One of the things people love about baseball is that the game is both very simple and very complicated all at once. Baseball is simple in that all you’re trying to do is score more runs than the other team during 162 finite, nine inning contests. You are trying to reach base and advance runners and you are trying to prevent the other team from doing the same. How you go about doing those things is where baseball gets complicated. Jeff Sullivan often refers to baseball as being “obnoxiously complicated,” which I find to be a fitting description.
Think of all of the different possible outcomes of every pitch and all of the different pitches and locations from which the pitcher can choose. The complicated part of baseball is what makes baseball interesting, but the simple part of baseball is where you need to start to get your head around sabermetrics and player evaluation. Baseball is about producing and preventing runs.
As a result of that simple reality, the heart of baseball analysis is determining what leads to run scoring and run prevention. Specifically, how many runs is each possible action worth? If a player hits a single, how much has that player just increased his team’s odds of scoring a run? If a fielder makes a nice running catch, how many runs has he prevented? We don’t actually care about hits and walks and double plays, we care about how those finite events contribute to the overall goal.
To that end, we talk a lot about run values when we’re evaluating players on FanGraphs. You may be interested in a player’s on base percentage as a matter of fact, but what you really care about when determining that player’s value is how his collection of offensive actions have lead to overall run production. That is the fundamental question we’re asking when talking about overall value.
This means that to carry out any sort of evaluation, you want to learn to speak the language of runs (and/or wins, they’re really the same thing). Knowing about wOBA and wRC+ is great, but a player with a .400 wOBA in 50 PA isn’t nearly as valuable or productive as a player with a .340 wOBA in 600 PA. The first is worth about 3.4 runs above average while the latter is worth about 13.4 runs above average at the plate.
You know this intuitively, but run values allow you to measure it. Would you rather have a player whom you could count on to play half a season at a .400 wOBA or a player who would play all year long while running a .380 wOBA? The answer is pretty clear as to which one is more productive. The second player helps his team score more runs than the first, and if you’re interested in answering a value based question, a run value stat is what you’re after.
These statistics aren’t anything other than rate performance scaled to playing time. To determine Weighted Runs Above Average (wRAA), our non-park adjusted batting runs above average, you simply do the following:
wRAA = ((wOBA = lgwOBA)/wOBA Scale)*PA
That equation takes a player’s wOBA (their performance per PA), compares it against league average, and then factors in the number of plate appearances for which they’ve generated this wOBA. It’s very simple.
We use run values for base running, defense, position, and replacement level as well. We either want to compare a player’s production to the average player or a replacement player and to do so we need to know their per PA performance and the number of PA in which they’ve participated.
When it comes down to it, we are trying to compare players to one another or to some common baseline. If your question is about how valuable a specific player has been to his team, speaking in runs (or wins) is the way to go. Let’s look at an example.
We’ll use Carlos Gomez and Evan Longoria from the 2013 season. You can compare their numbers for yourself here. Gomez had a .363 wOBA in 590 PA while Longoria had a .360 wOBA in 693 PA. Immediately, you know that if you leave park and league adjustments out of this, Longoria was the more valuable hitter due to his 100 extra PA. But how much are those 100 extra PA worth given virtually identical wOBA? Here’s where run values come in handy. If we plug Gomez into the equation above, we arrive at 22.7 wRAA. For Longoria, it’s 25.2 wRAA.
Relative to average, Longoria comes out three runs ahead offensively (he would get extra replacement level runs too). If you want to use wRC, which is essentially wRAA without league average set to zero, the gap is more like 14 runs over the course of the year. Run values give you a way to quantify how playing time interacts with rate stats and that’s very important.
And if talking about runs above and below average doesn’t feel comfortable, it’s a snap to turn runs in wins. Just take any run value and divide by that year’s runs per win number (which is usually between 9 and 10). If you want to ask yourself, how many wins better than average is this player or how many wins above replacement a player is worth, you just want to divide their run value by the league R/W found in the link above.
It’s a snap and it’s very powerful. Runs or wins, whichever you prefer, solve the problems that exist in rate and counting stats. If a player has a .500 wOBA over the span of a month (100 PA), how do you compare him to a player who has a .360 wOBA over an entire year (700 PA)? Using a rate stat will tell you to pick the first guy, but if you look at HR, RBI, R, Times on Base, the second guy will come out ahead. If you want to know which player helped his team score more runs, and therefore win more games, you need a stat that combines both dimensions into one number.
Our version of WAR and its components are context neutral, meaning that a double is always worth the same amount no matter the base-out state, but you could use something like RE24 if you want a context-dependent number. The entire enterprise is very intuitive. Once you learn the language, you’re set.
Sometimes you want to ask different questions, and that’s okay. If you want to know who has been the best hitter PA for PA, then a run value doesn’t do you any good. Or if you want to know how often something happens. But when you have a value question, rather than eyeballing it, use one of our statistics designed precisely for this purpose.
Have questions about runs and wins? Ask them in the comments or check out our Library entry on Off for an example!
Neil Weinberg is the Site Educator at FanGraphs and can be found writing enthusiastically about the Detroit Tigers at New English D. Follow and interact with him on Twitter @NeilWeinberg44.