In hindsight, it’s unfortunate that Ted Williams philosophies on hitting took so long to become universally accepted. His thoughts on batting were clearly ahead of his time and it has only been in the past few years that the more prevalent “swing down” views have largely exited the baseball community.

In his book, The Science of Hitting, Williams suggested an upward swing path that aligns the bat path and pitch path for a better chance of contact – about 5 degrees for a fastball and 10-to-15 degrees for a curveball. This research note is not about the total amount of loft in the swing today — everyone knows that swing loft is greater now than in Williams’ day. However, there are some very interesting findings in the data in terms of whether players are utilizing consistent amounts of swing loft for different pitch locations, which is implied in Williams’ book.

One observation that seems to hold in many sports is that the best performers are typically out in front of the popular views of the day in terms of changing mechanics for the better. However, as we will see in the data, this does not necessarily mean that these superior mechanics are being understood and directed by conscious understanding.

It turns out that there is a very important element that wasn’t considered by Williams in his book which the data shows the best hitters are “considering” — the amount of Vertical Bat Angle (VBA) in the swing. VBA can be defined as the amount of vertical swing tilt as viewed from the center field camera. The swings in Williams’ day as well as the illustrations in his book clearly have much less VBA than today’s hitters. While there is no broad data on VBA, a study of minor league hitters by David Fortenbaugh in 2011 showed the following averages of VBA at contact:

There is evidence which suggests that VBA goes well beyond player “style” and is more of a core swing mechanic that is associated with higher quality contact as well as superior levels of performance. Here is a chart showing VBA by playing level.

Read the rest of this entry »

I believe that pitches aren’t thrown in a vacuum, and the effectiveness of one pitch is certainly affected by the pitches that preceded it. Thus, I wanted to identify the most- and least-potent 1-2 pitch combinations in the 2018 Major League Baseball season. To accomplish this, I built a Pitch Combo Effectiveness Tool based on all 2018 pitches thrown in the major leagues.

The approach I took was to evaluate every pitch as the second pitch in a 1-2 combo (forcing us to exclude first pitches in an at-bat). I defined these pitch combos using the pitcher, the pitch types of both the first and second pitches (e.g. “four-seam fastball followed by a curveball”), and the pitch location change from the first to the second pitch (e.g. “the second pitch was further down and more inside than the first pitch”). I then gauged the effectiveness or value of these pitch combinations using the sum of the wOBA added for both the first and second pitches. Lastly, to ensure we were only looking at common pitch combos, we filtered the results to pitch combos observed at least 10 times in 2018.

The chart showing every pitch combo is below, and you can click it to go to the full tool and results:

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As the 2019 season begins, Las Vegas determines the annual over/under win totals for all 30 major league teams and gives us a chance to examine intriguing over/under win lines for the upcoming season. Not surprisingly, the Miami Marlins found a spot right at the bottom of the list at over/under 63.5 wins. Will the Miami Marlins, under the ownership of Derek Jeter and the tutelage of Michael Hill, elude the worst record in baseball? Call me crazy, but there are a number of reasons why Vegas’ determination of 63.5 wins is undervaluing the Marlins.

J.T. Realmuto, a 2018 All-Star and arguably the last star on the Marlins roster, was acquired by the Philadelphia Phillies for Jorge Alfaro, Sixto Sanchez, and Will Stewart this past offseason. While Sanchez is a potential budding ace pitcher and Stewart has a real future as a middle-of-the-rotation starter, Alfaro is the most interesting addition for the 2019 season. He rates as a guy with incredible raw power when he puts the bat on the ball, with the only issue thus far in his career being that his contact percentage is quite low:

The K% is good for 245th out of 247 players (min. 350 PAs) and the BB% ranks in the 8th percentile among those same 247. By looking at his O-Swing%, it’s good for second-to-last and 16% above the 2018 league average of 30.9%, and clearly he’s not making enough contact at 61%. However, when Alfaro does manage to put bat on ball, the results are quite impressive:

How about a video of the swing in action? This ball, at 115 mph off the bat of Alfaro, was absolutely crushed, and I think Junichi Tazawa’s reaction says it all…

With more patience and a better approach at the plate, the Marlins could have something special in Alfaro. It’s evident that this improved approach could be on it’s way by analyzing his second-half statistics from July 2018 to September 2018:

Alfaro managed to cut his K% and increase his BB%, while performing as an above-average hitter according to wRC+. He made strides at the plate by lowering his whiff percentage outside of the zone from 28% in the first half to 25% in the second half, and his batted ball quality improved against breaking pitches, which he had struggled with mightily in the first half, as his xwOBA increased from 0.246 to 0.338 in the second half and his whiff percentage on breaking balls decreased from 34.68% in the first half to 26.52% in the second half. Read the rest of this entry »

A UCL tear is a death sentence for a player’s season, and it can have large repercussions for the team and league as a whole, making it crucial for front offices to understand what puts players at a heightened risk for this injury. In this research, the height, weight, age, and fastball velocity of MLB pitchers in the years 2000-17 are analyzed to determine the impact of pitcher size on UCL tear probability. The results of this study will aid executives and front offices in evaluating pitchers and their risk of needing Tommy John surgery. Moreover, these findings may aid pitchers in lowering chances for injury by guiding their offseason training goals.

1. Introduction

As Tommy John surgery and UCL tears are thrust further into the spotlight, more is revealed about possible factors and causes. In this paper, I will inspect the correlation between pitcher size (BMI) and UCL tear probability in order to determine whether the former has a statistically significant impact on the latter. The data used in this study was taken from FanGraphs, the Lahman Database, and Jon Roegele’s Tommy John Database, all of which are publicly available sources. Due to the many variables which are closely correlated with BMI and have an impact on UCL health, such as age and velocity, pitcher size was analyzed independent of these variables, which are controlled through partial correlations.

2. Analysis

2.1 BMI and Tommy John: In Aggregate

When the data set is viewed in its entirety, the results are overwhelming. The mean BMI of pitchers who have undergone Tommy John surgery is 27.09, whereas the mean BMI of pitchers who have not is 26.34. The difference between these means is statistically significant, as the p-value (odds of the difference existing due to chance) in a two sample t-test is .000001153, far below the .05 benchmark commonly used in statistics. To test this relationship in a different way, the BMIs of the 2,383 pitchers in the data set (298 who had torn their UCL, 2085 who had not) were split into deciles. The correlation between decile number and probability of Tommy John was .91, with a p-value of .0002556, revealing that there is statistically significant linear correlation between UCL tears and BMI, with higher-BMI pitchers having higher risk for Tommy John surgery. The graph of these deciles and the probability of Tommy John is shown below. Read the rest of this entry »

Yoan Moncada has frustrated talent evaluators over the past two years. He’s about as physically talented as a baseball player can be; while still a prospect, the team here at FanGraphs thought he merited future grades of 60 hit, 60 power, 70 speed, 50 field, and 70 throw, with an OFP of 70 good for No. 1 overall prospect status. Prospects don’t get evaluated much better than that; in fact, a 70 OVR on a position player is as good as it gets. He was the kind of prospect that could headline a trade for a top-five starting pitcher, a bonafide ace, in his prime on a team-friendly contract with three years left.

Flash forward two years, about a year and a half into Moncada’s major league career, and he hasn’t performed quite as billed. Instead, in 901 career plate appearances before Opening Day 2019, he posted a 97 career wRC+ and 3.1 total fWAR, almost exactly league-average or slightly below. His defense at second base has not impressed, and so he’s being moved to the hot corner in the wake of 1) the White Sox whiffing on Manny Machado, and 2) the White Sox drafting “future Gold Glove second sacker” Nick Madrigal with the 4th overall pick in 2018. If nothing changes, he’s be in danger of becoming a utilityman.

