An Attempt to Quantify Quality At-Bats (Part 2) by hkingsley March 1, 2017 In my first article, I created a definition for what I feel like constitutes a quality at-bat. I also examined a few test cases1 and hypothesized different ways in which this data could be used going forward. As a reminder, my definition of a quality at-bat (QAB) is an at-bat that results in at least one of the following: Hit Walk Hit by pitch Reach on error Sac bunt Sac fly Pitcher throws at least six pitches Batter “barrels” the ball. To calculate a QAB percentage I divided the player’s total number of QABs by his total number of plate appearances. I then dove a little deeper into QABs to see what conclusions I could draw from this statistic. The first thing I did was run every hitter in 2016 who had more than 400 at-bats and created a leaderboard. I displayed the players with the best QAB% and the worst QAB% below. The average QAB percentage in 2016 was 48.54%. Not surprisingly, Mike Trout leads all hitters and is followed closely by Joey Votto — a player who always finds a way to get on base. The player that stuck out to me most on this list was Chris Carter. This is a player who had a lot of trouble getting a contract this offseason, despite leading the league in homers. In fact, he had so much trouble that he considered going to Japan before finally signing with the Yankees. However, he had the 10th highest QAB percentage. Mike Napoli’s QAB% also surprised me because I do not view him to be a particularly elite hitter; yet he ranked number four between two of baseball’s best hitters. Players with best QAB% Players with worst QAB% Name QAB % Name QAB % Mike Trout 64.02% Josh Harrison 41.83% Joey Votto 63.52% Rajai Davis 41.82% Freddie Freeman 57.93% Andrelton Simmons 41.74% Mike Napoli 57.89% Ryan Zimmerman 41.67% Josh Donaldson 57.71% Alcides Escobar 41.40% Paul Goldschmidt 57.65% Jason Heyward 41.34% Dexter Fowler 57.61% Adeiny Hechavarria 41.32% DJ LeMahieu 57.30% Jonathan Schoop 40.49% David Ortiz 55.27% Salvador Perez 40.22% Chris Carter 55.16% Alexei Ramirez 38.46% One commenter on my last post pointed out that OBP could be highly correlated with QAB%. They were right. In fact, there is a strong correlation of r2=.82 between OBP and QAB%, which makes sense since they share many of the same parameters. After this finding, I decided to create an interactive scatter plot of OBP and QAB% to see what the data looked like and to see if I could find any interesting patterns. If you interact with the graph you can see that the five players who seem to be a little above the data between .3 and .35 OBP are Chris Carter, Mike Napoli, Michael Saunders, Miguel Sano, and Jason Werth. Click here for an interactive version Why does QAB% seem to favor this group of players more than others? By investigating the other parameters in my definition of QABs, I found that these five hitters were taking a lot of pitches. In fact, all five of these hitters were in the top 15 last year in pitches per plate appearance, with Jason Werth and Mike Napoli being numbers one and two, respectively. Additionally, Chris Carter’s score was likely higher since he barreled the 8th most balls last season. This leads me to believe that QAB% tends to favor or distinguish hard-hitting, patient sluggers. Is QAB% another way in which we should be evaluating hitter performance? Probably not. As much as I love seeing Chris Carter on a list with the best players in baseball, this statistic uses an old-school mindset that does not show true value. That being said, it can still be helpful. It is a good way to show which hitters are taking a lot of pitches. It also helps quantify what coaches and broadcasters mean when they say a player had a “good at-bat.” Finally, perhaps you watched a lot of Indians games last season and you couldn’t help but feel like Mike Napoli was the best hitter ever. His QAB% may identify why you feel that way. Mike Napoli is a good hitter, but not nearly as good as former MVP Josh Donaldson despite the fact that they both have a very similar number of at-bats that a coach would call “quality”. Overall, I think this statistic does a good job of quantifying something that used to be a lot harder to quantify. At the very least, QAB% has given me a reason to be excited about Chris Carter joining the Yankees, my favorite team. Opening day cannot come soon enough. In my first article I made a mistake with my test cases. Barrels, a Statcast statistic, did not start being counted until 2015. I had provided QAB numbers starting in 2014. With the way I wrote my code this actually caused the barrels in 2015 and 2016 not to be counted. I should not have provided 2014 numbers at all, and the numbers for 2015 and 2016 were a little lower than they should have been. All of my calculations have been corrected for this article.