Archive for September, 2014

Why It’s Always Better to Use Multiple Statistics

One of the most common questions I get when talking about advanced metrics with people who are new to the experience is “what’s the best stat for looking at X?” My standard response depends on the particular question, but I almost always drop the caveat that you should always be looking at multiple pieces of information rather than one single stat and I don’t think I’m alone in offering that advice.

As our metrics for evaluating baseball improve there’s a desire among many for the new stats to push the old stats out of the conversation. Now that we have wOBA, why would you ever use OBP? And then once you have access to wRC+, is wOBA even necessary anymore? If we have K%, isn’t K/9 completely useless?

In some cases, that’s a fine idea, but in many you would rather have access to as much information as possible because stats that don’t do very well on their own can still be informative in the context of other statistics. Wins Above Replacement (WAR) is the best single metric we have to determine a player’s complete value, but WAR only conveys the answer to a very specific question. If you want to know about how good a player is overall, WAR is great. If you want to know if he’s a power hitter or a player with a good eye, WAR doesn’t do very much.

The same is true for wRC+. You know a 150 wRC+ means someone has had a very good season, but you don’t know if he’s doing it with a high average, good patience, excellent power or some combination of them. We’re striving for better measures of performance but you can’t only look at one or two numbers because baseball is full of questions that require a variety of tools to evaluate.

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Calculating Position Player WAR, A Complete Example

One of the hallmark statistics available at FanGraphs is Wins Above Replacement (WAR) and we’ve just rolled out an updated Library entry that spells out the precise calculations in more detail than ever before. There’s always been a clear sense of the the kinds of things that go into our WAR calculation, but we’re never just dropped an equation in front of you and said, “Here!”

As of today, we’ve done that and I encourage you to go check out our basic primer on WAR and our detailed breakdown of how we calculate it for position players. If you’re a hands on learner, grab a pen and paper or spreadsheet and follow along. I’m going to walk you through a complete examples of how to calculate WAR for position players. Let’s use the 2013 version of Joey Votto as our exemplar.

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The Beginner’s Guide to Using Statistics Properly

We’ve spilled a great deal of virtual ink and audible podcasting words on the nature of Wins Above Replacement (WAR) and defensive metrics recently. Jeff Passan of Yahoo! Sports and many who responded to his critique of the current WAR calculation dug into the relative merits of the metric itself and how well we’ve estimated it to date. That’s a great conversation to have and Dave has done the heavy lifting on behalf of FanGraphs in that regard. I’d like to pivot and discuss a very important point about the use of statistics in baseball: Everything has flaws.

Every single statistic is wrong. Your eyes are wrong. It is all wrong. Nothing we have will provide you with perfect information or even truly accurate information with respect to the underlying variables about which you care. You don’t get to choose between flawed and not flawed statistics, you get to choose between useful and not useful statistics. More importantly, statistics become useful based on your awareness of the proper way to wield them.

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The Beginner’s Guide to Measuring Defense

There’s a decent chance you’ve arrived at this page without a serious desire to hear more about defensive statistics. Trust me, I understand your frustration and your fatigue. Defensive stats like Ultimate Zone Rating and Defensive Runs Saved are controversial in some circles because they are reasonably new and the underlying data is somewhat hidden from view. You hear words like “flawed,” “absurd,” and “subjective” surrounding them. You’re tired of it.

Yet I’d like to lay out why we have advanced defensive statistics and how they work in the abstract. You won’t get to the end of this post and decide that UZR has perfectly measured Alex Gordon‘s defense, but hopefully you will have a better appreciation for why we measure defense the way that we do.

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