Does Consistent Messaging Impact Pitcher Performance?

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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.





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laughingstockMember since 2024
17 days ago

Three cheers for the return of the community blog!

shandykoufaxMember since 2024
7 days ago

I think this is a super interesting idea and a great way to start digging into it.
I think your analysis of organizational priorities is a little skewed by the small timeline, Kopech and Flaherty threw their 4 seamer pretty much the same, Honeywell went back to career norm after 2.1 innings with Pit. Paxton threw fewer 4seam but Keller pushed them to a career high.

I think the idea you’re trying to measure of consistent messaging is right in line with those quotes, and your second methodology is a better way to approach it (what they tell you in the minors is what the major league office wants you to do).
Probably it’s just too complex to prove with just pitch mix.

I think what makes the teams that develop best the best is that they aren’t a one size fits all kinda shop, but tailor their development to the player.
eg, the 2010s A’s took Sean Manaea, Frankie Montas, and Chris bassit from fringe prospects to playoff starters in 3 different ways, not all of which can be measured by pitch mix.
Maybe looking at all pitchers from minor and majors with more pitches (500 in each) grouped by organization will give more signal, but my thought is that it won’t show up at the organizational level. Also, I think you are technically double counting the difference (every percentage from 1 pitch to another is actually 2% percentage points of absolute difference), though I don’t think it affects the outcome.
Definitely appreciate the insight and approaching it in multiple ways.

Last edited 7 days ago by shandykoufax