

This is the question that gets really interesting. We've established that dig destination matters on individual plays, but does a team's overall ability to dig to better locations predict that team's success?

To figure that out, we can't just count what percentage of a team's digs land in the green zones on the heatmap and call it a day. The problem is opponent quality. If your team plays a schedule full of powerful attackers, your digs are naturally going to get pushed to worse locations than a team facing weaker hitters.

To account for this, I built a metric called Opponent-Normalized Dig Quality (ONDQ) that works on a match-by-match basis. For each match, I measure where your digs land and then compare that to where every other team's digs land against that same opponent.

The result tells you how much better or worse your team digs to high-value locations compared to what's typical against that opponent.

A positive ONDQ means your digs consistently land in better spots than what your opponents' other opponents manage. A negative ONDQ means your digs land in worse spots than average.

I ran this analysis for three groups: all 348 NCAA DI teams, just the 67 Power 4 teams, and the 281 non-P4 teams.

ONDQ vs Set Win%

Take a careful look at the Power 4 row. Dig% alone has essentially zero correlation with winning in the P4 (r = 0.13, p = 0.287). I showed that in a previous post. In the most competitive conferences, everyone digs at roughly the same rate, so Dig% doesn't differentiate winners from losers.

But ONDQ is a completely different story. Even in the Power 4, where Dig% fails, ONDQ correlates with winning at r = 0.45 (p < 0.001).

The implication is pretty cool: even at the Power 4 level, how many balls you dig doesn't predict winning, but where you dig them does. Teams that consistently dig to better locations (controlling for opponent quality) win more sets.

For non-P4 teams, both Dig% (r = 0.43) and ONDQ (r = 0.42) predict winning at about the same level. In those conferences, talent gaps are wide enough that digging more balls still matters.

Coaching takeaway: If you coach in a Power 4 conference, your team's dig rate is unlikely to separate you from your competition. Everyone in your conference can dig. What can separate you is where those digs go. Training your defenders to target the middle of the court (and away from the sidelines and deep corners) is a likely to pay dividends as dig location correlates with winning even at the highest level of college volleyball.

The Connection to Dig-Set-Kill%

Here's the piece that ties it all together. If ONDQ (dig quality by location) really does feed the transition attack chain, it should correlate with DSK%. And it does.

ONDQ is positively correlates with DSK% at r = 0.46-0.47 across all three samples. That's a slightly stronger correlation than ONDQ has with Set Win% itself. The mechanism is exactly what you'd expect: teams that dig to better locations give their setter better balls, which leads to better sets, which leads to more kills in transition.

And this relationship is remarkably consistent. Whether you look at the full DI landscape, just the Power 4, or just the non-P4 teams, ONDQ and DSK% correlate at virtually the same level. The relationship between dig location quality and transition attack success doesn't depend on what level you're competing at.

Coaching takeaway: Dig location quality feeds directly into your Dig-Set-Kill% chain. Teams that dig to the middle of the court produce higher DSK% regardless of conference level. If you're looking for ways to improve your DSK% beyond just "score more kills," consider working on where your defenders are sending the ball. It's the first link in the chain, and this data suggests it's a meaningful one.

What This Means for Your Program

Let me put this in context. ONDQ at r = 0.44-0.45 is not as strong as the dominant metrics in this study. Your team's ability to terminate is still what matters most.

But when it comes to defense, ONDQ (r = 0.44-0.45) is a metric worth paying attention to:

ONDQ slots in nicely as a component that feeds the bigger chain metrics. It's not the whole story, but it's a meaningful piece of the puzzle, and importantly, it's one that you can work with your team to improve.

The heatmap at the top of this post gives you a concrete target: zones 3 and 8 are your sweet spots. Zone 2D and 3D are solid alternatives. And the back corners (zones 1 and 5) are where transition attacks go to die. Print it out, post it in your gym, and give your players a visual target for where their digs should go. During a recent visit to meet Kasey Crider and his team at UMBC, I was excited to see a box taped on the floor and bonus points being awarded for digs that came down inside the box. I’ll certainly be doing that in my gym this week!

Coaching takeaway: Dig destination quality is a trainable skill that shows up in the data at every level of NCAA DI volleyball. It's not the most important thing (terminating still is), but for teams looking for an edge in transition defense, this is low-hanging fruit. Work on digging to the middle of the court, design drills that reward dig location and not just "keeping the ball alive," and track where your digs land alongside how many you get. The data says it will pay off in your DSK% and your win column.