Moncada’s offensive struggles are a little unusual. He has two traits required to be an offensive monster — power and patience — in abundance. Last year, his average exit velo of 90.6 mph was in the 86th percentile, while his 4.12 pitches seen per PA was in the 81st percentile. However, those positive traits were offset by the modern game’s bugaboo — strikeouts. Moncada struck out in an ugly 33.4% of his PAs last year, behind only Chris Davis and Joey Gallo, and his career K rate sat at 33.6% this offseason. This is very concerning, as contact issues are a flaw that are difficult to resolve.

The profile above seems to describe a three-true-outcomes hitter like the aforementioned Gallo. Dig a little deeper, though, and you’ll find that how Moncada struck out that often is not normal, and in a sense he doesn’t actually have contact issues, at least not 33.4% bad. He didn’t chase many pitches out of the zone last year — only 23.3% — sitting in the 87th percentile of qualified hitters. Neither does his whiff rate of 12.2% (league average in 2018 was 10.7%) jibe with that huge strikeout rate. Taken together, we can conclude that while Moncada’s contact ability may be somewhat below-average, he limits how much he swings-and-misses by rarely chasing pitches out of the zone. So if Moncada doesn’t chase much, and doesn’t swing and miss that much, how is he striking out so much? Read the rest of this entry »

Since the end of the BESR era, there was a downward trend of runs per game, home runs per game, and stolen bases per game in college baseball. After introducing flat-seam balls, home runs per game and runs per game have been on an upward trend. Both of these rule changes would seem to have no impact on stolen bases per game, and why would they? Analytics suggests that stealing bases is not worth the risk. I still believe there is value in stealing bases in today’s game, and the decline of it has hurt teams’ performance, especially squads that are at a disadvantage to Power 5 Conference teams. Programs such as Wright State, UCF, UCONN, and Campbell are able to stay competitive year after year by implementing the run game in their offense.

In 2018, 38 of the top 50 teams in stolen bases had a record above .500, while 38 of the bottom 50 teams in stolen bases have a record below .500. Out of the 35 non-Power 5 teams in the 2018 NCAA Tournament, 14 of those teams were ranked in the top 50 in stolen bases.

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My high-school-aged son sits at home yet again. Why? Because another of his baseball games has been canceled due to the wet and cold Ohio spring, and my thoughts turn again to our days playing baseball in Florida. Before we moved to this less-agreeable northern climate, it was a rarity to have a game canceled due to weather. Not only that, but games were scheduled year-round, which of course meant more baseball on the calendar. This situation reminded me of the familiar equation known to baseball fans:

Good weather leads to more playing.
More playing means better players.

But is this true? After all, it’s well-known that the best player in baseball, Mike Trout, is from cold-weather New Jersey. Many quickly point to the fact that California, Texas, and Florida are at the top of the list for states with the most MLB draftees, but they’re the three most populous states. Perhaps proportionally they don’t stack up to colder states after all.

I decided to look at the data from the last two drafts — 2017 and 2018 — to see if there is a relationship between a state’s average temperature and how well its players do in the draft. Do warmer-weather states really produce more MLB draftees than average?

To do this, I first gathered population data from each state to determine what percentage of the overall US population it contains. Then I did the same for each states’ MLB draft population. Finally, I compared those two figures and determined the percentage difference between their population proportion and their draft proportion. I call this figure the “Draft Difference”.

For example, let’s say State X makes up 10% of the US Population, but the State X’s draft class makes up only 8% of the overall class. Its Draft Difference is calculated as:

(Draft-Population)/Population = Draft Difference

In this case,

(8-10)/10 = -.20 = -20%

A state with 10% of the US population should, all things being equal, contribute 10% of all players in an MLB draft. But, in this case, State X did 20% worse than should be expected just from its population size. Read the rest of this entry »

Pitch selection is like Cold War game theory.

