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 »

There are few things I enjoy in baseball more than the pitcher vs. hitter dynamic. Everyone likes to see highlight plays like a great catch or a mammoth home run, but those plays are few and far between. I believe that the tension created in a drawn-out plate appearance is where baseball is most enjoyable. Every pitch is meaningful, and the strategy of the game is on full display. The pitcher is trying to decide the best way to get the hitter to produce an out and the hitter is doing everything he can to thwart the pitcher.

This dynamic of baseball has always fascinated me. I was curious how pitchers and catchers decided which pitch was correct to throw in a situation. There are plenty of tools available to them that were not readily available when I was a child, like heat maps made from pitch-tracking data, but they show results without the context of what previous pitches were thrown in the plate appearance. Heat maps provide useful data, but the real art of pitching is being able to set up a hitter to take advantage of their weaknesses. If a pitcher throws the same pitch in the same location every time, eventually the hitter is going to catch on and change his strategy accordingly. So which sequence of pitches is the most effective at retiring hitters? This is the question I attempted to answer with this article. Read the rest of this entry »

Mike Podhorzer has recently been publishing articles regarding his xK%, primarily using Baseball-Reference’s strike rates. I have been trying for a few years now to come up with my own using StatCast numbers, and you’d be surprised how little data is required to get a very close xK% and xBB% for both hitters and pitchers.

I love Mike’s work, but that xK% equation is a bit unwieldy, including seven inputs, two of which have a correlation of -.829. It also uses roughly 63% of the pitches seen by hitters by accounting for all strikes, in addition to three other variables on top of that. I wanted to come up with something simpler that didn’t require so much input and wasn’t so overly constrained. After all, if we throw so much data in there that we basically know the outcome before we start, what good is it going forward? To that end, I dove into StatCast to see what might work. Read the rest of this entry »

In 2018, the Philadelphia Phillies defense combined for -93 DRS, costing the team over half a run per game compared to an average defense. Though the pitching staff combined for a 3.83 FIP (seventh in MLB), defensive mistakes led to a middling 4.49 runs against per game. Much of the rotation underperformed their peripheral statistics, with Nick Pivetta and Vince Velasquez each having ERAs that exceeded their FIPs by more than a run. Against this backdrop, Aaron Nola’s performance was miraculous: a 2.37 ERA (fourth in MLB) over 212.1 innings pitched. Nola’s elite run prevention despite Philadelphia’s historically bad defense resulted in 10.2 bWAR, the 2second-best single season among all currently active pitchers.

According to FanGraphs, however, Nola’s performance was more All-Star-worthy than historic, as his 3.01 FIP notched him just 5.5 fWAR. Perhaps this case merely illustrates why one should opt for fWAR over bWAR and FIP over ERA; however, I don’t believe that bWAR is beyond salvaging, and philosophically I believe that to assess a pitcher’s value to a team we must examine the entirety of his work rather than just balls not in play. What this case illustrates is the need to rethink bWAR’s defensive component, which assumes that all pitchers are equally affected by a team’s good or bad defensive performance. This is obviously not the case. Defense is capricious, and a few bad or great plays behind a pitcher can cost or save several runs.

Thanks to Statcast’s Outs Above Average, which allows us to isolate a defense’s performance behind a specific pitcher, we can develop a new defensive adjustment that avoids outlier performances like Nola’s while not simply ignoring quality of contact on balls in play. Read the rest of this entry »