This is Part 5 in the "What Really Wins Sets" series. Previous posts: [What Really Wins Sets](#), [Service Errors](#), [FBSO Has Entered the Chat](#), [Digging into Point Scoring](#).What Really Wins Sets, Service Errors, FBSO Has Entered the Chat, Digging into Point Scoring.*

All analysis based on 2024-25 NCAA Division I season data. 579,767 dig-to-attack pairs across 348 teams. ONDQ computed with opponent normalization using a leave-one-out method across the full season schedule. Statistical significance assessed at p < 0.05.

For those of you who are new, in my first post, I analyzed 630 regular season matches across all four Power 4 conferences to find out which metrics are most correlated with winning. Side-Out%, Attack Efficiency%, Point Scoring%, and Dig-Set-Kill% topped the list. I learned that MTRP% acts as a gate. Transition In-System Conversion% emerged as an under-appreciated differentiator.

Now I'm turning my data analysis pipeline loose to answer my next question: do the same things win in the NCAA Tournament?

A note on methodology: For this post, I've expanded the season baseline from the 66 Power 4 teams in my first post to all NCAA DI matches across 349 teams. This gives us a more complete picture of how the tournament compares to the broader landscape of college volleyball. The season correlation values in this post won't match the first post exactly because of this change.

The Dataset

For this analysis, I processed all 63 matches from the 2025 NCAA Championship, from the First Round through the Championship match on December 21st.

However for analysis purposes I excluded teams that lost in the first round (teams that played only one match). Including them was making the data WAY TOO noisy. With those teams removed, my analysis focuses on the 32 teams that each won at least one tournament match, giving us a more reliable picture of what separated teams as the tournament progressed.

For each team's season baseline, I used their full 2025 regular season data, including preseason, non-conference, and conference matches. This gives us the most complete picture of who each team was before they entered the tournament bracket.

Same Metrics, Different Importance

Let's start with the obvious. Side-Out% and Point Scoring% are still strongly correlated with winning in the tournament (r = 0.72 and r = 0.59, both p < 0.001). That shouldn't surprise anyone. If you're winning more side-out points and more defensive points, you're winning more sets. Not super surprising.

The more interesting question is what happened to the more specific chain-of-contacts and skill metrics. Which became more important in the tournament, and which ones mattered less?

Green rows are statistically significant in the tournament (p < 0.05). The top five are all p < 0.001.

The Ability to Terminate is at a Premium in the Tournament

Kill% jumped from #5 to #1. Dig-Set-Kill% jumped from #4 to #3. Trans In-System Conv% moved up from #7 to #6. Point Scoring% fell from #3 to #5.

That reshuffling tells us something important. Kill%, Dig-Set-Kill% and Trans In-System Conv% are all different metrics tied to your team's ability to terminate.

In the regular season, broader outcome metrics like Side-Out% and Point Scoring% tend to be the strongest predictors because the talent gap gives you more ways to win a point. You can win on errors, on long rallies, on serve runs. But in the tournament, when every opponent is dangerous, those broader metrics become less useful for distinguishing between teams. The margin shrinks, and the ability to finish plays, to actually put the ball on the floor, becomes the most meaningful differentiator.

Meanwhile, the predictive power of Point Scoring% (winning points when you're serving) dropped quite a bit. This makes sense. When every team in the field can pass and hit at a high level, the variance in defensive outcomes (aces, blocks, digs leading to transition kills) narrows. There's less room for any one team's serving or blocking to separate them from the pack.

This shows up in the team spotlights too. Wisconsin's Transition In-System Conversion% jumped nearly 10 points. Pittsburgh's Kill% jumped significantly. The teams that went deepest were the ones whose hitters found another gear.

Coaching takeaway: Kill%, Dig-Set-Kill% and Trans In-System Conv% matter even more in the postseason than during the regular season. If you're preparing for the NCAA DI tournament, transition offense (converting digs into kills) is worth investing in.

Some of These Changes Are Meaningful, Some Aren't

Not every correlation changed by the same amount, and I did test to check which of these were statistically significant using a Fisher's z-transformation test. The results paint a clear picture:

Every metrics R value declined (mostly because of the smaller data set) but only some of those declines were statistically significant. Kill% (terminating attacks), Dig-Set-Kill% (converting defensive stops into kills, p = 0.067, borderline), Trans In-System Conversion% (killing in transition), and Block Impact% (stopping your opponents from terminating) all decreased small amounts, but those decreases were not significant. Meanwhile, the metrics that changed larger amounts that were statistically significantly (Side-Out%, Attack Efficiency%, Point Scoring%, FBSO%, and MTRP%) are all much less directly tied to terminating the ball or stopping your opponent from terminating the ball.

