Before Opening Day, the FanGraphs podcast, Effectively Wild, held its annual Preseason Predictions Game. Hosts Ben Lindbergh and Meg Rowley were joined by FanGraphs writers Michael Baumann and Ben Clemens, and each made 10 predictions about the upcoming baseball season. EW listeners voted on the likelihood of each prediction to determine its value, which will be kept secret until the final scores are revealed in November.

At times throughout the season, we’ll be tracking the various predictions here on the Community Blog to see how each competitor is faring. Having already covered Baumann and Clemens, we’ll now review the full ballot of Meg Rowley.

1. Isaac Paredes will earn more fWAR than Kyle Tucker.

Originally a close race in the early months, Kyle Tucker was beginning to pull away as May turned to June. July only brought more clarity to this prediction due to Isaac Paredes being placed on the IL for what was described as a “severe” hamstring injury. He’s attempting to avoid season-ending surgery in the hopes that he can return to action, but given that he’s since been moved to the 60-day IL, it will likely be too little too late for the purposes of this exercise. Should he find his way back onto the field, he’ll be at 2.7 fWAR — well behind whatever Tucker can add to his 4.0 fWAR in the interim. (The only thing keeping this from being an even more foregone conclusion? Tucker’s massive struggles in August mean he’s actually below his season high of 4.2 fWAR, having hit only .069 in his last eight games.)

Verdict: Very unlikely

2. At least three primary catchers (as defined by FanGraphs) will hit 30+ HR this season.

Regardless of how big Cal Raleigh’s dumper is, he still only counts as one player, and that’s going to be Meg’s biggest obstacle toward earning points. For Raleigh, we don’t even need to invoke a projection system — he’s already at 47 home runs and threatening to completely lap the field.

But that still leaves us waiting for two more names, and for ages it’s been difficult to feel confident about any candidates stepping forward. Logan O’Hoppe was the best of the rest through the first half, but was still well behind Raleigh, and he hasn’t been able shake a rough stretch of play that started in late May. He’s fallen down the leaderboards with only a single home run added since midseason, so he isn’t likely to factor in the result. Hunter Goodman was the only other name on the list that jumped out as a genuine threat during the first three months. He had 14 as June turned to July, and unlike O’Hoppe, Goodman has steadily added to his total, with 11 homers since then. He is projected to clip the target goal; Depth Charts and Steamer both expect him to smash six more, while ZiPS tabs him for five more, which would put him at exactly 30. Still, that would only make for two 30-homer catchers.

The good news for Meg is that reinforcements have arrived in the form of Shea Langeliers and Salvador Perez! The A’s backstop is now leading the league in second-half home runs (16), bringing him all the way up to 28 on the year and making him a virtual lock to reach 30 over the final six weeks. He is the third catcher — along with Raleigh and Goodman — projected to eclipse the threshold. Meanwhile, Perez has cooled off slightly from his post-All-Star Game barrage, but he enters this weekend with 22 homers and is projected for six more. He’s close enough that he provides Meg some insurance in the event that Goodman goes cold.

Verdict: Not so down in the dumps after all!

3. We will have no repeat division winners other than the Los Angeles Dodgers.

Thanks to FanGraphs’ Playoff Odds, we can actually quantify this precisely, by combining the odds that the Dodgers repeat along with the odds that nobody else does:

Besides the Dodgers repeating (which on its own looks not as certain as before), only one domino needs to fall for this prediction to go awry. For much of the season, the Yankees were in a commanding position to play spoiler, having peaked at 91.8%, but their slump (combined with the newfound momentum of the Blue Jays) has left them on the wrong side of the ledger. However, they have been supplanted by the Phillies, who now have a firmer grasp on the NL East, and the full goose bozo stampede that is the Brewers. The math doesn’t lie — the odds for this prediction are less than half of a percent.

Verdict: Very Unlikely

4. The New York Mets will miss the postseason.

Despite being 5.5 games behind the Phillies in the NL East, the Mets are projected to have a 77.4% chance of playing October baseball, even though they are only a half game ahead of the Reds for the final Wild Card spot. The reason? A softer remaining schedule coupled with a stronger lineup shake out to FanGraphs’ predicting 86.3 wins for the Mets, firmly ahead of 82.5 for the Reds. Yes, it’s the Mets we’re talking about here, so anything can happen, but at the moment they appear to be in the clear.

Verdict: LOLMets?

5. Nick Kurtz will earn more fWAR than Travis Bazzana.

Travis Bazzana isn’t even in the majors yet, but this prediction has still seen multiple twists and turns. The first was Nick Kurtz getting an early call-up, making his debut on April 23. But from there, he struggled to produce, and fell as low as -0.4 fWAR on May 19. So, technically, Bazzana was in the lead! Could he earn his own call-up to make this a true contest?

The answer to that question, however, has proven to be elusive. What initially felt like an expected 2025 debut suffered a setback when Bazzana was placed on the IL after straining his oblique during a doubleheader in Double-A action, only returning to the active roster on July 15. Yes, he’s been solid since, and was promoted to Triple-A on August 10, but any buzz regarding a trip to Cleveland has been completely absent. Eric Longenhagen concurred in his breakdown of the Top 48 Guardians prospects, believing that Bazzana’s timeline is “probably more in the late-2026 or early-2027 range”.

But, of course, you likely know what happened next: Kurtz hopped onto the express train to Rookie of the Year buzz, grabbing the attention of sportswriters everywhere. His four-homer, six-hit night against the Astros on July 25 firmly cemented that this was easily one of Meg’s best predictions since the exercise began, and he now is the proud owner of 3.9 fWAR at time of this writing. “Big Amish” can stop churning if he wants, because Meg’s got points coming her way.

I think @megrowler.fangraphs.com has earned the right to gloat a bit about her Kurtz > Bazzana prediction. WOW.

— Effectively Wild Stats (@ewstats.com) 2025-07-26T03:04:17.029Z

Verdict: Very Likely

6. Chandler Simpson will lead the league in stolen bases.

If I had written this update earlier, the vibes would have been immaculate. In only 35 games of action, Chandler Simpson had compiled 19 stolen bases, was among the league leaders, and was projected to finish the season ahead of all comers. And then, at the end of May, the Rays demoted him to Triple-A, a move which Rays manager Kevin Cash called a “tough decision,” and what I call a “crime against baseball” (though only one of us has to worry about being diplomatic about roster construction).

