One of the hardest things to do in basketball is evaluate whether production will translate.

A player averages:

19.4 PPG
6.2 RPG
3.8 APG
38% from 3

Those numbers look great.

But I think there’s another question we should be asking:

Does her game travel?

What happens when the opponent is bigger?

What happens when defenders are faster?

What happens when driving lanes disappear?

What happens when she can’t get to her favorite spots?

What happens when every mistake gets punished?

And maybe most importantly:

How much of her normal production survives when the game gets harder?

That’s what I mean when I ask whether a player’s game travels.

Season Averages Can Hide a Lot

Let’s create a hypothetical player.

We’ll call her Player A.

Her season numbers:

19.4 PPG
1.12 PPP
56% eFG
18% TO Rate

That’s a really productive offensive player.

Now let’s separate her games by quality of opponent.

Her overall production looks strong at 19.4 PPG, 1.12 PPP, and 56% eFG. But against Top-50 teams, that drops to 13.1 PPG, 0.91 PPP, and 47% eFG, while her turnover rate rises from 18% to 24%.

Now I see the player differently.

She’s still talented.

But her production is heavily affected by the quality of defense she’s facing.

And that’s useful information.

Now Look at Player B

Player B’s season numbers aren’t as exciting.

14.1 PPG
1.07 PPP
54% eFG
15% TO Rate

But against Top-50 opponents:

13.6 PPG
1.03 PPP
52% eFG
16% TO Rate

Her numbers barely move.

Player A might have the better season.

But Player B might have the more portable game.

And if I’m evaluating players for a higher level of competition, that’s something I want to know.

Efficiency Retention

This is where I think we could create a really simple number.

Efficiency Retention = PPP vs. Better Competition ÷ Overall PPP

For Player A:

0.91 ÷ 1.12 = 81.3%

For Player B:

1.03 ÷ 1.07 = 96.3%

So Player A retains about 81% of her normal efficiency against better competition.

Player B retains 96%.

I wouldn’t use that number by itself.

But now I have another piece of information when evaluating both players.

The next question becomes much more important:

Why does one player’s efficiency survive while the other’s doesn’t?

What Skills Actually Travel?

This is where I’d go back to the film.

Because saying someone’s production drops against better competition isn’t enough.

I want to know what disappeared.

Let’s break Player A’s offense into play types.

Her transition efficiency drops from 1.31 PPP to 0.98 against Top-50 teams, while isolation falls from 1.05 to 0.74. But her spot-up production barely moves, from 1.18 to 1.14, and cuts stay strong at 1.36 to 1.29.

That tells me her on-ball scoring may not travel as well, but her shooting and off-ball movement do.

Now we’re getting somewhere.

Her overall efficiency drops considerably.

But her spot-up shooting travels.

Her cutting travels.

Her pick-and-roll production drops some, but survives.

Her isolation scoring doesn’t.

That gives me a much better picture than saying:

“She averaged 19.”

Maybe Production Doesn’t Travel. Skills Do.

That’s the bigger idea I keep coming back to.

Points might not travel.

Usage might not travel.

Shot attempts definitely might not travel.

But certain skills might.

Shooting can travel.

Decision-making can travel.

Passing can travel.

Rebounding can travel.

Defensive activity can travel.

Quick decisions can travel.

Playing without the basketball can travel.

And that’s important when we’re evaluating a player who might have a completely different role at the next level.

A player taking 17 shots per game now might only take eight somewhere else.

So maybe asking:

“Can she average 19 here?”

is the wrong question.

Maybe the better question is:

“Which parts of the game that allowed her to average 19 can help us?”

Look at Shot Location

This is another layer I’d want to add.

Imagine Player A shoots:

At Rim: 67%

Midrange: 44%

3PT: 38%

Again, good numbers.

Now look against better competition:

At Rim: 51%

Midrange: 36%

3PT: 37%

That’s interesting.

Her 3-point shooting barely changes.

But her rim efficiency falls dramatically.

Why?

That’s where film matters.

Maybe she normally finishes over smaller defenders.

Against better competition, those defenders are longer.

Maybe she doesn’t have enough burst to create separation.

