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July 22, 2014

Moonshot

Accounting for Count

by Robert Arthur


The count controls all aspects of the batter-pitcher battle. From pitch type to location to swing tendencies all the way to the batted-ball outcome of a thrown pitch, nothing escapes the influence of the count. Like many statistics in baseball, credit for the count itself is difficult to parse. If a pitcher falls behind in a count, it could be because he is having trouble locating his pitches, or it could be because he is afraid to challenge the hitter in the zone. Conversely, a hitter’s ability to drive into favorable counts could be because of a good eye or a good approach or the ability to inspire a fear of the zone in the opposing pitcher.

I came to the conclusion recently that, because of the pervasive influence of the count, certain plate discipline statistics must be taken with a grain of salt (or an extra grain, if you were already taking them with the pre-recommended grain). I came to this realization because I was examining a certain hitter’s swing rate, and noticed that while his overall swing rate differed little between years, his swing rate on particular counts had changed quite substantially—that is to say, his approach, as a function of the count, had changed. However, when the positive and negative changes in swing rates were averaged, the overall difference in swing rate became muted, concealing the difference in approach.

It is well-known that swing rates differ depending on the count, but by way of introduction, consider this graph, which plots the league-average swing rate (among regulars in 2013) by count.

pastedGraphic.png

Hitters are generally more liable to swing as they get deeper into counts. The highest swing rate is on 3-2, somewhat surprising, and the lowest is 3-0, unsurprisingly (better to take the near-guaranteed walk). With each strike, swing rate increases massively, perhaps because there is less to lose when a strikeout is imminent.

The above pictures the league-average swing rates by count, however. If we are especially interested in what makes a good approach to the count, one way to examine the question would be to focus in only the best hitters. Consider, therefore, the swing rates of the top 10% (selected by TAv) hitters in baseball.

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11 comments have been left for this article. (Click to hide comments)

BP Comment Quick Links

MGL

Robert, of course swing rates by count will vary more than overall swing rate simply because of the sample sizes. Right?

Jul 22, 2014 08:03 AM
rating: 0
 
BP staff member Robert Arthur
BP staff

You are right, which is (part of) why I wasn't surprised that they did vary more (of course they would!). There's no way to get rid of this effect entirely, but I did weight the differences in swing rate by the frequency of each count, so that a 50% change on 3-0 is not as significant because 3-0 counts are rare overall.

However, another reason they vary more is because of the offsetting changes that I talked about--hitter increases his rate on 0-0 counts, but decreases it almost as much on 0-1+1-0 counts, so the total change gets diminished.

Actually, come to think of it, I suppose I could have gotten the 'expected' amount that count-adjusted swing rates would vary by random sampling, and then compared that to the actual differences. For next time.

Jul 22, 2014 08:15 AM
 
bmmillsy

Pet peeve complaint here: no reason to have so many colors on your bar plots. If you instead made different colors based on groups of counts with the same number of balls, it would be much easier to see the effect of the upward swing likelihood trend as there are more strikes on the batter.

Jul 22, 2014 12:05 PM
rating: 0
 
bmmillsy

Also, as to the substance, the Good vs. Bad stuff is really interesting! There is some fascinating game theory application here, and it would be interesting to see this broken down by pitcher quality as well.

Thanks for the insight!

Jul 22, 2014 12:07 PM
rating: 0
 
therealn0d

The colors are logically organized (I didn't see it until you lodged this complaint). You've got warm tones (red, orange, yellow) for 0 ball counts, greens for 1 ball counts, blues for 2 ball counts, and purples for 3 ball counts. Once you realize how it's organized it is easy to spot the trends, which are hardly surprising (tendency to swing more often as the count gets deeper).

Jul 22, 2014 13:31 PM
rating: -1
 
therealn0d

Also, the graphs show that hitters tend to swing more often in counts favorable to them (when they get ahead). Again not surprising.

Jul 22, 2014 13:36 PM
rating: -1
 
BP staff member Robert Arthur
BP staff

indeed, that was the idea, sorry if it ended up being unclear.

Jul 22, 2014 14:14 PM
 
bmmillsy

No worries, I see that. Just trying to help with the communication of the really interesting result. I think maybe doing less distinct shades within-group would help is all, and ensuring color progression is consistent within each group.

With no balls, you go from red to yellow (dark to light).

With 2 balls you go from light blue to dark blue (light to dark).

I like what you did with 2 balls, keeping the same general color and using a scale. But the consistency would help.

As I said, though, this is really interesting stuff.

Jul 23, 2014 09:40 AM
rating: 0
 
BP staff member Robert Arthur
BP staff

Thanks (for both your compliments and feedback), you are dead-on about the color progressions.

Jul 23, 2014 11:16 AM
 
Truthteller

I think there is a random variable you are overlooking in your model and will influence the batter and that is the consistency/inconsistency of the plate umpire. If the PU has a high strike count of pitches that are actually balls, the batter somehow accounts for that in his batting approach with the counts. You might want to use a co-variant for that in your analysis.

Jul 22, 2014 15:51 PM
rating: 0
 
BP staff member Robert Arthur
BP staff

You are absolutely right, Truthteller. There's a lot I'm not accounting for, like the pitcher, the location in the zone, pitch type, the ump, and so on. Really, for this first (overall) analysis, I just wanted to get a bird's eye view of swing rates by count and how they vary. In the future, I will be thinking about how to put all that stuff into a model to pull out how aggressive/patient individual hitters are.

Jul 22, 2014 19:42 PM
 
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