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.

Below are a few fundamental examples of why more information is always better and why you should be using multiple statistics to tell the richest possible story.

Hits, Walks, Extra Bases?

Take the 2013 versions of Paul Goldschmidt and Dustin Pedroia. Goldschmidt came to the plate 710 times compared to Pedroia’s 724, so their playing time was very similar and their batting averages were virtually identical at .302 and .301, respectively. Certainly most people know batting average is a very limited statistic, but it can be useful in the context of other information.

Goldschmidt had a higher walk rate (13.9% to 10.1%) which led to a higher on-base percentage and hit for a lot more power, leading to a higher ISO (.249 to .114) and slugging percentage (.551 and .415). The two players had virtually identical rates of hits per at bat, but nearly everything else about their offensive profile was different. You wouldn’t know that if you were just looking at batting average.

Now you may say that this kind of thing only happens when looking at outdated stats, but the same could be said for better measures of value. Take Shin-Soo Choo and Edwin Encarnacion from 2013. Choo (.393) and Encarnacion (.388) had nearly identical wOBAs and very similar batting averages (.285 and .272).

For every plate appearance, they were about equally productive at the plate during that season but they did it in very different ways. Choo walked a ton and hit for solid power (15.7 BB%, .178 ISO) while Encarnacion walked a good amount and hit for a ton of power (13.2 BB%, .262 ISO). Sure, a .390 wOBA is a .390 wOBA, but by only knowing their wOBA you fail to appreciate the differences in their approach.

wOBA tells you far more valuable information than batting average or slugging percentage alone, but batting average and slugging percentage in conjunction with wOBA provide added insight.

Rate Stats or Value Stats?

The same can be said for rate statistics and statistics that measure total value. If I had to pick the best single offensive statistic, I’d recommend wRC+, but a high wRC+ is much less impressive over small samples. For example, take a look at Choo’s 2013 wRC+ (151) and Ryan Raburn’s 2013 wRC+ (151).

By our best single measure, they were equally talented hitters. Except Choo had 435 more PA last year. Certainly wRC+ is a great measure of per PA performance, but knowing the number of PA and how the two statistics translate into Batting Runs is hugely important. For the season Choo had 40.9 to Raburn’s 16.0.

Consider the reverse as well. Jayson Werth (35.4) and Josh Donaldson (36.0) had nearly identical batting runs in 2013 but Werth had a 159 wRC+ and Donaldson had a 147 wRC+ thanks to a large gap in PA. They accumulated the same value at the plate but did so with substantial different per PA numbers.

You can find these types of comparisons everywhere. Is a K/BB ration of 200/50 the same as 40/10? Sort of, but you’d also really like to know the number of batters each pitcher faced. Are all 3.0 WAR players equally talented? Is a .300 OBP always indicative of a poor player? You can’t answer these questions without multiple indicators.

Context Neutral or Context Dependent?

Even when players have relatively similar styles of play and get largely the same number of chances to influence a game, there’s still a matter of timing. There isn’t necessarily skill involved in getting hits with men on base but you’d much rather get a hit with the bases loaded than with the bases empty if you were forced to pick one or the other.

But that’s not even the extent of the context debate. A hustle double and a double off the fence are sometimes equivalent plays and sometimes very different. With a man on first base, you would much prefer the double off the fence because it will almost certainly score the runner, but with no one on base you’re totally indifferent between the two types. Infield hits don’t advance runners more than one base nearly as often as hits to the outfield, but we can’t glean that information from certain statistics.

Consider Miguel Cabrera and Rajai Davis in 2013. Obviously Cabrera was the better hitter and better player overall, but comparing their context neutral and context dependent stats are very interesting. Cabrera had a 68 Batting + wSB (BwSB) and a 75.19 RE24, indicating that his hits were, on average, slightly more productive that we might expect. Davis had a 1.0 BwSB and a -13.31 RE24, indicating the opposite. I’m sure some of that is simply randomness of timing and who was on base in front of him, but a good chunk was also likely that Davis took extra bases with his legs but didn’t advance runners as far as Cabrera did given the same basic outcome (i.e a double).

Sometimes context is about timing, but sometimes it’s about the nuance of the actual play. You wouldn’t know the difference if you only looked at one measure or the other.

More work, but worthwhile

All of this circles back to a simple but important lesson. If you have access to lots of information, make use of it. You don’t need 50 tabs open on your laptop, but looking at a player’s PA, BB%, K%, ISO, wOBA, wRC+, and Contact%, among others, is going to tell you a lot more than just looking at their wOBA or wRC+. Maybe OBP and SLG won’t tell you better information on their own than wOBA, but looking at all three is better than looking at any individual metric.

Good analysis and good decisions are about processing quality information appropriately. Baseball is a complicated game and while we’ve spent many years developing better statistics that we had in decades past, that doesn’t mean we can answer complex questions without drawing from a diverse pool of resources. More information is better, even if it requires a little more effort.