For fans of the 29 teams whose autumns aren’t highlighted by a World Series parade (in a normal year at least), the offseason is a time of equality, when every team is zero games back from a playoff spot and hope springs eternal. Front offices have four months to write checks and strike deals with the hope of blocking off the streets come November, or at least sell some tickets along the way. Baseball Twitter and internet forums everywhere are filled with catchphrases like “winning the offseason,” “making a splash,” and of course, “going for it.”
In a perfect world, every team would try its hardest and “go for it” every year, but in today’s MLB, no offseason is without a large swathe of teams sitting on their hands if not outright tanking. The merits of managing a team for the sake of the bottom line or stockpiling prospects for some future championship run can be debated ad nauseum, but the teams that deserve our attention are the ones who spend the winter months actively trying to improve their on-field products and win the whole damn thing.
But what exactly does it look like when a team decides to go for it? A simple look at which teams sign the most free agents could be a start, but a team who signs an army of relievers to minor league contracts shouldn’t be regarded as trying harder than a team that adds a pair of high-profile bats. New dollars committed might be a step closer, but one massive long term contract would skew the results and heavily outweigh a team signing multiple short-term deals.
The best way, then, to judge to what extent a team “went for it” in an offseason would be to look at the perceived short-term value of the players added via trade or free agency compared to those who departed by those same avenues. Read the rest of this entry »
A few summers ago, Walker Buehler and the Los Angeles Dodgers came to Baltimore at the very end of the season. That night my buddy and I couldn’t figure out why the Dodgers, and the overwhelming mass of their fans in attendance, were so pumped about winning a single game in Baltimore. Once we saw staffers in ties and headsets running out with the “Division Champions” t-shirts, we realized what was going on.
Needless to say, Buehler was excellent, going 7 innings with 11 Ks and — because it was the 2019 Orioles — gave up no runs on four hits. During the game, while surrounded by very excited Dodgers fans, I mentioned that Buehler’s delivery seemed so efficient that his motion looked exactly the same every time he threw the ball. If you’ve ever worked on physical mechanics of any kind, be it baseball swings, golf swings, freestyle swim stroke, running stride, or maybe just proper form sitting at a desk to avoid that “work from home/pandemic backache,” you know how hard it can be to exactly replicate a motion over and over again. Buehler amazed us in his ability to do just that. We know that repetition in delivery mechanics leads to success in various forms, so with that in mind, the point of this analysis is to look at release point consistency and how that correlates with resulting pitching metrics. Read the rest of this entry »
During Game 6 of the World Series, Kevin Cash infamously replaced his cruising starting pitcher, Blake Snell, with reliever Nick Anderson. Anderson would give up the lead before registering an out, and the Los Angeles Dodgers won the Series for the first time in 32 years.
A heavily criticized decision by many, both in the moment and in hindsight, the move is representative of the new direction many clubs have been heading towards. This is calculated and analytics-heavy decision-making on reliever usage that has caused both a major shift in the value of relievers and a steady increase in pitchers used in games.
The consistent incline of pitchers used per game notably paired with the decline of average pitches and innings thrown by starters begs the question: how should pitch count factor into removing pitchers from games? If starters are removed for the fact that they are facing the top of the order for the third time rather than because they are fatigued or have seen a decline in their outing performance, is it important to pass on hittable pitches in order to drive pitch count up? Alternatively, is there value in being a pitcher who can record outs quickly if by the time Mookie Betts comes to the plate in the 6th inning, the threat of impending doom will chase an ace at 73 pitches out of the game? Read the rest of this entry »
In the 2020 season, American League MVP José Abreu faced 107 different pitchers, including the top four in Cy Young voting point totals (Shane Bieber, Trevor Bauer, Yu Darvish, and Kenta Maeda). Bauer was the only of the four not to allow a home run to Abreu in 2020. In comparison, MVP Runner-up José Ramírez faced 69 of the pitchers that Abreu faced. The third-place DJ LeMahieu faced a completely different set of pitchers, not a single one overlapping with Abreu’s.
While these batters were compared by their offensive production, it appears Abreu faced more challenging pitching. Using FanGraphs’s xFIP- (for which a lower number is better) as a measure of a pitcher’s quality, Abreu was up against a 96.75 xFIP- on average while LeMahieu faced pitchers with at a 105.93 mark. Both LeMahieu’s weighted on-base average (wOBA) of .429 and Abreu’s .411 were exceptional, but is the 18-point difference truly reflective of the difference between the two players’ seasons?