This confirms that the rank reshuffling isn't noise. The ability to kill the ball, especially in transition, and the ability to stop your opponents from killing the ball and then convert those stops into kills, are all relatively more differentiating in the tournament than they are in the regular season.

Why Kill% Beat Attack Efficiency%

One of the most intriguing findings is hiding in that table. Kill% (r = 0.75) and Attack Efficiency% (r = 0.67) are closely related metrics. Kill% is kills divided by total attacks. Attack Efficiency% is kills minus errors divided by total attacks. The only difference is the error component.

During the regular season, attack error rate is a meaningful predictor of winning (r = -0.64, p < 0.001). Bad teams make more errors, good teams make fewer, and that pattern is consistent enough that subtracting errors from kills improves the signal. That's why AtkEff% (0.87) beat Kill% (0.82) during the season.

In the tournament, that flipped. Attack error rate dropped to r = -0.34 (p = 0.057). Not only did the correlation weaken, it went from highly significant to merely suggestive. Error rate simply stopped distinguishing between winners and losers. And when you subtract something that's essentially noise from a clean signal, you make the signal worse. That's exactly what happened: AtkEff% inherited all of Kill%'s predictive power but got diluted by the error noise, dropping it below Kill% for the first time.

Digging into this some more, I found that after controlling for Kill%, the partial correlation of error rate with winning actually flips positive in the tournament (+0.18 vs -0.53 in the season). That means among teams with similar kill rates, the ones with slightly more errors tended to win more. That's consistent with aggressive play being rewarded. In elimination matches, teams that swing hard and accept some errors seem to come out ahead because the kills matter more than the errors cost them.

I also checked whether this was caused by improved blocking. After all, if tournament teams face better blockers, their attack errors would go up through no fault of their own. But the data rules this out: opponent block stuff rates are nearly identical in the tournament (7.0%) and the regular season (6.9%), and facing better blockers doesn't correlate with higher error rates in the tournament (r = 0.09, p = 0.61).

And most interesting of all: Removing block-induced errors from Attack Efficiency% doesn't improve its predictive power at all.

And it's not because tournament teams all have similar error rates. The standard deviation of attack error rate is actually higher in the tournament (2.1% vs 1.4% in the regular season). There's plenty of variability. It just doesn't predict who wins anymore. The teams making more errors aren't the ones losing. They're the ones swinging aggressively, and that aggression is paying off in kills.

What this tells us is that in elite tournament competition, error rate doesn't distinguish winners from losers the way it does during the regular season. The teams that go deep tend to be the ones that put balls on the floor, even if that means accepting a few more swings into the net or out of bounds along the way.

Coaching takeaway: The data suggests that aggressive attacking is rewarded in elimination play. Kill% matters more than Attack Efficiency% because the error component stops separating winners from losers at this level. If you're preparing for the postseason, you should probably be encouraging your hitters to swing aggressively rather than playing it safe.

Good Pass% Matters Less

One of the most surprising drops in the data is Good Pass%, which fell from r = 0.59 (p < 0.001) in the regular season to r = 0.27 (p = 0.134) in the tournament. Your first instinct might be that the range compressed, that all tournament teams pass at a similar level so there's nothing to differentiate. But that's not what happened. The standard deviation of Good Pass% is actually slightly higher in the tournament (5.8 vs 5.0), and the range is nearly identical. There's plenty of variability. It just stopped predicting who wins.

The real story is that the connection between passing well and killing well weakens significantly at the elite level:

During the regular season, teams that pass well also kill well. There's a clear chain: pass in system, get a good set, terminate the ball. But in the tournament, that chain weakens considerably. Good Pass% becomes essentially unrelated to Kill% (r = 0.03), MTRP% (r = 0.15), and Side-Out% (r = 0.07). Passing well doesn't translate into killing efficiently the way it does in the regular season.

This makes sense when you think about it. Every tournament team passes adequately (the average Good Pass% is actually higher in the tournament, 52.2% vs 45.7% across all of NCAA DI). The competitive advantage shifts to what you do with the pass. Two teams can both pass 55% of balls in system, but the one whose hitters can terminate against a tournament-caliber block is the one that wins. This is consistent with what I showed earlier: in the tournament, the ability to terminate is the premier skill. Passing gets you to the table. Killing wins the hand.

