I have found a gaping hole in the rules and laws of baseball. When you know the flaw, you won’t be able to ignore it, and you will wonder why teams haven’t tried to exploit this, just once. It all has to do with equipment regulations, or more specifically, baserunning equipment regulations. It’s brilliant (if I say so myself) and something I had to keep a secret, otherwise there would be madness on the basepaths.

In boxing, the competitors wear shorts with rather large waistbands. This is because any shot below the waistline is classified as “below the belt” and therefore an illegal hit. I don’t pretend to know boxing or the rules (I despise fighting sports), but this is one I’m pretty sure about. It probably leads to points deductions or a fine or a yellow card or a sinbin (imagine a two-minute penalty box in boxing, with one opponent dancing around the ring on his own… anyway…). Essentially, hitting below the belt is bad, so boxers try and maximize the size of their waistband and try to pull their shorts as high as possible.

In baseball, there are regulations on the size of a bat, the size of a glove, the way players and coaches dress themselves, their conduct during play, and the distance between the pitchers mound and home plate (thankfully the field dimensions are a recommendation and not specified like NFL/NBA etc, which allows for great and different ballparks to be made), and they are all laid out for everyone to see.

There is a piece of equipment that doesn’t have a set of dimensions or regulations. This small and insignificant bit of swag is the baserunning mitt, the single oven glove, the nubbin, the sock puppet, whatever you call it. It’s the thing you see those folks who like their fingers not to be treated to a studding from the baseman’s cleats wear while running the bases. It fits over one of their hands, and they use that hand to lead when sliding head-first into the bases.

While watching a game in the postseason, I noticed a hitter reach first base and be handed one of these mitts by one of the equipment guys. As he placed the device onto his hand, I couldn’t help but notice the size of it. It looked considerably bigger than the others I had laid eyes on previously. It then made me wonder, what length of mitt could you get away with before umpires start noticing? Clearly the ideal solution would be to have a 90-foot mitt on the end of your hand and simply tap the next base while being stood at your current location. Clearly this would attract a lot of attention, as the equipment guy comes out of the dugout, holding it horizontally over his two arms, bumping into umpires and players on the way out to second base. Read the rest of this entry »

We all know Billy Hamilton’s hitting stinks. In fact, since debuting in 2013, Hamilton’s wRC+ of 68 ranks as the 14th-worst among active, qualified hitters. His pre-2019 All-Star Break slash-line of .217/.284/.271 has done nothing more than hurt his cause. All told, since the start of his career, Hamilton has contributed a whopping -58.3 runs offensively.

Notably, however, among all of the cellar-dwelling hitters at the bottom of the offensive table, Hamilton’s 10.3 fWAR since 2013 ranks as the highest among the 75 lowest in wRC+. His 62.2 defensive runs contributed above average, in addition to his absurd 58.7 BsR, provides pretty much the entirety of Hamilton’s value.

To optimize Hamilton’s positive output, it would then make sense to limit his time hitting while simultaneously maximizing his baserunning and fielding opportunities. So here’s my proposal:

Given his weak career on-base numbers, when starting, Hamilton reaches base approximately once per game. Given this, if the Royals were to pinch-run Billy once every nine innings for a hypothetical average-running outfield replacement, Hamilton would contribute close to the same BsR as he does in a normal season, about 10.0.* This alone would be good for almost an entire WAR. Read the rest of this entry »

Sunday night baseball is such a great thing. Yes, I may fall asleep around the sixth or seventh inning, but I tend to fall asleep to baseball, which is nice. It’s such a summer feeling for me to have the window open, the summer breeze blowing in, and talk of baseball in the background. On June 9, the Cubs were aggressive early on the base paths. At one point, with Kyle Schwarber on first base and Kris Bryant at the plate, Bryant hit what would typically be a routine single to left-center. ​Now, with the Cardinals and the Cubs fighting it out for the top spot in the division, you saw an aggressive approach by Schwarber. Did mastermind Joe Maddon have that all planned and ready? Did he tell his team to run on Marcell Ozuna? Well, if so, maybe he (or his team of data scientists and analysts) was evaluating the rARM statistic.

Part 1: The Stat – Throwing Arm Runs Saved

A player’s total Throwing Arm Runs Saved is then the sum of our three halves: flyballs Runs Saved + groundballs Runs Saved + Miscellaneous Kills Runs Saved.

