After watching the great matchup between the Yankees and Rays in the 2020 ALDS, including Mike Brosseau’s epic at-bat against Aroldis Chapman in the deciding Game 5 of that series, I couldn’t help but take a look at the characteristics of the pitch he hit. Chapman is known as having one of the best fastballs in the game and a long track record of success as a closer. After battling back from 0-2, on the 10th pitch of the at-bat, Brosseau hit a 100.2-mph fastball thrown with 2386 rpms and 7.4 feet of extension over the left-field wall, allowing the Rays to advance to the ALCS.

This pitch was 6.9 mph, 80 rpms, and 1.1 feet above the average velocity, spin rate, and extension for four-seam fastballs in 2020. Given the same location, if the pitch was a little faster, had more RPMs, or was thrown even closer to home plate, would the result have changed? The aim of this article is to create a model to determine what the exact chances were of Mike Brosseau hitting that home run.

Using Baseball Savant and its wealth of Statcast data and more typical statistics, we can select all the four-seam fastballs thrown in 2020 and their related metrics. The data was cleaned for missing values, four-seam fastballs thrown by position players, eephus pitches, and four-seamers that may have been mislabeled as sliders or changeups. For the latter category, a minimum velocity of 87 mph was used to remove these potential label errors, and pitches with negative pfx_z values were removed as four-seam fastballs are expected to drop less relative to gravity. For pfx_x, the absolute value of the given value was used, as I want to look at the magnitude of the horizontal break as opposed to which side of the plate the movement is going towards. Read the rest of this entry »

Who should bat second? This question has been debated quite a bit in recent years, as the modern approach has become to slot the best hitter in the 2-hole to increase their total plate appearances in a season. Others argue that the second hitter, like the leadoff man, should be a table-setter and the goal should be to get the best hitters to the plate with runners on base. So which is more valuable: getting your best hitter to the plate with men on or getting them to the plate more often? A simple experiment suggests that we are wasting a lot of energy arguing either side, and it would be time better spent thinking about other elements of lineup construction.

Overview

I created nine fictional players that will be referred to by position. I arbitrarily provided probabilities for the players based on seven possible plate appearance outcomes: single, double, triple, homer, walk, hit by pitch, and out. To simulate the lineup playing a game, I used a simple base-to-base style (the runners on base move up the same number of bases as the batter). An oversimplification of play to be sure, but the goal is to get an approximation of potential lineups relative to each other. Each lineup “plays” 100,000 nine-inning games so that the run distribution is virtually identical on multiple simulations. Read the rest of this entry »

Building an MLB roster is anything but simple, to say the least.

It would be very convenient if it was as easy as playing MLB: The Show, but as we are well aware of, there are many complexities to roster construction. Not only do organizations need to have high-end talent, but they also need to have 26 quality big-leaguers as well other players in the pipeline when adversity hits.

In a perfect world, teams would be able to have tons of star talent as well as intriguing depth. However, we do not live in a perfect world, and for that reason, teams need to adopt a specific strategy when it comes to building the best roster possible in the most efficient way imaginable.

Teams have generally two courses: will they prioritize star talent, or will they look to have as deep a team as possible? The first option is typically known as the “stars and scrubs” approach, and it is one that you see often see in basketball. Meanwhile, the latter approach is one that you’ll see with sports with deeper rosters, primarily football. Overall, both methods are used frequently by teams, but it is unclear which one is the more efficient when it comes to roster building.

What good is there to posing a problem if we aren’t going to find the answer for it? We need to dig deep into these two approaches! Should teams prioritize star talent even if it means their depth is lacking? Or is quantity more valuable than quality? Let us try to discover the answer to this critical question! Read the rest of this entry »

After reading Alex Chamberlain’s piece on Kyle Hendricks‘ ability to suppress exit velocity, I was interested in attempting a similar investigation. Beginning with a linear mixed-effects model and Statcast batted ball data from the 2019 and 2020 seasons, I looked at which pitcher-pitch combinations were most effective at creating weaker contact and found similar results to Alex’s: Hendricks was still amazing. With a new toy in hand, I asked myself the next logical question — which pitcher-pitch combination improved the most from 2019 to 2020 (minimum 50 balls in play each season)?

The answer turned out to be Max Fried’s four-seam fastball.

When I consider Fried, the first thought that comes to mind is his breakout 2020 season. After establishing himself as a rotation mainstay the prior year with peripherals (3.72 FIP and 3.32 xFIP) that outpaced his results (4.02 ERA), Fried decided to stop giving up the long ball in 2020 (.32 HR/9). The reward for a season well-pitched was a fifth-place finish in Cy Young Award voting. The second thing that comes to mind about Fried is his devastating curveball. Since his first cup of coffee in 2017, Fried’s curveball spin rates have ranked between the 81st and 92nd percentiles, with hitters consistently struggling to square-up the pitch. For his career, Fried’s curveball xwOBA is just a hair above .200. It’s no surprise that FanGraphs’ 2018 prospects ratings gave Fried a 65/70 grade for the curve.

On the flip-side, Fried’s four-seam fastball may only be notable for being unconventional. In a period when four-seamers are supposed to be high-speed and high-spin, Fried’s does neither. Read the rest of this entry »

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?

Overview

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 »