Starting Pitchers Aren’t Leaning On Their Best Pitches

Nathan Ray Seebeck / USA TODAY Sports

The title of this post does not exactly mince words. Should that be all the context you need (TL;DR), it would be fair to move on. However, for those looking for a greater explanation, qualifications and nuance abound in what follows as justification for such a statement.

The impetus for doing some digging and eventually choosing this topic (and title) is pretty simple; I wondered whether starting pitchers, over the course of a long season, throw their best pitches more often than their less effective pitches.

Starters were the focus for a reason. Relievers, who most often face mere subsets of an opposing lineup (and face that subset crucially just once) in any given outing, are likely more inclined to defer to their strongest offerings at higher rates. Starting pitchers, meanwhile, often have to grapple with the phenomenon of diminishing returns on pitch usage. Should an opposing hitter see that “best” pitch over and over, what made it effective in the first place loses some of its value to a hitter’s heightened recognition. Starting pitchers, it turns out, probably should practice some moderation.

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How Does Seiya Suzuki Stack Up?

Yukihito Taguchi / USA TODAY Sports

With Japanese outfielder Seiya Suzuki signing with the Chicago Cubs (written about by Kevin Goldstein here while Dan Szymborski ran projections on him here), I wanted to compare him to other Japanese position players who have moved to MLB in recent years. The comparisons I made were to the final NPB years of Shohei Ohtani, Yoshitomo Tsutsugo, and Shogo Akiyama. But considering Ohtani was injured in 2017, his stats will be from both the 2016 and 2017 seasons. Data is based on 1.02 – Essence of Baseball public data managed by DELTAGRAPHS.

Comparing Recent Japanese Position Players’ WAR
Player Year PA Batting Base Running Fielding Pos WAR
Shohei Ohtani ’16 382 33.2 3.4 -0.2 -5.9 4.4
Shohei Ohtani ’17 231 15.9 0.3 0.2 -5.2 1.9
Tsutsugo Yoshitomo ’19 557 23.3 -1.5 -16.7 -9.8 1.3
Shogo Akiyama ’19 678 31.0 2.3 -4.3 4.7 5.6
Seiya Suzuki ’21 533 57.6 -0.4 11.2 -4.4 8.6

Suzuki posted 8.6 WAR in 2021, the best for position player last year. His high WAR is based on great batting value (+57.6 per 500 PA), higher than Ohtani (+43) and twice that of Tsutsugo (+21) and Akiyama (+23). His baserunning value of -0.4 is average in NPB, and his total baserunning value of +1.4 from 2019-2021 is neither good nor bad. Meanwhile, he is a good right fielder, putting up a fielding value of +11.2 (equal UZR). Tsutsugo (-16.7) is a left fielder and Akiyama(-4.3) is a center fielder, so we cannot simply compare them, but I think we can expect better fielding stats than Tsutsugo in MLB. Read the rest of this entry »

The Last Solo Umpire

Kyle Terada / USA TODAY Sports

July 11, 1923, was a sunny, seasonal day in Philadelphia. As National League umpires, Ernie Quiqley and Cy Pfirman were accustomed to living out of a suitcase and spending nights and game days in Philadelphia, Brooklyn, Manhattan, Boston, Pittsburgh, Cincinnati, Chicago, and St. Louis. Quigley had been at this for more than a decade, starting his NL career in 1913; the first of the day’s games was the 146th that he’d umpired in Philadelphia. And while it was only Pfirman’s second season, he’d already worked 24 Phillies home games. On this day they were going to work a doubleheader, which was unusual but not extraordinary for a Wednesday, as the Cincinnati Reds were in town to play their regularly scheduled game followed by a makeup of the May 15th tilt that had been rained out.

