On June 23, it was reported that Major League Baseball and the MLB Players Association had agreed to terms to resume play in 2020 following the sport’s suspension due to the COVID-19 pandemic. This agreement came on the heels of the now-infamous March 26th agreement that was the subject of debate and contention between both sides of the bargaining table. Among other things, the agreement does not foreclose the right of the MLBPA to file a grievance and seek financial damages as it relates to the interpretation of the Agreement.

Specifically, the players may look to challenge whether the league did in fact negotiate in good faith as it relates to how many games were to be played in the abbreviated 2020 season. Further, the agreement also states that the Office of the Commissioner’s effort to issue a schedule for the 2020 season shall only be performed to the extent it is “…practicable and economically feasible.”

The term “economically feasible” is likely another point of dispute, as the league did not reveal any financial data, supposedly requested by the union, that would help to justify their claim that a season without fans would be a detriment to its bottom line. As the season now rolls along, we can explore the process by which the MLBPA may file a grievance to have its claims be heard and adjudicated.

What Is A Grievance?

Article XI of the parties’ 2017–2021 Basic Agreement (the CBA) sets forth the terms and conditions of grievance procedure. As defined, a “grievance” is “a complaint which involves the existence or interpretation of, or compliance with, any agreement, or any provision of any agreement, between the [MLBPA] and the Club…or between a Player and a Club.” Presumably, the March 26th Agreement would fall within this definition. Read the rest of this entry »

This post was inspired by a couple of articles that I remembered reading from Jonah Pemstein back in 2014. The intention of those posts was to predict the result of any given batter/pitcher matchup, dubbed the “Outcome Machine.” Have you ever wondered what the probability Mike Trout strikes out when he steps into the box against Justin Verlander? Of course, there are variables that are specific to any plate appearance (umpires/situation/stadium/etc.) that are harder to quantify, but it set out to predict the outcome in a vacuum. Trout vs. Verlander and nothing else (For the record, in 2020, I would estimate the answer is about 27.5%).

Being able to predict the outcomes in sports would take most of the fun out of being a spectator, sure, but I still found myself coming back to those articles. While reading and re-reading in an attempt to understand the logic and fool around with the equations, I came to a few questions of my own:

• With all of the hubbub of juiced balls and increased launch angles, do equations that were based on data from 2003-13 still apply to the game today?
• The regression equations were composed of the at-bat result and the stats of the batter and pitcher from the same year. This stuck out to me as an issue because it means the player’s performance later in the season, say in July, influences the prediction of an at-bat in May, and to a lesser extent, the result of that specific at-bat is already baked into that season’s performance. Shouldn’t you use data exclusively before a given at-bat to predict the outcome? Hindsight is 20/20, after all.

Eventually curiosity got the best of me and I decided to emulate the original exercise. Before I really start to nerd out on the inner workings, you can find this iteration of the Outcome Machine as a Google Sheet here. You can either select a pitcher/batter combination through the dropdown or hard key in the rates in a custom, hypothetical matchup below that. League average is set by default to projections for 2020 but can be updated as desired in the custom matchup. I would note that the preset statistics in this tool are total projections for 2020 but not broken out into L/R splits, as to my knowledge that data is currently behind a paywall. Read the rest of this entry »

With the current trend of thinking in the front offices in MLB, we are seeing many aspects of the game at all-time highs and lows. Strikeouts, home runs, and a lack of stolen bases get a lot of content created about them, but there is also something else at an all-time high: hit by pitches. We saw 1,984 hit by pitches in 2019, the most in MLB history, surpassing the previous high of 1,922 in 2018. There has not been this rate of hit by pitches per game since 1900, and baseball is very different from how it was then.

You may say that there are more pitches thrown in games than ever before, so the rate per game may be rising because of that. But when we account for that and look at the rate per pitch (which we can do so from 2008 onwards), you can still see that sharp increase in the last two seasons.

What has gone on here? There should be something responsible for this increase in hit by pitches. Is it the pitchers? Do we have guys who can throw hard but have less command, so they are hitting more batsmen? Are they throwing inside more often? Is it the hitters? Do we have guys who are getting tighter to the plate or players who are just more willing to take the hit to get on-base?

