## What About Batted Ball Spin?

Recently, for my job, I got to mess around with Statcast data for fly balls. I have a good job. As part of the task I was working on, I attempted to calculate the maximum heights and travel distances of fly balls using my extensive ninth-grade physics knowledge. Now, I was excellent at ninth-grade physics, especially kinematics, but my estimates, compared to the official Statcast numbers, were terrible. Figuring the discrepancies must be due to air resistance, I did my best to remember AP physics (with the help of NASA) and adjusted my calculations for drag. The results improved, but were still way off. There are many additional factors that affect the flight of a fly ball such as wind, air temperature and altitude, but I think the biggest factor causing the inaccuracy of my estimates is batted-ball spin. (If you disagree, let me know in the comments.) Exit velocity and launch angle get all the attention when discussing batted-ball metrics, but the data I was looking at suggested that batted-ball spin merits attention too. Are there batters who are consistently better at spinning the ball than others, and if so, is this a valuable skill?

We already know that balls hit with top-spin sink faster than normal while balls hit with back-spin stay in the air longer. It’s unclear, though, whether it’s better for the batter to hit the ball with more or less spin, and whether top-spin or back-spin is more beneficial. Back-spin would seem to be better if you are a home-run hitter while top-spin might be more beneficial if you are a line-drive hitter.

As far as I know, Statcast doesn’t measure batted-ball spin, and if it does, it’s not available on Baseball Savant. So to act as a proxy for spin, I calculated the estimated travel distance (adjusted for air resistance) from its launch angle and exit velocity for every line drive, fly ball and pop up hit in 2016 and subtracted this number from the distance estimated by Statcast. The bigger the deviation between these two numbers, the faster the ball was spinning, theoretically. Balls with positive deviations (actual distance > estimated distance) must have been hit with back-spin and balls with negative deviations (actual distance < estimated distance) must have been hit with top-spin.

The following table shows the 20 hitters (min. 50 fly balls hit) who gained the most distance on average in 2016 due to back-spin:

 Batter Name Number of batted balls Avg Statcast Distance (ft) Avg Estimated Distance (ft) Avg Deviation (ft) Travis Jankowski 87 254 235 19 DJ LeMahieu 213 282 264 18 Carlos Gonzalez 226 293 276 17 Daniel Descalso 102 285 270 14 Max Kepler 150 285 271 14 Billy Burns 108 234 221 13 Rob Refsnyder 57 269 257 12 Jarrod Dyson 98 243 232 11 Martin Prado 256 262 251 11 Ketel Marte 154 250 239 11 Justin Morneau 73 278 268 11 Gary Sanchez 66 323 312 11 Tyler Saladino 107 270 260 10 Phil Gosselin 77 264 253 10 Jose Peraza 107 257 248 10 Mookie Betts 311 279 270 9 Melky Cabrera 280 271 261 9 Ichiro Suzuki 137 251 242 9 Omar Infante 68 269 261 9

With a few exceptions, these are not home-run hitters. This group of 20 players averaged 8.25 home runs in 2016. The players who are getting the most added distance on their fly balls are not the ones who need it most. (Note: four players on this list and three of the top four players played their home games at Coors Field. Did you forget that Daniel Descalso played for the Rockies last year? Me too.)

What about the other end of the spectrum? The following are the 20 players who lost the most distance on average in 2016 due to top-spin:

 Batter Name Number of batted balls Avg Statcast Distance (ft) Avg Estimated Distance (ft) Avg Deviation (ft) Colby Rasmus 136 285 306 -21 Tommy La Stella 72 273 294 -21 Brian McCann 195 273 294 -22 Todd Frazier 248 276 297 -22 Jorge Soler 88 278 300 -22 Brian Dozier 263 287 309 -22 Curtis Granderson 238 284 306 -22 Franklin Gutierrez 76 304 327 -23 James McCann 131 277 300 -23 Miguel Sano 158 301 324 -23 Khris Davis 213 303 326 -23 Freddie Freeman 269 289 312 -23 Mike Napoli 205 290 315 -25 Chris Davis 207 304 330 -26 Tyler Collins 54 270 296 -26 Ryan Howard 129 306 334 -28 Kris Bryant 284 281 309 -28 Jarrod Saltalamacchia 96 290 321 -31 Mike Zunino 63 295 327 -33 Ryan Schimpf 122 298 331 -33

Kris Bryant, Miguel Sano, Ryan Schimpf: this list is full of extreme fly-ball hitters with an average of 24 home runs last year. The scatter plot below with a correlation of -0.58 shows the relationship between batting spin and fly-ball percentage for all players in 2016.

