Simulating the Impact of Pitcher Inconsistency
I thought Matt Hunter’s FanGraphs debut article last week was really interesting. So interesting, in fact, that I’m going to rip it off right now. The difference is I’ll be using a Monte Carlo simulator I made for this sort of situation, which I’ll let you play with after you’re done reading (it’s at the bottom).
Matt posed the question of whether inconsistency could be a good thing for a pitcher. He brought up the example of Jered Weaver vs. Matt Cain in 2012 — two pitchers with nearly identical overall stats, except that Weaver was a lot less consistent. However, Weaver had a bit of an advantage in Win Probability Added (WPA), Matt points out. WPA factors in a bunch of things, e.g. how close the game is and how many outs are left in the game when events occur. Because of that, it’s a pretty noisy stat, heavily influenced by factors the pitcher doesn’t control much. It’s not a predictive stat. For that reason, I figured simulations might be fun and enlightening on the subject. They sort of accomplish the same thing that WPA does, except that they allow you to base conclusions off of a lot more possible conditions and outcomes than you’d see in a handful of starts (i.e., they can help de-noise the situation).