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May 20, 2014

Baseball Therapy

Beware the Genius Tag for Coaches

by Russell A. Carleton


How do you know whether your team’s hitting coach or pitching coach is doing a good job? Generally, the answer is “Well, how are his hitters/pitchers doing? Are they getting better?” That seems to be the justification given when he gets fired, after all.

Is that fair? Can a pitching coach really be blamed if his pitchers aren’t performing well? Like a manager, he’s not the one out there throwing the pitches, and the guys who are out there throwing the pitches may not be good. You can’t make a chicken salad out of a sow’s ear. It’s entirely possible that our hitting (or pitching) coach is actively tinkering with the swings of each hitter on his team, that he’s a mad genius, and that he could turn anyone into Babe Ruth. Or he might turn Babe Ruth into Mario Mendoza. Or maybe he’s more of a hands-off guy and just happens to be around when Mario Mendoza turns into Babe Ruth. If it does happen, can we safely proclaim him a coaching genius?

Warning! Gory Mathematical Details Ahead!
This is a tricky question to answer (and the math is going to get very gory this week). First, we ask the question of what a hitting (or pitching) coach’s job is. In theory, it’s to make the hitters (or pitchers) on his team better than they had previously been. Even if he’s not working with the most talented bunch, they should be showing some improvement.

Last year, I tried tackling this question, both with hitters and pitchers, using a very different method (mixed linear modeling, for the initiated). I tried to control, as best as I could, for the talent level that a coach had on hand, and then charged any variation from that talent level to the coach. I figured that some of it was random variance, but the randomness would largely cancel itself out across multiple hitters (or pitchers). At the end of the articles, I put together a “best and worst” section, but found myself asking whether I could actually trust the sample sizes that I had. For example, in the hitting coach article, I sang the praises of Kevin Seitzer, because his Royals hitters had been doing better than my model expected, and because he had what I guessed was a long enough track record. Was it really long enough?

I borrowed some methodology that I used in a previous article on figuring out whether we can trust changes in performance from year to year. The method, known as the reliable change indicator, looks at the difference between this year and last year in some stat (say, Smith picked up 30 points of OBP by going from .290 to .320) but also controls for what sample sizes produced those two numbers and the fact that those numbers are more reliable when they are produced at bigger sample sizes (for the initiated, I created a pooled standard error of the difference, based on the reliability of the stat at X PA and a good estimate of the population variance that we might expect at X PA). The result is something akin to a z-score. This gives us an indicator of whether a player has actually improved or declined.

From 1993-2013, I looked at the (raw, unadjusted) strikeout rate posted by all hitters, minimum 100 PA. In the following year (again, minimum 100 PA), I looked to see how much their strikeout rate had changed, by the reliable change index (RCI) method I just referenced. RCI can tell us whether the change in some measure has been positive or negative and how much faith we should put in those changes, in much the same way that a t-test can tell us whether the difference between two means is significant enough.

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Related Content:  Pitching Coaches,  Coaching,  Hitting Coaches

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