Let us all remember to support each other in these distressing times as we come to terms with the rapidly approaching conclusion of another glorious summer in baseball. Over the next few weeks, take some time to soak in the beloved sights of everything we will miss soon. The green grass, the blue skies, the dirty uniforms, the delicate bunts, the majestic dingers, the line drives into the gaps, the dugouts sprinkled with sunflower seeds, the stadium silhouettes proudly decorating city skylines where the soft vibrations of the living and breathing game of baseball spill into the dusky evenings to provide us company along our winding mortal journeys. Wherever possible, grab a little piece of it, turn it around in your fingertips, treasure it, and place it in your pocket for later.

Of course, with the season steamrolling toward its finish, bouts of exhilaration await. As one team after another sees its championship prospects dwindle down from improbable to impossible, we turn our attention to those that remain. First the field will narrow from 30 teams to 12, and though some will consider their playoff berth as a great achievement while others merely run-of-the-mill, it will nonetheless be celebrated by all. With each subsequent postseason round, our focus will magnify upon the teams who prove themselves superior by outdueling their opponents and punching their ticket forward. The fewer subjects left in the story, the higher the pedestal we place them on.

This last bit is also true, obviously, of the Incredibly Flawed Best Player in Baseball Bracket (IFBPIBB). Back at the end of April when the initial 64-player field was set, there was excitement to be found everywhere we looked – but there were too many places to look. One could not concentrate on any particular matchup given so many to choose from. It’s like having 15 MLB games slated for the day; it’s thrilling to know you’ll have 15 results to enthusiastically review the following morning, but you can’t possibly strap yourself into the passenger’s seat for every single ride.

Now, though, it’s getting serious. We are officially down to the bracket’s Final Four. This is not a common term in baseball circles; maybe sometimes the ALCS/NLCS participants are referred to as the final four teams, but the moniker is more typically used in March Madness-related discussions. Though we’re talking about a different sport here, I don’t mind borrowing it. This whole bracket was conceived as baseball’s response to March Madness anyway.

Speaking of the IFBPIBB’s creation, here’s the full chronicle of my fun little meaningless game, from the beginning to now:

• The Incredibly Flawed Best Player in Baseball Bracket

• June IFBPIBB update: Judge, Alvarez, other superstars eliminated; Cy Young favorites square off this month

• The IFBPIBB Sweet Sixteen

• The IFBPIBB Elite Eight

TL;DR: Using March/April stats, the top 64 MLB players by fWAR were placed in a bracket. Using monthly fWAR totals, the pool has been whittled down by half in each month since then via head-to-head matchups. There are now only 4 players remaining.

As outlined in the original introduction, the month of September will be split in two. The semifinal rounds will take place from September 1st through 13th. The finals will then consist of the final two weeks of the season, September 14th through 27th. From here on, winners will now be determined using even smaller sample sizes, but sports fans are already accustomed to the practice. Sure, it may not be “fair” or “accurate” to say this team or player is better than their opponent based on a short series of games, but overanalyzing tiny samples is what the playoffs are all about, baby!!!

Screenshots and links to the updated bracket will be provided at the end of this article. Before that, let’s take some time to analyze and celebrate our final month’s contestants.

#56 Pete Crow-Armstrong (2.50 fWAR in August) defeats #1 Shohei Ohtani (0.39)

For the third month in a row, Pete Crow-Armstrong has unleashed terror upon the baseball world and placed 1st atop the monthly fWAR leaderboard. As I pointed out last time, that means he could have been paired up against anybody in the league and it wouldn’t have changed this result. At the beginning of the season, I wouldn’t have been surprised if Shohei Ohtani had accomplished that feat and won this competition with a boring pole-to-pole triumph. Now, four months after barely slipping into the back half of the bracket, PCA has staked his claim as the “no for real though” best player in baseball by resoundingly eliminating the presumptive favorite.

At this point, everyone who’s been paying attention to baseball the past few months knows that Crow-Armstrong is having a special season. He’s currently at 9.3 fWAR and on pace for a top-30 season all-time. If he keeps up the pace he’s been at since June, we’re talking top-15 territory. Again, this is the list of best seasons ever. He’s looking at Babe Ruth, Barry Bonds, Lou Gehrig, Rogers Hornsby, Honus Wagner, Ted Williams, Mickey Mantle, and Ty Cobb, and legitimately saying, “Yeah, I belong in this conversation.” It is truly remarkable.

The most extraordinary aspect of PCA’s game has always been his defense. That’s not going away anytime soon. Though there are several defensive metrics that are tricky to comprehend and trickier to trust, he has top-notch grades in all of them and passes the eye test with flying colors. His outfield range is simply unmatched in today’s game. Much of this can be attributed to his blazing speed, which has also translated to superb baserunning value.

Where he has taken a massive leap this season, however, is his production at the plate.

In his rookie season (2024), he was quite definitively below average at the dish. He swung at everything, including a high volume of pitches outside the strike zone. He failed to make contact with many of those swings. And when he did make contact, it wasn’t like he was smacking the ball with authority. A clear improvement was made in those regards last year, when he started hitting homers at a surprising rate given his wiry frame. He still chased an alarming number of bad pitches, though, and second-half struggles prevented him from truly blossoming as a hitter.

He saved that for this year. The most influential change has been a -Chase% LASR that has jumped from an atrocious 25 to a much more respectable 45. His swing frequency is dramatically lower this season overall, and the resulting called strikes have led to slightly more strikeouts, but the tradeoff has been well worth it. He’s enjoying a 55 BB% LASR, taking free passes where he was giving them away before. Being more selective has also helped him do more damage, focusing his hacks on the more hittable offerings – and showing us just how hittable they are.

There is a degree of luck in his offensive success, but a great deal of it is real, and the improvements he’s made will go a long way in establishing his place in the upper echelon of this era’s best ballplayers.

#20 Parker Messick (1.12) defeats #21 Corbin Carroll (0.92)

Heading into this season, Cleveland Guardians’ young lefty Parker Messick’s name could be found on all of the highly reputed Top 100 Prospect lists, but you had to scroll for a while to find it. MLB Pipeline placed him 95th, Baseball America had him 88th, and FanGraphs ranked him 82nd. Various other publications largely agreed, or left him off their lists entirely. Though most analysts had confidence in Messick’s high floor and figured he’d contribute helpful innings to a big league roster, they all agreed that, as FanGraphs’ report put it, “He’s tracking like a contender’s fourth starter.”

The Guardians have long been suspected of having a secret magic lair deep within the dungeons of their development system, where pitching coaches and coordinators don lab coats and play around with mysterious potions until the right combination turns a AAAA-type pitcher into an ace. Corey Kluber, Carlos Carrasco, Shane Bieber, Tanner Bibee, and Gavin Williams have all emerged from that laboratory, equipped with special new powers designed specifically to give Cleveland a better starting rotation than anyone projected. It’s a tale as old as time.

And now, Parker Messick has added his name to that list.

Whether you consult fWAR, bWAR, regular old ERA, or any other measure of overall success, Messick’s case for the 3rd-best AL pitcher this season is pretty much undisputed. He’s clearly a step behind Dylan Cease and Cam Schlittler in the Cy Young race, but he’s also settled into a comfortable position ahead of the rest of the field. There’s little doubt that Messick is not a team’s fourth starter. At the moment he’s, at worst, the league’s fourth starter.

As a prospect, Messick was lauded for his excellent command. Given his results and that previous outlook, one might assume he would be among the best control artists this season. That hasn’t exactly been the case, with “only” a 55 -BB% LASR and related stats clocking in slightly below. Still, it’s not like there’s a weakness there – in fact, there aren’t any weaknesses anywhere. He isn’t a dominating strikeout guy, but he can pick them up when he needs them. He doesn’t rely heavily on grounders, but they are present. He’s simply been a formidable all-around hurler.

If there is one standout skill, it’s the ability to prevent hitters from getting good wood on the ball. His 4.6% Barrel rate (70 LASR) is lowest in the AL and second only to Jacob Misiorowski among all starters. Unseen on the scouting report card above is his Infield Flyball rate (IFFB%) which grades at 75 on the LASR scale and is best in baseball among qualified pitchers. Pop-ups are typically guaranteed outs, so pitchers who can consistently get those lollipops in the air will generally find success when they come back down.

Further understanding of Messick’s remarkable breakout would involve a deep analysis of Messick’s pitch mix that I hope to be able to conduct someday, but am currently incapable of. Other writers have, and I trust their word that his repertoire features unique movements that hitters aren’t used to seeing, which leads to poor contact and easy outs. This is a spot where my future translations of pitch characteristics (spin, arm angle, movement, velocity, etc.) onto the LASR scale will help me out a ton by contextualizing how good and how unique those metrics are.

Alas, I haven’t gotten there yet – but I’m putting this out there as a promise to Messick and all other pitchers who yearn for my analysis. Someday, Parker, I will comprehend just how impressive your 43.1 degree average arm angle or your curveball’s 14.0 inches of gloveside horizontal break are. Someday.

#10 Kevin McGonigle (0.47) defeats #63 Brayan Rocchio (-0.11)

Remember all that hype I just gave Parker Messick and his outstanding season? With rookie eligibility, you’d probably assume he’s the obvious frontrunner for AL Rookie of the Year, wouldn’t you? There can’t possibly be another rookie nearly as valuable as him, right?

WRONG!

Unlike Messick, Kevin Professor McGonigle was a highly regarded, consensus top-5 overall prospect at the onset of this season, and it was a big deal when he made the Detroit Tigers’ Opening Day roster. Though it feels like lifetimes ago now, it was also major news when he rewarded the Tigers with a 4-for-5 performance on that Opening Day, including a 2-run double in his first plate appearance to spark an eventual 8-2 victory. On the heels of consecutive postseason appearances, it looked like the beginning of another thrilling season in Detroit. That didn’t quite, uh... let’s just focus on McGonigle.

The Professor has been one of the best players in baseball right from the beginning (otherwise he wouldn’t have grabbed the #10 seed in this bracket). Among hitters, he is tied for 7th in fWAR and 3rd in bWAR, with the difference coming from slight disagreements in defensive value. Though he arrived as a shortstop prospect and has played there regularly this season, that may not be his permanent home on the diamond.

Whether he’s their shortstop, third baseman, or second baseman of the future, the Tigers are just grateful to pencil him in at the top of their lineup card for the next several years. McGonigle’s bat has answered the call, displaying an admirable contact ability (60 AVG and -K% LASRs) that is fully backed up by underlying pitch-level data (65 Whiff% LASR).

Though his profile doesn’t scream power hitter, there is hope for more slug. Prospect evaluators rated his power tool above average prior to his debut, and last season he posted a .278 ISO in 88 minor league games, smacking 19 homers and 31 doubles across three levels. As a big leaguer this season his ISO sits at .140, which is fine for someone whose profile doesn’t demand power, but tapping into that potential for more extra bases would be a nice little bonus to an already impressive stat sheet.

Where McGonigle really shines is his plate discipline. Interestingly, “Eye” is not a tool that scouts grade players on because it’s not technically a physical attribute, so I can’t cross-check strike zone recognition with past reports to see if this was expected. I think that’s pretty silly. Luckily, there are prospect summaries attached to scouting reports that can fill in the gaps. This is from the FanGraphs’ preseason rankings:

McGonigle has a case as the best pure hitter in the minor leagues. Short-levered with plus bat speed, elite hand-eye coordination, and a great approach, he’s the total package at the plate... He’ll track and line a back door slider to left in one at-bat, and then hammer heat on the inside corner out to right in the next. He’s difficult to pitch around because he’s both selective on pitches off the plate and aggressive when he gets a ball he wants to drive.

Alright, so it’s not an anomaly: this kid absolutely knows the strike zone. Laying off out-of-zone pitches and being aggressive in-zone is a formula for success, and when you add in his pre-existing contact rate and the promise of future power... is it too early to predict a Kevin McGonigle 2027 AL MVP?

#6 Matt Olson (0.53) defeats #19 Otto Lopez (0.19)

Have you heard the whispers? They are quiet, barely perceptible, nowhere on the radar if you aren’t paying attention. But if you listen closely, you’ll hear them trickling into the atmosphere like rumors no one dares lend credence to but can’t bear to ignore any longer. What is it they’re saying? Really, are you sure?

Matt Olson, future Hall of Famer?

Olson doesn’t seem like your typical Hall of Fame entrant. He has never been close to winning an MVP award, topping out at 4th place in 2023. Other than that, he has never finished higher than 8th. As a power-hitting first baseman whose value primarily rises and falls according to his home run totals, he has only led the league once (albeit with an incredible 54 in that aforementioned career year). He has 4 All-Star selections and 3 Gold Gloves, but only 1 Silver Slugger.

And yet, a case for Cooperstown might slowly be taking shape. Don’t get me wrong, it is definitely an outside shot right now. Considering typical aging curves, it’s quite unlikely. However, Olson has shown an iron man-esque level of durability throughout his career, currently carrying a streak of 900+ consecutive games played. Sure, you could argue that will weigh on him and drag him down as he enters his mid-30s. But I’m an optimist (or at least I tell the mirror I am), so I choose to believe it portends a capacity to fend off the sands of time a bit longer than his counterparts.

Olson’s best shot at achieving baseball immortality lies in his power numbers. And boy are those a treat.

Ever since his red-hot 59 games as a rookie in 2017, Olson has unquestionably been one of the top sluggers in baseball. Though some years have seen his HR% and ISO LASRs dip down to 55, the underlying power indicators – Barrel%, HardHit%, and Exit Velocity – have never strayed from the 60-70 range. To terrorize those poor little defenseless baseballs with such remarkable consistency is awe-inspiring, and there’s no sign of this trend coming to an end anytime soon.

As of this writing, Olson is 32 years old and has hit 325 career home runs. Since 2017, that ranks 3rd in baseball behind only Aaron Judge and Kyle Schwarber. His career-long 162-game average sits at 38.7. The pace isn’t much different if you only look at his Braves tenure, or his last 3 seasons, or just this season (obviously, since he’s already at 37 through the end of August). Of course, we can’t expect him to continue raking quite as much heading into the back half of his career, but even if he averages “only” 30 dingers per year, he’d get to 500 in the middle of his age-38 season.

The only non-Hall of Famers with 500 career home runs are shrouded in steroid suspicion or have retired too recently to be eligible. It’s a stretch to assume Olson will get there, as it’s entirely possible that he falls off a cliff at some point, but it’s certainly not out of the realm of possibilities. He’s a steady guy, never the best in the league, but always right around the top of the leaderboards, ready to clock in, give you everything he’s got, maybe smash a homer or two into the bleachers, clock out, and do it another 161 times, back to back to back to back to...

It’s time we start thinking of Matt Olson in a different, golden light. He’s starting to rack up the credentials. And if he picks up an IFBPIBB championship to add to his trophy case? Well, then there’s no question whatsoever.

The Bracket

Here’s a link to a PDF of the updated bracket. Here are the August hitter stats and August pitcher stats that were used to determine the winners. Below are screenshots of the four quadrants of the updated bracket. Thanks for following along, and stay tuned for the FINAL MATCHUP!!!

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In the first game of this Giants series, closer Trevor Megill had blown a 1-run lead in the top of the 9th but was bailed out in the bottom half on a walkoff home run from his catcher, William Contreras. It’s the sort of thing often cited as a factor that separates truly successful teams from the merely good teams: teammates picking each other up, having each other’s backs. If someone’s having a tough go of it, 25 colleagues are do-or-die ready to save the day.

Megill blew another save in this series finale, nearly pulling through before surrendering a 2-out 2-run single by Heliot Ramos to flip the score. After the game, Megill went on the Injured List with a right flexor strain. He had been a solid late inning reliever all season, but it was time for someone else to step up and take the pressure off his shoulders.

Even with their comfortable playoff standing, the Brewers knew there was danger in taking their foot off the gas in a league that also boasted dangerous teams like the Dodgers, Phillies, and Cubs. Milwaukee didn’t win like those traditional powerhouses; they relied on pitching depth, fundamentals, and sneaky skills like defense, baserunning, and patience at the plate. Maybe a little bit of magic. That hasn’t been ruled out. Whatever it was, it had worked tremendously up to this point, but the plan depended on every member of the roster pulling his weight, keeping the train moving, playing his part.

Bauers, who had just returned from a stint on the IL the day before, was in desperate need to be the next hero in the Brewers’ dreamlike season. After a strong start to the year, he had struggled to a .106/.286/.212 slashline in his last 32 games. He was a first base/corner outfield type, a role typically reserved for exemplary hitters you can rely on to come through in big moments. This was a big moment. This was his chance to turn his season around.

Instead, he struck out. Two batters later, the game was over and Milwaukee had lost some ground in their leads for the division and the top seed. For the team, it was a disappointing series loss, but one they could easily overcome (and they did). For Bauers, it was yet another frustrating day in an overall uninspiring career. With his 20s winding down, the chances he’d become a significant contributor at the Major League level were thinning. Sooner or later, teams would stop giving him opportunities to prove himself and he’d be stuck with a great big “What if?”

Now, it is 365 days later. Much like several other recent seasons, the Milwaukee Brewers are better than any preseason outlet predicted them to be, once again atop the NL Central, and on their way to a first round bye in the playoffs. Many elements look familiar: the patience, the baserunning, the pitching. Something is different this time though: for the first time since 2019, they have a top 10 hitter in baseball. And that hitter is Jake Bauers.

Missed opportunities

One of my favorite baseball player arcs is the late bloomer. In a sport dominated by snotty young kids saying, “Oh, baseball’s not that hard, look at me, super good at 21 years old, la-di-dah,” every grizzled veteran (aka, late 20s) who finally breaks through their obstacles to become a solid everyday player is a blessing. Especially to those of us on the wrong side of 30 who still get asked “But what are your long-term plans?” and appreciate any reminder that there’s plenty of time to figure things out.

Bauers himself is now 30, and a long distance from his MLB debut for someone who casual fans may have just become aware of within the past few months (or maybe paragraphs). That debut took place in 2018, when he was a 22-year old top 50 prospect with the Tampa Bay Rays. He had been drafted by the Padres 5 years prior and was sent to Tampa in 2014 as part of an intricate 3-team 11-player deal that included Wil Myers, Steven Souza Jr., and Joe Ross. Oh yeah, and a prospect by the name of Trea Turner found his way to the Nationals in that trade. Wonder if he ever amounted to anything.

Bauers impressed enough in the Rays farm system to earn significant runtime as the big league team’s everyday first baseman in 2018, playing in 96 of their final 102 games. He didn’t embarrass himself, but he certainly didn’t burst out of the gate. Though he showed promising plate discipline, the bat obviously needed some development and his subpar defense was never going to prop him up. Still, by OPS+ and wRC+ he was just barely below league average, which you’ll accept from a rookie with some upside.

However, the Rays didn’t believe in that upside anymore. Here’s a warning to anybody who ever finds themselves employed by the Tampa Bay Rays: At all times, you must be prepared to find yourself involved in an incredibly convoluted 3-team trade. Bauers learned this lesson on the way in and on the way out, heading to Cleveland in the 3-pronged deal that brought Yandy Diaz to Tampa Bay and also included big-name vets Carlos Santana and Edwin Encarnacion swapping places in Cleveland and Seattle.

Cleveland wasn’t able to unlock the Bauers powers, however. Neither were the next 3 organizations that took a chance on him. After pinballing from the Mariners to the Reds to the Yankees over the next 5 seasons, Bauers was 27 and presumably in the thick of his prime years. Without a meaningful Major League impact on his résumé, the Yankees sent him to Milwaukee for a couple of low-level lottery tickets in the 2023-2024 offseason.

The Brewers have garnered a reputation for helping players discover their missing pieces and live up to hidden potentials that other teams hadn’t seen or believed in. This is more common on the pitching side, with guys like Chad Patrick, Tobias Myers, and Quinn Priester all finding unexpected success on the Milwaukee mound. It isn’t quite as apparent at the plate, with Andrew Vaughn being the only notable hitter to improve at a comparable level. Even so, the notoriety of the organization’s player development makes any unanticipated breakout look less surprising. It’s just typical Brewers behavior.

One might be quick, then, to tack any improvements Bauers made in Milwaukee to the corkboard of Unexpected-Expected Brewers Breakouts. And while there might be some truth in that, the upgrade took much longer to take effect than you’d assume.

After his pinch-hit strikeout last August 24th, his Brewers tenure (including his full season in 2024) had amounted to a .197/.307/.356 slashline and an 89 wRC+, quite paltry from a bat-first player profile. This was over 502 plate appearances, a large enough sample for most teams to use as justification that they’d given him a shot and it hadn’t worked out. After 1900 total plate appearances in the majors, his career graded out worse than replacement level in both bWAR and fWAR. He was a first baseman/corner outfielder with bad defense and a below average bat.

And yet, the team with the best record in baseball still believed in him.

Don’t think so much

The switch didn’t flip immediately after that Giants game. It was a couple weeks later when a real change took place in his trajectory. Earlier this year, Bob Nightengale provided a behind-the-scenes look at Bauers and his turnaround in this article. The quotes that follow are from that piece.

After a pop out and a strikeout in his first two at bats on September 5th:

“I remember going down in the tunnel by myself and screaming, ‘What is this? Are we going to keep doing this? Can I even play? Do I have what it takes? Is this it?’ I remember looking up at the scoreboard and it says .180. Then, just saying, ‘Fuck it. I’m just going to go up there, not think about anything, and just swing. Whatever happens, happens. I really don’t care anymore.’ ”

Minor approach changes that lead to major results are always fascinating. Usually, though, they involve putting more focus on certain mechanical aspects. Hitters will switch up their batting stances by lowering their hands, raising their hands, moving up in the box, moving back in the box, and so on. You never hear about the changes that don’t take, but the ones you do hear about are often described as, “I started consciously doing this, and then it all clicked.”