Coaching takeaway: In the tournament, passing well appears to be more like table stakes than a competitive advantage. Every team at this level can pass. The differentiator is what your hitters do with those passes. If you're preparing for the postseason, this suggests investing more time on improving attack execution rather than trying to improve your serve receive.

Why Did All the Correlations Drop?

Every correlation dropped from the regular season, some significantly (as shown above). This isn't because these metrics suddenly stopped mattering. Two things are going on.

Smaller sample size. With 32 teams instead of 349, there's inherently less predictability. Statistical relationships need volume to show up clearly.

More uniform talent. In the regular season, Nebraska (91% set win) plays teams with 17% set win rates. In this analysis, every team was good enough to qualify and win at least one match. When the spread in talent narrows, correlations naturally compress because there's less variation to differentiate teams.

The fact that the correlations are still highly significant (p < 0.001 for the top five) despite these headwinds tells you these metrics genuinely matter. They're not artifacts of weak teams dragging down the averages.

Serving Errors: Still Not a Differentiator

Going into this analysis, I expected serving errors to matter even less in the tournament than they did in the regular season. In the regular season, Serve Error% is a weak predictor of winning (r = -0.12, p = 0.024). Statistically significant with 349 teams, but not practically meaningful. In the tournament, it's similarly correlated (r = -0.22, p = 0.220), but not statistically significant. Serving errors simply don't distinguish winners from losers in any meaningful way.

That's consistent with what I've found throughout this analysis. The metrics that matter most in the tournament are the ones tied to killing the ball and stopping your opponents from killing the ball. Serve errors are a cost of doing business, not a deciding factor.

Coaching takeaway: Once again, the data don't show any significant penalty for serving errors. Serve Error% is a weak predictor at best (r = -0.12 across NCAA DI). If your team serves tough during the season, stick with it in the postseason!

The Big Picture: Season vs. Tournament

Click here to see the full dataset with season and tournament values

I built a comparison table showing how each team's tournament metrics stacked up against their full season baselines. Make sure you take a careful look at the color coding. Green cells indicate metrics that improved in the tournament. Red cells indicate declines. The deeper the color, the bigger the change.

A few things jump out:

Almost every team's set win percentage dropped. This makes intuitive sense. In the regular season, you face the full range of opponents. In the tournament, you don't get any easy matches.

The teams that went deepest generally maintained or improved their attack metrics. Wisconsin, Pittsburgh, and Texas all saw their Kill% and Attack Efficiency% increase in the tournament. They elevated their games when it mattered, even as the competition made it harder to actually win sets.

MTRP% barely moved for most teams. The gate concept from my first post holds: teams that could get quality first-ball attacks during the season were still getting them in the tournament. It's a stable, repeatable skill, and that's exactly what you want in a high-pressure environment.

Now let's look at the individual stories. This is where things get really fun.

Spotlight: Kentucky, the Finalist Who Just Kept Playing Their Game

Kentucky made it all the way to the Championship match, and looking at their numbers, the most striking thing is how little changed from the regular season.

Kentucky's Kill% didn't change. Their MTRP% didn't change. Their Block Impact% didn't change. What did change was their FBSO% jumped 3.8 points, meaning they were killing the ball on first-ball attacks at a higher rate than during the season. That's how they maintained an elite Side-Out% of 68.3% (tied for third-best in the tournament) even though their overall attack efficiency dipped slightly.

The 7.9 point drop in set win% is almost entirely explained by the quality of opponents. Kentucky didn't get worse. They played 21 sets across six matches against exclusively tournament-caliber teams and still won 71.4% of them. That's the sixth-best rate in the entire tournament.

Their path tells the story: Wofford, UCLA, Cal Poly, Colorado, and Wisconsin on the way to the final before falling to Texas A&M. Five wins, all while playing essentially the same game they played all season. Sometimes the best tournament strategy is just being consistently very good.

But there's an interesting wrinkle here. Look at where Kentucky improved versus what the tournament data tells us matters most:

Kentucky's biggest improvements came in serve receive conversion metrics (SR OOS Setter Conv%, SR In-System Conv%, FBSO%), which are in the bottom half of the tournament predictive rankings. Meanwhile, they actually declined in Dig-Set-Kill% and Trans In-System Conversion%, the transition metrics that this analysis identifies as the most differentiating skills in tournament play. Kentucky got better at side-out, but not at the transition game that most separates tournament winners from losers. It's impossible to say for certain, but the data makes you wonder: if Kentucky had elevated their transition offense the way Wisconsin did, would the championship match have ended differently?