In their defense, the Rays genuinely were in a difficult spot: Jake Magnum was coming off the IL, and an already crowded outfield meant that their best move was to demote Simpson or lose Christopher Morel altogether, since he was out of options. The Rays chose the path that kept everyone available while giving Simpson regular playing time, instead of playing a bench role at Steinbrenner Field. He was back in less than a month’s time, added back to the roster on June 24 and swiping his first bag only three days later, and staying in form ever since.

So where does that leave us? Thankfully, with a competitive race! Simpson finds himself only four stolen bases behind current leader José Caballero, and ZiPS can’t foresee much daylight between the two over the remainder of the season. If Simpson stays healthy and the Rays continue to provide him with playing time he deserves, good things could be in store.

Verdict: If this doesn’t happen I will personally blame Jeff Sullivan

7. The Athletics will finish second in the AL West.

Unlike the White Sox, who had their pick of days to lead the AL Central for Ben Clemens, the Athletics need to end the season as runners-up. Also unlike the White Sox, the A’s actually managed to briefly grab hold of that rung on the ladder, sitting in second place for most of early May. Sadly, that stretch looks to be all they’re capable of mustering, and they have since returned to being cellar dwellers. Now 14 1/2 games back of the Astros and 11 behind the Mariners, who currently occupy the spot they need, I don’t know if there’s much in the way of silver linings to find here.

Verdict: Sell the team

8. At least two major league teams will announce new controlling ownership.

Before I begin my update, I just wish to quote the very end of this prediction’s discussion, during which the panel interrogated multiple qualifications:

Ben L: Alright. I think hopefully that’s specific and precise enough for us to be able to call this.
Meg: I think it is.
Ben L: Yeah, I think we’ll probably know it.
Meg: You’ll know! You’ll know. (defiantly) We’ll know!

*sigh*

The buzz around this prediction spiked when the Chicago White Sox revealed a deal between owner Jerry Reinsdorf and billionaire investor Justin Ishbia to transfer shares over the next several years, with Ishbia being able to hold a controlling stake as early as 2029. Based on the wording of the announcement and how the language of the prediction can be parsed, as well as Meg’s spelling out that the sale does not have to be completed by the deadline, it simply must be announced, I felt that it met the requirements. The evening that the news broke, I shared on social media that it counted toward this prediction being fulfilled.

However, some EW fans have disagreed, and I’m more than willing to say they have a valid argument: Technically, the announcement wasn’t about “new controlling ownership,” it was “a deal in place between two parties that builds a framework for said parties to have the option to transfer controlling ownership.” There is definitely a sequence of events where neither party exercises their part in the deal, and Reinsdorf remains owner of the White Sox past the spry young age of 98.

My counter to this is that a contract of this scope isn’t signed lightly, and doesn’t happen unless both sides go into the deal fully intending to see the deal through. Additionally, the tone of the press release, the quotes from both sides of the deal, and the analysis of media members close to the the relevant parties all communicated this is for all intents and purposes a succession plan. Given the long time frame that the deal required, it was more a pragmatic move for the transactions to be phrased as “options,” rather than an indication of indecision.

Meanwhile, the news cycle had barely cooled on this state of affairs when it was let slip that the Tampa Bay Rays are in “advanced negotiations” to be sold to a group of investors, who have reportedly signed a letter of intent. That letter has an expiration date, and should a deal not be struck, word is that another interested party is ready to step in and make a competing offer if given the opportunity. It’s been about a month since that report surfaced, and there’s no new info, but all in all, it’s looking promising for someone to put pen to paper and wrap this prediction up.

The background noise to all of this has been the ongoing saga that is my beloved Minnesota Twins. The Pohlad family making good on their professed desire to sell the Twins would, from my perspective, solve multiple problems: My favorite team would have someone less likely to cut payroll immediately following their greatest success in decades, and any controversy surrounding the White Sox factoring into this prediction would be moot — assuming the Rays rumors bear fruit. But those hopes were dashed the morning of August 13 by a press release reversing course, and instead simply adding two new groups of minority investment groups with no path toward a transfer of control.

With all of that laid before us, we await news from Florida, and any discourse that may follow.

Verdict: For the love of God, let’s make a deal!

9. Ethan Holliday will be the first pick in this year’s MLB amateur draft.

As All-Star weekend approached, Ethan Holliday topped multiple mock drafts, including Jim Callis’ projections for MLB.com. Holliday was spoken of as one of the two best high school picks, alongside fellow shortstop Eli Willits. That, along with multiple players on the collegiate side earning plenty of attention, made for a situation where there was no clear-cut prospect separating himself from the pack. The best evidence for this was FanGraphs having Holliday and Willits ranked ninth and 32nd on its draft board, respectively. In the end, the poetic result won out, with Holliday being selected fourth to play for his father’s Colorado Rockies. A fitting outcome, but not the one needed here.

Verdict: False

10. Ketel Marte will win the NL MVP.

Only days after inking a big extension with the Diamondbacks, Ketel Marte’s MVP campaign took a major hit when he suffered a left hamstring injury on April 4 and missed a full month of playing time. On the bright side, Marte has been playing well since then (4.3 fWAR as of August 21), and was selected as the starting second basement for the NL All-Star team.

However, his path toward an MVP award is hampered by obstacles on multiple fronts. Statistically, Marte would need to challenge Shohei Ohtani (which was predictable), Kyle Schwarber (which was less so), and Pete Crow-Armstrong (which was not). But with all three of these players comes the other major obstacle: media narrative. Ohtani’s star shines incredibly bright as it always has, and PCA’s ascendancy has made him a darling of sportswriters nationwide. Schwarber is in the middle of his best season yet, and he’s a fun and media-friendly player. It’s fair to say that Marte doesn’t have the same support behind him. In recent days, much ink has been spilled on accusations of his leaving the team to go on vacation and being “a diva in the clubhouse,” as well as on his full-blown apology tour trying to mend fences. It’s not a stretch to imagine that this could cloud his MVP case in the eyes of BBWAA voters.

If it were up to me, and it was my responsibility to coax Marte back into contention, my first question would be “any chance you’ve got a good knuckleball?” (This is one of the many reasons why I’m not employed in a front office.)

Verdict: Unlikely

Mookie Betts or Ronald Acuña Jr.? This was the question 2023 MVP voters were tasked with answering. There were cases for both: Acuña had just completed an incredible, record-breaking 40/70 season, while Betts had something others argued could not be quantified — positional versatility. The 2023 season marked his professional return to significant time in the infield, complementing his esteemed right field defense that had already earned him multiple Gold Glove awards.