For Bauers, the solution was simply to stop searching for a secret formula and start trusting his instincts, muscle memory, and talents.

“I was trying to be perfect. I mean, this game’s hard enough if you’re standing in the box trying to hit, and you’re thinking about where your hands are, what your lower half is, and everything else. I was handcuffing myself.”

Following his existential trip to the tunnel, Bauers smacked a couple hard-hit singles later in the game. The next day he collected another 2 hits including a homer. He finished off the series with a third consecutive 2-hit game, and suddenly the wheels were turning. He slashed .378/.472/.600 in 53 plate appearances to wrap up the season, then followed it up with a strong postseason, albeit in a tiny sample size.

“That’s when I realized, I just needed to get out of the way.”

The Brewers, however, were bounced in disgraceful fashion in an NLCS sweep at the hands of the Dodgers. They scored exactly 1 run in each of the 4 games. That’s not going to get the job done if they want to go further this year.

Perhaps it was just an unfortunately timed team-wide slump, the sort of valley that exists over 4-game stretches all the time in the regular season and goes unnoticed. Maybe it was the result of the Brewers not being built for October. The Dodgers’ elite starting rotation likely had something to do with it, but they need to be prepared for that this year too.

Either way, when the playoffs begin in a little over a month (!!!), Milwaukee’s question marks will be drawn around their offense. There are surefire threats in the lineup, and if opponents catch Brice Turang, Jackson Chourio, William Contreras, Andrew Vaughn, and Christian Yelich at the wrong time, they’re gonna have a hard time getting through in one piece. And when you factor in the new Jake Bauers, it’s not hard to envision a deeper playoff run this time around.

The breakout

Though I’ve implied it plenty, I haven’t provided an ounce of evidence yet that Jake Bauers is a productive hitter in 2026. Imagine if this whole article led to “Unfortunately, it’s all been downhill again for Bauers this year. He’s probably headed for indy ball.” Y’know what, someday I’m gonna write an article like that. Mark my words.

But it’s not this one. Here’s how Bauers shapes up in his LASR Scouting Report Card for 2026:

By all means, this is a breakout season if I’ve ever seen one. Though he’s still a liability on defense, he’s suddenly an unmistakable power-hitting offensive force with an excellent eye. Though some hints of these abilities were apparent before, Bauers had never come close to matching this output previously. In fact, I have convincing graphical evidence of that too, thanks to my new LASR Career Stat Sheets.

There’s no doubt something has clicked this year, with both his overall hitting production and peripheral estimators (65 wOBA and xwOBA LASRs) identifying true changes to his game. Of course, the big question is: What’s different? Is it sustainable? Did this happen out of nowhere, or were there signs?

As I’ve detailed before, I like to break hitting down into three general categories: eye, contact, and power. Does he swing at the pitches he should? When he does swing, does he make contact? When he makes contact, does he do damage?

First, let’s assess his plate discipline. (A reminder from a previous article: each of these groups of stats are available as predefined options in the Career Stat Sheet mode on my LASR graph generator.)

As mentioned earlier, Bauers showed real promise in his understanding of the strike zone as a rookie. He was generally patient, didn’t chase bad pitches, swung at pitches in the zone, and worked an exceptional number of walks. These skills have generally stuck around, with a noticeable decline in 2021. The 35 PtchAr LASR tells us that pitchers started attacking the strike zone that year, taking advantage of his previously established low swing rates. Perhaps getting thrown off his game, he started chasing more and losing his feel for where the hittable pitches were.

Though pitchers have continued to stay in the zone more often, his -Chase% and ZSw/Sw% have rebounded back to the 60-65 LASR range, and as a result he has renewed his status as one of the more proficient free base-takers in the league. His discipline has always been here, and though it temporarily dipped down, it is back. This is the type of skill that typically sticks around through a player’s 30s even as other abilities decline, which provides an optimistic floor for his future.

With a 65 ZSw/Sw LASR this year that clearly isn’t an anomaly, we know that he swings at the right pitches. Now, how often does he make contact with them?

There are a few stories being told here, though the main takeaway is that contact is not his calling card. Prior to this year, his AVG LASR was consistently in the 30-35 range, putting him in the bottom bracket of the league. While strikeouts are a primary ingredient, his swing-and-miss rate has actually been steadily around league average throughout his career.

The discrepancy between his whiffs and strikeouts can be traced to his patience. Though a low swing rate has graced him with a high number of walks, part of the tradeoff is more called strikes. The approach lets him hone in on finding the right pitch and hitting it hard, though, so it’s understandable to accept the strike threes that tag along. It was a major problem for him in 2023 and 2024, when an increase in whiffs led to Bauers going down on strikes at a 34.5% clip, which is essentially unplayable without significant power to counterbalance it. Since then, a moderately better contact rate has helped return the strikeout rate to a manageable level at 25.7% (40 LASR) this season. While there is still room for improvement, it’s acceptable where it currently is.

An eye-catching difference between 2026 and previous campaigns can be seen in the notoriously fluky BABIP. After never crossing 50 before, Bauers now sits at an elevated 60 BABIP LASR. Pessimists will see this and foretell a regression to the mean, chalking up his newfound success to dumb luck. And yes, I will acknowledge that’s a possibility.

However, there are reasons for optimism here too. Though hitters don’t have a ton of influence on which of their non-homer batted balls drop in for hits, it’s not like there’s a truly random number generator centered around .300 that determines everything. Contact that’s squared-up and/or hit at a high exit velocity has a better chance of getting past defenders. And if that contact happens at a lower launch angle, the ball is more likely to find grass before a fielder’s glove (though obviously less likely to go over the fence).

One of those metrics, Sq/Sw% (Squared-Up Per Swing%), is included above. Though there’s nothing special in the 50 LASR he’s put up this year, it’s better than his previous seasons. (Note: this stat is one of the bat tracking stats that were only introduced 3 years ago.) The other components mentioned – hitting the ball hard and with less airtime – are included below. Let’s see that power!

This is where we can see some real and obvious enhancements in 2026. Pretty much every measure of power has reached a career high this season, both in results and underlying metrics. He’s swinging faster, the truly fast swings designed to do damage have become more frequent, and as a result he’s hitting the ball with consistent authority. He has transformed undeniably into a power hitter you’d love to have in the middle of your lineup.

There is one area here that has fallen from its previous heights (pun intended): fly balls. When he first came to Milwaukee, it appears Bauers put heavy emphasis on generating lift in his swing, attempting to join the launch angle revolution that resurrected the careers of Jose Bautista, Justin Turner, Max Muncy, and many more. Put the ball in the air, and it’ll find its way to the seats.

Turns out, that isn’t a one-size-fits-all solution. Bauers has decreased both his Launch Angle and Flyball% LASRs from 65 in 2024 to 45 this year. Counterintuitively, his HR% and ISO have travelled the opposite direction. One last quote from the Nightengale article may shed some light on these changes. This is from his manager, Pat Murphy:

“When he got to the big leagues, because of the type of swing he had, people wanted him to pull. They wanted him to hit homers. He had some bouts of hitting homers, but he got away from what made him cook. I won’t say the organization, but he was with an organization that was saying, ‘We need you to do damage.’ I’m philosophically against telling a kid to do damage. You’ve got to be a pure hitter. So when he came to us, I said, ‘Jake, I don’t care if you’re hitting homers. I want you to be the hitter you can be.’ ”

With less compulsion to live up to the beefy slugger prototype, it seems Bauers has dialed back his focus on generating lift. The freedom to attack pitches in a way that feels more natural has allowed him to swing faster and smack the ball without tossing and turning over launch angles. Combined with the other factors mentioned, there is hope that his BABIP increase can at least somewhat be sustained. And as long as his bat path doesn’t drift into wormburner territory, enough dingers will follow to justify the approach.

What’s next?

In a vital series against the Atlanta Braves, Bauers came up to the plate this past Saturday with a runner on 3rd and 2 outs in the 3rd inning. After working the count and fouling off pitch after pitch until he found one he liked, he drilled the 12th pitch of the at bat over the left-center fence for a 2-run homer that proved to be the difference in the game. In the previous week’s 4-game series against the Dodgers, he capped a 3-run 9th inning comeback in the opening game with a go-ahead single, then in the closing game led the charge in defeating Tarik Skubal with a 4-for-4 day and 2 crucial RBI hits.

The Brewers will be jockeying with those two teams for a first round bye over the next few weeks. Those wins not only showed LA and Atlanta that they should be terrified of Milwaukee come October, but also might end up being key difference-makers in seeding tiebreakers. And Jake Bauers was responsible for those essential victories.

The Milwaukee Brewers still function like they did last year: as a complete 26-man roster that is greater than the sum of its parts. This is still a team that relies on little-known role players bridging the gaps, unsung heroes helping each other up, non-household names sneakily dominating box scores. It took a while for Bauers to find his place in the mix, but now he’s an integral part of it and he looks ready to play his part.

The revelation of his breakout spawns several follow-up questions and intriguing storylines. Will he carry this success into the playoffs and lead the Brewers to their first World Series berth since 1982? When he becomes a free agent this offseason, what will his payday look like and who will give it to him? Is this new and improved version the beginning of a solid late-blooming career, or will we look back on this as a peculiar flash in the pan?

It will be fun to track the answers to those questions. For now, let’s just enjoy the fact that Jake Bauers is making us ask them.

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I have now officially silenced my self-critic (for now) by putting together a new mode in my LASR (League-Adjusted Standardized Rating) graph generator application called the Career Stat Sheet. This is the first mode that does not include bar graphs; with all of the information I wanted to include in one graphic, it made sense to go with a heat map-style table instead of cramming a bunch of bar graphs into a space where they weren’t really necessary.

Here’s an example of a hitter LASR career stat sheet:

The stats that are included here are the default selection for hitters. This collection is labeled “Overall” in another, smaller new feature I’ve called Stat Group.

This is a stat selector that is now available in the Career Stat Sheet and the Multi-Stat Single-Season options. For these modes where users are able to select and customize a group of multiple stats to be shown at once, I wanted to provide a list of default groups. This way, if you’re interested in focusing your player analysis on a particular skill like Contact, Power, or Eye; or (for pitchers) Stuff, Control, or Contact Quality, you now have a quick way of gathering all of the related stats together. No more careful assembling of ISO, xISO, Brl%, EV, etc. to see Kyle Schwarber’s power and then combing through xAVG, -K%, -Whiff%, etc. to check on Luis Arraez’s contact and then going through the whole process again to see Yordan Alvarez’s… power and contact…

As you can see, there is also a Customize option where you can still pick and choose your favorite stats (with a maximum of 12 to prevent over-stuffing the sheet). Selecting one of the other options and then going to Customize will get you started with the pre-defined group, which you can then build upon. For example, if you want to get a sense of Juan Soto’s plate discipline and strikeout avoidance, you can choose Eye, then switch to Customize and add others like -K% and -Whiff%.

…Yeah, he sure knows his strike zone.

Of course, the Career Stat Sheet is available for pitchers too:

You may have noticed in the Adrian Beltre graphic, but it’s even more apparent in Pedro Martinez’s: as I’ve acknowledged before, some stats are only available for more recent seasons. For all players, the default stat groups will include all stats that were available by at least their final season. For example, pitch-level data like Chase, Whiff, and Groundball rates started being tracked in 2002, so any pitcher who retired in 2001 won’t have them on their sheet, but any pitcher who pitched in 2002 or later will have them.

With this representing (in my opinion) an upgrade from the original Single-Stat Full-Career mode, as well as last month’s Scouting Report Card being a better version of the Multi-Stat Single-Season mode, those original two graphics have been placed slightly in the background. To access them, you now have to open a dropdown called “Classic LASR Graph Types”:

Like I mentioned in the Scouting Report Card article, at this point I have been working on getting these modes ready for the MLB Hitters and MLB Pitchers datasets. The other two datasets I have translated LASR stats for, Negro League Hitters and Pitchers, have not been forgotten; however, for the time being I have taken them off the application while I work on bringing them up to speed. They’ll be included soon!

A few reminders of helpful resources for anyone who is new to this whole LASR thing:

• LASR Explainer

• LASR Stat Glossary

• LASR is open to the public!

And that’s about all I’ve got for you! If you’ve got some time in your schedule to partake in baseball nerdery, I would love for you to go check out the new additions. Here’s another link to the graph generator application. Take a look at your favorite players and see how they stacked up against their peers in multiple skills throughout their careers. Get a better look at potential fantasy league pickups. Marvel at what “power hitters” looked like in the 1910s. See whether Nolan Ryan’s thousand-year career can fit onto the graphic. The LASRs are at your command 🫡

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Let’s be honest, though: most plate appearances are pretty meaningless. When assembled together, yes, a season’s worth creates a magnificent masterpiece every single time. But when there are 180,000 pieces in a jigsaw puzzle, you don’t really notice when 1 or 2 get sucked up into the vacuum or eaten by the dog. The absence merely absorbs itself into the surrounding picture and your eyes pass right over the empty space, content with filling in the gaps using your imagination or maybe Baseball Reference in a pinch.

Side note: if you ever find yourself in possession of a 180,000-piece jigsaw puzzle, I recommend you donate it immediately. Or purchase a gymnasium.

Some plate appearances, however, are quite special. Though they follow the same rules as all their cousins and are just as short-lived, the shape they ultimately take is guaranteed to make real, far-reaching impacts on not only the history of baseball, but on millions of people’s lives. Think I’m exaggerating? No, I’m actually not.

A plate appearance like no other

Last November 1st, Toronto Blue Jays catcher Alejandro Kirk stepped up to the plate for the 589th time that year. Think of the things you’ve done 589 times in one calendar year. That’s about 1.6 times per day. Maybe it’s washing the dishes. Maybe it’s brushing your teeth (though you should probably bump that up a little bit). Maybe it’s walking your dog, cautiously searching for that missing puzzle piece in places you’d rather not be examining. Maybe it’s checking the mail. These are common events in your life, and there’s typically nothing that makes any singular instance more momentous than the others. Unless you find that puzzle piece – that’s a dog walk to remember.

With 1 out and runners on 1st and 3rd in the bottom of the 11th in Game 7 of the World Series, Kirk was quite obviously aware that this plate appearance held a particular significance not on par with the previous 588. The magnitude wasn’t a surprise. However, this was a moment in time where he was asked to do something he had done over and over again throughout his whole life. His job remained the same as it had been on his first day of spring training: hit the ball, preferably where nobody could get it.

If he had hit a grounder to the shortstop back in February, whatever, who cares. Kirk himself probably forgets the specifics by the next time he’s at the dish. On that fateful November day, however, there were immensely severe consequences for that ground ball.

Before Yoshinobu Yamamoto tossed that millionth pitch of his in that postseason, the baseball universe – and the grander universe consisting of everything we’ve ever known – met a fork in the road. Which path lay ahead? Blue Jays, 2026 World Series champions? Or Dodgers, 2026 World Series champions? Millions upon millions of lives faced meaningfully different futures based on this one tiny moment in time. The entire emotional atmospheres in the cities of Los Angeles and Toronto hung in the balance, not to mention all the other people and places rooting for one side or the other.

Within the next few moments, city officials would either be planning victory parades or consoling their despondent citizens. Team stores would have championship merch flying off the shelves or shipped off to third-world countries. Sentimental couples would either be getting recklessly engaged after a month of dating or storming out and never seeing each other again. Children for whom this baseball season represented a staggering percentage of their total time on Earth would go forth with the rest of their lives remembering the time their heroes either saved the day or destroyed their hopes.

Sure, sports are just a collection of games and we attribute way too much gravitas to their results, but that ship has sailed. We have decided this does matter. And once that decision was made, once we accepted that a group of strangers most of us will never meet will have the ability to seriously impact our livelihoods, our memories, our views of the world as a whole, we decided that not only does the gigantic 6-month, 162-game, 3- or 4-round playoff, exhausting, enthralling adventure mean something, but also that our entire recollection of it all will hinge primarily on this particular plate appearance. One singular plate appearance, out of the 180,000 that make up a baseball season.

When Freddie Freeman latched onto the throw from Mookie Betts to complete the double play, the universe had decisively chosen its way forward. We now live in that future, where the Dodgers are back-to-back champions and are endlessly accused of ruining baseball with their high payrolls and playoff success. It is easy to look around at where we currently stand and convince ourselves it was always meant to be. And in a way, that’s true. There is, after all, only the one universe (that we know of). All that has happened is all that really matters.

But that other universe was only an odd bounce away, a larger lead off third base away, a slightly deeper fly ball away. It’s impossible not to consider whether there’s a portal or a wormhole somewhere that offers a peek into what could have been.

A Golden Opportunity

The delicate circumstances under which Kirk came to the plate – more specifically, the pitches that were thrown to him under those circumstances – have been deemed “Golden Pitches” in this 2016 SABR article by Wade Kapszukiewicz. A Golden Pitch is defined as a pitch where either of the World Series participants could have theoretically won the championship as a result of the play. They represent the equivalent of “Golden Goals” in soccer or hockey, which occur when the next goal wins and automatically ends the game. A Golden Pitch must occur in the final game of the World Series (these days, that’s Game 7) with the road team ahead and the home team batting in either the 9th inning or later, with the winning run AND potential final out either at the plate or on base.

Below is an accounting of every Golden Plate Appearance in MLB history. I will admit that’s not as catchy of a moniker as Golden Pitch, and there’s certainly intrigue in breaking it down on a granular pitch-by-pitch level like Kapsukiewicz did, but I’d rather analyze the entire battle instead of each individual punch. Besides, I’ve already spent about a thousand words deliberating over just the Yamamoto-Kirk matchup; I don’t need to elongate the suspense any further and lose my two readers by telling them just how blue the curtains were.

There have only been 15 Golden Plate Appearances in MLB history. Though the conditions varied, such as pending franchise fortunes and number of television viewers (I believe the 1912 Fox broadcast ratings were quite low), the stakes were the same for each of the following. Everything I waxed poetically about in the 2025 finale applies to all of these matchups as well. With one little breeze shift or thumbnail trim, these flips of the coin could have gone the other way and we would live in a different world than we do now. Spooky, huh?

Here they are, summarized in reverse-chronological order:

15. 2025 - Alejandro Kirk vs. Yoshinobu Yamamoto

Dodgers at Blue Jays, Game 7, bottom of the 10th, down by 1, runners on 1st and 3rd, 1 out.

Yeah I think I’ve got this one covered, but it needed to be in the list.

Winner: PITCHER.

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14. 2025 - Addison Barger vs. Yoshinobu Yamamoto

Dodgers at Blue Jays, Game 7, bottom of the 10th, down by 1, runner on 3rd, 1 out.

We don’t have to go back much further to find the next most recent Golden Plate Appearance. Immediately before Kirk, Addison Barger had a similar opportunity to change things forever. A home run would have sparked celebrations all over Canada, while the Dodgers could have sealed the deal with either a lineout double play or, more likely, a sacrifice fly attempt where the runner (Vladimir Guerrero Jr.) was out at home. Instead, the Dodgers quite clearly decided not to let Barger beat them and Yamamoto threw four consecutive balls to unintentionally-intentionally walk him. It was risky putting the winning run on base, but they took that risk and chose the slow-running righty in a double play scenario. It paid off, but this particular matchup win technically goes to Barger. He did, after all, avoid chasing any of the offerings.

Winner: HITTER

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13. 2016 – Michael Martinez vs. Mike Montgomery

Cubs at Indians, Game 7, bottom of the 10th, down by 1, runner on 1st, 2 outs.

This all-time classic didn’t seem like it should come down to Michael Martinez, a late-inning defensive replacement whose defense wasn’t even tested in the game, but that’s how baseball works sometimes. The previous batter, Rajai Davis, had tied the game earlier with a clutch 2-run home run in the 8th inning, and remarkably he could have achieved the same result in the 10th. Instead, he merely hit an RBI single up the middle, bringing Cleveland to within 1 run and sending Martinez, a career .197 hitter, to the plate. He had been the last player on the bench when Terry Francona sent him out to replace weak-armed Coco Crisp in the 9th inning, so Francona was handcuffed here. Martinez hit a slow roller to Kris Bryant, who threw across the diamond to give the Cubs their first championship in 108 years.

Winner: PITCHER

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12. 2014 – Salvador Perez vs. Madison Bumgarner

Giants at Royals, Game 7, bottom of the 9th, down by 1, runner on 3rd, 2 outs.

The Royals may seem like a laughingstock now, and they were before the mid-2010s too, but for a couple of magical seasons they were a dominant, pesky force to be reckoned with. The Giants, meanwhile, were thriving under their own magic known as EYBS (Even-Year Bull Shit), seemingly finding the arm of God and attaching it to Madison Bumgarner’s body. Alex Gordon came oh-so-close to tying the game with a misplayed single into left-center that allowed him to reach 3rd base (SEND ALEX GORDON HOME YOU COWARDS – sorry, instinctive response). Knowing how dominant Bumgarner had been, the decision to keep Gordon at 3rd is one of the biggest what-ifs of our lifetimes. Instead, the Royals’ hopes came down to Salvador Perez, who could’ve won the game with a home run but instead popped out to Pablo Sandoval to end the Series. The Royals won the Series the following season, though, so it’s all good.

Winner: PITCHER

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11. 2001 – Tony Womack vs. Mariano Rivera

Yankees at Diamondbacks, Game 7, bottom of the 9th, down by 1, runners on 1st and 2nd, 1 out.

The Yankees had won the World Series in every year that the Diamondbacks existed when the two teams met up in 2001. This was a Series for the ages, with elite pitching and clutch hitting on both sides as one of the newest franchises in baseball history battled the highest-regarded franchise in baseball history down to the wire. After a failed Jay Bell sacrifice bunt (more on that very soon), light-hitting shortstop Tony Womack won the hearts of all non-Yankee fans by turning on a Mariano Rivera inside cutter – arguably the most unhittable pitch a pitcher has ever held in his repertoire – and tying the game with a double down the right field line. A couple batters later, a totally clean and unenhanced Luis Gonzalez put the Yankees’ dreams of a four-peat to bed with a legendary floater to shallow center field.