Let’s start with the pitchers. Thanks to the PITCHf/x and TrackMan data we have the location of every pitch since 2008. I will be using the Statcast zones to bucket the data. Read the rest of this entry »

Strikeout and walk rates are perhaps the most popular and widely used peripheral statistics, particularly for pitchers. However, with pitch level data, these statistics now have “peripherals” of their own. I was curious if I could create an accurate-yet-interpretable model using FanGraphs’ plate discipline metrics that could offer insight on what drives the differences in strikeout and walk rates between players.

While many have noted individual correlations between a single statistic and strikeout rate, I have not seen many unifying models that incorporate several plate discipline metrics. For the first part in this study, I will focus on hitter strikeout rate, but I intend on also looking at walk rate and, later on, pitchers’ strikeout and walk rates.

If you are not a fan of mathematical details, feel free to skim or skip these next few sections to get to my overall conclusions.

Methodology

Note: I used BIS discipline statistics rather than PITCHf/x. I do not think this made a significant difference, but I think it is important to keep in mind.

FanGraphs gives us nine plate discipline statistics to work with. However, several of them can be removed as they can be derived using the other statistics. In a regression setting, this phenomenon is called perfect multicollinearity, which is when an explanatory variable can be perfectly formulated by other explanatory variables. With a high degree of multicollinearity, it can be extremely difficult to tell which particular variable is responsible for a change in the response variable, which is problematic for inference. Using some basic dimensional analysis, I found formulas for all three of these: Read the rest of this entry »

Sometimes numbers tell a story. Sometimes that story is a mystery.

I came across the Baseball-Reference page for Raymond Fagan and was stunned by what I saw. It says Fagan went 13-0 with a 1.16 ERA for the Class D Oklahoma City Senators in 1915. Now the stunning part – it says it was his only professional season. Despite those dominant results, it appears Fagan never pitched again.

What happened to Raymond Fagan? Did he suffer a career-ending injury? Did he get into legal trouble and change his name? A Google search yielded no answers. This mystery required a deeper dive. Read the rest of this entry »

Fielding percentage is often criticized for the selection bias introduced by a player’s range (good defenders attempt more difficult plays, leading to more errors). A similar issue of selection bias is present in stolen bases. On any given pitch, it is at the sole discretion of the runner if he will steal a base or not. Naturally, the runner will only attempt a stolen base when he believes he has an advantage over the pitcher and catcher.

Ivan Rodriguez caught 46% of base-stealers throughout his career, topping out at a 60% caught stealing rate in his prime and leading the league in CS% in nine seasons. Knowing that stealing against Pudge is little more than a pipe dream for most, only the best baserunners would dare to attempt a steal. If this assumption holds, Rodriguez’s CS% would in fact be far more impressive than initially reported due to the level of competition he faces relative to a typical catcher.

To adjust for selection bias in stolen-base attempts, I developed an ELO model. For those unfamiliar, ELO ratings are a method of calculating the relative skill levels of players in zero-sum games. You might recognize ELO from chess rankings or FiveThirtyEight’s sports prediction models. These ratings can be used to directly estimate the probability of winning a match between two individuals or teams. The ratings change after each match, rewarding a win by an underdog more than a win by the favorite.

On a stolen-base attempt, the runner, pitcher, and catcher all play a major role in the outcome of the play. An argument could also be made for the importance of the fielder receiving the throw, especially when considering the select few who can make tags like this: Read the rest of this entry »

It’s a cool and breezy April afternoon down by Baltimore’s Inner Harbor, and the mid-rebuild Orioles are taking on the division-winning and record-breaking Minnesota Twins. Trying to salvage the final contest of a three-game series, the O’s — to no one’s surprise — find themselves trailing in the bottom of the ninth. But not all hope is lost. The Twins’ lead is small — two runs — and the Orioles have some of their best players due up. Out of the gate, Twins pitcher Taylor Rogers hits the first Orioles batter, Joey Rickard, in the foot. Then, after a Chris Davis lineout, Jesús Sucre resurrects the inning with a single to left that advances Rickard to third. The comeback is on.

Hanser Alberto then plunges the Orioles hopes back down to earth with a swinging strikeout that gives his team just one more out with which to work. But then comes Jonathan Villar, who rips a double to deep left, scoring Rickard and advancing Sucre to third. The Twins lead is cut in half. After an intentional walk to Trey Mancini that loads the bases, the game now rests in Pedro Severino’s hands. With two outs and the bases loaded, still down by one, Severino manages to work the count to 3-0. His team is one pitch away. The crowd is on its feet. Rogers winds and delivers his pitch. It’s outside! “Ball 4!” the commentator exclaims. The fans cheer, Severino begins to walk towards first, and the tying run starts his trot towards home. But suddenly, the umpire punches his arm through the air. He called it a strike. Severino walks back towards home plate, distraught. He pops up the very next pitch, and just like that, the game is over.

