Archive for wOBA

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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Last month, I did a two-part analysis that showed what happens — strike out-wise — when, say, a pitcher who strikes out 15% of batters faces a batter who strikes out 20% of the time. As a special bonus for you all, I included a few hundred other K%-matchup types too. I made handedness matchups central to the study, as I think it’s pretty well-established that you can expect a hitter to strike out more often against same-handed pitchers. That is, if I was trying to give an expected result for a righty batter against a lefty pitcher, I looked only at the hitter’s past performance rates against lefties and the pitcher’s history against righties. Before I moved on to performing a similar analysis on batted ball types (grounders, liners, outfield fly balls, and infield popups), I wanted to see whether handedness matchups mattered to these as well.

For this study, my sample was all non-switch-hitting batters from 2002-2012 with at least 300 PA against lefty pitchers plus at least 300 PA against righties. I’d have gone by number of batted balls, except I’m throwing some non-batted ball stats into the analysis.

Let’s get right to it — the following table shows the chances that handedness really makes no difference to each stat, according to paired t-tests:

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In my first article, I wrote about the limitations of the linear weights system that wOBA is based on when it comes to the context of unusual team offenses. In my second, I explained how Tom Tango, wOBA’s creator, also came up with a way of addressing some of these limitations by deriving a new set of linear weights for different run environments, thanks to BaseRuns. Today, I will tell you about the next step in the evolution of run estimators — the Markov model. Tom Tango created such a model that can be accessed through his website, and I’ve turned that model into a spreadsheet that I’ll share with you here.

I’ve told you that the problem with the standard run estimator formulas is that they make assumptions about what a hit is going to be worth, run-wise, based on what it was worth to an average team. That means it’s not going to apply very well to an unusual team. What’s so great about the Markov is that it makes no such assumptions — it figures all of that out itself, specific to each team. And when I say it figures it out, I mean it basically calculates out a typical game for that team, given the proportion of singles, walks, home runs, etc. the team gets in its plate appearances. It therefore estimates the run-scoring of typical teams better than just about anything, but it also theoretically should apply much, much better to very unusual or even made-up teams.
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In my last article, I explained how wOBA’s current implementation changes the value of walks, singles, home runs, etc., annually due to changing league characteristics.  Does this mean that the value of an event is the same for every team in the league each season?  Or in every park in the league?  No way.  If you’re talking about a weak offense in a high-offense era, then the overall constants for a weak offensive era are probably more applicable to that team.  However, it’s not really the point of standard wOBA to guess the run-producing contribution of a particular player to a particular team; I think it’s probably more accurate to say it’s about his probable productiveness in a typical team (although park effects aren’t taken into account, so not exactly… that would be more true of wRC+).

Anyway, Tom Tango realized this limitation, and produced a table that shows how the values change depending on a team’s runs scored.  He accomplished this system of “Custom Linear Weights” (“a necessary offshoot” of linear weights, he says) by making use of David Smyth’s BaseRuns formula, which is, in simplest terms, Runs Scored = base runners * (% of base runners that score) + home runs.  Home run hitters are not considered base runners, in this equation, by the way.  Makes perfect sense, right?

Tango realized that BaseRuns had a better handle on the team run-scoring process than his basic linear weights system (and all the other run estimators), so he translated the results of BaseRuns in various run environments into linear weights.  Specifically, the BaseRuns formula told him how many runs the team should score, and the linear weight value of each hit came from how many additional runs BaseRuns expected the team score if it had one more of that type of hit (the marginal value of each hit type).  Here are just the basics of his results, in graphical form:

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Well, it’s my first assignment as a real writer, having been promoted for my Community Research articles on pitcher BABIPs and ERA estimators, and I’ve been thrown into the deep end of the pool: linear weights.  It’s a tricky subject, but I’ll try to walk you through both the problems with linear weights and how they can be overcome.  This article series mainly draws from various works of Tom “Tango,” a.k.a. “tangotiger,” the creator of wOBA and FIP, as well as from David Smyth’s BaseRuns.  I’ll go deeper and deeper down the rabbit hole of stat geekishness as the series goes on, eventually emerging with a spreadsheet version of Tango’s Markov run modeler that I made for you all to play with.  Where the Markov mainly shines over wOBA is when it comes to extreme run environments, such as unusual offenses or extreme ball parks.

Who cares about extreme run environments?

Nerds like me, I guess?  Tom Tango cared enough to come up with ways to address the shortcomings his original wOBA formulation.  If you’ve ever wondered how valuable a certain player is to your favorite team, maybe you should care too; that low-OBP slugger might be more valuable than wOBA might suggest to your low-OBP team.  On the other end, a typical walk last year was worth considerably more to the high-OBP Cardinals than it was to the low-OBP Mariners (around 0.04-0.065 more runs each… which adds up over a season).

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With the increasing popularity of wOBA and other linear-weights-based offensive measures, OPS and its derivatives have become obsolete. That is as it should be. However, three “three slash” (AVG/OBP/SLG) still has its uses. While wOBA and its cousins are to be preferred as an evaluative measure of a player’s offense, the AVG/OBP/SLG combination still has a helpful descriptive role.

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