Maybe she’s getting to the same locations but the shot quality is worse.

Or maybe she’s not getting to the rim at all.

That distinction matters.

Don’t Just Track Efficiency…Track Access.

This might be one of the most important parts.

Let’s say a player shoots 65% at the rim.

Against Top-50 competition she still shoots 63%.

Great.

Her finishing travels.

But what if her rim attempts fall from:

7.2 per game — 2.8 per game?

That’s a different problem.

Her efficiency survived.

Her ability to access that shot didn’t.

So I’d track both:

Shot Efficiency

How well did she finish the opportunity?

Shot Access

Could she still create that opportunity?

That’s a huge difference.

A player can technically maintain her percentages while losing access to the shots that made her productive in the first place.

What Happens to Turnovers?

I think turnovers could tell us a lot here too.

Better defenses speed up decisions.

They close windows faster.

They apply more ball pressure.

They recover quicker.

So I’d want to know:

Overall TO%: 15%

Top-50 TO%: 16%

versus:

Overall TO%: 16%

Top-50 TO%: 26%

Those are two completely different players.

One player’s decision-making holds up when the game speeds up.

The other’s doesn’t.

That doesn’t automatically mean she’s a bad player.

It tells me what I need to investigate on film.

The Same Idea Works for Rebounding

A player averages 10 rebounds per game.

Great.

But does that rebounding translate against size?

Maybe:

Overall ORB%: 12.4%

Top-50 ORB%: 11.8%

That interests me.

She continues creating possessions even against bigger, more athletic opponents.

Another player might go:

Overall ORB%: 13.1%

Top-50 ORB%: 6.7%

Same season average.

Different story.

Build a Travel Profile

Instead of trying to create one giant rating, I’d want a profile.

Something like:

Player A — Travel Profile

Overall Efficiency Retention: 81%

3PT Efficiency Retention: 97%

Rim Efficiency Retention: 76%

Rim Access Retention: 54%

Turnover Retention: Poor

Rebounding Retention: Average

Spot-Up Retention: Elite

Isolation Retention: Poor

Now I have something actionable.

I know what parts of her game I’m comfortable projecting.

And I know which parts require more film.

Role Matters

This is probably the biggest warning I’d put on all of this.

A player doesn’t have to retain everything.

If a player is going from being the No. 1 option at one school to being the fourth option somewhere else, I’m not expecting her entire offensive profile to travel.

Maybe I don’t need her isolation game.

Maybe I don’t need 18 PPG.

I need:

38% catch-and-shoot 3PT shooting

Low turnover rate

Strong defensive rebounding

Good decisions against closeouts

If those skills survive better competition, she might actually become more valuable in a smaller role.

That’s why context matters.

This Isn’t Just for Recruiting

You can use the same idea when evaluating your own team.

What offense travels?

Maybe your team scores:

1.14 PPP in transition overall

but only:

0.88 PPP against Top-25 opponents.

Meanwhile your half-court ball-screen offense goes:

1.02 PPP overall

and:

1.00 PPP against Top-25 opponents.

Which one do you trust in March?

Maybe your most efficient offense isn’t necessarily your most reliable offense.

That’s a completely different conversation.

Reliability Might Matter More Than Peak Production

We naturally notice the biggest numbers.

20 points.

40% from 3.

1.20 PPP.

10 rebounds.

But when projecting basketball forward, maybe we should care about something else:

What survives?

When the opponent gets better…

When the space gets smaller…

When the game gets faster…

When the role changes…

When the shots get harder…

What is still there?

Because the most productive player isn’t always the player whose game translates best.

And maybe evaluating the next level isn’t about asking:

“How good was this player?”

It’s asking:

“Which parts of this player’s game can I trust to travel?”

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Before we get into today’s post, I just want to say thank you to everyone who continues to read and support the newsletter. The goal has always been simple: share basketball ideas that make us think about the game a little differently. Whether it’s analytics, film, Sportscode, recruiting, player development, or just a random basketball idea I’ve been working through, I want every post to give you something you can think about, test, build on, or use with your own team. I’m constantly building, testing, learning, and sometimes completely changing how I think about something. This newsletter has become a place for me to share that process with you. If you’re interested in any of the tools, dashboards, or resources I’ve built, you can find them on my Stan Store. I also post daily on X @JacksonDavisTTU about basketball analytics, video, workflows, and whatever else I’m working on. Alright, enough of that.