Yet, as this debate unfolded, I found myself wondering: How, in the most quantified sport in the world, was there such a gap in our numerical understanding of player value? Surprisingly little research has been done on the value of positional versatility beyond its defensive benefits, which, for the most part, are at least somewhat accounted for in existing metrics. Considering this, with my research, I focused on quantifying versatility in a different way: by assessing its impact on a team’s ability to use pinch-hitters. Positional versatility allows a manager to make more substitutions in favor of stronger hitters off the bench, potentially increasing wins in a way not currently reflected in WAR. Conversely, versatility may have no such effect or may provide only a marginal benefit that does not meaningfully impact wins. Is versatility truly undervalued in today’s analytical climate? Let’s take a look. Read the rest of this entry »

Before Opening Day, the FanGraphs podcast, Effectively Wild, held its annual Preseason Predictions Game. Hosts Ben Lindbergh and Meg Rowley were joined by FanGraphs writers Michael Baumann and Ben Clemens, and each made 10 predictions about the upcoming baseball season. EW listeners voted on the likelihood of each prediction to determine its value, which will be kept secret until the final scores are revealed in November.

At times throughout the season, we’ll be tracking the various predictions here on the Community Blog to see how each competitor is faring. Having already covered Michael Baumann, who recently is feeling more optimistic about one of his picks, we’ll now review some highlights from fellow guest Ben Clemens. Read the rest of this entry »

It’s not news to anyone that being on the receiving end of a 100-mph fastball is an unpleasant experience, but it’s easy for players and fans alike to forget that this little projectile is in fact a deadly weapon. While the tragic death of Ray Chapman along with decades of bruises and broken bones have spurred hitters to don an array of protective items in the box, our baseball culture does not afford pitchers the same protections. This is despite the fact that batted balls regularly exceed the speeds of the fastest fastballs, and occasionally top 120 mph, which may approach the limits of human reaction times at that distance. Additionally, while hitters are selected for their superhuman eyesight and reaction skills, those attributes are generally less important for pitchers. The issue may be further exacerbated by the increasing emphasis on exit velocities in modern hitting analytics:

That’s a concerning chart. In particular, notice the difference in scales across the panels; while the frequency of batted balls with exit velocities of at least 90 mph has increased by 8.7% in this span, batted balls hit 110+ mph occur a full 41% more often now than they did in 2015! Let’s find out if this trend is putting our already-fragile pitchers at even more risk. Read the rest of this entry »

Before Opening Day, the FanGraphs podcast Effectively Wild held its annual Preseason Predictions Game. Hosts Ben Lindbergh and Meg Rowley were joined by FanGraphs writers Michael Baumann and Ben Clemens, and each made 10 predictions about the upcoming baseball season. EW listeners voted on the likelihood of each prediction to determine its value, which will be kept secret until the final scores are revealed in November.

At times throughout the season, we’ll be tracking the various predictions here on the Community Blog to see how each competitor is faring. We’ll begin today with a curated selection from the crystal ball of Michael Baumann! Read the rest of this entry »

In late February, I decided to try my hand at building out my own pitch model similar to Stuff+. I had no coding or modeling experience, and outside of my overall baseball knowledge I was starting from scratch. However, with the help of Bradley Woodrum, a former Miami Marlins analyst and FanGraphs contributor, and AI, I was able to learn what I needed and develop Shape+ in R over the course of about six weeks.

Shape+ is a location independent, layered mixed effects model that aims to quantify the relationship between pitch shape and run prevention. It uses its layered model approach to isolate physical pitch characteristics and predict their expected impact on run value (xRV), producing standardized scores that are both descriptive and predictive of a pitcher’s performance.

No real outcomes were used in the training of the model. Validation was done using 2023 Shape+ scores and 2024 wOBA, xERA, and ERA. Shape+ is normally distributed, with a standard deviation of 35. This scale can be easily adjusted without affecting the performance of the model.

Note: The high median score for forkballs in 2023 is due to limited sample size — primarily Kodai Senga.

Data Processing

I used 2023 and 2024 MLB Statcast data for training my model — downloaded using the baseballr package. To prepare the data for modeling, the following preprocessing steps were executed:

• Filtering out all fastballs below 80 mph.
• Assigning a “game_year” column to each pitch (2023 or 2024).
• Standardizing pitch type labels.
• Assigning a platoon advantage binary indicator for batter handedness.
• Calculating IVB, VAA, and HAA, none of which are not explicitly included in standard Statcast data.
• Bucketing all batted balls by Hard Hit (≥ 95 mph), Soft GB, Soft LD, Soft FB, Soft Pop, and Not in Play.

After processing, I used the bucketed batted balls and fixed values for non-BIP to generate a run expectancy chart based on the average runs scored by bucket. Each pitch is now assigned a run value based on the chart, and the data are ready for modeling.

Model Structure

Shape+ is built using a layered mixed effects modeling framework. The modeling process consists of four sequential stages.

Model 1: xRV by Location
Model 1 is a large mixed effects model that is designed to predict expected run value (xRV) based on pitch type, location, platoon advantage, and count alone. The plate is sliced into a 150×150 grid to capture location effects at a granular level. Pitch types are bucketed into fastballs, changeups, and breaking balls to allow group-specific location interactions. Model 1’s goal is to quantify the value of pitch location, independent of actual outcomes or physical pitch shape.

Below are heatmaps I generated based on Model 1’s output:

Model 2: GAM Smoothing
Model 2 utilizes a Generalized Additive Model (GAM) to the Model 1 outputs, smoothing the xRV surface to reduce noise and stabilize estimates across the strike zone. In doing so, I am able to retain meaningful and important patterns while eliminating spikes caused by outliers.

The smoothed Model 2 output is used as the training target for Model 3 (xRV by Physical Characteristics), isolating pitch location from the physical characteristics. As depicted in the smoothed heatmaps below, the model is flexible enough to capture nuance by individual pitch type, such as cutters.

Model 3: xRV by Physical Characteristics
Model 3 is a linear mixed effects model that utilizes polynomial, quadratic, and interaction terms to capture non-linear relationships between pitch characteristics and xRV. It uses both fixed effects and random effects.

Fixed effects capture the impact of measurable pitch characteristics (velocity, spin, IVB, etc) across all pitchers. Random effects — implemented as ((1 | PitcherID)) — account for the unobserved, pitcher-specific variations (deception, mechanics consistency).

