Archive for simulator

Before the season, I put up a three-part series (1, 2, and 3) that explained how linearly-weighted stats like wOBA, while useful for comparing players to each other, don’t necessarily reflect each player’s true contribution to their team’s run scoring.  You see, the weights used to calculate wOBA are based on league averages.  So, for a team with league average breakdowns in walk rate, singles rate, home run rate, etc., wOBA (and its offspring, wRC+) ought to work very well in figuring out how valuable a player is (or would be) to an offense.  However, when it comes to particularly bad or good offenses, or to those with unusual breakdowns, wOBA will lose some of its efficacy.

Why?  There are synergistic effects in offenses to consider.  First of all, if a team gets on base a lot, there will be more team plate appearances to go around, which of course gives its batters more chances to contribute.  Second of all, if the team gets on base a lot, a batter’s hits are generally worth more, because they’ll tend to drive in more runs.  And, of course, once the batter gets on base in such a team, it will be likelier that there will be a hit (or series of hits) to drive him in.  The reverse of all three points is true in a team that rarely gets on base.

But it goes even beyond that.  Let’s say Team A gets on base 40% of the time, and Team B gets on only 20%, but their balances of the ways they get on base are equal (e.g. each hits 7x as many singles as they do HRs) .  A home run is going to be worth something like 14% more to Team A, due to more runners being on base.  However, to Team B, a home run is worth over ten times as much as a walk, whereas to Team A, it’s worth only about 5 times as much.  That’s because Team A has a much better chance of sustaining a rally that will eventually drive in that walked batter.  Team B will be much more reliant on home runs for scoring runs.

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A month ago, I submitted an article with something I came up with that I thought was pretty cool.  It was a simulator similar to the Coolstandings sim, except that it would use Steamer and ZiPS rest-of-season (RoS) projections instead of year-to-date statistics as the measure of each team’s true talent.  Well, as you may have noticed, the boss, David Appelman, must have thought it was a pretty cool idea too, as unbeknownst to me, he had been working on the same sort of thing since long before the idea popped into my head.  But my duplication of effort will hopefully not go entirely to waste, as I’ll be sharing and explaining the simulator I created.  You’ll be able to use it to analyze your own “what if” scenarios, if that’s your sort of thing.  Think ZiPS and/or Steamer is overly optimistic or pessimistic about some teams?  You can fix that by running your own simulations with this.  Or you can apply it to past or completely hypothetical teams.  Go nuts.

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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).

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