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Do More Assists Increase Your Chances of Winning?

Assists are one of the first numbers we look at in a box score.

Points. Rebounds. Assists. Turnovers.

But I think assists can tell us more about an offense than simply who passed the ball before a made shot.

What if we looked at assists as a team-level indicator of winning?

The question I would want to answer is simple:

As our assists increase, does our probability of winning increase too?

Start With Wins and Losses

The easiest place to start is comparing assists in wins versus losses.

For example:

Wins:
18.7 assists per game

Losses:
12.9 assists per game

That immediately gives me something to investigate.

It doesn’t mean the difference between winning and losing is automatically six assists.

But it does make me want to understand why our offense produces more assists when we’re winning.

Are we moving the ball more?

Are we getting more paint touches?

Are we creating more closeouts?

Are we playing faster?

Are we generating easier shots?

Or are we simply making more shots?

The number gives us somewhere to start.

Find Your Assist Benchmark

Instead of only comparing wins and losses, I would break every game into assist ranges.

Something simple:

Under 10 assists
10–14 assists
15–19 assists
20+ assists

Then calculate winning percentage in each group.

Maybe your team looks like this:

Under 10: 25% win rate
10–14: 43% win rate
15–19: 67% win rate
20+: 84% win rate

Now we have something interesting.

As assists increase, our winning percentage is increasing with them.

That doesn’t automatically mean assists are causing us to win.

But there may be a point where our offense starts operating differently.

Maybe for your team that number is 17 assists.

Now I want to study every game where we reached 17+.

Why Would More Assists Matter?

This is where I think the basketball becomes more important than the statistic.

Assists usually don’t happen by accident.

A lot of them come from creating an advantage.

Paint touch.

Help.

Kick-out.

Extra pass.

Shot.

Or maybe:

Ball screen.

Two defenders on the ball.

Pocket pass.

Rotation.

One more pass.

Open three.

The assist is the number that appears in the box score.

But underneath that assist are a bunch of offensive behaviors we probably want our team doing anyway.

Creating advantages.

Moving the defense.

Making quick decisions.

Playing off two defenders.

Finding the open player.

So maybe a high-assist game isn’t valuable because of the assists themselves.

Maybe it’s valuable because of everything that had to happen to create them.

Assist Rate Might Tell Us Even More

Raw assist totals have one obvious problem.

You have to make shots to record assists.

If one night you make 35 field goals and another night you make 20, you’re going to have very different opportunities to record assists.

That’s why I would also look at Assist Rate.

You can keep this extremely simple:

Assisted Made Field Goals ÷ Total Made Field Goals

If your team makes 30 field goals and 21 are assisted:

21 ÷ 30 = 70%

So 70% of your made baskets were assisted.

Now I can create another set of ranges:

Under 50% assisted
50–59% assisted
60–69% assisted
70%+ assisted

Then compare winning percentage again.

Maybe you discover that when 65% or more of your made field goals are assisted, you’re 12–3.

That’s something I would want to know.

Don’t Chase the Number

This is also where we have to be careful with analytics.

If I tell my team:

“We need 20 assists tonight.”

I might accidentally create the wrong behavior.

Players could pass up open shots.

They could make unnecessary passes.

They could start thinking about the statistic instead of reading the defense.

The goal isn’t to manufacture assists.

The goal is to create the basketball that leads to assists.

I would rather tell the team:

Touch the paint.

Play off two.

Make the next pass.

Attack closeouts.

Move it against rotations.

Create great shots for each other.

If we do those things consistently, the assist number should take care of itself.

What Else Changes When Assists Increase?

Once I find that assists are connected to winning, I wouldn’t stop there.

I’d start comparing other offensive numbers.