Coaching takeaway: Kentucky's tournament run is a case study in consistency, but also a cautionary tale. Consistency got them to the final, but the metrics that matter most in the tournament (Kill%, Dig-Set-Kill%, Trans IS Conv%) were the ones that didn't improve. If you're preparing for the postseason, maintaining your identity is valuable, but finding ways to elevate your transition game may be what separates a finalist from a champion.

Spotlight: Texas A&M, the Champion (Preview)

I'm not going to go deep on Texas A&M here because they deserve their own dedicated post. But here's a teaser.

At first glance, Texas A&M's championship run looks unremarkable. Their Kill% was essentially flat. Their set win% barely moved. But look at where they improved, and compare it to Kentucky:

Texas A&M is almost a mirror image of Kentucky. Kentucky improved in the lower-ranked serve receive metrics (FBSO%, SR OOS Setter Conv%, SR In-System Conv%) but declined in the transition metrics. Texas A&M did the opposite: they improved in Dig-Set-Kill% (+2.8) and Trans In-System Conversion% (+4.3), the transition metrics that this analysis identifies as the most differentiating skills in tournament play, while their serve receive conversion metrics declined.

Both teams were consistent. Both teams made it deep. But Texas A&M's improvements came in exactly the areas that matter most in the tournament, and they won the championship. Kentucky's improvements came in the areas that matter less, and they fell one match short.

I'll dig into Texas A&M's tournament path in more detail in an upcoming post. Stay tuned.

Spotlight: Wisconsin, Peaking at Exactly the Right Time

Wisconsin's path to the Final Four is one of the best stories in the data, and it has a name: Charlie Fuerbringer.

Look at where Wisconsin's improvements landed. Kill% up 3.8 points. Dig-Set-Kill% up 5.6 points. Attack Efficiency% up 4.5 points. Trans In-System Conversion% up 9.1 points. Those are the four metrics most correlated with winning in the tournament, and Wisconsin improved in every single one of them. This is what peaking at the right time looks like.

Here's the backstory. Wisconsin's starting setter, Charlie Fuerbringer (#24), was injured during the October 5th match against Michigan. She missed nine consecutive matches (October 10 through November 6). During that stretch, Wisconsin went 13-9 in sets, with their attack metrics dipping notably. Kill% averaged 39.2% and Attack Efficiency% averaged 32.9%.

Then Fuerbringer returned on November 9th against Indiana. From that point forward, Wisconsin went 21-1 in sets to close the regular season. Their Kill% jumped to 46.0%, Attack Efficiency% to 39.2%, and Side-Out% to 71.1%.

The late-season surge carried straight into the tournament, where Wisconsin's numbers climbed even higher. Their tournament Transition In-System Conversion% of 54.2% was up nearly 10 points from their season average. That's a team that was converting digs into kills at an incredible rate.

Wisconsin's tournament ended in the Final Four with a loss to Kentucky, but the data tells the story of a team that found another gear when their setter returned and never looked back. And unlike Kentucky, Wisconsin's improvements came in exactly the metrics that matter most in the tournament: termination and transition offense.

Coaching takeaway: Wisconsin is a great reminder of how much a setter can transform an offense. The same hitters, the same system, but a different distributor produced meaningfully different results. If your team is struggling to get kills, make sure to pay attention to the huge role your setter can play in making your offense more effective.

Spotlight: Pittsburgh, the Final Four's Attack Machine

Pittsburgh's run to the Final Four was powered by one thing: their hitters found another level.

A 5.0 point jump in Attack Efficiency% and a 4.0 point jump in Kill% against tournament-caliber opponents is impressive.

The interesting wrinkle: their Good Pass% actually dropped 4.4 points and their Point Scoring% declined. Pittsburgh wasn't passing better in the tournament. They were just finishing better. When they got an attack opportunity, they put the ball away at a higher rate, compensating for the tougher passing conditions.

Their set win% barely moved (75.8% to 75.0%), which means Pittsburgh was essentially the same caliber team in the tournament as during the season. They just shifted how they won: less through serving and defensive points, more through pure attacking execution.