Winner: BATTER

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10. 2001 - Jay Bell vs. Mariano Rivera

Yankees at Diamondbacks, Game 7, bottom of the 9th, down by 1, runners on 1st and 2nd, 0 outs.

Jay Bell’s plate appearance immediately before Womack’s is perhaps the biggest technicality on this list, as it would have taken a triple play for the Yankees to win or an extra-base hit for the D-backs to win. Both were extremely unlikely with Bell dropping down a bunt to nobody’s surprise. Mariano Rivera was on it quickly and, undeterred by the error he had committed on the previous play (another bunt), fielded this one cleanly and got a force-out at 3rd.

Winner: PITCHER

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9. 1997 – Craig Counsell vs. Jose Mesa

Indians at Marlins, Game 7, bottom of the 9th, down by 1, runners on 1st and 3rd, 1 out.

This was an incredible, historic game that doesn’t get talked about as much as it should, likely because of the small-market teams involved. This was another matchup between a franchise that had been around since the dawn of the American League and a franchise that would still be trying to wrap its head around the alphabet if it was a person. Unlike the Yankees in 2001, though, Cleveland wasn’t being greedy and demanding four championships in a row – they were gunning for their first title since 1948. Heading into the bottom of the 9th they were within striking distance of that dream, but Jose Mesa couldn’t close it out. With their backs against the wall, the Marlins completed their rally with a sacrifice fly from future manager Craig Counsell to tie it up. Later on, 21-year old Edgar Renteria hit a walkoff single to clinch the Marlins’ first World Series championship just 5 years into their existence.

Winner: BATTER

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8. 1997 - Charles Johnson vs. Jose Mesa

Indians at Marlins, Game 7, bottom of the 9th, down by 1, runner on 1st, 1 out.

Counsell wouldn’t have had the luxury of being able to settle for a productive out, however, if Charles Johnson hadn’t come through in the clutch right before him. The Marlins catcher came up to the plate with one worst-case scenario to avoid: a ground-ball double play. With some power in his bat (19 homers that season and 167 for his career), Johnson would have been THE hero of the Series with a game-winning blast. Mesa opened the plate appearance with three straight pitches on the outer part of the zone, however, gaining the edge with a 1-2 count. Rather than try to do too much, Johnson poked the next outside slider into right field for a single, moving the runner over to 3rd and setting Counsell up for success.

Winner: BATTER

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7. 1972 – Pete Rose vs. Rollie Fingers

Athletics at Reds, Game 7, bottom of the 9th, down by 1, runner on 1st, 2 outs.

In one of the closest World Series matchups of all-time (6 out of 7 games were decided by 1 run), the A’s narrowly came out on top. With 2 outs in the 9th of the deciding game and nobody on base, bullpen superhero Rollie Fingers hit pinch-hitter Darrel Chaney with a pitch to bring the winning run up to the plate. This hitter was somebody you could bet on: the eventual record-holder for career hits, Pete Rose. Though he made solid contact and drove the ball into the left-center gap, it stayed in the air long enough for the left fielder to make the catch, clinching the first Series victory for the A’s since they had left Philadelphia. To rub it in everyone’s faces, they promptly won the next two championships before ceding the crown to these Reds, who won back-to-back titles of their own and made the mid-70s World Series winner list look like a Christmas card.

Winner: PITCHER

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6. 1962 – Willie McCovey vs. Ralph Terry

Yankees at Giants, Game 7, bottom of the 9th, down by 1, runners on 2nd and 3rd, 2 outs.

The 1962 World Series is the only one that has given us 3 Golden Plate Appearances. That means 20% of all of the faceoffs in baseball history with potential championships on the line happened within a few minutes of each other. On the mound was Ralph Terry, the pitcher who had given up Bill Mazeroski’s Series-winner home run two years earlier (which isn’t on this list because the game was tied). It was the ultimate redemption story for Terry, who was the starting pitcher that day and was attempting to nail down a 1-0 shutout, a truly heroic feat to accomplish in Game 7 of the World Series. The finale of these critical plate appearances was a Willie McCovey line drive which he later claimed was “the hardest ball I ever hit.” Unfortunately for McCovey, it was sent straight to second baseman Bobby Richardson, who caught it to secure the series and prevent Terry from giving up 2 Series-losing walk-off hits.

Winner: PITCHER

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5. 1962 - Willie Mays vs. Ralph Terry

Yankees at Giants, Game 7, bottom of the 9th, down by 1, runner on 1st, 2 outs.

As if it wasn’t enough of a challenge for Terry to face one legendary Willie M. in his Herculean pursuit of the 27th out, the batter before McCovey had been none other than Willie Mays. The Say Hey Kid had smashed 49 dingers that year in the regular season and 660 total in his career, but in 25 career postseason games only ever hit 1, which didn’t come until the 1971 NLCS when he was 40 years old. This would’ve been a much better time for it, but a double down the right field line was a fine outcome as well. A quick relay from Roger Maris to Richardson kept the tying run 90 feet away with McCovey strolling to the plate.

Winner: BATTER

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4. 1962 - Chuck Hiller vs. Ralph Terry

Yankees at Giants, Game 7, bottom of the 9th, down by 1, runner on 1st, 1 out.

It was far from likely, but it just would’ve been so “baseball” for Chuck Hiller to have blasted a walkoff 2-run homer to win the Series. He played 161 games that season and hit only 3 home runs, with a grand total of 20 in his 8-year career. Alas, Terry had learned his lesson about unassuming second basemen and decided to strike this one out. The silver lining for Hiller was that it wasn’t a double play, which would have ended the game. He gave Mays and McCovey, the stars of the team, their shot at victory by humbly passing the torch.

Winner: PITCHER

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3. 1926 – Bob Meusel vs. Grover Alexander

Cardinals at Yankees, Game 7, bottom of the 9th, down by 1, runner on 1st, 2 outs.

Bob Meusel led the AL in home runs and RBI in 1925, and while he had a down year in ‘26 he was still a productive hitter more than capable of getting a hit or at least keeping the line moving. Unfortunately, despite being put in a position to decide that year’s World Series for himself, the opportunity was taken away from him. Known speedster Babe Ruth, the runner on 1st, attempted to steal second base and was promptly thrown out to end the Series. Ruth actually had 11 stolen bases that year and 123 in his career, but his success rate was pretty much 50/50, so his decision to try to steal with everything on the line was suspect at best. Given his fame (and the fact that the Yankees won the Series the following two seasons), Ruth was likely forgiven, but God, can you imagine how fans would react to something like this today?

Winner: PITCHER (& catcher?)

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2. 1912 – Tris Speaker vs. Christy Mathewson

Giants at Red Sox, Game 8, bottom of the 10th, down by 1, runners on 1st and 2nd, 1 out.

The 1912 World Series was the second (after 1909) to go to a decisive final game – though this one was Game 8 instead of Game 7, with a previous game being called a tie due to darkness and respect for future spring training rules – and was by far the most thrilling Series to date. Only one game ended with a margin of victory over 3, with 4 games decided by 1 run. In this final game, after the Giants had scored a run in the top of the 10th inning, times were looking dire for the Boston faithful. Star outfielder Tris Speaker nearly popped out for the 2nd out, but miscommunication between the catcher, first baseman, and legendary pitcher Christy Mathewson allowed the ball to drop innocently in foul territory. Given new life, Speaker smacked a single to right field to tie up the game. A couple batters later, Larry Gardner hit the only sacrifice fly to ever end a World Series.

Winner: BATTER

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1. 1912 - Steve Yerkes vs. Christy Mathewson

Giants at Red Sox, Game 8, bottom of the 10th, down by 1, runner on 2nd, 1 out.

The very first Golden Plate Appearance in baseball history went to light-hitting middle infielder Steve Yerkes. It would’ve taken something like a line-drive double play to lose, or a home run to win (of which he had 0 in 131 games that year), but they were both theoretically possible. Before Yerkes came up, center fielder Fred Snodgrass dropped a routine fly ball to start the inning in what is still considered one of the most infamous plays in World Series history, allowing the tying run into scoring position. Ironically, he followed up the flub with a great running catch, setting the stage for Yerkes. With the fate of the future in his hands, the infielder passed the buck and drew a clutch walk against control-artist Mathewson in the Hall of Famer’s 29th inning of the Series and 10th inning of the game. Ah, back when men were men and using the bullpen was an embarrassment. Did he walk Yerkes because of some fatigue? Maybe, but you better believe he’d never admit that.

Winner: BATTER

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Summary

In total, there have been 8 successes for the pitching team and 7 for the batting team – though “success” does not necessarily mean the game was won or lost on the plate appearance. In fact, only 6 of these matchups actually ended things, and all of those were on final outs by the pitcher. There has never been a Series-ending Golden Plate Appearance in which the hitter won it all right then and there. And yes, I am attempting an anti-jinx right now.

There are moments in everybody’s lives that define our futures. Some appear out of nowhere, and we must react in real time to the immense shift our timelines will take from there on. Others, like Golden Plate Appearances, carry clear and obvious alterations of our destinies right on their faces. Some may think it’s better to be prepared and well-measured when making these decisions or taking these actions; others may prefer to avoid overthinking and rely on their instincts.

Either way, they serve as important markers in our personal histories, the way Alejandro Kirk and Yoshinobu Yamamoto will now forever represent a moment that determined the future of baseball and everyone involved. Everything we know may be different if Bobby Richardson had been standing five feet to his left, or if Tony Womack had been looking for an outside pitch, or if Steve Yerkes had taken an errant swing. Isn’t that beautiful?

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To catch you up, here are the previous installments in this series:

• The Incredibly Flawed Best Player in Baseball Bracket

• June IFBPIBB update: Judge, Alvarez, other superstars eliminated; Cy Young favorites square off this month

• The IFBPIBB Sweet Sixteen

And a TL;DR on the rules: this is an MLB player bracket predicated on its flawfulness, in which matchups are determined and decided strictly via monthly FanGraphs Wins Above Replacement (fWAR). The initial group of 64 was determined using March/April stats, and in each subsequent month the field has been cut in half.

With the conclusion of July behind us, we now have only 8 players remaining in the IFBPIBB. Some are undoubtedly going to rack up significant MVP votes this offseason. Others are akin to the shy kid in the horror movie who, to the surprise of everyone, has withstood all manner of monster attacks and ghost summonings and now looks around at his fellow survivors like, “Really? Me?”

Screenshots and links to the full bracket and associated data can be found at the end of the article. Before that, let’s dive into each of July’s matchups and give the victors their due appreciation.

As I’ve been known to do, I’ll use this as an opportunity to showcase my LASR project. More specifically, I just introduced a brand new feature last week, LASR scouting report cards, and I’m excited to demonstrate their usefulness here. As we explore each player’s performance, I hope you take a moment to consider your gratitude that my barrage of stats is now presented in relevant subcategories rather than in a long monotonous line. Those old graphs really sucked by comparison, huh? Who thought that would be the preferred way of displaying information? We’re all trying to find the guy who did this...

And now, without further ado, I present the IFBPIBB Elite Eight!

#1 Shohei Ohtani (0.58 fWAR in July) defeats #49 Jordan Walker (0.45)

In his most vulnerable month yet, the unanimous real best player in baseball remains undefeated, as Shohei Ohtani barely squeaked his way past Home Run Derby champion Jordan Walker. Ohtani posted only a 127 wRC+ as a hitter in July, which would be a career month for some players but is quite forgettable for him. Overall, he’s enjoying another splendid offensive season. It’s not quite on par with his previous highs, but that’s partially a result of bad luck – he’s got 5 point LASR gaps in all of wOBA/xwOBA, AVG/xAVG, and ISO/xISO.

Ohtani’s pitching has been glorious this year, though with each of his -BABIP, -HR/FB, and LOB% LASRs at 60 or above, it appears the luck he lost at the plate was simply transferred to the rubber. More notably, he has a nagging left knee that has prevented him from pitching since his last start on July 3rd. However! That lone July start proved to be the difference-maker in this matchup! Walker actually had Ohtani beat in a tight hitter fWAR race, 0.45 to 0.42. If Ohtani’s knee had kept him off the mound a bit earlier, we would be looking at an overthrown #1 seed right now.

Though Ohtani soldiered on this time around, in August he will face his toughest competition yet: the man who is currently challenging him for the NL MVP throne.

#56 Pete Crow-Armstrong (1.87) defeats #57 Wilyer Abreu (0.26)

What a year Pete Crow-Armstrong is having. After debuting in 2023 and immediately establishing himself as an elite fielder with holes on offense, PCA has now blossomed into an offensive star and has kicked his pursuit of the NL MVP into high gear. He just led all players in fWAR for the second straight month, which means it literally didn’t matter who he was matched up against in June and July. He would’ve won no matter what.

Defensive metrics exist in a notoriously thorny conversational garden. The debate over FRV, DRS, OAA, dWAR, Fielding value, and even your oldschool Fielding Percentage will rage until humanity’s final inning. My approach is to put faith in the crowd and simply combine them (that is, just FRV and DRS; they each overlap with the others mentioned so I don’t want to double-count anything). With LASR, we can combine stats of different initial ranges and get a relatively balanced result. I’ve calculated the FRV/DRS averages for all fielders this season, and nobody else matches Crow-Armstrong’s 75. In 2024 and 2025, that value was 70. Somehow, his glove is getting better.

Oh and he’s magically walking now too, which is the main reason he’s transformed as a hitter this year. If he wins his August matchup with Ohtani, I’ll make sure to break that shocking development down next time.

#20 Parker Messick (0.46) defeats #29 Ozzie Albies (0.35)

As starting pitcher usage has become ever more conservative in the maximum effort era, high-end pitchers just can’t reach the top shelf as easily as high-end hitters. In 2021-2025, there were 10 pitcher seasons that accrued 6.0 fWAR or higher, with the highest being Corbin Burnes’s 7.5 fWAR in 2021. In that same period, 52 hitter seasons crossed the 6-WAR threshold, including 14 that were more valuable than Burnes in 2021.

Obviously, this pattern holds true on a month-by-month basis as well. With Parker Messick riding another solid month into the quarterfinals and Cristopher Sanchez being eliminated (oops, spoiler), we now have only Messick remaining among pitchers. (Well, one-way pitchers at least – thanks for always being the exception, Ohtani.) Messick is currently sporting a 3.5 fWAR that is 3rd best in the AL but has generally been overlooked by the more dominant hurlers. Thank you IFBPIBB for making sure his performance doesn’t go unnoticed.

Messick may not sustain the luck he’s basking in, but he should be a solid above-average pitcher for years to come. He’s great at limiting hard contact (65 grades in -Brl%, -EV, and -HH%) which may keep his -BABIP and -HR/FB afloat while he works on improving his already-steady strikeout and walk rates.

#21 Corbin Carroll (0.16) defeats #5 Bobby Witt Jr. (0.07)

Normally, I don’t recommend an fWAR showdown against Bobby Witt Jr. Even when he’s not lighting the world on fire at the plate, his defense and baserunning are basically cheat codes to an easy win. For example, Witt was the 5th seed in this bracket even though he had a fairly pedestrian 119 wRC+ in March/April. Even further, if you do find yourself in a battle of Witts, you should try not to slash .163/.284/.326 with a 68 wRC+. Having a Baserunning (BsR) value that’s tied for the best in baseball for the month will certainly help, of course, but I’d work on hitting a bit better if I were you.

When all else fails, you could just pray to the baseball gods that lower back tightness will sour Witt’s performance for half a month and then get him shelved on the IL for the other half. Looks like that’s the route Corbin Carroll took in July. It was a bold strategy, but it worked.

Carroll is one of the most exciting players in baseball with a power-speed combination that puts him in rare company (nobody else with a 70 BsR LASR has an ISO LASR above 55), but he has been prone to these extended slumps throughout his career. He’ll likely have to put this current slump to rest if he wants to kick the final pitcher out of the bracket and make the Final Four.

#63 Brayan Rocchio (0.52) defeats #18 Max Muncy (LAD) (0.38)

Dread it. Run from it. Light-hitting Guardians shortstop and #63 seed Brayan Rocchio arrives all the same. Somehow, he keeps riding his superlative defense to victory after victory in an impeccable, foolproof bracket designed to deliver the ultimate truth of baseball’s best players. You heard it here folks, Brayan Rocchio is officially a top 8 player in baseball! Like and subscribe for more hot takes!

Rocchio’s fielding stats are a great example of the troublesome idiosyncrasies we deal with when parsing defensive data. According to Statcast’s movement-based FRV, which doesn’t give a damn about pre-pitch positioning, game context, or even position, Rocchio is an excellent defender (65 LASR). However, turning to Sports Info Solutions and asking DRS the same question gets you a much different answer (50 LASR) because it does take those things into consideration – but doesn’t use high-tech optical tracking to do so.

I could (and maybe will someday) dive much further into these muddy waters, but this isn’t the place for it. The only important takeaway from this fielding foray is that FanGraphs relies on FRV instead of DRS in determining a player’s defensive value, and FanGraphs is where I get my data. Thus, Rocchio has once again defied the odds and maintained his weird placement in the bracket.

#10 Kevin McGonigle (0.83) defeats #26 Andy Pages (0.67)

Once again, Kevin Professor McGonigle has Avada Kedavra’d his opponent, collected another horcrux, and ridden his hippogriff into the sunset. The rookie’s defensive story is Rocchio’s inverse, with DRS grading him above average and FRV considering him part of the mediocre middle. Because of this, Rocchio has a leg up in their upcoming matchup.

Lucky for McGonigle, his bat is more than capable of making up that ground and defeating his fellow AL Central shortstop. He has burst out of the gate to open his career, establishing himself as an ideal top-of-the-order type hitter capable of laying off bad pitches and smacking the hittable ones into the outfield. He hasn’t shown the power that some of his contemporaries have, but it’s just a bit below average and I wouldn’t be surprised if he develops a bit more slugging in future seasons.

When I debuted these scouting report cards last week, I also introduced a new stat I’ve calculated and included: ZSw/Sw. That stands for Zone-Swing% divided by Swing%, which shows how often he swings at pitches in the strike zone in comparison to his total swings. His 75 means he’s one of the best players in baseball at recognizing his best opportunities to get good wood on the ball. This doesn’t seem like a skill that many 21-year olds have in their toolkit. What a future this guy has in front of him.

#19 Otto Lopez (0.83) defeats #30 Cristopher Sanchez (0.18)

Hurray, I still get to be annoying about predicting Otto Lopez’s success! Ahem. A reminder: I picked Lopez not only in my “Who’s Gonna Get Better?” article from before this season, but also in a Call to the Pen breakout series I wrote prior to the 2025 season. Sure, that didn’t quite work out last year, but I still believed in him enough to double down, and I’m glad I did (the other guy I picked both years was Luis Garcia Jr., who’s also having a career year).

As much as I don’t want to admit it, Lopez might be benefitting from some fair fortunes this year. His wOBA and AVG LASRs are each 10 points above their x-partners. His 65 BABIP LASR probably shouldn’t be expected to stay so elevated – although I don’t see it plummeting back to last year’s 40. He’s a speedy right-handed groundball hitter, which is typically a sign of naturally high BABIP capabilities, but his low Pull% (35 LASR) is a point off in that department. If he pulled more of his grounders, they’d be in an easier section of the infield to leg out infield hits on.

I don’t recommend he change his approach at all, though, at least unless he comes back down to earth. For now, it’s sure as hell working, and this former waiver claim is suddenly one of the best shortstops in baseball. Never forget, I knew it first. Please forget, Michael Toglia was also a breakout pick of mine for 2025. Shh. No he wasn’t. Nope.

#6 Matt Olson (0.69) defeats #22 Xavier Edwards (0.10)

Though his performance levels have fluctuated year-to-year, if there’s one thing Matt Olson is known for it’s consistency. At the time of this writing, he has just played in his 896th consecutive game, which passed Stan Musial for 8th all-time. Though he’ll likely fall well short of Cal Ripken Jr.’s famous streak, he could conceivably find his way into the top 5.

But that’s not the consistency I’m talking about. After putting up a very nice 0.69 fWAR in June, he liked that value so much he kept it around. That’s right, two months in a row have now featured a 0.69 fWAR total from Matt Olson, and both times it was enough to propel him onward. For those curious: if I were to put together a bracket consisting of only Matt Olson months, July Matt would have beaten June Matt by a score of 0.693 to 0.689.

As he’s been known for his whole career, Olson’s success this season rests on his superb power. In 2024 and 2025 his HR% and ISO had dipped to 55, so it’s exciting to see them jump back up to 70 this year. His walk rate has been steadily in the 55-70 range throughout his career, so its drop to 50 might be cause for concern – though his chase and swing rates haven’t seen any dramatic shifts, so it could bounce back up.

After defeating one Miami middle infielder, he’s onto the other. Does he have another 0.69 in him? Will that be enough?

The Bracket

Here’s a link to a PDF of the updated bracket. Here are the July hitter stats and July pitcher stats that were used to determine the winners. Below are screenshots of the four quadrants of the updated bracket. Thanks for following along, stay tuned next month for the Final Four!

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Here’s an example of the hitter version:

And here’s a pitcher:

Key details:

• Unlike the previous two modes, the included stats and their orders are not currently customizable. And they probably won’t be — the sidebar would look pretty messy if there were 5-7 separate customizable stat selector boxes. I think that’s okay, though. People don’t usually complain about not being able to rearrange things on other baseball stat sites.