Model 3 is trained exclusively on the smoothed xRV output from Model 2. It includes no location or outcome based variables, effectively isolating the value of the physical characteristics of a pitch. Variables included in Model 3 are as follows:

Physical Characteristics
• Velocity, standardized to create z-scores
• Induced Vertical Break
• Vertical Approach Angle
• Horizontal Approach Angle
• Horizontal Break
• Spin Rate
• Extension
• Release Height

Categorical Variables
• Pitch Group (Fastball, Breaking Ball, Changeup)
• Pitcher Throws (R/L)
• Batter Side (R/L)
• PitcherID

Model 4: Final Shape+ Output
The final step of the modeling pipeline is Model 4, converting the outputs of Model 2 and Model 3 into a standardized and interpretable Shape+ score. It subtracts Model 3’s predicted xRV (based on physical characteristics) from Model 2’s smoothed xRV (based on location). The result, arbitrarily called stuffimpact, reflects how much pitch shape alone contributes to run prevention.

Stuffimpact is then scaled and standardized, producing typical Shape+ values between 50 and 150 to improve interpretability.

Performance and Validation

Shape+ performs exceptionally well both descriptively and predictively. After conducting both in-sample and out-of-sample validation, I found that Shape+ scores correlate strongly with both current-season and next-season wOBA and xERA. I obtained validation data by downloading xERA, ERA, and wOBA numbers for 2024 from Baseball Savant.

Descriptive Correlations
In-sample validation testing was conducted using 2024 data, evaluating how well Shape+ scores aligned with real-world metrics such as xRV, wOBA, ERA, and xERA over the same season. These correlations can been seen below:

• 0.868 (2024 xRV and 2024 Shape+)
• -0.347 (2024 ERA and 2024 Shape+)
• -0.571 (2024 xERA and 2024 Shape+)
• -0.464 (2024 wOBA and 2024 Shape+)

The particularly strong correlation with xRV — the model’s training target — demonstrates excellent internal validity. In addition to this, these strong to moderate-strong correlations demonstrate that Shape+ accurately captures the quality of contact that pitchers are inducing in real time, confirming its descriptive power. The four scatterplots below depict the four descriptive correlations.

Predictive Correlations
Shape+ shows strong year-to-year consistency, reinforcing its reliability as a forecasting metric. The correlation between 2023 and 2024 Shape+ scores is 0.801, indicating a high degree of stickiness and model stability.

When used predictively, Shape+ correlates strongly with next-season performance metrics like xERA and wOBA. This suggests that Shape+ not only describes current pitch effectiveness, but that it also effectively anticipates future run prevention ability, making it a potential tool for forward-looking evaluation.

• -0.342 (2023 Shape+ and 2024 ERA)
• -0.590 (2023 Shape+ and 2024 xERA)
• -0.451 (2023 Shape+ and 2024 wOBA)

Below, I’ve included the three predictive correlation scatterplots:

I should note here that ERA is a noisy and context-dependent metric, heavily influenced by factors outside a pitcher’s control, such as defense, park effects, and weather. As a result, it is not a reliable target for evaluating pure pitch quality. Shape+, by contrast, is specifically designed to isolate and quantify the components that a pitcher can control. Metrics like xERA serve as better validation tools for this purpose, as they focus solely on outcomes driven by the pitcher’s own skillset.

Residuals and Error
Shape+ demonstrates excellent alignment with the values it is targeting, confirmed by strong error metrics and stable residuals.

• RMSE: 0.022
• MAE: 0.018

These low values indicate that predictions from the model are consistently close to the actual smoothed xRV values, verifying the model’s precision.

Residuals show a tight linear relationship with minimal spread and few outliers. They are evenly distributed across the Shape+ scale, indicating low bias and overall consistency. Taking both the RMSE/MAE and residuals plot into account, we can confirm that Shape+ reliably quantifies pitch-level run prevention.

Pitcher Cases

Shape+ can be easily applied to individual pitchers to evaluate the shape-based effectiveness of their arsenals. Using a few lines of code I can pull the 2024 Shape+ score for a given pitcher’s arsenal.

Robert Suarez, RHP, San Diego Padres

Josh Hader, LHP, Houston Astros:

Cole Ragans, LHP, Kansas City Royals:

MacKenzie Gore, LHP, Washington Nationals:

We can also pull the top 10 pitchers by Shape+ in 2024 (min. 1,800 pitches):

Conclusion

Shape+ is a location-independent model that quantifies the relationship between pitch shape and run prevention. By combining a layered modeling framework — including location modeling, GAM smoothing, and physical attribute regression — Shape+ aims to provide a robust and interpretable evaluation of pitch effectiveness.

Shape+ demonstrates both strong descriptive and predictive performance, and compares favorably to existing public models — particularly in its ability to forecast next-season xERA and wOBA.

Cade Cavin is the Assistant Director of Analytics for Point Loma Nazarene University in San Diego.

Last week, Ben Clemens wrote a fantastic piece comparing how well he, the FanGraphs crowd, and a set of other baseball websites and analysts did at predicting the fates of 2025 MLB free agents. His conclusion is that we, the crowd, did an overall great job. I concur. Yay, us!

Unaware that Ben had a piece in the works, I also spent time over the past few weeks testing the wisdom of the FanGraphs crowd against what free agents actually received this offseason, and hoped to publish my work in the newly revived Community Blog. After Ben’s article was published, the editorial staff at FanGraphs noted that I took slightly different slices of the data and conducted different analyses. They encouraged me to refine and update some of my analysis so they could then run it as a companion piece.

“Wisdom of the crowd” is the idea that the collective opinion of a diverse, independent group of individuals will come to a better decision than that of even the most well-informed individual expert. While it doesn’t always bear out, where would we be without the wisdom of crowdsources like Reddit, Quora, Wikipedia… or FanGraphs?

Beyond just being a fun way to engage with readers, the FanGraphs free agent crowdsource predictions exercise represents a good test of the “wisdom of the crowd” theory. Let’s see how wise this crowd turned out to be.

To test our wisdom, on February 17 and then again on March 8, I downloaded the excel file of the 2025 Free Agent Tracker. I then selected for every free agent who signed for a total contract value of at least $2 million, resulting in 105 cases. (Jon Berti and Amed Rosario just made the cut with one-year, $2 million deals; Mike Tauchman and Michael A. Taylor just missed it at one year and $1.95 million.) I then sorted out the players who didn’t receive a crowdsource estimate, reducing the pool to 67 players.

These 67 players were predicted by us to receive a collective 153 years of contracts (an average of 2.28 years per player) for a collective total of $2,886,520,000 and an average contract of $43,082,388.

