THE BOOK--Playing The Percentages In Baseball

Wednesday, November 16, 2011

HITf/x: (horizontal) batted ball speed

Great stuff from Mike:

Batters have a good deal of correlation between halves of the sample, with a correlation coefficient of r=0.76 with an average of 201 batted balls in each half. That means that we would add 63 batted balls (or about one month’s worth) at league average to the observed average speed for each batter in order to estimate his true skill.
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Pitchers have fairly good correlation between halves of the sample, though not as good as batters. The correlation coefficient is r=0.48 with an average of 251 batted balls in each half. That means that we would add 269 batted balls (or about three months’ worth for a starter) at league average to the observed average speed for each pitcher in order to estimate his true skill.

Just fantastic stuff, and I’m glad Mike did it, as well as showing the key points, which is the point at which r=.50.

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I’m not really surprised by the results.  The closer you get to someone’s base physical and mental skills, the less observations you need.  This is why scouts are so important.  And the F/X and Trackman systems are, at their heart, scouting tools. 

What we’ve had until recently are outcomes, results, things like OBP and K/PA, etc.  What drives OBP and the like are the players’ base skills AND luck.  That’s why we infer a players’ base skills by stripping out as much luck as we can figure out.  We do this through a Bayesian process (or its equivalent in regression toward the mean).  We need a few hundred contacted balls for a hitter, and in the thousands for a pitcher, in order for us to be able to strip out that luck to infer the base skill.

Inside a player’s contacted ball skill is not only the horizontal speed off the bat, but placement as well.

Unseen in Mike’s data is what the horizontal speed off the bat really means.  Let’s take a pitcher’s fastball speed.  We presume that there’s a high degree of correlation in a pitcher’s fastball speed.  I have no doubt that if you do a split-half correlation, you’ll get something ridiculous like r=.99 (really, it’s a question of how many nines) for pitchers who throw 1000 fastballs.  So, we can ascertain a scouting observation: we can readily and easily ascertain a pitcher’s underlying true fastball speed.

But, what does THAT give us?  He throws really hard or really soft.  But, that by itself, still doesn’t tell us how EFFECTIVE he is.

The next step is to correlate that particular base skill, that scouting-level observation, into results.  And Mike has given us that:

We see that a player who hits the ball at close to 80mph has a BACON of close to .300, while those who hit the ball at close to the league average (70mph) has a BACON of close to .200, and those at the league low (60mph) is just above .150. 

I have to say, all those numbers look pretty low.  I guess that’s what happens when you have non-linearity.  For example, suppose you hit one-third of your balls at under 60mph, another third at 60-80, and the last third at over 80mph.  (Numbers for illustration purposes only.) If it’s under 60mph, you get a batting average of .050 to .150, or say around an average of .120.  If you hit it between 60-80, it’s .150 to .300, or an average of .220.  And above 80mph, it’s from .300 all the way up to .650, for an average of say .500.  That gives you an average of .280, for an average of 70mph.  As you can see, the overall average for a distribution around 70mph is way above the batting average at the 70mph point.

Anyway, so what I’d like to see is this: create a DISTRIBUTION for each player, centered around his true talent horizontal speed off the bat, and apply the rates from the above chart (or a more smoothed version actually).  This way, we can end up with a player’s true talent BACON, if all we know is his horizontal speed off the bat.

THAT will tell us how valuable knowing his horizontal speed off the bat is.