Coaching takeaway: Pittsburgh shows that as long as your team's attacking is top notch (and hopefully improving), you can make up for backsliding in a few less important areas. Their passing got worse and their Point Scoring% declined, but their improved Kill% and Attack Efficiency% more than compensated.

Spotlight: Nebraska, the Queen in Waiting that Fell Short

Here's the most counterintuitive data point in the entire tournament: Nebraska had the highest set win percentage of any team in the tournament at 78.6%, and they didn't make it past the Elite 8.

At first glance, that 12.4 percentage point drop in set win looks dramatic. But look at the individual metrics. The changes are remarkably modest. Kill% dropped less than a point. MTRP% barely moved at all. DSK% actually went up slightly. Nebraska didn't fall apart. They were still the best team in the tournament by the numbers.

The real story here is about what happens when a historically dominant regular season team meets tournament-caliber opponents in every round. Nebraska went 60-1 in sets during Big 10 conference play (101-10 for the full season). That kind of dominance simply isn't sustainable when every opponent is a tournament qualifier. Their "decline" to 78.6% is less about Nebraska getting worse and more about the competition getting better.

And yet, 78.6% was still the best set win rate in the entire tournament. Better than the champion (Texas A&M at 78.3%). Better than every Final Four team. Nebraska's "underwhelming" tournament was still better than everyone else's good one. Their loss to Texas A&M in the Elite 8 came against the team that went on to win the whole thing.

Coaching takeaway: Nebraska played plenty of tough teams in the Big 10 regular season, but the end of their schedule was against teams who weren't as strong. Meanwhile, teams like Texas A&M, Kentucky, and Texas were competing in the SEC tournament, playing against many of the best teams in the country and getting tuned up for the NCAA Tournament. It will be interesting to see what happens next year when the Big 10 has a conference tournament for the first time and Nebraska gets to have that same kind of late-season, high-pressure tune-up.

Spotlight: Texas, the MTRP% Gate in Action

Texas is one of the most fun stories in the data.

Texas improved by a small amount in nearly every category, which is impressive in itself against tournament-caliber competition. They beat Florida Atlantic, Penn State, and Indiana on their way to the Elite 8 before falling to Wisconsin. But the only truly notable improvement was their MTRP%, which jumped from 83.9% to 88.2%, the second-highest in the tournament behind only Indiana.

That means nearly 9 out of 10 serve receptions resulted in a quality first-ball attack opportunity. But here's the thing: MTRP% is a gate metric. As I discussed in my first post, once you're above the ~82% threshold, additional improvement doesn't have a big payoff in terms of winning more sets. Texas was already well above the gate during the regular season at 83.9%. Going to 88.2% meant they were getting even more attack opportunities, but that alone doesn't win sets. You still have to terminate those opportunities, and Texas's Kill% and Dig-Set-Kill% barely moved.

Coaching takeaway: Texas's story is a good illustration of the MTRP% gate in action. Improving from 84% to 88% sounds impressive, but because MTRP% is a gate metric rather than a linear predictor, those additional attack opportunities didn't move the needle. Texas increased the number of times they got a good swing on their first ball after serve receive, but it didn't have a big payoff in Set Win% as I would have predicted based on this metric.

Spotlight: Minnesota, the Tournament Transformer

Minnesota's tournament run might be the most impressive performance improvement in the entire dataset. Their underlying metrics improved across the board, and they reached the Sweet 16.

Look at those numbers. A 7.7 point jump in MTRP%. A 6.0 point jump in Dig-Set-Kill%. Attack Efficiency% up 5.5 points. Transition In-System Conversion% up 8.4 points. Every single core metric improved substantially.

During the regular season, Minnesota's 78.1% MTRP was right at the "gate" threshold I identified in my first post, the zone where teams are basically 50/50 on having a winning record. In the tournament, they jumped to 85.8%, which would have put them among the elite in any regular season dataset.

With the caveat that tournament sample sizes are small (Minnesota played three matches), the data suggests Minnesota's first-ball execution simply reached another level. They passed better, got to attack more, finished at a higher rate, and converted their digs into kills far more effectively. Whether that's a team hitting its stride at the right time, a favorable draw, or some of both, the result is striking.

Coaching takeaway: Minnesota shows that it is possible for a team to really elevate their level of play for the tournament and perform well beyond expectations. Whatever my friend Keegan Cook did with this team to make this happen, he should definitely bottle and sell it!