When we have 15+ assists, what happens to:

• eFG%

• PPP

• Turnover %

• Rim Rate

• 3PT Rate

• Free-Throw Rate

• Paint Touches

• Catch-and-shoot attempts

This is where the analysis can get really interesting.

Maybe assists aren’t the actual story.

Maybe when your team has 15+ assists, your eFG% jumps from 47% to 56%.

Now we’re getting somewhere.

Why?

Go to the film.

Maybe you’re creating more uncontested threes.

Maybe you’re getting more layups.

Maybe you’re forcing longer defensive rotations.

The assist number helped us find the possessions.

The film helps us understand them.

Look at Who Is Creating the Assists

I would also want to know how concentrated our assists are.

For example:

Would I rather have one player with 10 assists and everyone else with one?

Or five players with 3–5 assists?

There isn’t necessarily a correct answer.

But it tells us something about how the offense is functioning.

If assists are spread throughout the lineup, maybe we’re playing with more multiple-side actions and making more decisions as a team.

If one player creates almost everything, maybe our offense is heavily dependent on that player’s ability to create advantages.

Neither is automatically bad.

But it’s information worth knowing.

Connect Assists to Paint Touches

One relationship I would really want to study is:

Paint Touches → Assists

How many of our assists come after the ball touches the paint?

Now we’re getting beyond simply saying:

“We had 19 assists.”

Maybe we had 19 assists and 14 came after a paint touch.

That’s useful.

Now I can start measuring:

Assist Rate after a Paint Touch

versus

Assist Rate without a Paint Touch

Maybe our offense creates an assisted basket on 28% of possessions with a paint touch but only 12% without one.

That starts giving coaches something actionable.

We’re not telling players to “get more assists.”

We’re showing them how we create assists.

Then Look at Ball Reversals

I would do the same thing with ball reversals.

Compare:

0 reversals
1 reversal
2+ reversals

Then look at assist rate in each group.

Maybe the ball touching the second side dramatically increases how often our made baskets are assisted.

Now you’ve connected three ideas:

Paint Touches + Ball Movement + Assists

And ultimately, I would want to connect all three back to PPP and winning percentage.

That’s when a basic box-score number starts becoming much more useful.

Correlation Doesn’t Mean Causation

This is probably the most important part.

If your team is 14–2 when recording 20+ assists, that doesn’t mean:

20 assists = automatic win.

There are other things happening.

You probably made more shots.

You might have played better competition in some games than others.

You might have gotten more transition opportunities.

Your best offensive players might have been available.

The game may have had more possessions.

That’s why I wouldn’t use assists as a prediction by themselves.

I’d use them as an indicator.

Something about our offense tends to be better when this number increases.

Now let’s figure out what.

Make It Simple for Your Players

The best part about assists is that every player already understands them.

You don’t have to explain an advanced formula.

Imagine showing your team:

Under 15 assists: 5–7

15+ assists: 16–3

That’s powerful because it’s simple.

Then don’t just show them the number.

Show them the film.

Drive → help → kick.

Post touch → double → skip.

Ball reversal → closeout → drive → drop-off.

Transition → advance pass → layup.

Now they can actually see what 15+ assists looks like.

That’s where the number becomes useful.

The Question I Would Build Around

I don’t think the question should simply be:

“How many assists did we have?”

I’d rather ask:

“At what assist number does our chance of winning begin to increase, and what are we doing offensively to create those assists?”

Find the benchmark.

Compare the win percentage.

Look at assist rate.

Connect it to paint touches, ball reversals, shot quality, and PPP.

Then pull the film.

Because the real value isn’t discovering that your team wins more when it gets 20 assists.

It’s discovering what your team is doing differently on the nights when it gets 20 assists.

The assist is just the clue.

The basketball behind it is what we’re really trying to find.

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Does Production Travel Better Than Skill?

When evaluating transfers, raw production is one of the easiest things to see.

Points per game. Rebounds. Assists.

But those numbers are also heavily influenced by role, usage, pace, competition level, and opportunity.

That is why I think transfer evaluation should focus less on whether a player can reproduce the same box-score numbers and more on whether their underlying skills can survive a change in environment.