– The Fielding Bible Read the rest of this entry »

Before we begin, here is the link to app being discussed: https://cargocultsabermetrics.shinyapps.io/Pitch_Design_Tool/

Two months ago, I wrote a blog post arguing José Berrios should learn a cutter. My argument hinged on the striking similarities between Berrios and Corey Kluber and the fact that Kluber has a good cutter and Berrios does not. Since then, I’ve developed a more objective way to evaluate a pitcher’s current pitches and make recommendations to guide the pitch design process. Pitch design is the process of a pitcher making changes to existing pitches or adding new ones, often using high-speed video and devices such as Rapsodo or TrackMan to get the spin axis of the pitch just right to create desired movement. The app I’ve built creates targets for pitchers and details ideal pitch characteristics to give objective, quantitative direction to the pitch design process.

My plan is to turn the tool into a service for college teams to use for their pitchers in pitch design, but I’ve also created a version which uses Statcast data to create pitch design plans for big leaguers that I’ve released for free. I figured this would be a good place to share the Statcast data version and give a brief explanation of how it works (if you’re interested in a more detailed explanation of the tool, check out this post on my blog). Read the rest of this entry »

Getting mad at an umpire, and then tossed from the game, may seem like an ineffective display of emotion since calls are never reversed after a little more yelling. But what about future calls? In order to answer this question, we need good data on a large number of adjudicated events. Close out and safe calls happen fairly rarely, and good data quantifying how close the play was would be difficult to collect. But the home plate umpire calls balls and strikes for every batter, and pitches at the edges of the zone provide plenty of opportunities to grow or shrink the zone slightly.

It’s difficult to measure the zone in a particular game since there aren’t enough pitches at each spot on the boundary of the zone, but by combining data from many games, we can get a clear idea of what the average zone looks like. As for quantifying the zone, it’s easy to get carried away with details (location of each side, correcting for player height, etc.), but with enough data, all of those variables should average out and we can focus on the simplest measure: zone size.

During the past four years, there have been 308 games featuring an ejection over the strike zone, containing about 47,000 pitches. Splitting by team (team with ejected player/coach/manager and opposing team) and before/after the ejection, we have groups with between 9,500 and 14,000 pitches, plenty for a good estimate of the strike zone.

The results, shown below, show two clear trends: first, one team is clearly justified in being upset as their hitters face a larger zone. Second, we see that umpires fix this, even over-correcting slightly, after making an ejection.

Umpires are Human

We all see the humanity of umpires in their fallibility, but it shows in other ways too: the zone shrinks on 0-2 counts and expands on 3-0 ones, showing that they don’t like ending an at-bat with their own judgement call. This doesn’t mesh well with the fiery persona of the umpire and their emotive strike-three calls, but we have to remember that they are playing a part, and their main goal is to keep the game firmly in their control. We see more evidence of this here: if umpires ejected arguing players out of a sense of holy wrath, we would expect no change in the strike zone at all.

Instead, we see a clear reaction in the direction that the arguing player desires. While the data cannot point to the exact mechanism, I see two distinct explanations: signaling and aversion to conflict.

In the signaling hypothesis, we suggest that players are frequently sending messages to the umpire, but the umpire considers these messages according to the cost in sending it. A few words muttered under their breath doesn’t cost them anything, and so it is usually ignored. An ejection is costly, so the umpire takes that signal seriously.

The second hypothesis is a simple human aversion to being yelled at in front of a crowd of thousands. It’s not a fun experience for anyone, so they take action to avoid it happening again.

About the Models

To measure the zone, I took two approaches, k-nearest neighbor (which knows nothing about the expected shape of the strike zone) and a logistic regression based model (which looks for a rounded rectangle). Error estimates were calculated using bootstrapped samples. Both gave similar results, and the code and data behind this post are available on Kaggle.

I believe that pitches aren’t thrown in a vacuum, and the effectiveness of one pitch is certainly affected by the pitches that preceded it. Thus, I wanted to identify the most- and least-potent 1-2 pitch combinations in the 2018 Major League Baseball season. To accomplish this, I built a Pitch Combo Effectiveness Tool based on all 2018 pitches thrown in the major leagues.

The approach I took was to evaluate every pitch as the second pitch in a 1-2 combo (forcing us to exclude first pitches in an at-bat). I defined these pitch combos using the pitcher, the pitch types of both the first and second pitches (e.g. “four-seam fastball followed by a curveball”), and the pitch location change from the first to the second pitch (e.g. “the second pitch was further down and more inside than the first pitch”). I then gauged the effectiveness or value of these pitch combinations using the sum of the wOBA added for both the first and second pitches. Lastly, to ensure we were only looking at common pitch combos, we filtered the results to pitch combos observed at least 10 times in 2018.