• As before, these cards are able to be generated for every player-season in MLB history dating back to 1876. Of course, not all stats are available for every season (those frickin’ Statcast losers can’t go back in time and get me Teddy Ballgame’s barrel rates for some reason) but those that are available are included:

• There have always been playing time adjustments in the LASR calculations; now, those multipliers are included on the graph. If a player falls short of being a LASR qualifier, that is noted. And if so, the scalar that all of their underlying z-scores were multiplied by is listed. For example, all of Jacob deGrom’s 2021 z-scores were multiplied by approximately 0.66 before being translated to the 20-80 scale here (and they’re still wildly phenomenal):

• A couple new stats have been added, and several others have had their abbreviations shortened so they could fit better.

  • “ZSw/Sw” stands for Zone-Swing% divided by Swing%, which is designed to identify players who recognize strikes when they see them and concentrate a larger portion of their swings on pitches in the strike zone.

  • “PtchAr” (Pitch Around) is simply Zone% negated, so it identifies hitters against whom opposing pitchers avoided the strike zone more often.

  • “SpdSc” (Speed Score) is a Bill James-invented stat that takes into account stolen base percentage and attempts, triples percentage, and runs scored percentage. It’s a bit old-school, but I wanted to include more than one running-based stat and it gives a decent estimate of a player’s baserunning prowess.

  • These additions and the abbreviation updates are reflected in the LASR Stat Glossary, which I encourage you to check out if you have questions about any stats that are included on the cards (or elsewhere in LASR).

• Annoyingly, I don’t have Bats and Throws information available for any data before 1974. I’m sure I can get that somewhere, but for now those little pieces of info are not included in the player details section (as you can see in the Ted Williams card).

• The scouting report cards are not ready for my Negro League data yet, so I’ve taken those datasets off the site for the time being. I’ll get around to that at some point. (If it is the year 2100 and I haven’t gotten around to it yet, or at least haven’t updated this to reflect it, please feel free to reach out and remind me. Just keep in mind I’m 104 years old and don’t know what the hell you’re talking about.)

That’s all for now! If you’re reading this, I’d love for you to go check it out and generate your favorite players’ scouting reports. I’m really enjoying expanding this project, and you can rest assured there’s lots more updates on the way. Thank you ✌️

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Coming to America

When Miguel approached his parents about defecting from Cuba in pursuit of his baseball career, it was the type of conversation that illustrates the ground that humanity still has to cover – and is currently losing – in understanding the process by which we should be able to share physical spaces with each other. This wasn’t about the Vargases packing their possessions into a U-Haul and happily traversing to their new house in the suburbs. This was a family dangerously risking everything they knew and literally escaping their homeland. For their 14-year old to go play baseball.

“I had a lot of emotions, obviously,” Miguel said (as quoted in this article). “I think we all made the decision. My mom definitely cried when she left her mom there and her family. It’s hard for all of us. But I was ready, and I knew the moment was going to come when the time was right. I just wanted to play in the Majors.”

Miguel’s father Lazaro had been a star in the Cuban National Series, the country’s top professional league, and had seen teammates defect to the United States to chase their ambitions. He hadn’t seriously considered it himself – the practice was very rare at the time, and even more treacherous – but he understood his son’s desires and believed in Miguel’s ability to make the life-changing move worth the risk. A year later, the family uprooted their lives and left Cuba behind.

There are many more awe-inspiring details in Miguel’s journey stateside in the article linked above (written by Juan Toribio), including considerations of quitting, his mother shagging fly balls during his scouting evaluation because nobody else was around, and his eventual signing being impacted by Shohei Ohtani’s initial venture to the states. I encourage you to give it a read. It is an example of the rough road many big leaguers take to achieve the ultimate goal of putting on an MLB uniform even one time.

Even for those who make it, however, things don’t always go as planned.

Tossed into the pits of hell

After signing as an international free agent with the powerhouse Los Angeles Dodgers, rocketing up their minor league system, and making his MLB debut at 22, Vargas took a few years to find steady footing in the majors. A few opportunities presented themselves amidst LA’s dynastic run, but he struggled to make his mark and was considered expendable after 129 games with the team across 3 partial seasons.

Still considered a promising young player, he was traded near the 2024 trade deadline to the Chicago White Sox as part of a 3-team trade that sent Michael Kopech and Tommy Edman to the Dodgers. Over the next couple months, Kopech and Edman became significant contributors to the eventual World Series winners. Meanwhile, Vargas was essentially sent from baseball heaven to baseball hell. The 2024 White Sox were enveloped in quite possibly the worst season of all-time, finishing the season with a ridiculous 121 losses and becoming the clear laughingstock of the league.

Vargas’s performance after coming to Chicago reflected that of the team. Swept into the shit-nado against his wishes, he batted .104/.217/.170 in 42 games and 157 plate appearances. His first impression to Chicago fans was, “Hey, I see you’ve got a pretty bad team here. Somehow, I’m going to make it even worse.” He arrived in the middle of a 21-game losing streak (not a typo) and after the last of those losses, became the unfortunate face of the streak with this solemn rumination in the dugout:

Boy, what a sad story this is, huh? A young boy with a desperate hope to taste the arcadian bliss of the big leagues finally getting there and being greeted by failure of utterly embarrassing levels? What am I, a masochist? What is the lesson here, never listen to your kids?

Fortunately, no. The lesson is, in baseball there’s always next year.

A step in the right direction

...Well, and the year after that (we’ll get there). The White Sox were still bottom feeders in 2025, adding another 100-loss season to the rebuilding checklist. However, they did so with much more dignity. They improved by a full 19 games and were a normal bad team instead of a historic bad team. Important pieces of a potential playoff team started to debut or take steps forward, even if the October they envisioned was still a few years down the line. When your starting point is “worst team of all-time” there’s generally nowhere to go but up; nonetheless, it was promising to see them realize that truth instead of challenging it.

Vargas was a microcosm of the development, rebounding from his unspeakable introduction to the team and becoming a league-average hitter. Like, pretty much exactly average, putting up a 100 OPS+ and 101 wRC+ in 569 PA. After bouncing between third base, second base, and left field with the Dodgers, he established his role on the roster as a corner infielder, splitting his time pretty evenly between first and third base. He wasn’t great defensively, but he found a regular spot on the lineup card and gave Chicago confidence that he could be penciled in for the foreseeable future.

The breakout

As a proud lover of stat sheet scrutinizing, one of my favorite elements to spot in a player’s career is the season where they turn a corner and tell the league, “This is me, I’m a character in this drama now, and you better get used to me because I’ll be around for a while.” In 2026, Miguel Vargas is leading a White Sox team full of those new characters.

Only two years after an atrocious performance that will be mentioned anytime a future team spirals out of control in a similar manner, Chicago seems to be reaping the rewards of their rebuild. It’s a few years earlier than expected – coming into the season, you’d be hard pressed to find a baseball talking head who didn’t project another last-place finish for the Southsiders. And yes, there is danger in writing this article midseason when there’s plenty of time for a collapse (knock on wood), but I’m starting to learn that being a baseball writer sometimes means putting words into the universe that might be ridiculed in the near future. How ‘bout that Eric Wagaman breakout, huh?

As of today (7/27), the White Sox are in first place in a weak AL Central (and overall weak American League). They are 55-49 and a season-high 2.5 games ahead of the Guardians, who have a tenuous hold on the third Wild Card spot. Even if the division crown slips out of their hands, the playoffs are a real possibility. Heading into the final week before the trade deadline, they are looking to be buyers after spending the past few deadlines and offseasons selling off anything of value. It’s a breath of fresh air and the vibes have soared into the stratosphere.

Lots of credit deserves to go to Munetaka Murakami, Colson Montgomery, Chase Meidroth, Tristan Peters, Sam Antonacci, Sean Burke, Davis Martin, Grant Taylor, and more, but this is a Miguel Vargas article. And now I’ve mentioned them anyway, so I’ve done what Moneyball refused to do.

Let’s break down what Vargas has done this year the way I typically do. It’s LASR time.

Miguel Vargas 2026 LASR

Yeah, it’s another LASR article in disguise. Surprise, surprise.

Simply put, Vargas has been an excellent hitter thus far in 2026. There’s a reason he was an All-Star earlier this month (and hit the only home run of the All-Star Game!). In addition, his 60/70 wOBA/xwOBA gap implies that he’s actually been quite unlucky. Refresher stat course for those who need it: wOBA (Weighted On-Base Average) is an all-around measure of a hitter’s value, with built-in weights for different outcomes (strikeout, walk, double, etc.) that match their run values. Meanwhile, xwOBA (eXpected wOBA) utilizes contact quality and quantity to estimate its x-namesake. It’s dressed up to sound all fancy, but all it’s trying to tell you is: based on what he’s doing, how well should this guy be hitting?

How good of a hitter does Vargas appear to be this year? Here are the other guys with a 70 or higher xwOBA LASR: Yordan Alvarez, James Wood, Juan Soto, Mike Trout, Shohei Ohtani, and Aaron Judge. That list is just absurd. If you asked 1000 baseball fans before the season who the best 7 hitters in baseball would be in 2026, approximately 1 of them would have mentioned Miguel Vargas and that respondent would have been Miguel Vargas’s mother. Or maybe Pope Leo.

Let’s take a look at his year-by-year improvements.

Since he debuted (and generally before he debuted too), Vargas has had above-average plate discipline. Even in his Dodger days and the horrible first couple months with the White Sox, he was walking at an admirable clip, especially for a youngster. That has continued this year, but is now being complemented by a noteworthy growth to his power. Never cracking league average in isolated power before this season, he’s now far above it: his 65 ISO LASR suddenly has him looking like one of the premier power hitters in the league.

His contact ability, usually measured by batting average, has climbed steadily from the doldrums of its past, but is still slightly below average. However, as we can see on the LASR graph above, most of his poor luck this year comes from here. There is a 10 point gap between his AVG and xAVG LASRs, and his 35 BABIP is low enough to expect a bounceback. With that in mind, he does generally have a low BABIP each year, perhaps due to his flyball tendency, so it might be something he just has to live with. Still, a slight increase wouldn’t be surprising.

Let’s continue taking a peek under the hood.

Whenever a player enjoys a season like this, there are always doubters. So-called “fans” who salivate at the opportunity to point out that it’s a fluke, there’s no way it’ll last, and the team should sell high while they can before the player burns out and crashes into the 1-year-wonder graveyard. I’m happy to report that those high and mighty pundits have absolutely no evidence to point to if they want to make those claims about Vargas.

Whiff rate lines up with strikeout rate? Check. Chase rate and walk rate? Check. Underlying power metrics like xISO, Brl%, EV, and LA? When asked whether the homers and extra bases look legit, every single one responds in the affirmative.

I want to specifically marvel at the difference between his Swing% and Z-Swing% LASR. The ability to exhibit patience on pitches outside the zone (i.e. a 65 -Chase% LASR and 45 overall Swing% LASR) while aggressively attacking those in the zone (i.e. a 60 Z-Sw% LASR) is an oft-overlooked player quality that’s on my list to look into further. I am aware of Baseball Prospectus’s stat “SEAGER” (SElective AGgression Engagement Rate) which measures this ability; perhaps someday it – or something like it – will find its way into LASR. For now, I’ll be content simply saying: Hey Miguel, I notice you’ve been making great swing decisions. Keep it up, bud.

Conclusion: Miguel Vargas is good, and will probably stay good

All signs point toward this breakout being real, and if Vargas can keep playing like the player he has been this year, he has a long and successful career ahead of him. I’m not the type of baseball knower capable of analyzing minute physical characteristics of a batter’s swing and describing why tiny adjustments to tiny motions result in grand improvements (here’s a James Fegan article on Vargas that does do that), but I am getting better at recognizing true developments where they can be dissected in the data. At least, I’m telling myself I am. Guess we’ll see.

No matter what the end of this White Sox season looks like, it will most likely be considered a success for both the team and Vargas. And on a personal level, the Vargas family can already look back on their decision of a lifetime – several lifetimes – and pat themselves on the back for believing in their 14-year old’s dreams. He’s made it, and he’s got a long way left to go.

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The Incredibly Flawed Best Player in Baseball Bracket (Introduction)

June IFBPIBB update: Judge, Alvarez, other superstars eliminated; Cy Young favorites square off this month

While I didn’t incorporate LASR into those first two articles, I’m going to bring it onboard with this one. The rules of the bracket don’t utilize LASR at all, but I want to demonstrate its usefulness in analyzing player performance as much as possible. And a reminder, it’s now publicly available so you can generate LASR graphs too!

Links and screenshots of the July 1st bracket updates can be found at the bottom of this article. I know last time around I gave them to you early, but this time I’m switching things up and drawing out the drama of the matchups one by one. Enjoy!

#1 Shohei Ohtani (2.09 June fWAR) defeats #33 Brandon Lowe (0.22 June fWAR)

It feels like this whole thing is destined to come down to “Is Ohtani going to win, or will someone else surprise us?” Even though the two-way superstar has set the league aflame every year in the 2020s, the burn may be at its hottest this season. That’s largely due to full health and dominance on the mound for the first time since 2023 while his offense continues its elite pace. His pitching excellence has come from a typical above average strikeout rate, along with a new and improved ability to keep batted balls on the ground: his 65 GB% LASR is eclipsing his career high by 10 points.

#49 Jordan Walker (0.15) defeats #48 Christian Walker (0.05)

Jordan didn’t win the Battle of the Walkers with a compelling victory, merely managing not to struggle as much as his opponent (who wasn’t the only Christian to face self-afflicted adversity in June). Overall, though, he’s been enjoying an incredible turnaround season that could represent a significant step forward in the Cardinals’ rebuild. Strikeouts were his main issue in 2025, and while his improvement in that area is encouraging, I’m wary of the Whiff% LASR that has stayed at 35. He’s got one of the fastest swings in baseball and can clearly do damage as well as anyone – as we all saw in the Home Run Derby – but time will tell if his newfound success can be sustained at this level.

#57 Wilyer Abreu (0.39) defeats #40 James Wood (0.33)

After being traded at the 2022 trade deadline as part of a prospect package for a homegrown key contributor to a late 2010s World Series-winning team, Wilyer Abreu has proven to be a nice find for the Red Sox. What, did you think I was talking about Wood? After a scorching start at the plate, Abreu has cooled down and settled into his typical near-average offensive production. His strikeout rate has improved but at the expense of a decreased home run rate, which seems to be the result of less pull in his approach. His glove has been magnificent, as always, assisting this surprising victory over ol’ Jimmy Lumber.

#56 Pete Crow-Armstrong (3.07) annihilates #41 Carlos Cortes (-0.71)

Principal Component Analysis is only in this bracket in the first place because his defensive wizardry kept his production afloat early in the year. Since then, he has made that #56 seed look like a typo. He now dons silver to Shohei’s gold on the overall fWAR season leaderboard, making good use of a new brake system he seems to have installed in his hands. His previously league-worst chase levels have trended in a promising direction, leading to a 55 BB% LASR that would’ve been impossible to imagine coming into the season. If Ohtani shows any signs of being human, Crow-Armstrong might have an MVP on his shelf sooner than later.

#29 Ozzie Albies (0.34) defeats #4 Ben Rice (-0.22)

Looking at Ozzie’s LASR stats has provided me with a Eureka moment. This may seem obvious to even oldschool fans, but somehow I’ve only just now discovered it for myself: a high swing rate helps with strikeout prevention. Albies has always avoided strikeouts, with a 55 or higher -K% LASR in 9 of his 10 seasons. However, he has never had a -Whiff% LASR above 50. I’m used to these metrics lining up more than that, so I’m hypothesizing – and maybe testing someday with more rigor – that for hitters with high swing rates, the ball gets put in play earlier and less 2-strike counts leads to a better K% than the whiffs would imply.

#20 Parker Messick (0.93) defeats #52 Randy Arozarena (0.42)

The Cleveland Pitching Factory continues its endless churn. Messick got a decent taste of big league action at the end of last season but maintained rookie eligibility for this year by literally one out. His prospect pedigree centered around his excellent command, so naturally that’s the only aspect of his game that hasn’t quite crossed the league average threshold this year. Instead, he has racked up a decent share of strikeouts and has limited hard contact at an impressive rate. He may be getting a little lucky, but he’s one of only two pitchers left in this bracket, which obviously means he’s a Top 2 pitcher in the sport. Obviously.

#5 Bobby Witt Jr. (1.54) defeats #28 Colson Montgomery (0.42)

For a few years now, it has seemed obvious that the AL MVP award will one day have Bobby Witt Jr.’s name on it. With Aaron Judge now missing significant time due to his rib injury, 2026 looks like a good time for Witt to take advantage of his opportunity. Much like the last two seasons, his superb defense and baserunning have carried him to his lofty heights – and though he’s been good at the plate, he’s actually underperforming according to his expected stats, with an xwOBA, xAVG, and ISO LASRs all 10 points above their real counterparts. Already leading the AL in fWAR, Witt looks poised to be even better in the second half.

#21 Corbin Carroll (0.83) defeats #12 Tarik Skubal (0.08)

A speed demon who looks like he’s running from a pack of wolves every time he rounds second, Corbin Carroll is having a typical Corbin Carroll season. He’s got a decent bat with an eye for the gaps, a plus right field glove, and wheels a’spinnin’ on the basepaths. His signature continues to be the triple, where he’s already in double digits and leading the league by 3. If he tops the NL list again this year, he would tie Lance Johnson (1991-1994) for the most all-time consecutive seasons leading the league in triples with 4. Sure it’s not the first number that jumps out on the stat sheet, but there’s nothing more exciting than a good old fashioned triple. I thank Carroll for providing more than his share.

#63 Brayan Rocchio (0.36) defeats #34 Jose Soriano (-0.04)

The second-to-last seed survives another round! All hail bracket chaos. Brayan Rocchio is a solid defensive shortstop with improving contact skills at the plate, but there’s no way I can dress this up as anything but a funny quirk of the format. The Hitter and Pitcher of the Month for the American League in March/April (when the stats that this bracket is based upon were accrued) were Yordan Alvarez and Jose Soriano, respectively. Facing those two back-to-back, this unsung Guardians infielder had a Herculean task ahead of him – and now, two months later, he has somehow survived it.

#18 Max Muncy (LAD) (0.95) defeats #15 Mike Trout (0.19)

The elder Max Muncy is now 35 years old, a full 8 seasons removed from a late-blooming breakout, and remarkably still near his offensive peak. However, a crack in the armor may be quietly forming. Always known for his impeccable plate discipline, Muncy’s BB% LASR has dipped down to 55 after a consistent and elite run from 2018 to 2025 in which it was 65 or higher each year, including a 75 just last season. He’s chasing more, his P/PA LASR is league average for the first time ever, and he’s swinging more than he ever has. Maybe he’s trying to take advantage of pitchers trying to sneak gimmes past him, maybe he’s losing his feel for the zone. Maybe it’s both. Or a random anomaly. How can any of us pretend to know?

#26 Andy Pages (0.29) defeats #7 Elly De La Cruz (0.04)

On a team full of stars, an important corner of the Dodgers roster has been claimed by an oft-overlooked, severely-underpaid-relative-to-teammates, homegrown young outfielder. Andy Pages hasn’t quite kept up the pace of his incredible hot start, but he’s been a reliable contributor on the quest for a three-peat. Most importantly, he did not spend most of June on the Injured List like De La Cruz did. Though he hasn’t been exemplary in any individual element of the game, Pages has been an all-around good player and an essential cog in the Dynastic Dodger regime.

#10 Kevin McGonigle (1.16) defeats #42 Michael Harris II (0.23)

Entering the season as a consensus top 5 prospect, Kevin “Professor” McGonigle (please help it catch on) delivered on his promise right from day one. He’s an old school top of the lineup hitter, combining excellent bat-to-ball abilities with knowledge of the strike zone and proficiency on the basepaths. The best case I can make for the Professor’s long-term outlook is his selective aggression, a rare find in a player this young. He’s got a 40 Swing% LASR and a 55 Z-Sw% LASR, a combination that typifies players who take advantage of hittable pitches and let the rest fly by. McGonigle is one of 3 qualified hitters with a 15 point gap there, joining Miguel Vargas and Freddie Freeman.

#30 Cristopher Sanchez (0.91) defeats #3 Cam Schlittler (0.34)

Now in his second season as a bona fide ace, Cristopher Sanchez has maintained his transition from groundball artist to overall dominant pitcher. He’s still the top wormkiller in the majors – the only pitcher (starter or reliever, qualified or not) with an 80 LASR in both -LA and GB% – and his control remains decidedly above average. He completed the pitcher skill trifecta last year with a nice leap in strikeout rate, and it has slightly risen again this year. This matchup was my favorite going into June, and even though Schlittler is still leading the pack in the AL Cy Young race, Sanchez took this one handily as he battles Jacob Misiorowski for the NL award.

#19 Otto Lopez (1.41) defeats #14 JJ Wetherhold (0.84)

“Oh wow, this guy came out of nowhere, he wasn’t on anybody’s radar preseason.” WRONG! You’re talking to a founding member of the Otto Lopez bandwagon. He was not only featured in my “Who’s Gonna Get Better?” article from before the 2026 season, but was also one of my picks in a Call to the Pen breakout series I conducted prior to the 2025 season. I needed some self-convincing this March to double down on my confidence in Lopez, but it seems to have paid off. No no no, he’s not overperforming his expected stats, I don’t know what you’re talking about. He’s a cornerpiece to the upcoming Marlins dynasty and I will accept no prophecy that says otherwise.

#6 Matt Olson (0.69) defeats #38 Davis Martin (0.30)

This is awkward: it appears Matt Olson has missed the memo that 2026 is an even number. Olson has strangely been much more productive in odd years ever since he debuted, posting a 60 or higher wOBA LASR in 2017, 2019, 2021, 2023, and 2025. The only seasons where that value has been 55 or lower? Yeah, that’d be 2016, 2018, 2020, 2022, and 2024. He’s currently on pace to break the pattern, thanks to a continuation of exemplary power numbers. His walk rate has fallen to a league average 50 LASR for the first time since 2019, which may be cause for concern, but he’s getting more pitches in the strike zone so he might as well whack ‘em.