The two umpires had been paired up since the season started on April 17th, having worked 70 games together over the first 85 days. As the more veteran member, Quigley was clearly the “chief.” Of those 70 games, he had been the home plate umpire in 68, even presiding over the plate in both ends of five doubleheaders. That’s how it had worked with Major League umpires since professional baseball started. In the early days, a single umpire worked most games. 1909 was the first NL season that had more games worked with two umpires than one, 442 games to 179. By 1910, the single-ump game had nearly been eliminated altogether, with less than 10 such games every year. Most of those rare solo games were necessitated by travel constraints — it was hard to get a person from far-flung St. Louis after a game to the east coast for another game the next day. Prior to 1923, there hadn’t been a game worked by only one umpire since 1917. In fact, the NL had begun incorporating three umpires into games occasionally in 1917.

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What Makes a Good Four-Seamer Good?

There used to be a lot of debate about the four-seam fastball and the relationship of velocity, vertical movement, and spin rate. But now there is a new concept called Vertical Approach Angle (VAA) that includes the height of the release and the height of the pitch’s path. With that in mind, let’s think again about what is needed for a good four-seam fastball.

Cross-Tabulating To Determine the Impact of Each Element

A cross-tabulation was performed for four-seamers thrown in MLB from 2017-2021, with velocity ticked to 4 km/h, vertical movement ticked to 7.5 cm, release height ticked to 10 cm, and plate height ticked to 15 cm. Each element was tabulated and color-scaled with the MLB average as the middle value in white, good values for pitchers in red, and bad values in blue. The indicators are Whiff%, xwOBAcon, and xPV/100 (expected Pitch Value per 100 pitches, which I wrote about here). Read the rest of this entry »

Are Hitters Hitting It Where It’s Being Pitched?

If you watch basebll games, which you probably do if you find yourself reading this, then you’re likely familiar with announcers employing phrases like “he just went with it,” or “hit it where it was pitched.” These phrases suggest hitters have made contact with the baseball such that outside pitches are hit to the opposite field and pitches on the inner half are put in play to the hitter’s pull side.

These comments beg the question: are hitters “going with” the pitches they are thrown with any discernible frequency? In today’s game, wherein the value of tapping into pull power and raising average launch angles has been well established, are hitters still hitting it where it’s pitched? To what extent do team’s defensive alignments correspond to how their pitchers will approach any given hitter should that hitter go with pitches? Given that pitchers who throw higher in the zone more often allow fly ball contact and those who throw lower induce more groundballs, does something similar apply for hitters given how they are pitched on a horizontal plane, i.e. inside and outside? Read the rest of this entry »

A Peta Perspective on the Hot Stove So Far

Cold snowy days here in our nation’s capital, combined with the owners’ and players’ seeming determination to kill the golden goose, provides an opportunity for me to look at the hot stove (pre-lockout) through the lens of the Peta methodology. For those unfamiliar with the Peta methodology, I refer you to this deeper dive here on the Community Blog published last January. Based on Joe Peta’s groundbreaking 2013 book Trading Bases, the methodology derives each team’s upcoming season win-loss record based on the utilization of its previous season performance (runs scored/runs allowed), adjusted for cluster luck (my proxy is FG BaseRuns), and the team’s upcoming-season projected WAR.

Just before Opening Day, the product of this calculation is compared to the money line. Peta suggests that in a 162-game season, win totals produced by the model that deviate from the money line by more than four games (1.5 games in a 60- game season) represented “unrepeatable results” and therefore were worth a possible wager. Read the rest of this entry »

Analyzing Joc Pederson’s Free Agency

Joc Pederson is selling pearls, but is anyone buying? After earning his second World Series ring, your favorite bedazzled outfielder is on the market and could be coming to a city near you.

This past year, Pederson slashed .238/.310/.732 OPS between his stints with the Cubs and Braves, finishing the season with 18 home runs, 61 RBIs, two stolen bases, and an OPS+ of 93. If we dive a bit deeper we can see that despite modest traditional numbers, Baseball Savant has him ranked in the 80th percentile for average exit velocity and the 90th percentile for max exit velocity. His ability to hit the ball hard is nearing an elite level, and though a subpar batting average is certainly not helping his case, his skill at driving the ball may entice a team in need of some lefty power.