***

Using data from Baseball Savant’s pitch-by-pitch library, we can begin to understand the role that these incorrect calls play in baseball. By matching up the database’s pitch locations to the calls associated with those pitches, we can see which calls were supposedly correct, and more importantly, which were not. The results are pretty astounding. Last year, by this data, MLB umpires made a total of 33,277 incorrect calls. That’s good for 13.8 per game, or just over 1.5 per inning. While not every bad call is a comeback-killer, these mistakes have the ability to greatly alter an at-bat, a game, and maybe even a season. Read the rest of this entry »

With the advent of Statcast, statistics like exit velocity, spin rate, and launch angle have become easily accessible to baseball fans. Catcher pop time data has also become available. However, unlike some of the other Statcast metrics, catcher pop time data has existed for much longer, with scouts measuring pop times in the minor leagues years before Statcast entered the mix.

This sounds all well and dandy, right? Well, it would be, if the Statcast numbers were consistent with scouting pop time tool grades. Baseball Prospectus, for example, calls a pop time from 1.7-1.8 a 70 pop time, which sounds reasonable enough without any context. However, considering the best average Statcast pop time to second base from 2015 to 2019 was JT Realmuto’s 1.88 (minimum 10 throws to second), something seems amiss here. I decided to take a deeper look into Statcast’s pop time data to get a better idea of what’s going on.

Read the rest of this entry »

Why don’t teams ever sign former quarterbacks to try and turn them into pitchers?

This thought stems from watching Patrick Mahomes and his pre-draft NFL tape and discovering that his father was a former major league pitcher. Can a quarterback’s arm strength transfer to pitching? What can be learned from football velocity to uncover a future successful pitcher?

ESPN was ramping up their coverage in the weeks leading up to the 2017 NFL Draft, and Mahomes was gaining momentum. A SportsCenter interview with the future MVP explored his multi-sport background, which caught my attention.

I was vaguely familiar with the story about Mahomes’ father reaching MLB as a pitcher. Apparently there was a time when Mahomes considered following in his father’s baseball footsteps. The interview spilled over into the prospect’s appearance in the Gruden QB Camp. He mentioned then that he was drafted by the Detroit Tigers in high school, but due to a strong desire to play quarterback at Texas Tech, he went in the 37th round. If his football passion wasn’t as strong, scouts told him that the top three rounds were a likely landing spot.

As the video continued, it featured highlights of in-game play and practices where Mahomes showed a dynamic skill set. He had special throwing abilities, and his baseball background and natural talent was obvious in just a few of his tosses. There were impressive clips of him throwing a football from his knees about 50 or so yards, and another highlighting a final pregame warmup toss and ritual: throwing the ball about 75-80 yards in the air. Read the rest of this entry »

The other day I began pulling together Savant data to determine whether there was an ideal zone percentage for different types of curveballs (CUs) and sliders (SLs). I haven’t found much on that front yet. However, I did realize that I don’t really know what makes curveballs effective, both from a results standpoint (extra whiffs, weaker contact, etc.) or a trait standpoint (vertical break, horizontal break, velo). I took a look at all of these factors for the curveballs in the 2019 baseball season to see if anything stuck out.

I analyzed a sample of 214 pitchers, representing everyone from 2019 who threw at least 20 innings, a curveball at least 10% of the time, and qualified for Savant’s pitch movement leaderboard. From this sample I pulled info on every pitcher’s spin profile, wOBA, xwOBACON, zone percentage, SwStr %, and RHB/LHB splits. I even noted all that same info for the rest of their arsenal as well as just to have a full view. Then they were bucketed in every way imaginable with averages and standard deviations to see which ones stood out. I do want to preface all my findings by saying that the sample size is not ideal, as the buckets were mostly of decent size (roughly 100-plus players), but I did get granular at times (the smallest group was 48).

I am most focused on the following metrics: CU wOBA, CU xwOBACON, CU SwStr %, CU Drop & Tail (as a % difference vs. the average pitcher at similar velocity). Here are the averages across the entire sample: Read the rest of this entry »