The pitcher/catcher (battery) and the hitter are trying to balance a guessing game of what their counterpart is thinking with their own capabilities to develop a decision or expectation about the next pitch thrown.

The battery is trying to strike the delicate balance of a pitch that will result in a strike or an out (usually by being put into play) and give the hitter the least opportunity to get on base. The hitter is trying to anticipate that decision to maximize their ability to react successfully. This becomes circular, since the hitter’s ability to anticipate correctly improves their ability to get on-base, which changes the calculus and pitch decision for the battery, which changes the hitter’s ability to anticipate correctly. Just like the nuclear stand-off of the Cold War, a low-and-inside slider hit into the gap or a Soviet Sarmak from Siberia shot down by Star Wars lasers. Same thing, right?

Pitcher: I should throw this.

Hitter: I will anticipate this.

Pitcher: Then I should throw that.

But it’s not – because baseball is fun and the Cold War was humans (not) trying to murder each other by the millions. Instead let’s say pitch selection is just like keeping secrets from your Friends:

Given this stand-off of anticipation, the battery can take one of two approaches:

1.) Complete randomness, or…

2.) Sequencing pitches that build on each other to keep the hitter off balance.

This is the old pitching-coach speak of “changing the hitter’s eye level, keeping him on his heels, and mixing speeds.” Read the rest of this entry »

The Franklin Institute is a science and research museum located in Philadelphia, Pennsylvania. Among its many draws are a giant heart you can walk through, the SportsZone where you can sprint the 40-yard dash and compare your time to professional athletes, and a Changing Earth exhibit made entirely of sustainable materials that focuses on the ways the planet has transformed over time. Through all of that, plus rotating feature exhibits, it’s easy to lose sight of a tried and true experiment: The Ruler Drop Test.

If you never performed the experiment in middle school, the Ruler Drop Test is exactly as it sounds. Take a ruler — or, in the case of the Franklin Institute, a yardstick — and hold it vertically between your index finger and thumb on your dominant hand, about one-fourth from the bottom. Then release it and see where you can catch it. The shorter the distance between where you let go and where you catch it, the faster your reactions are. Science!

It’s a simple experiment, but it is illustrative. And with how it’s centered on vertical drop and expectations, it could help us understand how the Houston Astros have used advanced technology and data to tweak pitchers’ repertories to reach new levels of success. Read the rest of this entry »

Baseball fans have seen it time and again: a starting pitcher will twirl a masterpiece, but because his team doesn’t score, he’ll be tagged with a loss. Or a reliever will come into a game, pitch to one or two batters, and end up with the win.

The vagaries of assigning wins and losses to pitchers are a well-known irritant to serious baseball fans (though perhaps not to old-timers like Bob Costas or John Smoltz). Here is the pitching decision statistic explained:

The winning pitcher is defined as the pitcher who last pitched prior to the half-inning when the winning team took the lead for the last time.

The losing pitcher is the pitcher who allows the go-ahead run to reach base for a lead that the winning team never relinquishes.

Often timing — particularly the timing of a team’s offense — affects the statistic more than a pitcher’s actual contribution to his team’s win or loss. In other words, the decision frequently fails to reflect which pitcher made the biggest difference for the winning team (or was most detrimental for the losing team). In these cases, it simply tags the pitcher lucky or unlucky enough to pitch at a certain time in the game.

In an effort to create a more accurate stat to reflect a pitcher’s contribution to his team’s win or loss, I’d like to propose new stats, which I’ll call the “WPA-Win” and “WPA-Loss.” Let’s start with the WPA-Win:

The “WPA-Win” is given to the pitcher on the winning team with the highest WPA for that game.

I’ll address how to calculate the “WPA-Loss” (which is more complicated) later in the article. For now, we’ll just assume it goes to the pitcher on the losing team with the lowest WPA. Read the rest of this entry »