However, this was an overall good year for free agents — just ask Juan Soto (15 years, $765 million) or Max Fried (8 years, $218 million) — and they ended up collectively out-earning our expectations by 10.4%. These 67 free agents signed a collective 150 years of contracts (we were 98.7% accurate in cumulative contract length!) for a collective $3,186,325,000, with an average contract of $47,557,090 (an AAV of just over $19 million).

Our collective wisdom was pretty good, but we underestimated the market by 10.4%. Let’s take a more detailed look at what happened.

Analyzing the Widest Prediction Gaps
In my research, I looked at the differences between what the FanGraphs crowd predicted and the actual signings. Again, I compared total contract value to the median crowdsourced prediction. (I know this is an oversimplification. I couldn’t think of an elegant way to include things like the “value” of opt-outs, etc.)

The FanGraphs crowd was within $2 million of the actual contract value for 22 of the free agents and got two exactly right, the one-year deals that Justin Verlander ($15 million) and Caleb Ferguson ($3 million) signed.

Some free agents signed for considerably more than we expected. In terms of total amounts, Soto, Fried, Blake Snell (5 years, $182 million), Tanner Scott (4 years, $72 million), Willy Adames (7 years, $182 million), and Corbin Burnes (6 years, $210 million) all exceeded their contract predictions by at least $30 million, with Soto, Fried, and Snell earning over $60 million more than we predicted. No other player signed a deal worth more than $20 million above what we predicted for him. Five relief pitchers — A.J. Minter (2 years, $22 million), Andrew Kittredge (1 year, $10 million), Yimi García (2 years, $15 million), Blake Treinen (2 years, $22 million), and Scott — netted contracts worth more than double what we expected they’d get. Minter especially scored a bonanza, getting a total value that was more than four times higher than what we predicted. It was a very good year to be a high-upside relief arm.

On the flip side, there were other free agents who signed contracts below our predictions, with many of these players settling for short-term deals that curbed their total values. Pete Alonso (2 years, $54 million), Jack Flaherty (2 years, $35 million), Ha-Seong Kim (2 years, $29 million), Alex Bregman (3 years, $120 million), and Gleyber Torres (1 year, $15 million) all received contracts that were worth at least $30 million less than we expected, with only one other player, Andrew Heaney (1 year, $5.25 million), underperforming his predicted deal by more than $14 million. In terms of percentage, Alonso, Flaherty, Kim, Torres, Heaney, and Max Kepler (1 year, $10 million) received less than 50% of what the crowd predicted. It’s worth pointing out that Alonso and Bregman received higher average annual values than expected, owing to their shorter contract lengths. Bregman is set to make $4.7 million more per year than the $27 million AAV the crowd predicted for him, while Alonso’s $27 million AAV is $2 million per year more than expected.

Eyeballing the data, a pattern seemed to emerge in which the top of the market did as well or better than expected, while the middle-to-bottom of the market fared worse. (This would be consistent with many author and reader hypotheses of how the market would shake out.) So I ran a quick correlation between the rank of the size of the signed contract and the difference between prediction and actual total contract value. The correlation coefficient was -.52, indicating that indeed those who signed the smaller contracts signed for less than predicted more so than those who signed larger total contracts.

The Implications of Signing Late
Once again, Ben pre-empted some of my analyses with his own deeper dives. Specifically, back in January, he examined whether those who signed late in the offseason (a) signed for less, and (b) performed worse than those who signed earlier. In short, yes and yes.

When one of my favorite players, Alonso, signed late and for far less than we expected, I also hypothesized that those who sign late in free agency get less than expected relative to those who sign early. After all, late signees usually do so after their options have dwindled. However, the 2025 free agent tracker does not include date of signing, so I could not directly test this proposition. (I suppose if I’d really wanted to spend time researching this, I could have gone back and logged every signing date, but, you know, this is the Community Blog and not my full-time job.) That said, I did find the list of players who’ve signed since the start of February as opposed to earlier in the offseason and ran some quick comparisons.

Of the players in the main sample, nine signed between February 1 and March 8. Nick Pivetta, Enrique Hernández, and Tommy Pham all signed for more than expected, although Hernández and Pham signed one-year deals worth $1.5 million and $1 million above their modest one-year expectations. Pivetta signed for an additional year and $10 million over his predicted deal (5 years and $55 million, compared to the crowd’s prediction of 4 years and $45 million). Kanley Jansen (1 year, $12 million) and Justin Turner (1 year $6 million) signed one-year contracts worth $2 million less than we expected — not very far off expectations.

The remaining four deals since the start of February demonstrate the downside of signing near the end of the offseason. In fact, those four players (Bregman, Alonso, Flaherty, and Heaney) represent four of the six deals listed in the overestimated contracts sample included in the previous section.

Comparison to Six Years Ago
Back in 2019, I performed a similar analysis that was published here in the community notes. That year’s free agent class, led by Bryce Harper and Manny Machado, came in under our expectations. Recall 2019 was a very cold “hot stove,” with major signings not occurring until late in the offseason. (Harper wasn’t signed until February 28!)

We predicted that year’s sample of 47 free agents would receive a collective 123 years of contracts (an average of 2.61 years per player) for a collective total of $1,801,000,000 and an average contract of $38,320,000. However, they ultimately received 91.4% of what was expected – a collective 109 years of contracts (an average of 2.32 years per player) for a collective total of $1,619,500,000 and an average contract of $34,460,000.

Our collective wisdom back then was also pretty solid, except we overestimated the market for free agents by just about the same margin as we underestimated it this year.

Conclusions?
I don’t think we can take any major lessons from this analysis — perhaps just two small ones: 1) relief pitchers with upside did well, and 2) prominent free agents shouldn’t wait too long to sign. That said, running the numbers was fun, and I hope you enjoyed the article. I’ll be back next year at this time with another update. Until then, let the wisdom of crowds protect you from the madness of crowds.

An aging curve illustrates how a performance changes throughout a career. It plays a crucial role in various fields of baseball, particularly in player evaluation and forecasting. While any performance measure could theoretically be the subject of an aging curve, we will focus on WAR hereafter. This is because WAR captures a player’s actual playing time. No player can help his team win while sitting at bench (or hospital).

Before we get into the technical stuff, let’s talk about what we pursue, or expect from an aging curve. I expect an aging curve to be the average trajectory of players. In other words, I expect a player to follow the trajectory over his career.

Keep in mind that this is not the same as simply the average WAR of all player-seasons at each age. That approach would be valid only if players started and ended their careers at the same ages. But that is not the case.