#22 Xavier Edwards (0.23) defeats #11 Shea Langeliers (-0.07)

If I had a nickel for every contact-oriented Marlins middle infielder surprisingly flying above his fins, I’d have two nickels. Which isn’t a lot, but it’s weird I have two. Edwards and the aforementioned Lopez have made a compelling pair this year, shocking the other participants in the shapeshifting NL East with a great first half. Compared to Lopez, Edwards has much better plate discipline, less power, and less value outside of the batter’s box. Though they may both experience a descent in the near future, there’s hope it won’t be too steep. There’s a chance they meet up in the bracket next month, which would be the first teammate matchup since... Otto Lopez and Liam Hicks faced off back in May.

The Bracket

Here’s a link to a PDF of the updated bracket. Here are the June hitter stats and June pitcher stats that were used to determine the winners. Below are screenshots of the four quadrants of the updated bracket.

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Here it is: the interactive LASR graph generator

(Note: it works on both mobile and desktop, but is easier to use on desktop.)

I have also, today, started a YouTube channel in which I’ve posted a walkthrough of the site. I don’t know how often I’ll use the channel, but the option is at least open for me to post more LASR and/or baseball videos over there. We’ll see.

For those who may be stumbling upon this project for the first time, below are examples of the LASR graphs that I’m talking about. They provide helpful context to baseball stats by putting them all on the same scale, modeled after the traditional 20-80 scouting scale. By comparing players to their peers based on the means and standard deviations for each stat in each season, LASR intends to help its users better understand player strengths and weaknesses. More information can be found in my LASR Explainer article.

UPDATE 7/31/26: LASR Scouting Report Cards

I’m excited to announce a new, third mode has been added to the LASR graph generator application! I call this one a Scouting Report Card. I consider it a step up from the Multi-Stat Single-Season mode, with stats separated into categories rather than presented altogether in a line. More information can be found in this post about the update.

Unlike most of my articles, I’m going to keep this one short and sweet to avoid taking away from your graph-generating playtime. I’ve tried to make the site rather self-explanatory and easy to use, with the video walkthrough as a potential go-to resource to help you figure out any features you’re confused by. Of course, this is just the first iteration, so if I’ve overlooked anything or if you notice anything breaking, please feel free to let me know!

I intend to do a lot more with LASR, as I’ve droned on and on about here before, and I’m all amped up about all the different directions I can turn now that this is finally out there. If you’d like to help me on this journey, I’d greatly appreciate your support in the form of a subscription. If you’d prefer to watch from afar, I totally get it; thanks for being here in the first place!

Have fun with your LASRs, baseball fans! ✌️

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With May in the rearview, our first month of head-to-head contests has now concluded. And boy, are there some wild results. Later in the season, I will likely have something to say about each matchup, but there were 32 of them here and I’m really trying my best not to write 3000 words in every article (I’ve managed to keep this one at only 2000, success!). Some victories will go unmentioned this time around, but if these guys truly care about this highly esteemed bracket which could certainly affect their Cooperstown chances and overall eternal legacy, they’ll make me talk about them sooner or later.

Before notes on the notables, however, let’s just jump into what we’re all here for: updates to the bracket. Below are screenshots of the four quadrants as of June 1st. Yes, I know it is now a full week later. It’s been a busy month. Here is a PDF version of the full bracket; as I said last month, I encourage zooming in unless you’re a hawk. Here are the hitter fWAR totals and pitcher fWAR totals (along with many other stats) for the month of May, which were used to determine these results.

Close Contests

While some defeats were comfortable and convincing, others came right down to the wire. This seems like as good a place as any to reiterate: I understand WAR totals are merely approximations of value, and a cautious approach should be taken when using WAR for high-stakes player evaluations. Much like a successfully assembled tent, however, the stakes here couldn’t be lower. With that in mind, let’s check out some of the players who barely eked out punching their tickets into June:

• Christian Walker (0.3868) defeats Max Fried (0.3860). When establishing my rules last month I anticipated having to use the FanGraphs data export feature, which provides several more decimal places for fWAR calculations than you’ll see on the on-site leaderboard tables. A few tight races proved me right by requiring a hundredths place, but those were wide margins compared to this one. Walker, who is enjoying a decent bounceback season after a disappointing first year in Houston, had an up-and-down month and eventually landed so close to Fried, who was injured after only 3 starts in May, that I needed a jaw-dropping four decimal places to settle the score.

• Shea Langeliers (0.76) defeats Josh Jung (0.75). Quick, I’m thinking of a right-handed 28-year old from Texas who was drafted 8th or 9th overall in 2019, debuted in 2022, became an established starter in 2023, is currently having the best season of his career for an AL West team, and has averaged 0.018 fWAR per game in his career. If you said Langeliers, you’re wrong, I was thinking of Jung. And if you said Jung, you’re also wrong, Langeliers was my guy. I never expected to find this many similarities when looking deeper into them, but now it makes sense why this race came down to a photo finish.

Superstars Crumble Under the Pressure

No matter how good they are, some guys just can’t handle the IFBPIBB.

• Aaron Judge (0.60) loses to Pete Crow-Armstrong (1.40). Judge will miss at least the next month or so with a rib injury, but that won’t cost him in this bracket because the 3-time MVP has already been eliminated. The generationally talented slugger has had outrageous months in previous seasons that would have doubled Crow-Armstrong’s impressive 1.4 showing, but it wasn’t to be this time around. One of the most obvious candidates to win the whole thing, gone already.

• Yordan Alvarez (0.61) loses to Brayan Rocchio (1.01). A #2 seed falling to a #63 seed is what meaningless brackets are all about, baby. Alvarez was the AL Player of the Month in April and still had a decent month of May, but he went through a rough stretch and doesn’t provide much value in the field or on the bases. Rocchio’s offensive production is shaped quite differently from Alvarez’s, but overall they put up comparable months at the plate (134 wRC+ for Rocchio, 140 for Alvarez) and in the end Rocchio’s defense and baserunning made this upset possible.

• Vladimir Guerrero Jr. (-0.08) loses to Ozzie Albies (0.08). Yikes. After an excellent (albeit powerless) opening to the season, Guerrero slumped to a 79 wRC+ in May with only 1 home run. The face of the Blue Jays franchise who tore through the competition last October simply has not arrived yet in 2026. Albies was right around replacement-level himself, but managed to barely stay in positive territory and got the awkward victory.

• Jose Ramirez (0.62) loses to Max Muncy (LAD) (0.72). This battle of long-tenured third basemen did not go to the surefire Hall of Famer, as J-Ram put up a forgettable month by his standards. I didn’t look into this for every matchup, but I did for this one: on the last day of May, Muncy went 2-for-4 with a homer in his last at bat. He gained about 0.16 fWAR that day while Ramirez was right around 0.00 in his last game, so it seems Muncy clinched this win with his very last swing.

• Kyle Schwarber (0.60) loses to JJ Wetherholt (0.71). Much like the Alvarez-Rocchio contest, superior defensive value outweighed high voltage power here. It was a typical month for Schwarber, posting a strong 143 wRC+ but almost all of it in the highly-penalized DH spot. Wetherholt’s bat was a bit below average at 93 wRC+, but his fWAR defensive value for the month was 6th best in baseball and that sneaky skill gave him the edge.

Tough-Luck Losses

In the London Marathon this April, humanity celebrated its first official sub-2 hour marathon. Sabastian Sawe limboed under the previously unbroken barrier with a total time of 1:59:30... and then merely 11 seconds later, Yomif Kejelcha also crossed the finish line, also under 2 hours. Imagine that. Kejelcha accomplished something that day that when he woke up in the morning had never been done before. He put in a historic performance, one of the greatest feats of mankind. And yet, he didn’t win the race.

Sometimes, no matter how impressive your own showing is, all that matters is who you’re up against.

• Bobby Witt Jr. (1.74) defeats Nick Kurtz (1.42). Kurtz put all whispers of a sophomore slump to rest in May, leading all qualified hitters with a 183 wRC+ and stringing together a 48-game on-base streak that tied Mark McGwire for the longest in franchise history until it snapped on May 26th. His fWAR for the month would have defeated anybody in this bracket except Cristopher Sanchez and... Bobby Witt Jr., the guy he was facing. How heartbreaking. Kejelcha, meet Kurtz.

• Colson Montgomery (1.12) defeats CJ Abrams (1.06). I’m really enjoying the coincidental matchups of players with similar defining characteristics. Montgomery and Abrams are both young and exciting shortstops on rebuilding teams overperforming their season expectations so far. Pitting these two against each other in a “Who would you rather have on your team?” exercise would be a really fun debate. Montgomery barely grabbed this win, showcasing defensive wizardry that outweighed Abrams’s superior bat, but the conclusion of May won’t stop me from continuing to compare these budding superstars.

• Xavier Edwards (1.13) defeats Joe Ryan (0.99). In case nobody has told you to do so yet, go check out how the Marlins’ middle infielders are doing in 2026. Alongside Otto Lopez, Edwards has been excellent on both sides of the stat sheet this year, giving Miami a promising pair to build their next core around. Meanwhile, Joe Ryan is looking at his 1.73 ERA and 2.13 FIP in May and saying “What more do I have to do??”

• Corbin Carroll (1.05) defeats Munetaka Murakami (0.92). The light tower power of Murakami has been the primary highlight of the surprising surge into contention for the White Sox this year. Unfortunately, a hamstring injury suffered on May 29 cost him a couple games at the end of the month, in which he could have easily hit a dinger or two to make up the difference here. Since he’s likely out for the entirety of June, though, it’s probably for the best that Carroll is moving on instead.

Easy Wins

Sometimes you have to fight tooth and nail for a chance at toppling your mighty opponent. Sometimes, you just have to sit there and watch them trip over their own feet.

• Tarik Skubal (0.00) defeats Taylor Ward (-0.21). On May 4th, in what would have been his first start of the month after a great April, Skubal was scratched from his start in what was initially described as a precautionary move. What followed was quite disastrous: Skubal required surgery to remove bone chips, and though he underwent an experimental procedure that may speed up his recovery, the Tigers spiraled into a 6-22 record in May. Now, once he returns, the back-to-back Cy Young winner may be on his way out of town. It’s too bad, but the silver lining is, thanks to Ward’s subpar month, Skubal is somehow still in this bracket despite not pitching at all in May.

• Carlos Cortes (0.02) defeats Daniel Schneemann (-0.15). Cortes followed up an unexpected hot start to the season with a return to earth, serving as a mere replacement-level player for the month. Lucky for him, Schneemann’s season has followed a similar path, just a little worse. I want to take this opportunity to tell anyone who doesn’t already know that Cortes is the first recorded switch-throwing fielder in MLB history. He plays third base as a righty and mans the outfield as a left-handed thrower. Pretty cool, and not talked about enough.

Most Intriguing June Matchup

Later in the year, once the bracket has been whittled down to a select few, I’ll be tracking individual head-to-heads with intense scrutiny. Right now there’s a few too many to be able to check on every day without having to dedicate a significant portion of my morning to it. Instead, I’m going to select just one, and I gotta say it’s a banger.

• Cristopher Sanchez vs. Cam Schlittler. These two are arguably the current frontrunners for each league’s Cy Young Award, and they’ve just so happened to line up against each other in June. Schlittler has been dominant all year in his first full MLB season, grabbing the #3 seed at the end of April. Sanchez was merely good to start out the year, settling into the #30 seed, but absolutely shut everybody down in May. He didn’t give up a single run the entire month, tossing 39 scoreless innings as part of a scoreless streak that ended up 5th-longest all time. Even though he’s the lower seed, I’d say he’s the favorite in this matchup – but not by much. Buckle your seatbelts, folks, we’re set for a wild ride.

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A baseball play represents a singular moment in time, a snapshot where a series of recognizable actions has been captured and preserved for eternity – or at least until they decide to put this whole internet thing out of its misery. However, contrary to their monotonous representations in the game logs, we all know not every moment is created equal. The fan who receives the same level of excitement from reading each of “Bobby Richardson lines out to shortstop” and “Bill Mazeroski hits a home run to left field” (the first and last plays in Game 7 of the 1960 World Series) has eith…

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I am, however, a big fan of the bracket. No, not the tournament. Not the games. The bracket itself. The structure is so visually pleasing that whenever I stumble upon a bracket, any bracket – even if the teams are completely inconsequential to me – I will always take a moment out of my day to study it. It could be the college basketball tournament or the NHL playoffs or my neighbor’s cousin’s drunken backyard cornhole extravaganza. I don’t care. Give me a bunch of numerically ranked words on symmetric lines with the victors listed and paired up on the following lines, and I will nod and say thank you every single time.

Of course, the MLB playoff bracket is a natural favorite of mine, simply because it consists of teams I have a great deal of familiarity with. However, there are two issues preventing me from accepting this as the best bracket the sport is capable of:

• The number of teams is not a power of 2. Back when only 2, 4, or 8 teams made the playoffs (which was true for every MLB season until 2011) we had perfect balance and symmetry. With the expansion of the playoffs to 10 and now 12 teams, though, byes are necessary. Sure, it may be a good thing that superior regular season teams are now more rewarded for their efforts, but the aesthetic charm of the bracket has been tarnished.

• It’s just too freakin’ small. 12 teams? C’mon man, that’s boring as hell. Way too easy to follow. I want to have to put my finger on the paper and carefully trace a team’s expedition across the page and feel the rush of potentially getting lost along the way.

Recently, it was announced that March Madness will expand from 68 to 76 teams. When I started writing this article I had no idea that would happen, and to be honest I had forgotten about the previous total being 68, since those first play-in games are usually treated as a little side quest, separate from the rest of the tournament. I know there are several other factors to consider in the benefits and drawbacks of the 68- or 76-team setups, but for the sake of pure bracket beauty I’d like to go on record saying they’re both terrible.

Any reasonable bracket enthusiast will agree that the ideal number of participants is 64. It’s a significant enough number that there’s a plethora of exciting matchups and the ultimate winner must make a truly heroic journey, but it’s not too overwhelming an amount to keep track of. And most importantly, it’s a power of 2. Nobody gets to arrogantly waltz into the second round by virtue of their past performance. Though there are theoretically uneven matchups – seed #64 vs. #1 may feel like an adorable salamander facing off against a terrifying dragon – the number of wins required to go all the way is satisfyingly equal. And who knows, maybe that salamander has a better fastball than we assume.

Rob Manfred has yet to respond to my requests for 34 expansion teams and every team making the playoffs, so my pursuit of a 64-member MLB bracket must take a more creative route. Instead of using teams, then, how about players? I’ve heard there’s at least 64 baseball players out there. So, in my limited free time where I surely could have been working on more important tasks but how dare you point that out, I created this little game that I’m going to keep track of throughout the 2026 season. I am proud to announce the historic introduction of the first annual Incredibly Flawed Best Player in Baseball Bracket.

The Rules:

• At the end of April, the top 64 players by total fWAR will earn their spots in the bracket. If there are ties to one decimal place, that’s okay – exporting data from FanGraphs allows you to see several decimal places out. Yes I know WAR is an estimation and even WARs within 1 or so are essentially rounding errors, but... did I mention this is supposed to be incredibly flawed? Get over it.

• At the end of each subsequent month, the winners of each matchup (1 vs. 64, 2 vs. 63, etc.) are determined by the amount of fWAR the player accumulated in that month. An exact tie is more likely here, since it’s possible a duo of players could each be injured for an entire month and achieve exactly 0.0 fWAR. In this case, the previous month’s fWAR will be used to determine the winner. This is less likely to be 0.0 since the player will have won that matchup, but if the previous months are also 0.0 on both sides, I’ll just keep going back in time until there’s a month either player actually played.

• By the beginning of September, there will be 4 players left. With only the one month remaining, the month must be cut in half. The final day of the season is September 27th, so the fWAR accumulated between September 1st and 13th will determine the winners of the semifinals, and the rest of the season after that will represent the finals. So for the last couple weeks of the season, as pennant races and wild card chases swirl around us in a wild frenzy, I will also be madly following the performances of 2 otherwise completely unconnected players. Or maybe they will be connected in some manner! Who knows? That’s the fun of it.

• When the regular season draws to a close, as Commissioner of this highly esteemed contest I will crown the inaugural Winner of the Incredibly Flawed Best Player in Baseball Bracket. You all will be encouraged to congratulate this player as much as you would for an MVP award, and I will certainly appreciate your support in getting this honor recognized by Major League Baseball, the Elias Sports Bureau, FanGraphs, Baseball Reference, and all other reputable baseball data sources. Time will only tell just how much weight this will carry on a Hall of Fame resumé, but I predict vigorous debates on the topic for decades, possibly centuries, to come.

In creating this pointless-but-fun-to-me-so-who-are-you-to-stop-me game, I have leaned into what makes brackets awesome: unpredictability. Here, there is no guarantee that the champion will have actually been 2026’s best player in baseball. The tournament is rooted in chaos, designed around its flaws, proud of its defects. It represents everything we love about baseball.

With the rules established and the month of April now in the rearview, it’s time to reveal the official bracket. Below are zoomed-in screenshots of the four quadrants, since the full 64-player image would have been impossible to read when embedded into this article. If you’d like to see the fWAR totals to all the decimal places you could ever want, here is a CSV file from my WAR leaderboard export. If you’re interested in seeing the full glorious bracket, here is a link to the PDF. To aid in your exploration of it, I suggest zooming in and out, or possibly enhancing your eyesight to superhuman levels, or maybe even printing it out as a gigantic poster and hanging it up above your bed.

Ladies and gentlemen, let the meaningless games begin ⚾

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Since the list is so long, I am only including very brief definitions of each stat. If you’d like to learn more, I suggest Googling the full name and checking out primary resources like FanGraphs or Baseball Savant.

“Category” attempts to simplify things by identifying the aspect of the player profile that this stat is most associated with. For hitters, there are 8 categories: Overall, Offense, Hitting, Contact, Power, Eye, Baserunning, and Fielding. For pitchers, there are 6 categories: Overall, Stuff, Control, Contact Quality, Luck, and Miscellaneous.

The order is generally based on relevance and strategic groupings. I know glossaries are supposed to be ordered alphabetically, but it felt all over the place conceptually. I suggest using Ctrl-F (or “Find on page”) to easily find the stat you’re looking for.

All stats that aren’t expressed as rates in their intial form have been translated to rates prior to LASR calculations.

Statistics for Hitters

fWAR (fWAR/PA) FanGraphs Wins Above Replacement (per Plate Appearance)

• Brief Description All-encompassing measurement of player value.

• Availability All Seasons

• Category Overall

Off fWAR Offense Value

• Brief Description Overall offense value contribution to fWAR; consists of Bat and BsR for most seasons.

• Availability All Seasons

• Category Offense

Bat fWAR Batting Value

• Brief Description Hitting value contribution to fWAR; combines with BsR to create Off.

• Availability All Seasons

• Category Hitting

BsR fWAR Baserunning Value

• Brief Description Baserunning value contribution to fWAR; combines with Bat to create Off.

• Availability Since 1886

• Category Baserunning

wRC+ Weighted Runs Created Plus

• Brief Description Overall hitting value based on wOBA with ballpark/league adjustments.

• Availability All Seasons

• Category Hitting

wOBA Weighted On-Base Average

• Brief Description Overall offensive value; weights outcomes based on run value.

• Availability All Seasons

• Category Hitting

xwOBA Expected Weighted On-Base Average

• Brief Description What wOBA was expected to be based on underlying metrics.

• Availability Since 2015

• Category Hitting

AVG Batting Average

• Brief Description Hits divided by at-bats.

• Availability All Seasons

• Category Contact

OBP On-Base Percentage

• Brief Description Times on base divided by plate appearances.

• Availability All Seasons

• Category Eye

SLG Slugging Percentage

• Brief Description Total bases divided by at-bats.

• Availability All Seasons

• Category Power

OPS On-Base (Percentage) Plus Slugging (Percentage)

• Brief Description OBP plus SLG.

• Availability All Seasons

• Category Hitting

ISO Isolated Slugging Percentage

• Brief Description SLG minus AVG; measures power ability without conflating it with contact ability (like SLG does).

• Availability All Seasons

• Category Power

HR% Home Run Percentage

• Brief Description Percentage of plate appearances hitting a home run.

• Availability All Seasons

• Category Power

BB% Base on Balls (Walk) Percentage

• Brief Description Percentage of plate appearances drawing a walk.

• Availability All Seasons

• Category Eye

-K% Strikeout Percentage (Negated)

• Brief Description Percentage of plate appearances avoiding a strikeout.

• Availability All Seasons

• Category Contact

BABIP Batting Average on Balls In Play

• Brief Description AVG without including strikeouts and home runs. Can be used as a measure of luck.

• Availability All Seasons

• Category Contact

-Chase% Chase Percentage (Negated)

• Brief Description Percentage of pitches outside the strike zone not swung at.

• Availability Since 2002

• Category Eye

-Whiff% Whiff Percentage (Negated)

• Brief Description Percentage of pitches not swung and missed at.

• Availability Since 2002

• Category Contact

EV Average Exit Velocity

• Brief Description Average speed of batted balls off the bat.

• Availability Since 2015

• Category Power

LA Average Launch Angle

• Brief Description Average angle of batted balls off the bat.

• Availability Since 2015

• Category Power

Brl% Barrel Percentage

• Brief Description Percentage of batted balls classified to have been hit on the bat barrel based on exit velocity and launch angle.

• Availability Since 2015

• Category Power

maxEV Maximum Exit Velocity

• Brief Description Speed of hitter’s hardest hit batted ball.