Start With Usage

If a player averages 18 points per game, the first thing I want to know is:

How much offense did it take to get there?

A player scoring 18 points on 30% usage is different from a player scoring 18 on 22% usage.

High production can sometimes be the result of simply having more possessions.

So I would compare:

• Usage %

• Shot attempts per 40

• Touches, if available

• Possessions used

• Minutes played

Then ask what their role will realistically look like at the next school.

If their usage drops from 30% to 20%, what parts of their game can still create value?

Efficiency Matters More When Role Changes

The next thing I would look at is efficiency.

Not just FG%.

I would want:

• True Shooting %

• eFG%

• 2PT%

• 3PT%

• Free-throw rate

• Turnover %

• Points per possession by play type

If a high-usage player is already inefficient at a lower level, I would be cautious about assuming their efficiency improves against better competition.

But if a player is carrying a large role while maintaining strong efficiency and a manageable turnover rate, that could be a better indicator that their game can scale.

Shooting Is One of the Most Portable Skills

Shooting is one skill I would trust to translate more than raw scoring.

But even shooting needs context.

Instead of only looking at 3PT%, I would look at:

Volume + efficiency + shot type.

For example:

Player A:
41% from 3 on 1.5 attempts per game

Player B:
37% from 3 on 6.0 attempts per game

I may trust Player B more as a transferable shooter because the volume tells me the shooting is a larger part of their role.

I would also want to know how those shots are being created.

Catch-and-shoot?

Off movement?

Off the dribble?

Wide open?

Contested?

A player who can shoot efficiently on volume without needing the ball can maintain value even if their usage drops dramatically.

Look at What Happens Without the Ball

This is where I think traditional transfer evaluation can miss players.

A high-scoring player may become less effective when they no longer control the offense.

So I would ask:

What happens when this player isn’t the primary option?

Can they:

• space the floor?

• cut?

• attack closeouts?

• make quick decisions?

• defend multiple positions?

• rebound their position?

Those skills give a player more ways to stay valuable when their touches decrease.

Turnover Rate Can Tell You a Lot

I would pay close attention to turnover percentage, especially for guards and high-usage creators.

Moving up a level usually means:

better ball pressure, more length, faster rotations, and smaller windows.

If a player already has a high turnover rate against weaker competition, that is something I would want to investigate.

On the other hand, a player handling a high usage rate while keeping turnovers low may have decision-making that translates.

I would even look at this relationship:

Usage ↑ vs TO% ↑

How much does their turnover rate change as their responsibility increases?

That may tell you more than their season-long average.

Adjust for Competition

One of the hardest parts of transfer evaluation is comparing players across leagues.

A 1.10 PPP ball-screen player in one conference is not automatically equivalent to a 1.10 PPP player in another.

Ideally, I would compare performance against stronger opponents.

For example:

Overall:
18.2 PPG
58% TS

vs. Top-50 opponents:
13.4 PPG
51% TS

That drop does not automatically eliminate the player, but it gives you another piece of information about how their production might react to better competition.

Separate Production From Translation

This is probably the biggest distinction I would make.

Production tells you what a player did in their current environment.

Translation asks what they can still do when that environment changes.

A player could go from:

18 PPG → 11 PPG

but improve a team because they still provide:

38% 3PT shooting
low turnover rate
strong perimeter defense
good decision-making
positional rebounding

Their box-score production declined.

Their value may not have.

The Question I Would Build the Evaluation Around

Instead of asking:

“Can this player average the same numbers here?”

I would ask:

“Which parts of this player’s statistical profile are most likely to survive a change in usage, role, system, and competition?”

That is where I think analytics can help the most with transfers.

Don’t just project the points.

Project the skills underneath the points.

The Turnovers We Don’t Track

What if a bad shot can be just as damaging as giving the ball away?

I was thinking about something while watching basketball:

A bad shot out of your team’s rhythm can basically become a live-ball turnover.

Not because the box score records it as one.

Not because the player actually lost possession of the ball.

But because the end result can look almost identical.

A player takes a bad shot early in the clock. The defense barely has to guard anything. Your team isn’t organized behind the ball. The shot misses, the opponent grabs it, and five seconds later they’re finishing a layup on the other end.

