What’s 0/573?

Baseball Savant knows, but they also know it’s useless information. This is precisely why they do not display it. And it’s a shame that they don’t display it.

If they did, it would show that David Fletcher is in the zeroth Percentile for Barrels.

For a quick refresher, Barrels are “a batted ball with the perfect combination of exit velocity and launch angle.” To qualify, a ball must be hit at least 98 mph. For that exit velocity, a launch angle of between 26-30 degrees is required. For every single mph increase, the range of acceptable launch angle degrees increases by two or three, up until 116 mph. At that level, any ball hit between 8-50 launch degrees is considered Barreled.

Fletcher didn’t do that once in 2021. Instead, he mustered eight “close calls” among his 573 batted ball events. Read the rest of this entry »

One piece from FanGraphs this season has stayed with me more than any other article on the website. In early September, Kevin Goldstein wrote a piece called The Rays’ Unique Ability To Mitigate Risk.

For most of the piece, Goldstein examined why the Rays pitch effectively even though they use so many relief pitchers. Most of the time, a team that cycles though relief pitchers in bunches is a bad one, like the Baltimore Orioles this year. But the Rays, as they often do, defy common practice.

I actually did not remember that part of Goldstein’s article; I only remembered it when I re-read it before writing this. What stuck with me was a short section at the beginning in which he explained why the Rays score so many runs.

Goldstein’s question was how does a team that has no high-priced free agent slugger, like Bryce Harper or Manny Machado, or no home-grown young stud, like Juan Soto or Fernando Tatis Jr., score so many runs? (You will see in a moment why I am ignoring the Rays’ young phenom Wander Franco.) Read the rest of this entry »

October 4, 1972: Yankees righty Larry Gowell hits a double off of Milwaukee Brewers pitcher Jim Lonborg. The American League Brewers played that game in an American League park in the Bronx, with no designated hitter on either side.

October 3, 2021: Dodgers righty Andre Jackson hits for himself, in relief, grounding out against Milwaukee Brewers pitcher Daniel Norris. The National League Brewers played that game in a National League park in Los Angeles, with no designated hitter on either side.

Gowell started his game and went five innings. In the third he led off, got his double, and advanced on a 6-3 groundout before being stranded at third base. In the bottom of the inning, he gave up a sac fly to John Briggs. That proved to be the only run, tagging Gowell with the loss.

Jackson was in relief of Phil Bickford, himself in relief of Walker Buehler. When a reliever hits for himself, rarely is the game competitive: here, Jackson had already pitched two innings with a nice lead. Immediately before Jackson’s spot in the batting order came up, outfielder Matt Beaty drove in catcher Will Smith, utility man Chris Taylor, and himself. Dodgers skipper Dave Roberts surely saw the score in LA, the score in San Francisco, and Jackson’s roster status for the playoffs, and let him hit and finish out the ninth. (Jackson collected a save for his three-inning effort, the first of his career.)

Gowell probably wasn’t the last AL pitcher to bat before the DH. The Angels and Royals had night games on the same day with pitcher at-bats in the Pacific and Central time zones. If baseball should adopt the designated hitter rule for the National League effective next year, Jackson will probably be the last NL pitcher to bat under these rules. The Reds’ Reiver Sanmartin collected three at-bats before being lifted for a pinch-hitter on the same day, but two of those came to start and end the fifth inning in his game against the Pirates. The Giants’ Logan Webb collected three at-bats too, but his day as a batter ended after a home run in the fifth. All those games began around the same time, so Jackson’s appearance in the eighth inning was a bit later on. Read the rest of this entry »

It shouldn’t come as any great surprise to a typical baseball fan that Dominican players play an outsized role in Major League Baseball today. In fact, the Dominican Republic, which has a population roughly just 3.3% that of the United States, supplies MLB with upwards of 10% of its players. Major League Baseball and baseball fans are better off because of this. After all, who wants to live in a baseball world without Nelson Cruz or Fernando Tatis Jr., for instance?

With this point in mind, the following takes a look at players from the Dominican Republic. More specifically, where in the D.R. players were born and when they made their way to MLB. What follows will be split into three brief sections: a description of the data utilized, some insights into the growth of the D.R.’s influence in MLB, and finally some map-based depictions of the players’ provinces of birth within the Dominican Republic. Read the rest of this entry »

Imagine you are coaching third base. Your team is at bat with a runner on third and one out. There is a flyball caught in marginally shallow left field. You think your runner has about a 50/50 chance of scoring if you send him. Do you send him?

Many of you would probably say no. This is a risky call. There is a 50% chance the runner would be out, which would be a huge momentum killer. Furthermore, if he gets caught and your team loses by a run, you are going to be the person blamed by the media.

My hypothesis is that third base coaches are leaving runs on the table. Over the past four seasons, third base runners scored 98% of the time when sent in sac fly situations, suggesting that coaches are sending them only when they have a very high degree of confidence of success. I hypothesize they won’t send runners unless they feel they have at least an 80% chance of scoring, but my analysis says they should be sent even with much lower chances. Read the rest of this entry »

Quantifying catcher framing was a huge step for the analytical community in trying to understand the position more fully. It has allowed evaluators to have more accurate numbers on what a catcher is adding to the team. It has seemingly also brought more organizational focus to framing at the expense of blocking across the league, as can be seen in the increased prevalence of catching from a knee.