Coaching Takeaways

The tournament data mostly confirms the findings from the regular season analysis, so I won't repeat everything from my first post. But a few tournament-specific observations are worth highlighting:

The same metrics predict winning in the tournament, but the emphasis shifts. Kill% and Dig-Set-Kill% rise to the top, while Point Scoring% drops significantly (Fisher z = 3.18, p = 0.001). The ability to terminate, especially in transition, becomes the key differentiator when the talent gap narrows.

Aggressive attacking is rewarded. Kill% outperformed Attack Efficiency% for the first time because error rate stopped distinguishing winners from losers at the tournament level. Teams that swung hard and accepted some errors tended to come out ahead.

The teams that went deepest improved in the metrics that matter most. Wisconsin and Pittsburgh saw big jumps in Kill% and Attack Efficiency%. Texas A&M elevated their transition game (Dig-Set-Kill%, Trans IS Conv%). Kentucky was the exception: their improvements came in less predictive serve receive metrics, and they fell one match short of the title.

The MTRP% gate still holds, but it's a gate, not a ladder. As Texas dramatically illustrates, MTRP% is a gate metric: once you're above ~82%, further improvements don't have a big payoff. Texas jumped from 84% to 88% and didn't see a corresponding improvement in the metrics that matter most. Minnesota, on the other hand, improved from 78% (just above the lower gate) to 86% (just above the upper gate), and likely saw a much more dramatic payoff from that shift. The gate matters for getting in the door. Once you're through it, termination is what separates teams.

Consistency may be the ultimate championship quality. Texas A&M and Kentucky, the two finalists, had the smallest metric shifts from season to tournament. They didn't need to become different teams. They just kept being who they were.

Conclusions

The 2025 NCAA Tournament data tells a story that's both reassuring and nuanced. The same core metrics that predict regular season success also predict tournament success. But the emphasis shifts: Kill%, Dig-Set-Kill%, and Trans In-System Conversion% all increase in importance during the tournament, while broader outcome metrics like Point Scoring% drop, telling us that the ability to terminate, especially in transition, becomes the most important skill when the competition level rises. And the data suggests that aggression is rewarded: error rate stops distinguishing winners from losers at the elite level, meaning Kill% outperforms Attack Efficiency% for the first time.

But the tournament also reveals its own character. Correlations compress because the talent pool is more uniform. Individual team stories show that aggregate metrics only tell part of the tale. Minnesota's transformation, Nebraska's paradox of dominance, Wisconsin's perfectly timed peak, and Kentucky's unwavering consistency all remind us that volleyball is played by people, not spreadsheets.

As always, I'd love to hear your thoughts. Which teams or metrics surprised you? What should I dig into next? Drop a comment, email me at alex_simons@hotmail.com, or find me on X (@VballAlexSi) and Threads (@coach.alex71).

See you on the court.

Alex

Appendix: Full Tournament Correlation Table

I went back to the data to find out.

The Setup

Same dataset as before: 5,107 match files from the 2025 NCAA Women's DI season, 349 teams, non-DI opponents excluded. This time I filtered down to only matches that went to five sets and looked exclusively at set 5 outcomes. That gave me 1,035 five-set matches and 2,070 team-set observations (two teams per match, one served first, one received first).

For each set 5, I identified which team served first, then tracked whether that team or the receiving team won the deciding set.

The Answer: Receive is the Right Choice in Set 5, by an Even Bigger Margin.

Across all of NCAA DI, here are the set 5 results compared to the all-sets results from my previous post:

The receive-first advantage nearly quintuples in the deciding set. Across all sets, receiving first is worth about +0.8 percentage points. In set 5, it jumps to +3.7 percentage points.

Why might this be? In a 5th set to 15, every point carries more weight. The structural +1 real point advantage that Coach Swing describes takes up a bigger share of the total points needed to win. In a set to 25, one real point is roughly 4% of the points needed. In a set to 15, it's closer to 7%.

Coaching takeaway: The receive-first advantage matters even more in a set to 15. When you win the coin toss before a deciding set, receive unless your SO% is well below 50%.

Does Side-Out Quality Change the Picture?

In my previous post, I broke teams into four tiers based on their season-long Side-Out%. The same tiers apply here, using the NCAA DI mean of 57.1% and standard deviation of 4.0%:

The elite tier tells the clearest story. Teams with a Side-Out% of 60% or higher won 63.3% of their 5th sets when receiving first, compared to 54.7% when serving first. That's an 8.6 percentage point edge, far larger than the +0.5pp elite edge across all sets.