Technically?

0 turnovers.

Functionally?

We might have just given the ball away.

And that made me wonder if there is another type of turnover we should be thinking about.

Functional Turnovers

My idea is pretty simple:

Functional Turnovers = Traditional Turnovers + Rhythm-Breaking Shots

We already track traditional turnovers.

Bad passes. Travels. Offensive fouls. Stepping out of bounds. Losing the ball.

Those are easy because the possession clearly ends without a shot.

But what about possessions where we technically get a shot up, yet never really make the defense work?

That’s where I think Rhythm-Breaking Shots become interesting.

Instead of only asking:

“Did we turn it over?”

Maybe we should also ask:

“How many possessions did we give away without forcing the defense to actually defend us?”

So What Is a Rhythm-Breaking Shot?

This is where the idea gets complicated.

Because a bad shot cannot simply mean a missed shot.

It can’t even automatically mean a contested shot.

Imagine two possessions.

Possession A

There are four seconds left on the shot clock.

The offense has already gone through multiple actions.

Nothing develops.

A player has to take a heavily contested three.

That’s probably not a rhythm-breaking shot.

The offense exhausted its options and had to create something before the clock expired.

Now imagine:

Possession B

There are 24 seconds remaining.

One pass.

No paint touch.

No advantage created.

A player takes a heavily contested three.

Miss.

The opponent grabs the rebound and immediately attacks in transition.

Those two shots might look identical in a traditional shot chart.

3PA. Missed. Contested.

But they aren’t the same possession.

Context matters.

The Shot Clock Has to Matter

One of the first things I would include when defining a Rhythm-Breaking Shot is when the shot occurred.

A difficult shot with four seconds left is sometimes necessary.

A difficult shot with 24 seconds left is a decision.

That distinction matters.

You could start separating shots into shot-clock windows:

• Early clock: 22–30 seconds

• Middle clock: 10–21 seconds

• Late clock: 0–9 seconds

Then start asking:

Where are our low-quality shots actually happening?

If most of them occur late in the clock, maybe the bigger problem is the offense failing to create an advantage earlier.

But if they’re consistently happening early?

Now we might have a decision-making problem.

Did We Make the Defense Work?

This might be the part I’m most interested in.

Basketball analytics usually evaluates what happened at the end of a possession.

Make.

Miss.

Turnover.

Foul.

But what happened before the shot?

Did we touch the paint?

Did we reverse the ball?

Did we force a closeout?

Did we create a rotation?

Did we screen?

Did we attack a mismatch?

Did the defense ever have to make a decision?

Because there’s a massive difference between missing a three after collapsing the defense, kicking the ball out and forcing two rotations...

and missing a contested three after one pass.

Both are missed threes.

Only one made the defense actually defend.

That’s why I wouldn’t want this metric to become:

Bad shot = Functional Turnover.

It needs context.

The Transition Piece

This is where I think the concept becomes even more valuable.

How often does a bad offensive decision immediately create offense for the opponent?

Think about the sequence:

Bad shot → Poor floor balance → Defensive rebound → Transition opportunity → Basket

That possession might be more damaging than a traditional dead-ball turnover.

At least after some turnovers, your defense gets a chance to get set.

A poor shot with terrible floor balance can create instant numbers going the other direction.

Now I want to know:

Opponent transition PPP after Rhythm-Breaking Shots

That could tell a much bigger story.

Maybe a team only has 12 official turnovers.

But they also have six Rhythm-Breaking Shots, four of which lead directly to transition opportunities.

The box score says:

12 turnovers.

Your internal analytics might say:

18 possessions where we failed to make the defense work.

That’s a very different conversation.

How I Would Start Tracking It

I wouldn’t try to make the definition perfect immediately.

I would start tagging potential Rhythm-Breaking Shots and look for patterns.

A shot could be flagged based on a combination of:

Shot Clock + Shot Quality + Offensive Process + Floor Balance + Transition Result

For example:

Early-clock contested shot.

No paint touch.

Zero or one pass.

No defensive rotation created.

Poor floor balance.