Consider this example: Imagine a league established in 2015 with two distinct groups. As of 2015, half of the batters are ordinary players in their age-20 seasons, while the other half are 30 but very talented. If we track these players until 2024 and average their player-season WAR by age, we’d get a curve spanning ages 20-39. But this curve does NOT represent a single player’s trajectory, since two parts of the curve (20-29 and 30-39) are constructed from whole different populations.

Below is the crude aging curve of batters, constructed by simply averaging each batter-season WAR by age. I looked at the player-seasons of the Statcast era (2015-present, excluding 2020), and includes all primary position non-pitcher players with at least one plate appearance. To keep the sample size reasonable, I only looked at players 21-35 years old.

In this injury-epidemic era, not being hurt is getting increasingly important. To account for this, I imputed a WAR of zero for a player who missed an entire season if he has a record in the majors both before that missed season AND after that missed season.

Take Fernando Tatis Jr. as an example. He missed all of 2022 due to injuries and his PED suspension, and because he played both before and after 2022 (2019-21, 2023-24), I counted his 2022 WAR as zero. (This is a small bonus of using WAR. We can easily impute zero to these seasons. With other stats like wRC+, picking a value to fill in would be much trickier.)

In this crude model, players peak at age 30 with a WAR of 1.2. This might look different from aging curves you’ve seen before.

The point of this is to account for the fact that players debut and retire at different ages. Previous research tackled this by looking at WAR differences between consecutive seasons for individual players. Here’s how it works: Take Shin-Soo Choo, who posted 0.5 WAR at age 33 and 0.3 WAR at age 34. That -0.2 WAR difference becomes one data point for the age 33-to-34 change. In contrast, Félix Hernández’s final season came at age 33, so we do not use his data to calculate 33-to-34 difference. This way, we’re comparing players to themselves, using the same group of players for each year-to-year change. (Note that the mean of differences is the same as the difference of means, if the population is fixed.)

This “difference method” helps with the population mix problem, but it’s not perfect. We run into trouble when comparing changes across multiple years because we’re dealing with different groups of players. Let’s say we use 100 players to find the average WAR change from age 33 to 34. When we look at the change from 34 to 35, we’re working with a different group – not even necessarily a subset of those original 100 players.

This all comes back to players starting and ending their careers at different times. If everyone played from the same age to the same age, we’d have consistent groups to compare. Even with varied career lengths, it would work if retirement and debut ages were totally random – like if they were decided by flipping a coin rather than being tied to how well a player performs. But that’s not how baseball works – performance definitely affects how long players stay in the league.

Let’s try a different approach to building an aging curve. Instead of just averaging WAR (or difference of WAR) by age, we’ll use what is called a mixed effects model. This is great for our purposes because it can handle a correlation problem: A player’s performance in one season tends to relate to his performance in other seasons. For example, Choo’s WAR at age 33 is definitely related to his WAR at age 34, but not related to the 3.1 WAR Nolan Arenado put up last season at age 33. Assuming the quadratic curve of WAR by age, I fitted a model like this:

WAR_i = β0 + β1age + β2age^2 + b0i + b1iage

The subscript ‘i’ represents a player. The model has two parts for each player:

Part 1: b0i is called “random intercept” and represents how much a player i is departed from the average player.

Part 2: b1i is called “random slope” and represents how much a player i’s rate of change by age is different from the average player.

But usually, those “random effects” are not of primary interest and considered to have a mean of 0. What we really want is the average trend. That comes from the β values, which turned out to be: β0 = -15.46, β1 = 1.20, and β2 = -0.02. I plotted these values to create the aging curve below. Note that I used a different color for ages above 35 since those are not based on the data, but extrapolated by the model.

Using this model, we find players peak at age 27.2 – three years earlier than what our crude model suggests. But we’re still running into the same issue we had with the difference method: survivor bias.

The players who stick around into their mid-30s aren’t your average players – they’re usually the most talented ones who can still perform at a high level. So when we look at the numbers for older ages, we’re really just looking at the success stories, which makes our estimates too optimistic. This is well illustrated by the pitchers. If we draw a crude WAR average graph, it has a peak at age 34! (For pitchers, any player of primary position pitcher, with at least one total batter faced was included in the study.)

Previous studies tried to handle this bias by making educated guesses about what players would have done if they hadn’t retired early. For example, if a player retired at 34, researchers would estimate what his WAR might have been at 35 and include that in the analysis.

But there’s another approach: using what’s called a joint model. As the name suggests, this combines two different models: 1) A longitudinal model that tracks how WAR changes with age, and 2) A survival model that looks at how WAR affects when players retire.

Instead of just looking at how players age OR just looking at who stays in the league, we’re considering both at the same time. This gives us a much more complete picture of player aging.

I’ll omit the technical details. I used an R package called JMBayes. Below you can see how this joint model compares to our simpler naïve model.

The blue curve is the naïve model, and the red curve is the joint model. The peak of batter is at age 26.5 in the joint model, approximately 0.7 years earlier than the one by the naïve model. Below are the curves for pitchers. The peak was at 26.8 in the joint model, 0.9 years earlier than the peak at the naïve model (27.7). (Just remember that any values shown beyond age 35 are projections based on our model, not actual data.)

This is all I want to introduce.

But one more thing. Should we adjust for the survivor bias at all?

The answer depends on what you’re trying to figure out. Let’s look at two common scenarios:

Scenario #1: If you’re trying to predict how a 34-year-old free agent pitcher will perform this year, the unadjusted difference method works just fine. After all, this pitcher is still in the league, so we know he has “survived” to this point.

Scenario #2: But if you’re projecting a 20-year-old prospect’s career path, you definitely want to use the adjusted model. Here’s why: The unadjusted aging curve is actually showing you two different things. The data for players in their 30s only includes the most talented players who stuck around, while the data for players in their 20s includes pretty much everybody who made it to the majors. So you end up with a curve that doesn’t truly represent either group – it’s not showing you the path of an average player OR the path of a star player.

One final note about the difference method that’s easy to misinterpret: When it shows something like “WAR decreases by 0.3 between ages 34 and 35,” that’s what statisticians call a conditional difference. This means “IF a player is good enough to still be playing at both age 34 AND age 35, he typically declines by 0.3 WAR.” It’s not telling you about all players who reached age 34 — just the ones who kept playing through 35.

“One for the Money. Two for the Show.” That’s how the saying goes anyways. And that’s how Cristopher Sánchez’s career has gone thus far.

