How Many Pitches Does it Take? Part One

I’ve been talking about pitches, pitching patterns, and pitch usage a lot lately. Whether it be through PitchFx charts, simply sharing observations, or talking about a pitcher who needs an additional pitch. Finally, I broke down and gathered the data needed to see whether having a surplus of pitches or only a couple mattered to performance.

Most people have the idea that quality matters more than quantity in mind. I know I did. In fact, while running the query (last three years, at least 5% usage of the pitch, at least 150 innings) only one pitcher recorded more than five pitches and that was Ryan Franklin with six. As you’ll see, Ryan Franklin is not a particularly good pitcher. Franklin is passable, but I think you would expect more from someone who has a constant advantage in game theory. Now, it is possible that Franklin falls into patterns, tips his pitches, or simply throws hittable garbage, I’ll leave that up to you to figure out, my only interest is the amount of pitches used modestly and whether it makes for better pitchers.

We begin today with that query I mentioned earlier. No restriction on amount of games started and only 150 innings over the last three years; meaning relievers like Joe Nathan, Mariano Rivera, and Jonathan Papelbon were eligible to make the cut. Let’s get to the data, shall we?

Franklin was the only pitcher with six pitches and 28 pitchers had five pitches qualify. Tradition has most starters throwing 3-4 pitches and most relievers having one or two. Tradition holds true here. 119 pitchers had four pitches qualify, 134 had three, and 39 had two. Franklin failed to make a start over the last three years meanwhile pitchers with 5 pitches saw 64% of their games come as starters, 60.2% for four pitch qualifiers, 32.2% for three pitches, and 11.7% for two pitches.

Let’s look at how they actually performed:

Are the relievers skewing the two and three pitch numbers? Tomorrow we’ll separate the starters from the pack and see if that’s the case.



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Thor
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Thor
7 years 6 months ago

This is an interesting line of analysis. I’d never thought about actually trying to see whether more pitches helps. Still, though, it seems like the actual value of having more pitches is going to be dwarfed by the ‘junk-ball effect’–the pitchers who don’t have enough stuff or speed to make it on their first 2-3 pitches are going to work harder on developing different pitches.

Maybe you could focus on individual pitchers’ difference in performance in the year or two after they develop a new pitch?

Evan
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Evan
7 years 6 months ago

I’d be interested to the distributions in this chart by team. I know that Toronto seems to encourage its pitchers to throw lots of different pitches in games; do other teams have easily identifiable tendencies?

Kazinski
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Kazinski
7 years 6 months ago

I think the results you see are self-selection bias. The more marginal a pitcher is then the more pitches he try to throw. The better a pitcher is then he needs fewer pitches to be effective.

So the conclusion is definitely not that Ryan Franklin needs to mothball 3 of his pitches to get better results.

And wow, I didn’t realize FIP correlated so well with ERA when aggregating pitchers.

mrbmc
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mrbmc
7 years 6 months ago

Are you distinguishing between fastballs? e.g. are 2 seamers and 4 seamers and cutters considered distinct pitches?

RMR
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7 years 6 months ago

I think Kazinski is likely on to something, though it will be interesting to see what happens with the relievers removed. The lower the quality of your offerings, the more need you have to deceive the hitter through other pitch types. If you’re Mariano Rivera and can throw an unhittable cutter, why develop a curve — or at least use it.

Rafa
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Rafa
7 years 6 months ago

Less pitch variety = more dominant pitches = higher k/9, lower HR/9, and lower FIP, but leads to overthrowing = higher BB/9 (usually).

BABIP is flat meaning a hitters BABIP is a true measure of their own skill and independent of pitching?

Great stuff

MG
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MG
7 years 6 months ago

Two things instantly pop-up to make this largely a futile exercise:

– Sample sizes issues. Only really have sample sizes large enough for the pitchers with 3/4 pitches to draw some real conclusions. I am willing to bet the 95% CI intervals are pretty large for the other sample sizes and makes it pretty dubious to draw any conclusions from them with much certainty.

– pitch f/x data is interesting but as several others have pointed out it still does a fairly inaccurate job of classifying a pitchers’ true repertoire because of the number of pitchers who throw a modified fastball or a offspeed pitch that can be difficult to classify.

– Given that you have larger sample sizes for the 3/4 pitch groups, I would be curious to see breakouts between starters who are primarily fastball/sliders guys (norm usually today) vs. fastball/other type of breaking pitch

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