For context, here's how the same tiers looked across all sets in the previous post:

The set 5 effect is dramatically larger for elite teams. These are the programs that side-out most reliably, so the structural +1 real point from receiving first is most likely to actually convert. And in a shorter set, that converted advantage looms larger. You might wonder what is going on with the Above Average group here as well. Why is its advantage so small? I dug into this and the answer is pretty interesting. If you're curious, check out the Appendix at the bottom of this post.

Coaching takeaway: If your team is an elite passing team, the 5th set is where the receive-first advantage really shows its teeth. An 8.6 percentage point edge is substantial. If you're in the above average or below average range, the advantage is smaller and noisier, but it never flips to favor serving first.

Power 4 Conference Play

I also looked at this specifically within Power 4 conference matches (Big Ten, SEC, Big 12, ACC), filtering to only head-to-head matchups between P4 teams so the numbers are apples to apples.

The receive-first edge is even larger in P4 conference play: +5.7 percentage points compared to +3.7pp across all of DI. This lines up with what the tier data shows. P4 teams side-out at 60.1% on average (compared to 57.1% for all of DI), so they're better positioned to cash in on that structural first-point advantage. Meanwhile, Point Scoring% is virtually identical (42.7% vs 42.9%), meaning the difference isn't about serving or defending better. It's about passing and attacking better, which is exactly the mechanism that makes receiving first valuable.

Coaching takeaway: In P4 conference play, where side-out rates are highest, the receive-first edge in the 5th set is even more meaningful. Out of 157 P4-vs-P4 five-set matches, the receiving team won nearly 53% of the time.

The Bottom Line

The receive-first advantage that Coach Swing laid out mathematically, and that I confirmed across 18,919 sets in my previous post, gets amplified in the 5th set. The shorter set makes the structural +1 advantage worth relatively more, and the data reflects it: +3.7pp overall, +8.6pp for elite teams, +5.7pp in P4 conference play.

If you were already choosing to receive after winning the coin toss, keep doing it. If you were on the fence about whether it matters in a deciding set, the data is clear: it matters more, not less.

As always, I'd love to hear your thoughts. Feel free to leave comments, email me at alex_simons@hotmail.com, or find me on X (@VballAlexSi) and Threads (@coach.alex71).

See you on the court.

— Alex

Methodology Notes

• Dataset: 5,107 match files from the 2025 NCAA DI season

• Teams: 349 NCAA DI teams (52 non-DI teams excluded)

• Five-set matches analyzed: 1,035 (2,070 team-set observations)

• SO% tiers: Based on NCAA DI mean (57.1%) and standard deviation (4.0%)

• Power 4 conference filter: Only matches where both teams are from Big Ten, SEC, Big 12, or ACC (157 matches, 314 team-set observations)

• Served first identification: First "Serve" skill action in each set's play-by-play data

• Set winner determination: From the file header metadata, with fallback to points-won calculation

• Tool: Data pipeline built with R, Claude Code, and the openvolley/datavolley package

Appendix: What Is Going On with the Above Average Tier?

The Above Average tier's near-zero receive-first edge (+0.3pp) in set 5 looks weird next to the Elite tier's +8.6pp. So I broke it down by opponent tier to see what's actually happening under the hood.

Here's who Above Average teams (57-60% SO%) are playing in their 1,035 set 5 appearances:

And here's how the receive-first edge breaks down by opponent:

The +0.3pp overall number isn't a uniform lack of advantage. It's opposing effects canceling each other out. When Above Average teams play down against Below Average opponents, the receive-first edge is a healthy +5.2pp. But when they play each other or play up against Elite teams, the edge disappears or even flips slightly toward serve-first.

For comparison, look at Elite vs Elite matchups: 57.8% RF vs 42.2% SF, a massive +15.6pp edge across 180 set 5 appearances. At the 60%+ SO% level, both teams are converting side-outs reliably enough that the structural +1 real point consistently cashes in. At the 57-60% level, teams apparently side-out well enough to be competitive but not reliably enough for that first-point advantage to consistently convert in a short, high-pressure set.

A few caveats: the per-matchup sample sizes are small (76 to 308 set 5s), so I wouldn't read too much into any single row. And these tiers mix cross-tier matches, so the RF% and SF% within a row won't add to 100%. The big picture takeaway is that the Above Average tier's flat overall number masks real variation underneath, and the receive-first advantage is most clearly demonstrated when both teams can reliably side-out.