To answer the question, I derived a value with a similar intuition to Baseball Prospectus’s Deserved Run Average (DRA). DRA is a measure that adjusts a pitcher’s performance by the quality of the batters they are facing. This statistic also accounts for numerous context factors to attempt to better isolate the pitcher’s contribution. DRA shows that the quality of the batter can be influential in a pitcher’s performance, so it makes sense that the quality of pitcher is influential in a batter’s performance.
As for the statistic I will be working with, I choose to refer to this as “pitcher-adjusted weighted on-base average,” or pwOBA. The intuition is simple: a batter should get credit for offensive production against challenging pitching. The formula for pwOBA is based on the formula for wOBA. With wOBA, every event has a run value (ex. 1.979 for home runs in 2020) and a batter gets credit for these values accumulated over the course of the season. The sum of these values is then divided by (AB + BB – IBB + SF + HBP). Read the rest of this entry »
In one of the later chapters of The MVP Machine, the authors describe a working relationship between a professional baseball player (an unnamed position player) and a writer at an “analytically inclined” baseball website. The player felt that his club’s advanced scouting data wasn’t granular enough and asked the writer to supplement the information with more detail. The writer summarized that the player was basically looking at three things: “Am I squaring up the ball? Am I swinging and missing? Am I swinging at strikes?”
That last question got me thinking. As a pitcher, it is rarely a bad idea to have batters look at called strikes and swing at balls. Which pitchers, in 2020, were particularly effective at doing just that? To make that determination, I looked at Statcast data for all pitchers who threw at least 60 innings in 2020. Specifically, I looked at their outside-zone swing rate and their zone take rate – calculated as just (1 – zone swing rate) – and took the average of the two. Note that this analysis completely omits what happens if contact is made with the ball; We’re merely interested in strikes that were taken and balls that were swung at. (If you’re interested in the Statcast query and the few lines of code for this, click here.) The top 10 was as follows: Read the rest of this entry »
People have always been looking to understand what makes a good pitch. With advances in pitch tracking technology and computing power, we can begin to use large amounts of data to answer this question more definitively. I’ve created a model called PitchingBot which uses machine learning to try and find what makes a good pitch.
Machine learning describes a general class of algorithms that are very flexible and “learn” patterns from large amounts of data. This means I don’t have to tell PitchingBot what I think a good pitch is, but instead I can give it a load of pitches (and the results of those pitches) and it will train itself to recognize a pitch that gives good results.
I intend to investigate a couple of key questions:
Does PitchingBot reach the same conclusions as conventional wisdom about what makes a good pitch?
Naively, I would expect a good pitch to have the following qualities: high velocity, plenty of movement, and good location in the corner of the strike zone. I will look at whether these are true for PitchingBot and how the definition of a good pitch changes with the ball/strike count.
Can we meaningfully compare and evaluate pitchers using PitchingBot?
Are the pitchers who are best according to PitchingBot those who get the best results? PitchingBot isn’t very useful if it does not agree with real pitcher performance. Read the rest of this entry »
How well do you think you can predict the future of a minor leaguer? My computer may be able to help. Towards the end of the regular season, I found the prospects page at FanGraphs and started experimenting with it. I have always had a lot of fun thinking about the future and predicting outcomes, so I decided to try to build a model to predict whether or not a prospect would make it to the majors. I had all the data I needed thanks to FanGraphs, and I had recently been looking into similar models built by others to figure out how I could accomplish this project. I realized that all these articles I was reading detailed the results of their models, but not the code and behind-the-scenes work that goes into creating them.
With that in mind, I decided to figure it out on my own. I had a good idea of what statistics I wanted to use, but there were a few issues I needed to consider before I started throwing data around:
Prospects playing multiple years at a single level isn’t too difficult to deal with because I can just aggregate the stats from those seasons. The fact that not all prospects play in every level of the minor leagues before reaching the majors is tough, however, because that makes for a lot of missing data that needs to be handled before building the model. I decided to replace all the missing values with the means of the existing data, and I created variables to indicate whether or not a player’s season stats for that particular level of the minor leagues were real. To make this model useful, I would want to take out certain variables. For example, I figured I wouldn’t need or want Triple-A stats included in the model because typically once a player has reached that level of the minors, you are more interested in how well they will do in the majors. Read the rest of this entry »
Earlier this month, Blake Treinen returned to the Dodgers on a two-year, $17.5 million deal. Treinen was non-tendered by the Athletics after a down season in 2019 before signing a one-year, $10 million deal with Los Angeles that led to a decent bounceback in 2020.