Although many fans consider Pederson a clutch postseason hitter, primarily because of his self-proclaimed “Joctober,” his stats looked grim as this postseason winded down. Pederson went 5-for-22 with one bomb in the NLCS, which is not especially great for a postseason power hitter of his caliber. He was worse in the World Series, going 1-for-15 with no homers. Despite having won a pair of championships, “Joctober” seems to have been a classic case of small sample size. His recent performance, or lack thereof, will weigh heavy on the mind of executives and undoubtedly bring down his value. Read the rest of this entry »

Predicting Hall of Famers with Machine Learning

The questions of who should and who will make it into the Baseball Hall of Fame have inspired countless debates, books, articles, and statistics. From the early days of statistical milestones like 3,000 hits, 500 home runs, and 300 wins to more advanced measurements like WAR and JAWS, and throughout baseball’s many eras, many have attempted to tackle the task. The discussion is more or less ongoing but peaks whenever a prominent player retires and during every winter ballot season. Innovations like the Hall of Fame Tracker have only added fuel to the fire.

I wanted to see if machine learning was up to the task of predicting who’ll get enshrined. I trained and evaluated a prediction model and used it to predict induction chances for current and recently retired players. I specifically wanted to see if I could get a sense of how some of the game’s younger superstars are doing, because who doesn’t want to talk about how good Juan Soto is?

In this article I discuss building and evaluating the model and show the predictions it makes. If you’re interested in the former, continue reading; if you’re interested only in the predictions, feel free to skip to the end. Read the rest of this entry »

Modeling One-Run and Extra-Inning Games

When the 2021 regular season concluded, there was the following exchange in the “Hey Bill” section of the Bill James Online baseball community, with Bill’s response starting at “Answered:”

Hey Bill! 

 Is it possible to calculate an expected number of 1-run games for a team in a season? The reason I ask is that the Mets played in 66 1-run games this year, 40.7% of their games. That seems like a whopping big number . . . but is it? 



Asked by: kgh

Answered: 10/4/2021

It’s a very large number, but I wouldn’t know how to calculate an expected number. I don’t even know what the variables would be. I suppose one-run games are slightly more common among teams which are near .500, and obviously they would be significantly more common in a low-run environment than in a high-run environment.

Inspired by this interaction, I built a dataset to answer those questions and a few more that popped up along the way. Let’s start with the easiest one:

The 2021 Mets played 66 one-run games, or 40.7% of their contests. Is that a whopping big number?

Yes, that is a big number, but not “whopping” big.

The Mets did play 66 one-run games, with 13 of those in extra-innings and 53 in “regulation.” They played 18 total extra-inning games. This gave them a total of 71 games that were decided by one run or in extra-innings. Several teams listed below played more one-run games than the Mets did in 2021. Read the rest of this entry »

No Pitch Is an Island: Pitch Prediction With Sequence-to-Sequence Deep Learning

One of the signature dishes of baseball-related machine learning is pitch prediction, whereby the analysis aims to predict what type of pitch will be thrown next in a game. The strategic advantages of knowing what a pitcher will throw beforehand are obvious due to the lengths teams go (both legal and illegal) to gain such information. Analysts that solve the issue through data have taken various approaches in the past, but here are some commonalities among them:

  • Supervised learning is incorporated with numerous variables (batter-handedness, count, inning, etc.) to fit models on training data, which are then used to make predictions on test data.
  • The models are fit on a pitcher-by-pitcher basis. That is, algorithms are applied to each pitcher individually to account for their unique tendencies and repertoire. Results are reported as an aggregate of all these individual models.
  • There is a minimum cut-off for the number of pitches thrown. In order for a pitcher’s work to be considered they must have crossed that threshold.

An example can be found here. The goal of this study is not to reproduce or match those strong results, but to introduce a new, natural-fitting ingredient that can improve on their limitations. The most constraining restriction in other works is the sample size requirement; by only including pitchers with substantial histories, the scope of the pitch prediction task is drastically reduced. We hope to produce a model capable of making predictions for all pitchers regardless of their individual sample size. Read the rest of this entry »