• Availability Since 2015

• Category Power

HH% Hard Hit Percentage

• Brief Description Percentage of batted balls hit with an exit velocity of at least 95 MPH.

• Availability Since 2002

• Category Power

xAVG Expected Batting Average

• Brief Description What AVG was expected to be based on underlying metrics.

• Availability Since 2015

• Category Contact

xSLG Expected Slugging Percentage

• Brief Description What SLG was expected to be based on underlying metrics.

• Availability Since 2015

• Category Power

xISO Expected Isolated Slugging Percentage

• Brief Description What ISO was expected to be based on underlying metrics.

• Availability Since 2015

• Category Power

BtSpd Average Bat Speed

• Brief Description Average speed of a hitter’s competitive swings.

• Availability Since 2023

• Category Power

FSw% Fast Swing Percentage

• Brief Description Percentage of a hitter’s swings that are tracked at 75 MPH or faster.

• Availability Since 2023

• Category Power

SwLgth Swing Length

• Brief Description Distance traveled by the head of the bat before making contact.

• Availability Since 2023

• Category Power

Sq/Sw% Squared-Up per Swing%

• Brief Description Percentage of swings where contact with the ball is “squared up,” which is defined as the contact generating at least 80% of total possible exit velocity. Note: I have included Squared-Up per Swing and not Squared-Up per Contact because the former has stronger correlations with what it’s measuring, and I’d like to keep things simple-ish wherever I can.

• Availability Since 2023

• Category Contact

Bl/C% Blast per Contact%

• Brief Description Percentage of swings with contact where the ball is squared up on a fast swing. Note: I have included Blast per Contact and not Blast per Swing because the former has stronger correlations with what it’s measuring, and I’d like to keep things simple-ish wherever I can.

• Availability Since 2023

• Category Power

AttAng Attack Angle

• Brief Description The average vertical angle at which the sweet spot of the bat is traveling when contact is made.

• Availability Since 2023

• Category Power

IdAtAn% Ideal Attack Angle Percentage

• Brief Description Percentage of batted balls hit at an Attack Angle between 5 and 20 degrees.

• Availability Since 2023

• Category Power

FB% Flyball Percentage

• Brief Description Percentage of batted balls classified as fly balls.

• Availability Since 2002

• Category Power

LD% Line Drive Percentage

• Brief Description Percentage of batted balls classified as line drives.

• Availability Since 2002

• Category Contact

-IFFB% Infield Fly Ball Percentage (Negated)

• Brief Description Avoidance of fly balls that remain in the infield (aka, pop-ups).

• Availability Since 2002

• Category Hitting

IFH% Infield Hit Percentage

• Brief Description Percentage of ground balls that become hits.

• Availability Since 2002

• Category Baserunning

HR/FB Home Run per Fly Ball Rate

• Brief Description Percentage of fly balls that were home runs. Can be used as a measure of luck.

• Availability Since 2002

• Category Power

Swing% Swing Percentage

• Brief Description Percentage of pitches the batter swung at.

• Availability Since 2002

• Category Eye

PtchAr (-Zone%) In-Zone Pitch Percentage (Negated)

• Brief Description Percentage of pitches to the batter that were not in the strike zone.

• Availability Since 2002

• Category Eye

ZSw% Zone Swing Percentage

• Brief Description Percentage of pitches inside the strike zone the batter swung at. Note: OSw% is Chase%.

• Availability Since 2002

• Category Eye

ZSw/Sw Zone Swing Percentage divided by Swing Percentage

• Brief Description Ratio of swings at pitches in the strike zone to overall swings. Measures a hitter’s recognition of hittable pitches.

• Availability Since 2002

• Category Eye

ZCon% Zone Contact Percentage

• Brief Description Percentage of swings on pitches inside the strike zone the batter made contact with.

• Availability Since 2002

• Category Contact

OCon% Outside-zone Contact Percentage

• Brief Description Percentage of swings on pitches outside the strike zone the batter made contact with.

• Availability Since 2002

• Category Contact

Pull% Pulled Batted Ball Percentage

• Brief Description Percentage of batted balls that were hit to the batter’s pull side.

• Availability Since 2002

• Category Power

Oppo% Opposite-Field Batted Ball Percentage

• Brief Description Percentage of batted balls that were hit to the batter’s opposite field side.

• Availability Since 2002

• Category Contact

CStr% Called Strike Percentage

• Brief Description Percentage of pitches to the batter that were called strikes.

• Availability Since 2002

• Category Eye

P/PA Pitches per Plate Appearance

• Brief Description Average number of pitches in the batter’s plate appearances.

• Availability Since 2002

• Category Eye

Def fWAR Defensive Value

• Brief Description Defensive value contribution to fWAR; consists of Fielding value and a positional adjustment. The positional adjustment is not currently included in LASR stats due to limitations on its descriptive effectiveness.

• Availability All Seasons

• Category Fielding

Field fWAR Fielding Value

• Brief Description Fielding value contribution to fWAR; combines with a positional adjustment to create Def. The positional adjustment is not currently included in LASR stats due to limitations on its descriptive effectiveness.

• Availability All Seasons

• Category Fielding

FRV Fielding Run Value

• Brief Description Overall defensive value as measured by Statcast’s advanced metrics. Note: FRV combines OAA (a range statistic) with other defensive elements, essentially making it a more comprehensive defensive measurement of, and with significant overlap with, OAA.

• Availability Since 2016

• Category Fielding

OAA Outs Above Average

• Brief Description Defensive value specifically from the fielder’s range, as measured by Statcast’s advanced metrics.

• Availability Since 2016

• Category Fielding

UZR Ultimate Zone Rating

• Brief Description A pre-Statcast measurement of defensive value. A bit more flawed than newer methods of measuring defense, FanGraphs has stopped reporting it.

• Availability From 2002 to 2024

• Category Fielding

DRS Defensive Runs Saved

• Brief Description Measures defensive value with game context taken into account. Similar to UZR with subtle differences and more recent upgrades.

• Availability Since 2003

• Category Fielding

TZ Total Zone

• Brief Description A rough measurement of defensive ability based only on play-by-play data (no advanced metrics). This makes it possible to be calculated for historical seasons, but should be taken with more caution than newer defensive stats.

• Availability From 1876 to 2002

• Category Fielding

SpdSc Speed Score

• Brief Description Created by Bill James, a stat designed to rate a player’s speed and baserunning ability. Incorporates stolen base percentage, frequency of stolen base attempts, percentage of triples, and runs scored percentage.

• Availability Since 1886

• Category Baserunning

Framing Catcher Framing Value

• Brief Description For catchers, measures the defensive value they provide by framing pitches as strikes.

• Availability Since 2008

• Category Fielding

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Statistics for Pitchers

fWAR (fWAR/TBF) FanGraphs Wins Above Replacement (per Total Batters Faced)

• Brief Description All-encompassing measurement of player value. Note: sometimes differs significantly from Baseball Reference WAR (bWAR) because it is based on FIP instead of ERA.

• Availability All Seasons

• Category Overall

ERA Earned Run Average

• Brief Description Earned runs per 9 innings pitched. Oldest, most traditional measurement of pitcher run prevention.

• Availability All Seasons

• Category Overall

FIP Fielding-Independent Pitching

• Brief Description An ERA estimator that only takes into account strikeouts, walks (and hit by pitches), and home runs – the outcomes the pitcher has the most control over.

• Availability All Seasons

• Category Overall

xERA Expected Earned Run Average

• Brief Description An ERA estimator that uses Statcast metrics like exit velocity and launch angles to predict what ERA should be based on quality (and quantity) of contact.

• Availability Since 2015

• Category Overall

xFIP Expected Fielding-Independent Pitching

• Brief Description An ERA estimator that adjusts FIP by setting home run per fly ball rate to the league average, since high and low HR/FB values are often matters of luck.

• Availability Since 2002

• Category Overall

SIERA Skill-Interactive Earned Run Average

• Brief Description An ERA estimator that essentially takes FIP a step further by accounting for types of batted balls (i.e. groundballs and pop-ups).

• Availability Since 2002

• Category Overall

K% Strikeout Percentage

• Brief Description Percentage of plate appearances ending in a strikeout.

• Availability All Seasons

• Category Stuff

-BB% Base on Ball (Walk) Percentage (Negated)

• Brief Description Percentage of plate appearances ending in a walk.

• Availability All Seasons

• Category Control

-HR% Home Run Percentage (Negated)

• Brief Description Percentage of plate appearances ending in a home run.

• Availability All Seasons

• Category Contact Quality

WHIP Walks and Hits per Inning Pitched

• Brief Description Walks plus hits divided by innings pitched.

• Availability All Seasons

• Category Overall

-BABIP Batting Average on Balls in Play (Negated)

• Brief Description Avoidance of giving up hits on batted balls in play. Can be used as a measure of luck.

• Availability All Seasons

• Category Luck

Chase% Chase Percentage

• Brief Description Percentage of pitches thrown outside the strike zone and swung at.

• Availability Since 2002

• Category Control

Whiff% Whiff Percentage

• Brief Description Percentage of pitches swung at and missed.

• Availability Since 2002

• Category Stuff

-EV Average Exit Velocity (Negated)

• Brief Description Average speed of batted balls off the bat (negated, so higher LASR means a lower average exit velocity).

• Availability Since 2015

• Category Contact Quality

-LA Average Launch Angle (Negated)

• Brief Description Average angle of batted balls off the bat (negated, so higher LASR means lower launch angles).

• Availability Since 2015

• Category Contact Quality

-Brl% Barrel Percentage (Negated)

• Brief Description Avoidance of batted balls classified to have been hit on the bat barrel based on exit velocity and launch angle.

• Availability Since 2015

• Category Contact Quality

-maxEV Maximum Exit Velocity (Negated)

• Brief Description Speed of opposing hitter’s hardest hit batted ball (negated, so higher LASR means a lower maximum exit velocity).

• Availability Since 2015

• Category Contact Quality

-HH% Hard Hit Percentage (Negated)

• Brief Description Avoidance of batted balls hit with an exit velocity of at least 95 MPH.

• Availability Since 2002

• Category Contact Quality

PB Ovr PitchBot Overall

• Brief Description Overall pitching ability as measured by the Pitch Bot pitch quality model

• Availability Since 2020

• Category Overall

PB Stf PitchBot Stuff

• Brief Description Stuff (strikeout ability) as measured by the Pitch Bot pitch quality model

• Availability Since 2020

• Category Stuff

PB Com PitchBot Command

• Brief Description Command (ability to locate pitches) as measured by the Pitch Bot pitch quality model

• Availability Since 2020

• Category Control

S+ Pit Stuff+ Pitching

• Brief Description Overall pitching ability as measured by the Stuff Plus pitch quality model

• Availability Since 2020

• Category Overall

S+ Stf Stuff+ Stuff

• Brief Description Stuff (strikeout ability) as measured by the Stuff Plus pitch quality model

• Availability Since 2020

• Category Stuff

S+ Loc Stuff+ Location

• Brief Description Location (ability to locate pitches) as measured by the Stuff Plus pitch quality model

• Availability Since 2020

• Category Control

GB% Groundball Percentage

• Brief Description Percentage of batted balls hit on the ground

• Availability Since 2002

• Category Contact Quality

-LD% Line Drive Percentage

• Brief Description Avoidance of batted balls classified as line drives.

• Availability Since 2002

• Category Miscellaneous

IFFB% Infield Fly Ball Percentage

• Brief Description Percentage of fly balls that remain in the infield (aka, pop-ups).

• Availability Since 2002

• Category Miscellaneous

-HR/FB Home Run per Fly Ball Rate (Negated)

• Brief Description Avoidance of fly balls that become home runs. Can be used as a measure of luck.

• Availability Since 2002

• Category Luck

Swing% Swing Percentage

• Brief Description Percentage of pitches the batter swung at.

• Availability Since 2002

• Category Control

Zone% In-Zone Pitch Percentage

• Brief Description Percentage of pitches that were in the strike zone.

• Availability Since 2002

• Category Control

-ZSw% Zone Swing Percentage (Negated)

• Brief Description Prevention of batters swinging at pitches inside the strike zone. Note: O-Sw% is Chase%.

• Availability Since 2002

• Category Control

-ZCon% Zone Contact Percentage (Negated)

• Brief Description Prevention of batters making contact with pitches inside the strike zone.

• Availability Since 2002

• Category Stuff

-OCon% Outside-zone Contact Percentage (Negated)

• Brief Description Prevention of batters making contact with pitches outside the strike zone.

• Availability Since 2002

• Category Stuff

CStr% Called Strike Percentage

• Brief Description Percentage of pitches that were called strikes.

• Availability Since 2002

• Category Miscellaneous

-P/PA Pitches per Plate Appearance

• Brief Description Average number of pitches thrown per plate appearance (negated, so a higher LASR means more efficiency).

• Availability Since 2002

• Category Control

Str% Strike Percentage

• Brief Description Percentage of pitches that were strikes, either swinging, called, or made contact with.

• Availability Since 2002

• Category Control

Pull% Pulled Batted Ball Percentage

• Brief Description Percentage of batted balls that were hit to the batter’s pull side.

• Availability Since 2002

• Category Miscellaneous

Oppo% Opposite-Field Batted Ball Percentage

• Brief Description Percentage of batted balls that were hit to the batter’s opposite field side.

• Availability Since 2002

• Category Miscellaneous

QS% Quality Start Percentage

• Brief Description Percentage of starts where the pitcher pitched at least 6 innings and gave up 3 or less earned runs.

• Availability Since 1974

• Category Miscellaneous

F-Str% First-pitch Strike Percentage

• Brief Description Percentage of plate appearances where the first pitch was a strike.

• Availability Since 2002

• Category Miscellaneous

LOB% Left-on-Base Percentage

• Brief Description Percentage of baserunners pitcher allowed that did not score. Can be used as a measure of luck.

• Availability All Seasons

• Category Luck

Framing Catcher Framing Value

• Brief Description Value produced by the framing ability of the pitcher’s catcher(s). Can be used as a measure of luck, especially if the pitcher is changing teams/catchers.

• Availability Since 2008

• Category Miscellaneous

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Much like when I actually was a teen, I am not adept at keeping up with these things. I’m not here to be popular, I’m just here to say hi to the other nice kids and stress about standardized tests. Here in the baseball writing world, I usually don’t join in on the fast-fashion discourse that dominates news circles with more vigor than a lunch table goss sesh. In my last article, for example, I scoffed at the writers who dare muse on the future outcomes of this offseason’s moves and instead boldly declared my thoughts on old transactions whose fortunes we already know. I really went out on a limb with that one.

In this article, however, I am joining the crowd of “Who’s Gonna Get Better?” articles (“breakouts”, “sleepers”, “picks to click”, etc.) and conceding there is entertainment value in acting like I can predict the future – not to mention an opportunity to assess my analyst acumen. Every year there are players whose statuses in the grand baseball echelon soar or plummet between April and September. And every year, there are writers who point to a piece they published in March to prove their clairvoyance... while the more miscalculated yawps are either erased from existence or blamed on injuries.

This year, I am shamelessly eyeing a spot among the fortune- and fib-tellers. Sure, sometimes it’s nice to just lay around in the box score meadows and look at the pretty shapes last year’s stats make in the clouds, but do it long enough and you’ll start to wonder whether it’s going to rain tomorrow.

While my meteorological metaphors could use some time in the batting cage, I have been perfecting my swing on a tool that I hope will help me forecast upcoming ripples in the baseball atmosphere. LASR is designed to help fans wrap their heads around the contextual quality of raw stat values, and I’m excited to test its usefulness in this exercise. While I do know that fresh-and-shiny process-based metrics are helpful in predicting the basic result-based tallies, if you give me a list of paired Barrel% and HR% values, I’d never be able to pinpoint under- and over-achievers on my own. If one stat has a 55 LASR grade and the other has a 65 LASR grade, though? Now we’re getting somewhere.

Last year, I made some predictions that didn’t use LASR to self-score my first published rubbing of the crystal balls, and the results were about as Not Safe For Work as that just sounded. This year, I’m hoping to have better luck by setting my game rules along the measuring stick of my own handy-dandy tool.

I did conduct some semi-serious research in the makings of this article – calculating correlations and designing predictive models and overthinking spring training claims of new pitches and batting stances – but I’m going to leave it behind the scenes for the time being. Once I am confident that my studies are ironclad and not half-baked versions of other analysts’ superior work, I will lay out my tangled web of old-timey and new-age stats and share the Eureka moments with you. For now, my predictive persona hovers between “Here’s what my current, flawed-but-improving, understanding of advanced stats is telling me” and “Yeahhh my gut’s sayin’ this guy’s DUE this year!!!”

Someday it would be fun to play this game against others, but in the interim I growl solely at the mirror. Here are the rules I’ve set for myself:

• Pick one player from each team to improve in 2026.

• The player can be a hitter or pitcher, but must have been a LASR Qualifier in 2025. This means their 2025 LASR stats will not have been affected by low playing time.

• No superstars (using the “You know it when you see it” definition).

• For hitters, improvement will be measured in wOBA LASR (a general all-encompassing benchmark of offensive production) and a second, more specific LASR stat out of the following: AVG, ISO, and BB%. These, respectively, represent three broad categories that effectively define hitter type: Contact, Power, and Eye. Overall, 5 players will be selected for each more-specific stat.

• For pitchers, improvement will be measured in ERA LASR and FIP LASR. I wanted to be more specific like I was with hitters, but I feel the need to do more work on incorporating individual pitch characteristics into LASR and evaluating their impacts on the categories I was aiming for – Stuff (K%), Movement (-HR%), and Control (-BB%). Maybe next year I’ll be ready to do that. For now, I’ve challenged myself by looking at both the most traditional run-prevention stat (ERA) and its modern-day predictive counterpart (FIP), which is often used to foresee ERA improvements. This way, my picks can’t be made using FIP alone. Overall, 10 starters and 5 relievers will be selected.

• Points (or negative points!) are accrued by tallying the differences in 2025 and 2026 LASR values for the specified ratings. On all LASR graphs below, improvement in the first two bars will determine scoring.

• Though I’m preventing some playing-time shenanigans by only including 2025 LASR Qualifiers, obviously there’s no way to avoid selecting players who might not end up being 2026 LASR Qualifiers. I don’t want to get credit for foreseeing a 40 to 50 improvement if the 50 is only there because of a playing time adjustment. I also don’t want to just throw those results out the window. So, for any 2026 non-LASR Qualifiers, I’m going to initially compare their 2025 scores to the 2026 non-adjusted scores, get those differences, then apply the adjustments. I’ll also update this later if that’s not the solution in the slightest, but I think it’ll take care of the potential problem quite nicely.

• Publish results no matter what. Don’t let them go to your head (if good) or your heart (if bad).

Now that that’s settled, let’s go see who Samantha is talking to...

The Predictions

The LASR Graphs

Note for the following graphs:

UPDATE: Previously I had a paragraph here explaining why I didn’t define every stat on these graphs. I’ve now published a stat glossary you can use as a reference if needed.

Improvement: wOBA and AVG

Rice had a great first full season in pinstripes, but there’s signs he could’ve been even better. He hit the ball with authority, posting a 70 xwOBA LASR that put Rice in a slugger stir-fry that included Vladimir Guerrero Jr., Ronald Acuna Jr., Pete Alonso, and Ketel Marte. His strikeout avoidance was above average and his BABIP was low, suggesting an AVG LASR above last season’s 50 is in his future.

Durbin’s wOBA and AVG LASRs were right in line with their expected counterparts, but there’s reason to dream of loftier heights. He’s one of the best in baseball at getting his bat on the ball and squaring it up (SqUpSw% is highly correlated with contact-oriented stats like AVG), and once again the low BABIP might see a natural regression toward league average. He’s also going from one of the worst stadiums in Hit Park Factor to one of the best.

Lopez was one of my breakout picks last year; in my Call to the Pen article that included him, I pointed out his outstanding defense and excellent contact ability. Surprisingly, even though his xAVG and other contact indicators were comfortably above average in 2025 – along with low FB% and P/PA, which are both negatively correlated with AVG – he didn’t enjoy the success he deserved. This year, perhaps Otto will slam the brakes on his bad luck.

Garcia was another one I previously wrote about who did not deliver on my high hopes but has not dissuaded me yet (I swear not all of these are repeats from last year). Much like Lopez, he has all the promising features of an above-average contact hitter and could see some positive regression this season. He’s looking to reclaim the crown of Best 2020s Luis Garcia (out of 3 contestants) which he won in 2023 and 2024.

It was a bad year for Rengifo in 2025, but now he’s headed from a team infamous for its player development black hole (the Angels) to a team that seems to have a neverending supply of magic (the Brewers). He may be supplanted soon in Milwaukee by their infield prospect embarrassment of riches, but it’s as good a place as any to try finding the missing piece in your swing.

Improvement: wOBA and ISO

Blue Jays hitting coach David Popkins earned the honor of Major League Coach of the Year last year, helping Toronto improve their Runs Scored from 23rd in MLB in 2024 to 4th in 2025. He emphasized finding the best possible approach for each hitter uniquely, rather than a one-size-fits-all fix. Sanchez has already shown extraordinary underlying skills; now he just needs someone to show him the best way to take advantage of it.

In 2025, Wagaman was a 27-year old rookie corner infielder/outfielder who did not turn any heads. That position profile typically requires power, and his 40 ISO LASR won’t get the job done. He smacked the ball harder than those results would suggest, however, so I’d like to keep track of whether that means anything. Probably not, but who knows? Does anything mean anything?

Diaz was another unlucky customer to the batter’s box in 2025, with wOBA and ISO LASRs that were both 10 below their expected versions. He has boasted better outcomes in the past, and still at only 27 years old, there’s a good chance his best years are ahead. I shouldn’t mention this because it detracts from my pick, but there is also a reason to be concerned: a -Chase% LASR that dropped to 20 last year. Yikes.