Perhaps all this work will be moot if robo-umpires are ever implemented, but teams clearly see marginal advantages to be gained by research and development on this topic for now. With this in mind, the quantification of a catcher’s ability to frame is only the first step in the journey. Next we should be looking to find what makes a catcher good or bad at framing in order to improve player development practices. Finding this from a statistical perspective is tricky, as we don’t really have easily accessible data on what the catcher is doing behind the plate other than the video of it happening. This may not be the case on the team side as markerless motion capture is a developing technology in this space which can record more data, but publicly, we just have video. Instead of sitting down and trying to watch thousands of pitches like surely many coaches have done, I’ll try my hand with OpenCV and Tensorflow. Read the rest of this entry »

In a previous project, I attempted to determine what types of pitches are most effective in 1-2 and 0-2 counts based on suspicions that wasting pitches was not inherently strategic. I did this by analyzing league average wOBA values of different types of pitches in and out of the strike zone. The findings showed that on average, breaking and off-speed pitches outside of the zone were the most effective pitch to throw in order to minimize wOBA in both 0-2 and 1-2 counts.

While using league-average data produced some interesting results, I was still unsatisfied, since trying to project pitching strategy to a single pitcher doesn’t work when the data is league-wide. My goal was then to write an algorithm that could use a specific pitcher’s career pitching history to analyze the results of each of their pitches and determine every pitcher’s most effective pitch mix.

After a long time writing and editing code, I believe I have written a script that can do just that: evaluate each pitcher who has thrown more than 1,250 pitches since the start of 2019 and determine the wOBA value of each of their pitches at every count. Read the rest of this entry »

Like millions across the country, minor league players are facing a housing crisis. The practice of using host families to house prospects was put on hold due to the pandemic, leaving players responsible for obtaining their own housing. Things have not gone well. While stories have come to light bit-by-bit, team-by-team, a piece last month by Brittany Ghiroli of The Athletic is one of the more comprehensive looks at the minor league housing crisis to date.

Ghiroli’s story details a number of ways in which minor league players get squeezed by housing, all of which is best summed up by this quote from catcher Caleb Joseph: “Finding a place to put your head at night is the hardest, most stressful thing to do as a minor leaguer.” Joseph would know, as he slept in his team’s clubhouse one year to save on housing.

The comments by Joseph, who spent 2014-2020 in the majors, also underscore that while the situation with host families is specific to this season, housing has long been an issue for minor leaguers. But in light of Ghiroli’s piece and the amount of reporting on this issue recently, I was interested in putting some numbers to the stories players have shared, particularly since housing costs can vary greatly from market to market and minor league teams are scattered across the country. Read the rest of this entry »

In today’s game, reliever usage may be more important than ever. As starters go less deep into games, more emphasis is placed on bullpen strategy to survive the mid-to-late innings. Teams can use data to streamline this process, strategizing relief pitcher usage based on their pitch repertoires and batter ability. My goal is to produce a matchup tool that can potentially give us some insight as to how the big league teams “play the matchups.”

The basis of a bullpen matchup recommender will be at the pitch level: what types of pitches does a particular hitter struggle against, and how do they align with what a particular pitcher throws? To do this, I will first use clustering methods in order to redefine pitcher arsenals based on pitch flight characteristics. Matchups will then be selected according to which pitcher is expected to perform the best against a given batter, optimizing pitcher strengths against batter weaknesses.

Data

To conduct this research I used available Statcast data from 2016-2021 (through this year’s trade deadline). My variables of interest are as follows: pitch location (plate_x & plate_z), perceived pitch speed derived from release extension (effective_speed), pitch movement (pfx_x & pfx_z), spin rate (release_spin_rate), and the newly introduced spin axis (spin_axis). I elected to include spin axis in order to account for how the batter may see the pitch as it’s released. All in all, the variables selected measure the stuff and location of each pitch so that we may classify them more accurately beyond the basic pitch type labels. After cleaning this dataset and removing outliers, I was ready to move on to the modeling process. Read the rest of this entry »

By Bryan Woolley, JP Wong, and Nick Skiera.

Baseball, like all sports, is exciting because of the concept of variance. No team scores the exact same number of runs every game. That is why the Dodgers (5.82 runs/game) were not 60-0 in 2020. Runs per game strongly correlates with winning percentage for obvious reasons, but a team’s variance (essentially their consistency) plays a crucial role in their ability to win baseball games

Relating to this, we came across an interesting game theory concept. Given certain properties of the run-scoring distributions, the competitor with the lower output can increase their win probability by increasing the variance in their output. Conversely, the competitor with the higher output can increase their win probability by decreasing the variance in their output. Were this to apply to baseball, lower-scoring teams could win more games by becoming more inconsistent. Of course this is all just in theory, so the requirements for it to be relevant in reality to baseball might not be met.

We will examine the importance of variance in baseball both to test the theory and to attempt to uncover interesting trends in the sport. In our analysis we find that variance plays a significant role in a team’s success, suggesting that roster and lineup construction can be optimized by going beyond mean production. So as our title proposes, 1 WAR + 1 WAR and 2 WAR might not always be worth the same amount to a team if they are produced with different consistencies. Read the rest of this entry »