And this isn’t just a one-year phenomenon. I was relieved to find out that the correlation between 2016 average distance deviations and 2015 average distance deviations is 0.75. Players who hit balls with a lot of spin in 2015 overwhelmingly did so again in 2016. Again, the plot below shows the strong relationship.

Mechanically, this is not such a surprising result. Players with a more dramatic uppercut swing (like a tennis swing) will impart more top spin onto the ball while the opposite should be true for players with a more level swing.

It remains to be seen whether this knowledge is useful in any way or if it falls more into the “interesting but mostly irrelevant” category of FanGraphs articles. There is essentially no relationship between a player’s average distance deviation and his wRC+ (correlation = -0.13), so we cannot say that spinning the ball more or in either direction leads to better results. And I imagine it is difficult to alter one’s swing to decrease top-spin while still trying to hit fly balls. At best, maybe this is a cautionary tale for players who want to be more hip and trendy and hit more fly balls like James McCann (FB% = 0.41), but don’t have the raw power to absorb a loss of 28 feet per fly ball (HR = 12, wRC+ = 66).

Let me know what you think in the comments.

## When Do Pitchers Try Harder?

Pitch counts have become an integral part of the game of baseball, so much so that it’s impossible to find a TV telecast that doesn’t display the pitch count side-by-side with the score and the inning. Yet pitch counts continue to be maybe the most annoyingly simple and arbitrary metric used to craft crucial in-game strategy. 99 mph fastball down the middle: +1 pitch. 76 mph curveball in the dirt: +1 pitch. Intentional ball: +1 pitch. Dirty ball tossed to the umpire: +0 pitches. Pitchout +1 pitch. Warmup pitches: +0 pitches. My goal here is not to fix this problem — just explore some interesting data that I believe should eventually be used to bring pitch count into the modern era.

Right now, I’m just going to look at 4-seam fastballs and how hard they’re thrown. All data comes from the 2016 regular season. Thank you Baseball Savant. The question I set out to answer is simple: When a pitcher needs to make a pitch, does he try harder? Common sense says yes, of course this is what happens. Relievers throw harder than starters in general because they don’t have to worry about throwing more quality pitches in later innings. But the data shows that pitchers change their effort levels within innings as well, especially when they have two strikes and/or runners in scoring position. Eventually, we should be able to use this knowledge to craft a better pitch count that takes this extra effort into account. Read the rest of this entry »

## Should the Best Team Win Each Year?

The Cubs won the 2016 World Series. Though that hopefully isn’t news to anyone, it is still interesting for a variety of reasons. Notably, it was the Cubs’ first World Championship since 1908. I have nothing new or interesting to add to the conversation about the Cubs’ accomplishment. The reason I want to talk about the Cubs now is because not only are they World Champions, they were also clearly the best team in the MLB this year.

Most fans recognize that those two statements are saying vastly different things. The Cubs won more games in 2016 than any other team, had the greatest run differential and had the highest team WAR total, so it is fairly safe to say that they were, in fact, the best team in 2016. But in 21 seasons from 1995-2015 (wild-card era) the team with the best regular-season record (or tied) has only won the World Series four times: the Red Sox in 2007 and 2013 and the Yankees in 1998 and 2009. That’s a 19% success rate. Also since 1995 only three teams that have led the major leagues in team WAR have won the World Series: again the 2007 Red Sox and 2009 Yankees, and also the 2010 Giants. That’s 14%. So that raises the question: is this a problem? Should the World Series champion more frequently be the best regular-season team? Should MLB change things to fix this problem?