It’s gotta happen one of these years, right? Cruz has consistently been atop any leaderboards that measure brunt force impact on the ball, but his awe-inspiring rocket blastoffs have been counteracted by an all-too-common failure to launch. He’ll probably need a make-or-break adjustment if he’s ever going to cut down on the strikeouts, but if that happens... watch out.

According to my own rules, I had to choose a Rockie, and I’d be damned if I didn’t pick the power category in a plea to Coors Field for assistance. Doyle was my best option as someone with, ahem, room for improvement, along with an enticing 70 Ideal Attack Angle% LASR. Who knows how telling that is? Let’s find out together.

Improvement: wOBA and BB%

In this section, I’m mostly looking for players who know the strike zone. We’re talking someone who doesn’t chase bad pitches, lets a fair number of pitches go by, and punishes the pitchers who dare put one in the zone. Dingler isn’t quite the best example of this, but he was at least better at those skills than his BB% LASR last year. And as a catcher, he’ll have significantly more opportunities to get accustomed to the new ABS-assisted strike zone than most other hitters.

India has always been extremely patient and continued to be last year, but didn’t rack up walks like he should have. That may have had something to do with his unintimidating bat, but he’s earned more free passes before. With the Royals moving in their fences, everyone will be watching for an uptick in home runs, but a possible offshoot of that might be pitchers showing more caution in Kansas City than they had in recent seasons.

Meidroth doesn’t chase often, which is too bad because nominative determinism is always fun. Though his extremely low Z-Swing% LASR shows he might be too picky for his own good, his eye-catching 80 Called Strike% LASR is worth watching to see if its connection to BB% might foretell an improvement. This is why we do it, folks. For the called strikes. There’s nothing on Earth as beautiful as a called strike.

Stott must hate the pitch clock, because he makes it his mission to elongate the game as much as possible. His 75 P/PA LASR topped the league alongside Mike Trout, Isaac Paredes, and Will Smith, but his 55 BB% LASR was below each of theirs. He’ll likely continue to get plenty of pitches in the strike zone as pitchers avoid the more prominent sluggers in the lineup, which will limit his walks, but the disparity in BB% and P/PA was too great to ignore.

The Giants have brought in several notable veterans since Lee joined the Giants: Willy Adames, Rafael Devers, Harrison Bader, and Luis Arraez. I have absolutely no data that says they can help Lee hone his game, but I also have no data saying they can’t. Like with previous hitters in this section, I’m banking on an above-average -Chase% and below-average Swing%, perhaps with a maturing approach, translating into an above-average walker.

Improvement: ERA and FIP - Starting Pitchers

As far as I understand, the difference between FIP and xFIP is a home run rate adjustment toward league average for the latter. While pitchers do have some control over fly ball rate, the amount of fly balls that go over the fence is often a matter of luck. Baz’s 35 -HR/FB LASR suggests last year’s 45 FIP LASR may be a result of misfortunes and his 55 xFIP LASR offers optimism for better luck, especially now that he’s not pitching in a spring training ballpark.

Cleveland has been notorious for its pitcher development for years now, so it would be foolish not to toe the rubber for their selection. Bibee was the best choice, despite only one ERA estimator (xERA) disagreeing with the consensus that he’s a league average pitcher. Honestly, everything else was so close to average that nothing here is super exciting, but... it’s Cleveland. They’ll turn someone into a Cy Young contender out of nowhere, and maybe it’ll be Bibee.

A former top prospect who has always shown the promise to be among the best starters in MLB, Gore never quite fulfilled that prophecy in DC over a full season. However, his ability to rack up strikeouts is legit and he has been freed from the Nationals’ perpetual rebuild where gory defense likely contributed to his tough-luck 35 -BABIP LASR. Now that he finds himself in one of the most pitcher-friendly stadiums, he stands a good chance to see a Texas-sized improvement.

Someday I’ll develop LASR split stats and Severino’s 2025 home/road splits will be Case Study #1. For now I’ll use traditional stats and highlight his 3.02 ERA on the road and 6.01 ERA in the A’s temporary-home minor league ballpark. He’ll still be pitching his home games there, but maybe last year’s experience will help him neutralize things this time around. He may also see a rebound from his 35 LOB% LASR – a stat, like BABIP, that often regresses toward league average.

Kochanowicz better hope the only way to go is up after a disastrous 2025 season. Though there isn’t much sign of him being even an average pitcher anytime soon, he can at least keep the ball on the ground. When opposing batters did put it in the air in 2025, they enjoyed a home run rate that shouldn’t be expected to stay so high. With well-regarded pitching coach Mike Maddux joining the Angels, perhaps he can help Kochanowicz become merely below-average. Promising, I know.

Peterson’s weakness has always been a tendency to allow free passes, and though his Zone% and -BB% LASR were matching 40s in 2025, he posted an above-average Chase% LASR for the first time in his career. If he continues improving at generating swings on his inevitable misses, he may ride his excellent home run prevention, a bounceback in -BABIP and LOB%, and contract-year motivation to take a step up.

Similar to Baz and Gore, Cabrera is a strikeout-reliant former top prospect who, despite having shown flashes of brilliance, still feels like he has another gear to unlock. Also like those two, he was traded this offseason and his new team (the Cubs) might be itching to teach him some new tricks. Even outside this little game of mine, it will be interesting to follow and compare their progressions and/or declines this year.

During Pallante’s 2025 starts, Cardinal outfielders could’ve spent the entire time picking dandelions in the outfield grass and not much would’ve changed. Pallante has been the most prolific groundballer in MLB since his 2022 debut, putting up at least a 75 GB% LASR every year (the only pitcher to have done so). Last year, many of his rare fly balls found the bleachers; with St. Louis being known for home run suppression, that should reverse its course in 2026.

Marquez’s nose-dive from mediocre to terrible last year served as a painfully accurate representation of the Rockies’ season as a whole. Mercifully, he has finally been freed from Colorado, a pitcher’s graveyard he had toiled in since 2016. Headed to San Diego on a perform-or-your-career-is-over type of deal, Marquez’s MLB existence may be saved by the friendlier pitching environment and highly regarded pitching coach Ruben Niebla.

E-Rod’s first two seasons in Arizona have been quite forgettable, and most of his peripheral stats don’t provide much hope that he’ll return to his Detroit and/or Boston form. So why did I pick him? Because the Diamondbacks were my toughest assignment, and Rodriguez’s 35 -BABIP LASR was the biggest sign of potential improvement I could find. At one point I strongly considered picking Carlos Santana, a soon-to-be 40-year old, but I just couldn’t talk myself into it.

Improvement: ERA and FIP - Relief Pitchers

Along with Otto Lopez and Luis Garcia Jr. above, Uceta completes the trifecta of players I’ve chosen in consecutive preseason predictions. Uceta actually took a step back in 2025 after a dazzling half-season the previous year, but he still shows signs of potentially being a high leverage reliever. His -HR% LASR dropped from 60 to 40, and while his GB% and -LA LASRs indicate that value might not cross 50 again, he may benefit from leaving the Rays’ temporary minor league home.

Vargas is a rare reliever who may not need to rely on strikeouts to succeed – though I’m sure the Mariners would appreciate him missing more bats. He has shown an ability to find the strike zone, though, which is always a surefire way to maintain employment in a major league bullpen. Perhaps in time he will figure out how to further translate his groundball tendencies to greater home run prevention, especially as long as he stays in Seattle.

Dylan Lee and Jung Hoo Lee win the title of “Players with the same last name and most dissimilar first names” in this exercise. I love baseball. Anyway, this Lee was an easy pick, with xERA, xFIP, and SIERA LASRs convincingly higher than the two I’m scoring points on. Even if they all meet in the middle, I’ll enjoy an effortless bounty. His -HR/FB LASR had been around average in previous seasons, so a bounceback there could be all it takes.

Ashcraft’s above-average Whiff% and outstanding Chase% should’ve translated into better K% and -BB% results in 2025 (though his 35 Zone% LASR might be to blame), but he made up for it by refusing hard contact to hitters. His -BABIP and LOB% should regress to league average, though his -HR/FB LASR absolutely won’t because it doesn’t fit my narrative. No further questions.

Oh woe is the Dodgers. They signed Scott last offseason to join the deathstar they’re building, but he was about as effective as a stormtrooper’s shooting. How will they ever get by? Simply by signing one of the top closers in the game, Edwin Diaz, to push Scott down the bullpen hierarchy. Maybe with less pressure and better luck than he was subject to last year, Scott can put that terrible 2025 Dodgers season behind him.

_______________________________

Phew. That’s all folks. Can’t wait to see how spectacular this all looks when it comes crashing down.

Happy Opening Day! What a beautiful sentence that is✌️

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The meaningless nature of spring training games leaves us wanting more to discuss, though, and the concurrence of this thumb-twiddling with the slowing trickle of major transactions makes right now a popular time of year to take a bird’s-eye view look at the roster shuffling of the offseason we’re leaving behind. Who got better? Who got worse? Which free agent signing will prove to be the steal of the winter? Which seemingly minor trade will keep us up at night 10 years from now?

Only time will tell, of course. At the moment we have n…

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This article will serve as the primary guidebook for my League-Adjusted Standardized Rating (LASR) project. I will update it as necessary when relevant changes are made.

Below is a list of my articles chronicling the creation, development, and exploration of various aspects of the project.

• Introducing LASR, Providing Much-Needed Context to Your Favorite Baseball Statistics

• LASR Update: It’s Prettier Now!

• What does LASR provide that + stats don’t?

• Wait, LASR isn’t the same thing as Baseball Savant percentiles?

• LASR now has Negro League Stats!

• The Collapse of the Qualifier (And a solution I’ve applied to my LASR project)

• LASR Stat Glossary

• LASR is open to the public!

• Introducing LASR Scouting Report Cards

Generally I will not be revising those articles, so some may contain outdated explanations. All changes to the process and scope of LASR, both past and future, will be summarized in this article.

Overview

League-Adjusted Standardized Rating (LASR) is a project in which a wide range of baseball statistics are translated to the same scale, one modeled after the traditional 20-80 scouting scale. On the scale, 50 represents average and every 10 points above or below that is equal to 1 standard deviation. The project is designed to provide context to stats we may not have a comprehensive notion of quality for. It tells us exactly how impressive a .300 batting average is these days, whether a 15% whiff rate is desirable or not, the difference between a 3% home run rate (HR%) in 1915 and 2015, and much more.

I created this project because I like being able to contextualize stats to make more sense of them, but the current most popular methods of doing so in baseball (+ stats and percentiles) do not account for the spread of data. I believe the data’s spread is too important to continue ignoring. My background and a more thorough explanation of why I began this project is included in my LASR introduction article.

Public Access

I’m happy to announce that my interactive LASR site is now publicly available and can be found here: https://highandoutside.shinyapps.io/lasr_shiny_public/

Data

I have calculated LASR scores for most available stats for every individual player season in the following datasets:

• AL/NL Hitters (1876-2026)

• AL/NL Pitchers (1876-2026)

• Negro League Hitters (1920-1948)

• Negro League Pitchers (1920-1948)

Currently, all of the data comes from FanGraphs. If you are not already a supporter, I highly encourage you to become one. They deserve every ounce of support they can get.

All of the coding I have conducted for this project has been done in the programming language R.

There are some new advanced stats (i.e. pitch characteristics) that I have begun translating, but I need to put more time into ensuring their accuracy and reliability before officially adding them to the project. It’s on the list.

Calculation Process

The ratings are essentially z-scores. That’s the number of standard deviations above or below the mean (average) an individual data point is. A standard deviation is a measure of spread in a dataset; most data falls within -3 and 3 standard deviations of the mean. Perhaps someday I’ll write an article on the calculation and value of standard deviations, but for now if you’re interested in learning more about them, I encourage you to peruse the extensive information you can find online.

Here’s the step-by-step process in calculating a LASR value:

1. Establish a pool of “league regular” players for each season. These qualifier pools are composed of the top X players by playing time (Total Batters Faced (TBF) for pitchers, Plate Appearances (PA) for hitters, and Innings Played (Inn) for fielders), where X is a predetermined value multiplied by the number of teams in the league. These values for the AL/NL datasets are listed below. (Note: for Negro League data, I use 6 position players (for both hitting and fielding stats, since position and innings played data is incomplete) and 3 pitchers per team (the lines between SP and RP were quite blurry).) The reasoning for using this method is discussed in my article on the qualifier threshold.

2. Among these established pools, calculate the mean and standard deviation for all stats I deem reliable and relevant. (Note: if stats are not already expressed as rates, I turn them into rates. This means FanGraphs Wins Above Replacement (fWAR), for example, is actually fWAR/PA for hitters and fWAR/TBF for pitchers).

3. For each individual player, calculate their z-scores in all of these stats. This is done by subtracting the league mean from the player’s value, then dividing by the league standard deviation.

4. For players who fell short of qualifying for the league regular pool, their z-scores are multiplied by a scalar equal to their progress toward qualification.

5. For stats in which it is preferable to have a low value (such as HR% for pitchers and K% for hitters), z-scores are negated. This is denoted with a “-” in front of the stat on LASR graphics (except on pitcher stats like ERA where that is obvious).

6. All z-scores are then translated to the 20-80 scale. This is done by multiplying the value by 10 and adding it to 50.

7. Since the traditional scale typically uses increments of 5, the LASR value is finalized with a rounding to the nearest 5.

Example

Let’s calculate Giancarlo Stanton’s 2025 HR% LASR value as an example.

1. Establish a qualifier pool of “league regular” players.

   1. Based on the framework laid out above, the pool will be the top 180 hitters by PA (30 teams, 6 hitters per team in the league).

   2. In 2025 the pool actually included the top 181 hitters, since Austin Riley and Andrew Vaughn tied for 180th place with 447 PA. They are in the pool along with any hitter with more PA; any hitter with 446 PA or fewer is not included.

2. Calculate the mean and standard deviation.

   1. Among this pool, the mean HR% was 0.0348 and the standard deviation was 0.0151.

3. Calculate the player’s z-score.

   1. Stanton’s HR% was 0.0854. The z-score is (0.0854 - 0.0348) / 0.0151 = 3.3510.

4. Adjust for non-qualified players.

   1. Stanton only had 281 PA in 2025. To be in the qualifier pool, he would have needed 447 PA. So, his progress toward qualification is 281/447 = 0.6286.

   2. His z-score is multiplied by this scalar: 3.3510 * 0.6286 = 2.1066.

5. If a lower value is preferable, negate the stat.

   1. Hitters want higher HR%, so this step is not applicable (if it was, I would just multiply 2.1066 by -1).

6. Translate to the 20-80 scale.

   1. 2.1066 * 10 + 50 = 71.066

7. Round to the nearest 5.

   1. 71.066 is rounded to 70.

Graphics

I currently have developed three different types of graphics to display LASR scores:

• Scouting Report Card. Example: Bobby Witt Jr.’s LASR stats from 2025 broken into separate categories with a new player detail box.

• Multi-Stat Single-Season. Example: several statistics from Juan Soto’s 2025 season.

• Single-Stat Full-Career. Example: career progression of Max Scherzer’s strikeout rate (K%).

FAQ

What is the usefulness of LASR?

I have primarily been using LASR to get a better idea of player quality and type. Even though I’m a lifelong baseball fan, I find it difficult to keep track of “how good” a stat value is. That might be because of major shifts in modern trends (like batting average generally decreasing) or because I don’t have the same level of experience assessing the quality of advanced stats (like chase rate or zone rate) as I do traditional stats (like Earned Run Average (ERA) or its estimators). Whenever I conduct a player evaluation and find myself frustrated in wrapping my head around their strengths and weaknesses, LASR helps me put it all in a helpful context.

Others may find different uses for LASR (hopefully), but that’s how I use it.

Why are there values below 20 or above 80 if it’s called a 20-80 scale?

The scale is generally referred to as 20-80 because most data points (approximately 99.7% in normally distributed datasets) fall within -3 and 3 standard deviations of the mean. Values below 20 and above 80 are true outliers and quite rare. Admittedly, baseball’s scouting scale typically puts hard caps on those boundaries – i.e. a scout will not report a player’s Power tool as an 85 or 90 even if it truly is that remarkable. However, I like to measure how much of an outlier a player really is, so I do not include those caps.

Where do I learn more about the “regular” versions of these stats?

At some point I want to put together a glossary of my own, but for now I encourage you to check out the FanGraphs Library Stat Glossary to learn more. I know I’ve had to consult it often to make sure I know what’s being measured. Often, a quick Google search will give you a good idea of the basics. And once you know what a stat aims to measure, you don’t even need to figure out what the good or bad values are – just use LASR!

Do you plan on translating data from non-MLB datasets?

Yes, future expansions of the project will hopefully include the following datasets and more: Minor League, NPB, KBO, WPBL, college baseball, independent league baseball, and possibly even different sports.

Why are all underlying counting stats turned into rate stats?

Though baseball fans do often have a good sense of quality for some counting stats, all underlying stats are expressed as rates because they more accurately reflect player abilities. Someone who hits 20 home runs in 500 PA has displayed a different power profile than someone who hit the same amount in 300 PA. The playing time adjustment I incorporate works well in tandem with this concept, rewarding players who contribute full-time and fairly dinging players who may have achieved abnormally high rates through small sample size flukes.

Are LASR scores for starting pitchers and relief pitchers calculated separately?

Yes. Any pitcher who started at least half of his games pitched is treated as a starter; all others are treated as relievers. This does create the rare strange outcome, such as a reliever having an extremely high Quality Start rate (QS%) because most of his peers threw none. However, this is the best method of dealing with that blurry SP/RP line that I’ve come up with.

What are some updates you’re planning on making?

This project has a long way to go before I feel like I’m “done” with it. Here’s just a few things I plan to work on in the indefinite future:

• As mentioned above, the inclusion of more baseball datasets and potentially other sports data.

• Different types of graphics, such as league leaders, grouping players by type, career progressions for multiple stats, and more.

• An option for users to change the scale, such as regular z-scores (-3 to 3) or “video game ratings” (0 to 100), as well as options for varying degrees of rounding.

• Projections of future performance. This may include creating a projection system of my own, but since there’s no need to reinvent a wheel, there are already effective projection systems out there that I can look into translating.

• An option to include the raw value of a stat on the graphic – though this would likely have to involve flipping the orientation of the bars.

• Pitch characteristics for different pitch types, such as velocity, perceived velocity, horizontal/vertical movement, spin rate, etc.

I’ll cut myself off here to avoid giving away ALL of my plans.

Thank You

If you made it this far, I can’t tell you how much I appreciate your interest in this project. Thank you. I’ve put quite a bit of work into it already and I’m hopeful it’s still in its pre-arb years.

I’m terrible at promoting myself, but the ugly truth is, in order for me to be able to dedicate significant time and energy to this project, I need to at least have the illusion that it will compensate me somehow. As of now, that strictly comes from paid subscriptions to this Substack. I’ve been posting here about once a month, which I’m hoping to improve upon, but I try my best to write thoughtful and entertaining articles that are full of substance and worth your time. I know the world is falling to pieces and money is hard to come by these days, but if you are able to provide a bit of support, I would be very grateful.

Go baseball ✌️

“Yes, yes. This is severely important. We cannot simply watch these contests for pleasure; we must measure the performances with statistical rigor, and subsequently argue about the veracity of those measurements for generations to come.”

“If I may start the proposals, good sirs: a pitcher’s main objective is to win ballgames. Therefore, I propose the tosser who accumulates the highest total of victories shall be declared the league’s best.”

“A fine suggestion. However, what becomes of the poor fellow stranded on a ballclub whose wretched batsmen refuse to tally runs in his support? Might it be possible, friends, for the most exemplary pitcher to have lost a number of games he might have won had he competed alongside a higher-scoring offense?”

“Indeed, it is possible. In that case, while I suggest we maintain our immense reverence for these ‘pitcher wins’ for at least another century, I do advise we calculate an additional assessment of quality.”

“If I may – the thrower’s goal is to limit his opponents’ scoring totals. Perhaps the hurler who allowed the fewest runs is our target?”

“Ah, but only if he has earned them.”

“Yes, of course. It is necessary to separate a moundsman’s talents from the foolish mistakes of his fielders.”

“A problem, gentlemen: our league’s most effective tossers will naturally be given the opportunity to complete more innings, and thus concede more earned runs, than their lesser contemporaries.”

“Hm, could this be surmounted by using highly advanced mathematics to calculate the amount of earned runs one might expect a pitcher to relinquish over the course of a standard 9-inning ballgame?”

“I believe this concept is referred to as an ‘average.’”

“There are indeed some mighty fine thinkers in this room. Very well then: we shall deliver the crown of top pitcher to the owner of the season’s lowest Earned Run Average.”

“Huzzah! We’ve done it!”

“What about FIP?”

“What?”

“Never mind.”

“Hold on folks, just one moment. With such a calculation, a pitcher may toss but one scoreless inning and achieve an ERA of 0. He would therefore be entitled at least a tie in the top spot. Is this fair?”

“No, of course not. In such a case, it appears crucial to establish a minimum requirement for total innings thrown. Those who fail to eclipse our standard will not qualify for leadership consideration.”

“Well, what should that requirement be?”

[cue: decades upon decades of indefinite disagreement]

The Qualifier Threshold

This conversation (reported word for word, I assure you) took place around 1912, when the National League introduced Earned Run Average (ERA) as an official statistic. The American League followed suit the next year, and ERA has been a standard measure of pitcher quality ever since. (Note: Though I am aware of its flaws, I will be using ERA as an example throughout this article because it was the original pitching stat that necessitated a qualifier benchmark.)