After a stretch of 33 starts from 2023 through mid-June 2024, in which the lanky lefty compiled 183 2/3 innings of a 3.09 ERA, he was really into the money, to the tune of a four-year, $22.5 million extension – with room to grow to six years and $56.5 million with incentives and club options.

He followed up this showing with 15 more strong starts, along with a five-inning performance in Game 2 of the NLDS, the Phillies’ only win against the Mets, to put a bow on his second year as a rising star in the majors.

But why isn’t he treated as such? You won’t often find the 28-year-old gracing MLB’s Instagram account, or even Pitching Ninja’s for that matter. He is not marketed as an ace despite his relative youth, place in a big-market postseason rotation, and proclivity to go deep into games.

Maybe the better question is, why should he be? I’ve decided to take up that mantle, at least within the friendly confines of the FanGraphs Community Blog.

For starters (pun definitely not intended), Sánchez carries a modern ace workload. Across 31 starts in 2024, Sánchez reached 181 2/3 innings. Like a true ace, he faced 24.32 batters per start – for context, this lines up with the volume of notable innings eaters Corbin Burnes and Tarik Skubal, and was certainly in the upper echelon of 2024 starting pitchers.

So, he faced a lot of batters. He also eclipsed 90 pitches per start. Both of these statistics matter because, to an extent, the best ability is availability. Cliché, sure, but true for postseason hopefuls like the Phillies, and true when it comes to staying at the top of fans’ minds. Put differently, Sánchez’s 3.32 ERA was stretched over more innings than most starters in the league threw last season. That is valuable.

Under the hood, the most notable pieces of Sánchez’s profile are his sky-high 39.3% O-Swing% (Sports Info Solutions) and solid 65.8% first pitch strike rate. That 39.3% O-Swing% was good for first in baseball last season. That’s right: Cristopher Sánchez – League Leader. Let that sink in.

As I mentioned earlier, Sánchez is not thought of as a particularly nasty pitcher. His stuff is not commonly GIF’d. He is relatively obscure, as far as sub-3.50 ERA starters on playoff teams go. Yet, he clearly stands out, especially when it comes to generating swings on pitches outside of the zone.

So how does he generate all of this chase? And why aren’t we calling him Chasetopher Sánchez (that may be more obvious)?

Sánchez throws three pitches according to Baseball Savant: sinker (47.3%), changeup (35.7%), and slider (16.9%). And he generally throws them all low in the zone.

This makes sense. He is a groundball specialist, generating a 58.3% groundball rate last season, good for a 95th percentile finish and the consequent bright red bar on his Savant page. And if we all are trained to do one thing, it’s to love when a player has bright red on his Savant page. It’s akin to a “Happy Hour” sign in a bar window or “BOGO” sign at the deli. It’s the modern day siren song. How could you not follow it?

The graphic above shows Sánchez’s three primary pitches and plots only those that generated swings and misses. Clearly, changeups below the zone make up the vast majority of his impressive O-Swing%, followed by sliders below the zone.

Sánchez threw his changeup outside the zone 71.8% of the time last season when ahead in the count (compared to 51.1% of the time when behind) and threw sliders outside the zone 60.4% of the time when ahead. Another note – the changeups in these situations skew glove side, while the sliders skew arm side.

Here was a particularly nice changeup thrown to Freddie Freeman in a 1-1 count.

This same pitch and location worked against lefties and righties. See the following string-pull presented to Aaron Judge.

The changeup plays well off of the sinker, his most commonly thrown pitch. There is a nearly 10-mph velocity gap between the two offerings, and both come from the same arm angle and have nearly identical arm-side tail. In other words, this is a nasty pitch, which is borne out in the 18 run value that the pitch generated in 2024, according to Baseball Savant.

Sánchez is elite at generating chase swings, likely because his best pitch is spotted to do just that. But part of why he may not get the consequent publicity his ERA would suggest he deserves is because, even as he gets all those swings on pitches outside the zone, he doesn’t miss as many bats as other high-chase hurlers.

Cubs lefty Shota Imanaga and Twins righty Bailey Ober finished second and third in O-Swing% last season, respectively. Both also allowed less contact on pitches in the zone and generated more swinging strikes than Sánchez. Not surprisingly, thanks to these whiffs, both struck out more batters than Sánchez as well.

I think this is, in part, because Imanaga and Ober are fly ball pitchers whereas Sánchez is a ground-and-pound specialist. Famously, balls hit in the air come with a greater risk of going over the fence than grounders, so the best way for Imanaga and Ober to avoid damage is to miss bats. Sánchez, though, can get away with pitching to contact, which can help to keep his pitch count down and allow him to go deeper in games.

Considering this, the better comparison for Sánchez is another Dominican lefty with an affinity for the sinker: Framber Valdez. Sánchez doesn’t have the pedigree of the Astros ace, but their arsenals and pitching approaches are similar.

What’s perhaps even more interesting about this comparison is Valdez’s embrace of the changeup in 2024, as pointed out by Ben Clemens this offseason.

Last season, Valdez threw the changeup in two-strike counts nearly 20% of the time, compared to 13.9% in 2023 and less than 10% of the time in all other prior seasons. Sánchez, meanwhile, threw changeups 51.4% of the time with two strikes last season. This makes plenty of sense, as the pitch generated a healthy 17.8% swinging strike rate.

So perhaps both have something to learn from each other, with Valdez leaning more into the changeup and Sánchez learning from new teammate Jesús Luzardo on his breaking stuff – wouldn’t that be fun? Even more likely, perhaps both are poised to continue frustrating opposing hitters with their bowling ball sinker/change combinations.

Ultimately, for as much love and attention as Valdez has earned at the top of Houston’s rotation, I think it is about time “Chasetopher” catches on and Sánchez gains a bit of respect as an emerging ace.

Editor’s Note: A version of this post was first published on Liam Delehanty’s personal blog on Medium.com.

As someone whose playing career fizzled out after high school, the realm of scouting and player development is one that has always drawn my interest. With the tools that MLB organizations and third-party facilities like Driveline and Tread Athletics utilize, it is easier than ever for players look at their unique biomechanical profiles, identify areas of improvement, and optimize everything from their spin rates and movement profiles to their throwing workloads. Despite the ability to objectively evaluate such a wide range of player traits, drafting and development remains an inexact science, with 26% of signed draftees between 2012–2019 reaching the majors, and only 3% of drafted players contributing more than 5 WAR with team that drafted them. (For reference, Dillon Gee generated 5.0 WAR over the course of his MLB career.) What gives players the ability to reach or exceed their potential? What makes certain organizations (Astros, Dodgers, Rays) more likely to develop major league contributors, while others consistently struggle to do so?