While the Dodgers were also reportedly interested in adding Liam Hendriks, now a White Sox, the fact that they eventually signed Treinen to a multi-year deal suggests that they were looking closely at his performance in 2020. However, from looking at various data and video, Treinen in 2020 appears to be a particularly different pitcher than Treinen in 2018.
I’d like to take a closer look at how Treinen has changed since his time with the A’s. The first thing to note about his performance after joining the Dodgers is that his groundball rate was 65.3%, up more than 20 points from 45.0% the year before. This is more than 10% higher than in 2018, when he had the best performance of his career. Meanwhile, his strikeout rate was 20.6%, the lowest since his debut in 2015 and well under his career high of 31.7% in 2018. These numbers lead me to believe that Treinen’s change in pitching style is intentional. Read the rest of this entry »
For those unfamiliar with Joe Peta’s groundbreaking 2013 book Trading Bases, the author is a successful financial analyst and former Wall Street trader. Seriously injured in a traffic accident, Peta’s long and painful recovery included employing his professional skills to develop a baseball wagering methodology. His book is about more than that though, including observations about the 2008 economic meltdown and sports wagering writ large. Peta’s anecdotes alone make it worth the read — imagine being hit by a NYC ambulance and then being billed by the city for the ride to the hospital.
At its highest level, the Peta methodology is based on the utilization of a team’s previous season performance adjusted for cluster luck (a regression of OBP/SLG/ISO to arrive at “hits per run”) and WAR, as well as upcoming-season projected WAR. Arriving at an estimate of a team’s season win total, it is then used to identify and capitalize on inefficiencies between the model’s estimates and wagering lines.
Peta’s work produces two products: a season-long projection of wins (the long game) and the ability to handicap individual games through adjustments to each team’s lineup, starting pitcher, and home field. While conceptually straightforward, it is time-consuming to operate, requiring familiarity with Excel (particularly the ability to link sheets). In lieu of Peta’s regression calculation of cluster luck, I utilized FanGraphs’ calculation of BaseRuns, convinced of its utility as a proxy after reading a 2019 article at samkonmodels.com arguing it was one of a number of comparable and readily available such calculations. Read the rest of this entry »
While it might not appear so, baseball games constantly portray economic thought, such as in the mathematical model of game theory. There are many ways game theory takes place, but a classic example is the prisoner’s dilemma. Imagine a police officer is interrogating two suspects of robbing a bank together. The police officer has some evidence to put them in jail, but a confession would go a long way. Each suspect is contemplating confessing to the crime. If both suspects keep quiet, they will each receive five years in jail. If one suspect confesses and the other keeps quiet, the one who kept quiet will receive 20 years in jail while the suspect who confessed will receive just one year. If both confess, they each receive 10 years in jail. The logical choice for each suspect is called the dominant strategy. The end result, or the combination of each suspects decision, is called the Nash Equilibrium. By using game theory, we come to the conclusion that each suspect should confess to the crime, meaning they will each get 10 years in prison. I won’t go much into why this is the case, but feel free to research more about game theory and the Nash Equilibrium on your own.
What does this have to with baseball? We can think of each pitch as game theory, with each suspect as the pitcher and batter. Instead of confessing to a crime, the pitcher is contemplating throwing a ball in the strike zone while the batter is contemplating swinging. While the prisoner’s dilemma has a Nash Equilibrium, not only does a pitch to a batter not have a Nash Equilibrium, but the combination of decisions is constantly changing. If the batter’s dominant strategy is to swing, then pitchers will throw more balls outside the batter’s reach. If the pitcher’s dominant strategy is to throw a ball, then the batter will take more pitches.
We could observe this thought process for every pitch thrown. However, let’s look at one type of pitch: 3-0 counts. If you are the batter, it might seem obvious to take the pitch. The worst-case scenario is you end up with a 3-1 count. If you are the pitcher, it might seem obvious to throw an easy strike. You do not want to walk the batter, and you know the batter doesn’t want to swing and risk giving you an easy popup to get out of good count. So I guess the batter should take every pitch and the pitcher should throw the ball right down the middle every time. Read the rest of this entry »