That tricky question of where to draw the qualification line in the sand did not have a consistent answer until 1951. In fact, for those nearly four decades the AL and NL had separate guidelines, and they varied season by season. In some years, starters needed to pitch at least 10 complete games to qualify; in other years it was 10 total games pitched, or 15 games, or 45 innings, or 100 innings, or (in the AL’s case) the requirements were often unlisted and inconsistent. This 2005 Dan Levitt article provides an excellent recap of the pre-1951 ever-changing ERA title landscape. Compared to the pedantry of today’s hyper-scrutinized stat tracking environment, it was the wild wild west.

In 1951, both leagues finally decided it was about time to agree on a clear and common threshold. It was decided that pitchers who accumulated at least one inning pitched (IP) per scheduled team game would qualify for the ERA crown. At the time there were 154 games in a standard season, so the target was set at 154 IP. This became today’s minimum of 162 IP when schedules expanded to 162 games a few years later. On a less official basis, this policy has been extended to the leaderboards for other pitcher rate stats, such as walks and hits per inning pitched (WHIP), strikeout rate (K%), and the like.

The need for a cutoff is obvious. In small sample sizes, rates go full free-range and are unreliable in describing what we want them to describe: player talents. Paul Skenes put together a remarkable sophomore season in 2025, posting a 1.97 ERA in 187.2 IP. Any baseball fan can tell you, that’s pretty damn good. Without any playing time cutoffs, however, Skenes ranked a mere 79th in MLB. Were there 78 pitchers who performed better than Skenes in 2025? Absolutely not – they didn’t pitch nearly enough to earn that credit.

A major problem, though, has also become obvious: under the current rules, qualifiers are on a rapid decline.

When the qualification threshold was more concretely defined in 1951, teams followed a standard four-man rotation and those four were expected to remain on the mound as deep into the game as possible. Here in 2025, even the modern five-man rotation is sometimes stretched out to six, and seeing a starter tossing warmups in the 9th is like catching a glimpse of a unicorn.

Nearly every modern trend in starting pitcher usage is leading to fewer qualifiers. Teams have decided it makes sense to sacrifice endurance for maximum effectiveness on individual pitches. When one guy runs out of gas, there’s a conga line of decent flamethrowing relievers ready to show off their own max efforts. When teams exhaust these resources by calling upon the same guys multiple games in a row, they simply swap out their tired relievers for comparable minor league replacements. Though there are limits on these options to the minors, there is enough leeway that constant roster shuffling is commonplace.

The emphasis on rearing back and exerting full force on every pitch has also led to an alarming increase in arm injuries over the past few decades. Though there are efforts attempting to curtail this progression, teams and pitchers consider Tommy John surgery – among other injuries with major recovery timelines – a grim inevitability. Between missed playing time due to injuries and intentional load management as an injury prevention tactic, many pitchers who would otherwise appear on end-of-season leaderboards let enough innings slip away to lose qualification.

Pitcher usage strategies have been reimagined at the beginning of games too, as teams often go the opener route, leaving the bulk of the contest to others – which is sometimes just a string of more relievers each pitching an inning or so. This is related to advancements in the understanding of how well batters perform when seeing the same pitcher three or four times in one game, which teams now avoid more often. Everywhere we look, some element of today’s game has taken a hack at the top-end inning totals and distributed the offcut to the grander reliever pool.

It’s also important to note that this shift in starting pitcher usage is not the pitchers’ faults. One might be tempted to proclaim that the qualifier decrease is due to a lack of durable, workhorse-type pitchers capable of consistently toughing out nine innings, and changing the threshold would be an unjust reward for a lack of effort. Today’s pitchers are soft compared to their fearsome forefathers, and if they can’t get through a measly 162 innings, I spit on their statsheet!

That may look like the story here, but it’s just not true. At the end of the day, you can only pitch as long as your team lets you, and when that team is training and operating under the strategies I’ve detailed, you’re simply not going to rack up the totals that earlier generations did. As Sam Miller put it in his 2016 plea to change the qualifier threshold:

It’s also at least partially a myth that pitchers like [Don] Drysdale were tougher or exerted more effort than pitchers today; pitchers today throw fewer starts and fewer innings per start, but they throw more pitches in each inning, because evolving strategies (on offense and defense) require them to; and they throw with more effort on each individual pitch, because evolving strategies ask them to. Measure the energy exerted in 40 Drysdale starts and 33 Max Scherzer starts, and I bet it’s pretty close—or, perhaps, not all that close at all, with the modern pitchers exerting far more energy. (I’d bet confidently that Scherzer’s between-starts routine to keep conditioned for those pitchers is greater than Drysdale’s was, too.)

The evidence and its reasoning, at this point, is clear and convincing: we need to change the qualification rules. And I’d like to propose a solution, one that I have already applied to my LASR project.

Oh, it’s a LASR article in disguise, huh?

I have to admit, my attack on this relatively arbitrary and ultimately meaningless benchmark is at least partially for the sake of my own selfish goals. For my LASR (League-Adjusted Standardized Rating) project, in order to compare players to their peers I have to define a specified pool of players for whom I calculate means and standard deviations. Similar to how rate stat leaderboards need to filter out tiny sample sizes to avoid funny business, a barrier to entry must be applied to my groups so that the resulting calculations accurately reflect the statistical environment.

My primary goal with LASR is to compare to the average player with regular playing time. When a starting pitcher has a 65 ERA LASR, I want that to mean his ERA was approximately 1.5 standard deviations better than the average regular starting pitcher in that season. Now, “regular player” is tough to define, but that’s essentially what the qualification threshold was created to do. So when I first created LASR, I used the one-inning-per-team-game criteria to assemble my starting pitcher pool.

As I became more acquainted with the results from my LASR calculations, though, I noticed some flaws in that decision. Using a concrete and unchanging value leads to pools of varying sizes for each season, including major discrepancies across eras. The largest group of qualifiers came in 1998, when 96 starting pitchers crossed the line in MLB’s first season with 30 teams. Outside of the late 1800s – when teams often relied heavily on only one or two pitchers – the group size reached its nadir in 2021 with only 39 qualifiers. There were still 30 teams, which means the total number of opportunities to be a regular starting pitcher remained relatively similar (aside from minor roster size adjustments). Using dataset sizes ranging from 39 to 96 to set the standards for around the same number of total pitchers leads to inconsistent processes and results.

More specifically, as less pitchers qualify, the ones who do are generally the better pitchers in the league. They are the guys who pitch well enough to stay in individual games longer and keep their spots in the starting rotation throughout the season if they remain healthy. Average and below average starters who used to put up 162 innings with regularity are now falling under the mark. They still occupy important roles in filling out MLB rosters just as they did 70 years ago, though, and excluding them artificially increases expectations for the “regular starting pitcher.” It’s time to invite the low-bar-setters back to the party.

The New LASR Qualification Method

My new method of selecting qualifiers is to use the top X pitchers by playing time, where X is a predetermined value multiplied by the number of teams. Back when MLB made its fateful 1951 decision, the retroactive number of qualified starting pitchers per team had been hovering around 3.5 for a while (and continued to do so), which hints at the relative pool size the league was shooting for. Therefore, I’ve settled on including 3.5 starting pitchers per team (that is, 3.5 multiplied by number of teams in the league). This doesn’t quite work as well in the 1800s era referenced above, so for seasons prior to 1892, I’ve set the multiplier at 2.

This means, outside of seasons with new expansion teams when the overall player pool proportionally increases anyway, my pool size stays consistent year-to-year. A total of 105 starting pitchers are used to determine “league regular” means and standard deviations in 2025, and that exact total is used for every season since 1998.

Admittedly, the effects of this switch weren’t huge. With more players in the pool, the means moved almost unanimously toward the “worse” end of the spectrum (i.e. higher for ERA, lower for K%) as expected, but the standard deviations generally expanded since there was a higher variation among the players. Here’s a snapshot of the changes to select stats from 2025. Most years’ stats followed a similar pattern.

For top-end seasons like Skenes in 2025, these changes more or less cancel each other out – the further they get from the new higher average, the more of an impact the new larger standard deviation has. For players near league average, however, many have played jump rope with that line and now find themselves in slightly more favorable positions.

The most noticeable repercussions come from players who had been (or still are, but to a lesser extent) subject to playing time adjustments. A refresher: for “unqualified” players, I still calculate their underlying z-scores based on qualifiers, but I adjust those values based on their progress toward qualification. Now, with that bar lowered, the adjustments aren’t as harsh – or have disappeared. In my original introduction to LASR (linked above), I used Skenes in 2024 as my calculation example, which included a playing time adjustment that knocked his ERA LASR down from 75 to 70. That example is now out of date, because Skenes is retroactively a qualifier in that season (he had 133 IP).

I also should note that I’m not using innings pitched to measure playing time anymore. Instead, I’m using Total Batters Faced (TBF) which is a more literal assessment of how much a pitcher has appeared in games. The innings pitched total only goes up when a pitcher gets an out, which means pitchers who aren’t as efficient at getting those outs aren’t receiving “playing time credit” when they fail to do so. This could be seen as unfairly discounting better pitchers who don’t need to face as many hitters to get their outs, but this is now parallel to how hitters’ playing time is measured (plate appearances) which makes the most sense to me.

Overall, the LASR values have not moved incredible distances. Some have been bumped up by 5, a rare few by 10, and many have not changed a bit. But this wasn’t about making players look better or worse. This was about identifying a squeaky wheel in my LASR process and oiling it up to preserve the long-term stability of the project.

In the Sam Miller piece linked above, along with a more recent article of his, he proposed lowering the innings threshold to something like 130 IP. Rob Mains in 2017 and Devan Fink in 2018 also put together excellent arguments and proposals to solve this problem. Surprisingly, none of them suggested simply agreeing on a pool size that only fluctuates when the total number of playing opportunities goes up or down. While I will now advocate for that modification to MLB’s leaderboards, I have my doubts anything will actually be done about it. Baseball is known to be slow-moving when it comes to making changes, and something like this will likely need a significant public movement. For now, I can at least rest well knowing my pet project boasts a slightly better system.

Wait, what about relief pitchers and position players?

Oh yeah – though I won’t spend 2700 words explaining my reasoning for them, I have also applied similar changes to the way I calculate LASR stats for relief pitchers, hitters, and fielders (for fielding statistics). There are separate issues to tackle for each of those player groups, which I might address in future articles (no promises). The general reasoning is about the same, and it was an easy decision to make adjustments for each group so that the process remained consistent.

To determine each pool, I use the top X players by playing time (TBF for pitchers, PA for hitters), where X is equal to the following predetermined values multiplied by number of teams. I arrived at each of these values by evaluating player usage throughout history and taking into account the number of players MLB intended to include in each group with its 1951 qualification designations. In some cases, I’ve touched the sliders where it made sense to do so.

“Cheerio then, gentlemen. I am overjoyed we have agreed upon these stipulations.”

“And what luck, we managed to do so with merely three bloodied noses and a bruised eye socket.”

“My sincerest apologies for the enthusiasm.”

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A bit of background on my player-focused dartboard tosses. Last fall through this spring, I wrote a few articles for the FanSided publication Call to the Pen. During the offseason, I was given an opportunity to write deep-dive analyses that used my LASR project to understand player strengths and weaknesses. I needed to frame those analyses as intriguing and click-worthy, so I did two series that are common finds in the baseball offseason writing landscape: potential breakout players and underrated/overrated players.

Here on High and Outside, I wanted to stage a little predictions game against myself. As I learned more and more about player forecasting – using “deeper” stats to predict the basics (i.e. Barrel% to HR%, Whiff% to K%, SIERA…

Read more

Standing sixty feet and six inches away is a legend-in-the-making of absolute postseason dominance. The away team has placed its fortunes entirely in the hands of its ace, a young man who has followed up an impressive regular season by taking his game to the next level in the playoffs, establishing himself as a truly unstoppable force. His postseason ERA starts with a 1, he has tossed 2 complete games, and now he’s pitching on short rest with only one thing on his mind: etching his name and his team into the preferable column of the history books.

The stakes in each clubhouse are far from equal. The home team, a blue American League contact-oriented squad, has not won or even played in a World Series in about three decades. Their recent playoff success prior to this deep World Series run has been startingly bleak. The away team, hailing from the National League West, has two very recent World Series trophies sitting proudly on the shelf. Most neutral fans are rooting for the long-suffering AL franchise to break through and keep the NL dynasty from selfishly claiming another title.

But it is not to be. With fate on the line and baseball fans everywhere feeling their hearts in their throats, Madison Bumgarner throws an inside fastball that jams Salvador Perez, inducing a foul pop-up that Pablo Sandoval squeezes for the final out of the game and the 2014 season. The relieved and elated Giants storm the Kansas City field and celebrate their third World Series championship in five years, leaving the scrappy, hopeful Royals heartbroken and begging desperately with the baseball gods for just one tiny shred of a moment to have gone the opposite way.

Anyway, enough reliving old memories. Let’s take a look at how the 2025 World Series went.

It’s Game 7 of the World Series. The home team is batting with their backs against the wall, down by one with the tying run on third base. Their beloved, solidly built catcher strolls to the... Hey, wait a minute here...

• Salvador Perez... Alejandro Kirk...

• Madison Bumgarner... Yoshinobu Yamamoto...

• Kansas City Royals... Toronto Blue Jays...

• San Francisco Giants... Los Angeles Dodgers...

• Final pitches could win the game for either team...

• I’m still trying to figure out what I want to do with my life...

HEY GUYS, WE’VE DONE THIS ONE BEFORE!

Alright sure, I will acknowledge that in my weirdly-specific-but-retrospectively-vague opening that I conveniently picked out the similarities between the 2014 and 2025 World Series and left out their differences, of which there are plenty. Aside from a focus on contact, those Royals and these Blue Jays got to their respective World Series Game 7s with significantly different strengths and weaknesses, particularly in the bullpen. Both were financial underdogs compared to their counterparts in the series, but they were on completely different planets in terms of payroll. And while the 2025 Blue Jays entered the field with five playoff appearances in the previous ten years (without much to show for it), the 2014 Royals hadn’t even been to the playoffs since their 1985 championship.

Meanwhile, the 2014 Giants and 2025 Dodgers were even further apart from a production distribution standpoint. San Francisco relied heavily on their defense to support an average offense and pitching staff, while the Dodgers sported top-5 offense and pitching to go with average defense. Though those Giants were no slouch when it came to spending, these Dodgers’ gigantic financial commitments have painted them as much more of an evil enemy-of-all than those Giants ever were. Those public perceptions are also tinted by a contrast in recent playoff appearances: the Dodgers are currently on a 13-year postseason streak, while the Giants only made the playoffs three times between 2004 and 2015 – winning the World Series each time.

Still, the parallels exist, and if you sneak a peek into those Royals’ futures, there may be a modicum of hope to be found for current Toronto fans who have spent the past week sifting through the rubble of their intense and memorable 2025 season.

In the moments and days after the 2014 World Series concluded, I remember feeling utter heartbreak for Kansas City. The Royals had been dreadful for a long time, finishing with a losing record (often by a wide margin) in 17 out of 18 years from 1995 to 2012. Finally in 2013 they put together a respectable 86-win third place finish, in which a young and improving core of position players paired with a sturdy crew of veteran starting pitchers brought some promise to the franchise’s future that hadn’t been there – or at least hadn’t been fulfilled – for many fans’ entire lives.

They had an unspectacular start to the following season, but they rode a 24-6 stretch in late July and August to the playoffs, ultimately losing the division by a single game but grabbing a Wild Card spot in the final week of the season. I could take the next 3000 words and dedicate them solely to the outrageously dramatic 2014 AL Wild Card game, but I’ll restrain for now. The important part is they won, and then continued winning all the way through the AL portion of the postseason bracket. They swept the Angels and Orioles in the ALDS and ALCS respectively, stampeding into the World Series as a surefire Team of Destiny. After multiple generations of Kansas City fans had suffered through season after season of maddening incompetence – and, perhaps worse, bland irrelevance – the 2014 Royals were finally a team to talk about.

Then, Madison Bumgarner happened. Of course, he wasn’t the only reason San Francisco won the series, but in much the same way Toronto fans never want to see Yoshinobu Yamamoto toe the rubber against them ever again, Bumgarner put his team on his shoulders and dragged them heroically across the finish line. Heading into Game 7, Bumgarner and Yamamoto had each claimed 2 of their team’s 3 victories in the series with dazzling starts. Then in the season-deciding final innings, they each entered the game on short rest out of the bullpen to put a few more zeroes on the board and seal the deal.

Bumgarner pitched the final 5 innings of Game 7 and looked nearly unhittable – emphasis on nearly. For those who don’t remember or need a refresher, Salvador Perez wasn’t the first potential last out of that game. Right before him, Alex Gordon stood at the plate representing Kansas City’s last hope, batting left-on-left with 2 outs against what appeared to be an actual pitching god. Miraculously, he lofted a sinking liner to left-center field that dropped in for a hit. For a split second, Royal fans’ hopes slightly lifted: Gordon had successfully passed the baton and had gotten on base.

Then those hopes soared through the roof. Gregor Blanco, the center fielder, misjudged the ball’s hop and it bounced right past him, skipping chaotically to the outfield wall where Juan Pérez, the left fielder, briefly bobbled it as he attempted to coral it. It was as if the Giants’ outfielders had suddenly and inexplicably decided to let the Royals tie up the game. The TV broadcast did not show Gordon circling the bases until after Pérez’s goof, and when that camera cut was finally made, many of us expected to see Gordon rounding third and sprinting home with the game-tying run. Alas, being only an average runner, he was only then rounding second. He made it to third easily as the ball was relayed in to the cutoff man, Brandon Crawford, but he received a crucial stop sign from his third base coach to keep him there.

In the years since, I have often woken up in the middle of the night drenched in sweat, screaming “SEND ALEX GORDON HOME YOU COWARDS!” We had the potential to witness one of the most exciting moments in baseball history: a bang-bang play at the plate with the fate of the World Series on the line. If safe, a tie game where anything could happen. If out, a wildly intense ending for us to marvel at for the rest of time.

It was very likely the correct call to stop Gordon at third, since Crawford received the ball right around the time Gordon would’ve tagged third if he hadn’t slowed down. In an alternate reality, Gordon never slows down but Crawford’s throw home beats him by a mile, and the series ends with the third base coach tarred and feathered on the field. However, in other alternate realities, Crawford’s throw home is off-line, or is not handled well by the catcher, or Gordon turns on the jets and sneaks in with a marvelous slide. In this reality, we know that Perez subsequently popped up and ended the game anyway, so we’d give Gordon the hindsight green light 100 times out of 100. What would change? Anything? Everything?

I know I’ve gone off on a tangent, but that’s on purpose. I’m not here to hash out all of the nitty gritty details of the “What If?” moments that the Blue Jays experienced last weekend – the rest of baseball media seems to have that covered – I’m simply here to say my baseball heart goes out to you, Toronto. I am not a Blue Jay or Royal fan, but in these moments where miniscule changes to a coach’s stop sign or a runner’s lead or a flyball’s arc could have flipped the script at the flip of a coin, there is not a soul in baseball who doesn’t see the gloomy faces in the losing team’s dugout and feel the yearning heartache, the agonizing grasp of air when the gold was a mere inch from your fingertips, the griefstricken plea for a Goddamn rewind button.

And remember: the story of this season has concluded, but the larger plot in the movie about this Blue Jays era is likely unfinished. The next season, those Royals came back with a vengeance and completed the mission, winning the 2015 World Series in a relatively laidback series against the Mets. In retrospect, you can imagine it as all part of their master plan. They used 2014 simply to establish their place among baseball’s main characters, telling the world, “Hey! We don’t lose 90 games a season anymore!” By the time the 2015 playoffs arrived, we were already familiar with Gordon, Perez, Lorenzo Cain, Mike Moustakas, Eric Hosmer, Wade Davis, Greg Holland, and the rest of the gang. They knew they had what it took to go all the way, and we knew they were serious about it.

The Blue Jays didn’t win a single game when they made playoff bids in 2020, 2022, and 2023. Entering this season, the era defined by Vladimir Guerrero Jr.’s and Bo Bichette’s rises to stardom seemed doomed for perpetual disappointment. And okay, yeah, they’re certainly disappointed right now, but they have successfully broken through the relevance barrier and established themselves as one of the current main characters of baseball. If you didn’t know Vladdy & Bo, Alejandro Kirk, Addison Barger, Ernie Clement, Kevin Gausman, and Trey Yesavage before, you certainly do now. A little more than a month ago, we didn’t know what they were capable of. Now we do. Perhaps a sequel is in the works.

(Oh, and congratulations to the Dodgers for actually winning the whole thing (again). I decided not to focus on them here because, y’know, “been there, done that.” But I am happy for Shohei, Mookie, Freddie, Yamamoto, the rest of those guys, and most importantly for my Dodger-fan aunt Lisa, who kept me company (virtually) during each game of the series, including all the way through the glorious 18-inning masterpiece in Game 3. Congrats Aunt Lisa, next time we see each other we’ll have to do a shot every time one of us mentions Will Klein! ✌️)

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Earlier in the season, I made a few posts I called “LASR Beams” which were meant to highlight several well-performing players by briefly exploring their LASR (League-Adjusted Standardized Rating) values. I did one at the end of April, one at the end of May, and one at the All-Star break. Then I quietly decided I didn’t like the structure (I hadn’t settled on a consistent one anyway) and stopped. I was trying to include too many players at once, and the writeups ended up too shallow for my liking.

So, I’m now trying a new type of LASR Beam: an analytical deep dive into a singular player’s LASR grades and the patterns that often show up, in the context of the player’s career (with some light commentary mixed in). Who knows if this ends up being a regular thing, but I’m gonna try one and see if I like it – and who better to start with than the current star of the postseason...

Vladimir Guerrero Jr.

Before the playoffs began, Vladimir Guerrero…

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