The chart showing every pitch combo is below, and you can click it to go to the full tool and results:

Read the rest of this entry »

Since the end of the BESR era, there was a downward trend of runs per game, home runs per game, and stolen bases per game in college baseball. After introducing flat-seam balls, home runs per game and runs per game have been on an upward trend. Both of these rule changes would seem to have no impact on stolen bases per game, and why would they? Analytics suggests that stealing bases is not worth the risk. I still believe there is value in stealing bases in today’s game, and the decline of it has hurt teams’ performance, especially squads that are at a disadvantage to Power 5 Conference teams. Programs such as Wright State, UCF, UCONN, and Campbell are able to stay competitive year after year by implementing the run game in their offense.

In 2018, 38 of the top 50 teams in stolen bases had a record above .500, while 38 of the bottom 50 teams in stolen bases have a record below .500. Out of the 35 non-Power 5 teams in the 2018 NCAA Tournament, 14 of those teams were ranked in the top 50 in stolen bases.

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Pitch selection is like Cold War game theory.

The pitcher/catcher (battery) and the hitter are trying to balance a guessing game of what their counterpart is thinking with their own capabilities to develop a decision or expectation about the next pitch thrown.

The battery is trying to strike the delicate balance of a pitch that will result in a strike or an out (usually by being put into play) and give the hitter the least opportunity to get on base. The hitter is trying to anticipate that decision to maximize their ability to react successfully. This becomes circular, since the hitter’s ability to anticipate correctly improves their ability to get on-base, which changes the calculus and pitch decision for the battery, which changes the hitter’s ability to anticipate correctly. Just like the nuclear stand-off of the Cold War, a low-and-inside slider hit into the gap or a Soviet Sarmak from Siberia shot down by Star Wars lasers. Same thing, right?

Pitcher: I should throw this.

Hitter: I will anticipate this.

Pitcher: Then I should throw that.

But it’s not – because baseball is fun and the Cold War was humans (not) trying to murder each other by the millions. Instead let’s say pitch selection is just like keeping secrets from your Friends:

Given this stand-off of anticipation, the battery can take one of two approaches:

1.) Complete randomness, or…

2.) Sequencing pitches that build on each other to keep the hitter off balance.

This is the old pitching-coach speak of “changing the hitter’s eye level, keeping him on his heels, and mixing speeds.” Read the rest of this entry »

The infield shift is a much-maligned defensive strategy, hounded as one of the worst analytics-based changes to baseball. Multiple times each season there will be some conversation about banning the shift, and each time pros, ex-players/managers, commentators, and analysts will chip in with their two cents. But for now, the shift is here, and it is as popular (with the fielding teams) as it has ever been. Just under 26% of all pitches were thrown with some form of infield shift in place in 2018, 22% of at-bats had a shift for the entirety of it, and 30% had at least one pitch shifted.

As you can see, left-handed hitters are far more likely to be shifted than their right-handed counterparts, with 46% of left-handed ABs seeing a shifted pitch versus 19% for righties. This makes rudimentary sense as the shifted players for a left-hander are closer to first base, so they have a greater chance of impacting the play to first and therefore stopping a potential single.

I have taken players who have 100-plus at-bats in 2018 both against a shifted and non-shifted infield, then I compared the outcomes. There were 132 such players, and their combined number of at-bats was 72,389 (39% of the seasons total). I have split these up into four categories based on the handedness of the batter and the pitcher.

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Andrew Miller has an undeniably nasty slider. As a Red Sox fan, I remember it far too well from the 2016 postseason. Big Papi’s farewell tour didn’t seem all that fair when you consider the way the Red Sox ran into the buzz-saw that was Miller and the Cleveland Indians. Sure, I’m grateful for Miller helping the 2013 version of the Red Sox win a third world title since 2004, but come on Andrew, you had to ruin Papi’s goodbye?

With Miller’s recent signing with the St. Louis Cardinals, I found myself exploring his FanGraphs page. I stumbled upon this article, Andrew Miller on the Evolution of his Slider, and I instantly began to wonder if pitchers had similar experiences developing their sliders in the 2018 season. The first step in this analysis was to evaluate the evolution of Miller’s slider.

What jumps off the page is the change in velocity. Miller saw a 4.6 mph increase in his slider from 2011 to 2012, then another 3 mph added from 2012 to 2013. This in large part had to do with Miller moving from a starting role to a relief role during his time with the Red Sox. Given that information, however, an increase in velocity that drastic not only shows a pitcher’s willingness to adapt, but also a pitcher’s ability to adapt. By observing Miller’s slider splits, we see that ability to adapt almost immediately.

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