In recent years, organizations have made more concerted efforts to bridge the gap between analytics and traditional coaching, with former players like Brian Bannister serving as conduits between the front office and on-field staff. Bannister described much of his role as VP of Pitching Development with the Red Sox as “…during a bullpen session, holding my phone and showing it to a pitcher, showing him what the data says and then telling him why I think he should make an adjustment and backing it up on the spot.” Despite the industry shift toward a more player-friendly deployment of data, Driveline’s Kyle Boddy believes “player development departments are still highly fractured — there is strong resistance in most organizations to unify under a data-driven message.”

Motivated by my interest in the field and a Saberseminar presentation in 2023 by Katie Krall, which emphasized organizational efforts to fight “stereo coaching” and deliver a consistent message around a player’s development plan to all levels of the organization, I am examining if it is possible to find a link between organizational consistency and performance relative to projections. This study formed the basis of my 2024 Saberseminar presentation, titled “Consistency is Key? Examining Pitcher Arsenals Across Organizations.”

Org Level

Given publicly available data, I decided to use pitch arsenals as a proxy, as Statcast added Triple-A pitch-level data in 2023. Pulling team-level statistics and differentiating by pitch type percentage, we can get an overview of which organizations had the closest alignment in pitch arsenals across Triple-A and MLB.

From the data above, we can see that the Giants, Nationals, and Marlins saw the greatest overall difference in arsenals across Triple-A and MLB, while the Braves, Mets, and Orioles had the most consistency across levels. Did this impact how the staff performed relative to projections? To examine this, I pulled year-end individual statistics for the 2023 season, and compared them to preseason PECOTA projections, grouped by team. I then calculated correlations between the delta in pitch category and average difference in actual ERA, FIP, WHIP, and DRA vs. projections by team, then further broke out the data into plots for WHIP and DRA, the two metrics with the (relatively) strongest correlations:

While all of these correlations range from weak to nonexistent, the inverse nature of the correlations suggests there may be a small relationship between greater variance in arsenals across levels, and better performance compared to preseason projections. While a variety of other factors can play into this, it aligns with the idea that the most successful organizations are not those that necessarily preach one message across levels, but can identify and implement tweaks throughout the season.

If we are able to get more granular with these data, can we identify a more solid relationship between arsenal variance and performance?

Player Level

As a next step, we will be looking at a subset of players who spent significant time in Triple-A and MLB to see if there is a stronger relationship between individual arsenal variance and performance. Using Python, I gathered a list of pitchers who had thrown at least 100 pitches in both Triple-A and MLB during the 2023 season. (Apologies to the Alex Wood lovers, he fell one Triple-A pitch short of making the dataset.) With a group of 395 pitchers, I calculated the pitch percentage difference for each pitch category (fastball, offspeed, breaking, other) in a player’s arsenal by taking the absolute difference of pitch percentages of each pitch category in Triple-A and in MLB and calculating a total and average difference. For example, Logan Allen’s 2023 season broke down to a 14.35% total arsenal difference and a 3.59% mean arsenal difference.

Across the entire dataset, the median of all Total % Difference is 13.44% and the Average % Difference is 3.36%.

Performance vs. Projections

To measure if a smaller variance in arsenal aligned with stronger performance relative to projections, I pulled statistics from 2023, and compared them to Baseball Prospectus’ 50th Percentile PECOTA projections for each player, focusing on ERA, FIP, WHIP, and DRA. For both the total and average pitch percentage differences, our correlations ranged from incredibly weak to nonexistent. Total and Average % Difference were perfectly correlated, as evidenced below. The results show that these correlations were even weaker than those found at the organization level.

A Sample: Organizational Philosophy in Player Acquisition

Finally, let’s look at three organizations with notable philosophies or histories of pitching development, to see what we could glean from the types of players they acquire and subsequent tweaks to pitcher arsenals. First, we’ll start with the Red Sox, for their anti-fastball approach. Then, we’ll go to the Yankees, due to their history of developing pitchers both internally, Luis Gil for example, and those acquired externally, like Clay Holmes. Lastly, we will look at the Dodgers, as their decade-long run of success has been sustained by their ability to, in the words of Noah Syndergaard, “turn everything they touch into gold.”

For this portion, I looked at players acquired by these three organizations between May 1 and August 1 of 2024. This range was selected to exclude any offseason arsenal tweaks that the pitchers may have made, and to isolate changes to those that can be attributed directly to the shift in organizations. While this is a relatively small sample size, certain trends do begin to emerge:

The Red Sox tend to acquire pitchers with four-seam fastball usage roughly at league average, and, true to form, reduce that number drastically to 19%. This difference is made up through more than doubling usage of the cutter, while also increasing the frequency of the sinker.

Unlike their AL East rivals, the Yankees employ a different tact — acquiring pitchers with lower four-seam usage relative to league average, and increasing that along with slider percentage — encouraging their acquisitions to lean on higher-velocity offerings, while decreasing the overall usage of breaking and offspeed pitches.

Over on the West Coast, the Dodgers have acquired pitchers who are heavily four-seam dependent (driven by Michael Kopech’s MLB-leading 79.1% four-seam usage) and encouraged a more diverse pitch mix, boosting sinker usage by almost 5 times, and adding one of the league’s two screwballers in Brent Honeywell.

What Can We Take From This?

In short, this analysis shows us that there is no meaningful relationship between arsenal consistency across levels and performance relative to projections at either the organization or individual player level within the same season. What we can deduce from this is that organizational philosophies can be discerned from a player acquisition level, and this can be used to identify pitchers who may be able to unlock another level of performance if they are put in the right situation. For example, the Red Sox maximize usage of a pitcher’s secondary offerings, which can cover up a poor fastball, while the Yankees seek out pitchers who could stand to benefit from leaning on their pure velocity and stuff to boost the fastball/slider combo.

Additionally, there are many more avenues that can be explored in this area. Is there an even smaller subset of players that should be considered? Can we focus on performance across specific pitch types? Would performance across seasons be more telling? Are there other proxies that would work better than pitch arsenals?

Fundamentally, what makes this such an inexact science is the fact that it’s difficult to measure! Even if there is not publicly available player performance data to back up the notion, entire industries in the business world have been built around ‘breaking down silos’ within organizations, and there is no reason why baseball should be an outlier in that regard.