Opponent creates a transition opportunity.

The more boxes a possession checks, the stronger the case that it functioned like a turnover.

You could even create different levels rather than making everything binary.

RBS 1: Questionable shot selection.

RBS 2: Poor shot that ends the offense before an advantage is created.

RBS 3: Poor shot that also directly creates a transition opportunity.

Now we’re not simply labeling shots “good” or “bad.”

We’re measuring their effect on the possession.

This Isn’t About Telling Players Not to Shoot

That’s an important distinction.

I wouldn’t want an analytics concept like this to make players afraid to shoot.

Sometimes an open three five seconds into the possession is exactly what you want.

Sometimes your best scorer taking a difficult shot is still better offense than continuing the possession.

Personnel matters.

Role matters.

Game situation matters.

The goal isn’t:

“Stop taking early shots.”

The goal is understanding which shots are ending possessions before we’ve created the type of advantage our offense is designed to create.

That’s completely different.

Maybe Turnover Percentage Isn’t Telling the Whole Story

Turnover percentage is one of my favorite offensive metrics because it tells us how frequently we’re ending possessions without getting a shot.

But maybe there’s another layer.

A team can have a low TO% and still waste possessions.

That’s the part I’m interested in.

Because protecting the basketball shouldn’t only mean not turning it over.

It should also mean valuing the possession enough to make the defense work.

If we finish a game with only nine turnovers but repeatedly take shots that lead directly to transition opportunities, did we really take care of the basketball?

Technically, maybe.

Functionally?

I’m not sure.

And that’s why I think Functional Turnovers could be worth exploring.

Because sometimes the possession doesn’t end with a turnover in the box score.

Sometimes you give the possession away with the shot you choose.

The bigger question isn’t just: “Did we turn it over?”

It’s:

How many possessions did we give away without forcing the defense to actually defend us?

The best analytics don’t just tell you what happened. They tell you what to repeat.

One of the biggest shifts I’ve made in how I think about analytics is this:

Stop measuring outcomes. Start measuring habits.

Most teams are really good at tracking results.

Field goal percentage.

Turnovers.

Rebounds.

Points per possession.

Those numbers matter. But they’re all looking in the rearview mirror. They tell you what happened, not necessarily why it happened.

That’s where I think analytics can become much more valuable.

Instead of asking:

“Did we make the shot?”

Ask:

• Did we create the shot we wanted?

• Did we make the defense rotate?

• Did we get the paint touch we were looking for?

• Did we force a closeout?

• Did we execute the action the way we practiced it?

• Did everyone fulfill their role on that possession?

Now you’re measuring habits.

Because here’s the reality: players don’t control whether every shot goes in.

They do control the decisions they make.

They control whether they sprint into a screen.

Whether they make the extra pass.

Whether they attack the closeout.

Whether they relocate after passing.

Whether they box out.

Whether they communicate on defense.

Those are habits.

And over time, good habits create good outcomes.

Let’s say your team runs a post-first offense.

Instead of grading every possession by whether the post scored, what if you tracked:

• Did we get the post touch we wanted?

• Was the catch inside the paint or outside?

• Did the weak side stay spaced?

• Did we create an open kick-out three?

• Did we force a double team?

Maybe the possession ends with a missed shot.

The box score calls it a failure.

But your process says you created exactly what you wanted.

If you continue creating those same opportunities over the course of a season, the results usually take care of themselves.

The same idea applies on defense.

Instead of celebrating only steals and blocks, measure the habits that consistently lead to stops.

Did you force the offense into a second action?

Did you make them reset?

Did everyone rotate correctly?

Did you finish the possession with a rebound?

Those habits rarely make the highlight reel, but they win games.

This is where analytics and film work together.

The numbers reveal the patterns.

The film explains the patterns.

Together, they tell you what needs to be repeated and what needs to change.

I think that’s where analytics has the biggest impact.

Not by telling coaches who made or missed a shot.

But by helping them identify the habits that consistently create winning basketball.

Because at the end of the day, outcomes will always fluctuate.

Habits are what you can coach.

And the best analytics don’t just measure success.

They measure the behaviors that create it.

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