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:

1. Hit
2. Walk
3. Hit by pitch
4. Reach on error
5. Sac bunt
6. Sac fly
7. Pitcher throws at least six pitches
8. 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 10^th 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.

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 8^th 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.

1. 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.

Several of my childhood baseball coaches believed in the idea of “quality at-bats.” It’s a somewhat subjective statistic that rewards a hitter for doing something beneficial regardless of how obvious it is. This would include actions such as getting on base, as well as less noticeably beneficial things like making an out but forcing the pitcher to throw a lot of pitches. There is some evidence that major league coaches use quality at-bats and, through my experience working for the Florida Gators, I noticed that some college coaches like using it too. However, how it is used varies from coach to coach and it is a stat that is rarely talked about in the online community. Since there doesn’t seem to be a consensus of what a quality at-bat is, I decided to define a quality at-bat as an at-bat that results in at least one of any of the following:

1. Hit
2. Walk
3. Hit by pitch
4. Reach on error
5. Sac bunt
6. Sac fly
7. Pitcher throws at least six pitches
8. Batter “barrels” the ball.

There is some room for debate on a few of these parameters (e.g. if six pitches is enough, whether or not sacrifices should be included, etc.). However, in my experience this is roughly in line with what most coaches use, and I think it does a good job of determining whether or not a hitter has a “quality” at-bat. In my analysis I was excited to be able to include the new Statcast statistic, barrels. I have seen coaches subjectively reward a hitter with a quality at-bat for hitting the ball hard, but barrels gives us an exact definition of a well-hit ball based on a combination of exit velocity and launch angle.

The first player I used to test this definition was Billy Hamilton. Hamilton is a player that has always interested me, partially because stealing bases is entertaining, but also because there has always been speculation about whether or not he will ever be able to develop into an average hitter. I also find him interesting because his career has consisted of one awful offensive season sandwiched between two less horrible but still sub-par offensive seasons. His wRC+ in 2014 was 79, in 2015 it was an unsightly 53, and in 2016 it was back up to 78. I thought that his quality at-bat percentages might be able to give us a clue as to whether or not he could become a better hitter. By pulling Baseball Savant data from Bill Petti’s amazing baseballr package, I counted all of Billy Hamilton’s quality at-bats in each of his three MLB seasons. I then divided those quality at-bat totals by his total plate appearances to get his quality at-bat percentages:

2014:  41.75%

2015:  42.28%

2016:  47.52%

It is never ideal to make sweeping conclusions about statistics — especially new ones that are not widely used or understood — without putting them in context. However, at the very least, I think it is a good sign that Billy Hamilton has experienced an upward trend in his quality at-bat percentages. Based on my definition, these results show that he is making more effective use of his at-bats and that he is continuing to develop as a hitter.

To put Hamilton’s scores in some context, I calculated the quality at-bat percentages for several other players and provided them below. I have not had a chance to run every player as of yet, but I think this chart can give you a feel of where Billy Hamilton stands compared to other players. It is also interesting to point out Jason Heyward’s large drop-off in quality at-bat percentage. This is yet another indicator of how poor his 2016 season was. Additionally, and not surprisingly, Joey Votto and Mike Trout have, relatively, very high quality at-bat percentages, while Adeiny Hechavarria (a player who had a wRC+ just north of 50 last season) had a quality at-bat percentage well below that of even Billy Hamilton.

There is more research that needs to be done here in order to make more intelligent conclusions. I would like to run more players through my statistic, including minor leaguers, to see just how well quality at-bats can be used in evaluating talent, development, and predicting future success. I believe that quality at-bats are something that could be relevant in many of the same ways as quality starts. Neither of these statistics inform you of the nuances that make a player great (or not so great), but they do give you an idea of a player’s reliability in having a passable performance. I believe that with further analysis into quality at-bat percentages using the definition I created, we may be able to learn more about how hitters make use of each and every at-bat.