A TYA stat primer

Chances are if you’re a regular reader of TYA, you’ve got a pretty good comfort level with advanced statistics. If you didn’t, I’m not sure how engaging or interesting this site would be considering that statistics are the driving factor behind almost every post we write.

However, we’ve often been asked to add a stat glossary for added convenience, and so without further adieu, here’s a quick rundown on the stats I’d say we favor the most. I’m going to assume everyone has an extreme comfort level with the basics — AVG/OBP/SLG — and doesn’t need a refresher here. Because Fangraphs has already done the legwork and built such a wonderful Sabermetrics Library, I’m going to borrow liberally from our holy grail. And remember, if one of us ever cites a stat you’ve never heard of, please don’t hesitate to ask us what it is.

The following represent what I would estimate to be TYA’s most frequently cited stats. I’m excluding things like GB%, LD%, FB%, K/9, BB/9 and HR/9, because those should be self-evident.

Offense

wOBA – Hopefully you already know and love wOBA, which is Weighted On-Base Average, which, per Fangraphs, combines all the different aspects of hitting into one metric, weighting each of them in proportion to their actual run value.

wOBA is put on the same scale as OBP, so any score that would be a great OBP is also a great wOBA, i.e., if you’re putting up a wOBA above .400, you’re one of the very best players in the game; .380 is excellent; .360 is very good; .340 is good; and league-average is typically around .330, although it varies from year to year. In 2011′s down offensive year league-average wOBA is currently all the way down to .315.

wRC+ – Weighted Runs Created Plus. This has recently become my personal favorite offensive stat. The best way to think of it is the same way you think about OPS+, only wRC+ is way better because it’s based on wOBA. Like OPS+, 100 is league average, 125 is very good, 150 is insane, and ~82 is replacement level. The other wonderful thing about wRC+ is that it’s also park- and league-adjusted, meaning you can use it to compare players that played in different years, parks, and leagues.

Pitching

FIP – Fielding Independent Pitching. Per Fangraphs, FIP measures what a player’s ERA should have looked like over a give time period, assuming that performance on balls in play and timing were league average. Back in the early 2000s, research by Voros McCracken revealed that the amount of balls that fall in for hits against pitchers do not correlate well across seasons. In other words, pitchers have little control over balls in play, and as such, FIP assesses a pitcher’s talent level by looking at things a pitcher can control: strikeouts, walks, hit by pitches, and homeruns. Obviously, a walk is not as hurtful as a homerun and a strikeout has less impact than both. FIP accounts for these differences, and presents results on the same scale as ERA. It has been proven to be much more effective than ERA at predicting future performance.

xFIP – Expected Fielding Independent Pitching is a regressed version of FIP, developed by Dave Studeman from The Hardball Times. Per Fangraphs, it’s calculated exactly the same as FIP, except it replaces a pitcher’s homerun rate with the league-average rate (10.6% HR/FB) since pitcher homerun rates have been shown to be very unstable over time. A pitcher may allow a homeruns on 12% of their flyballs one year, yet then turn around and only allow 7% the next year. Homerun rates can be very difficult to predict, so xFIP attempts to correct for that. Along with FIP, xFIP is one of the best metrics at predicting a pitcher’s future performance.

Pitch Type Linear Weights – Again, I’m going to let Fangraphs take it away: Pitch Type Linear Weights (or “Pitch Values”) attempt to answer the question, “Which pitch is a pitcher’s best weapon?” The changes in run expectancy between an 0-0 count and a 0-1 or 1-0 count are obviously very small, but when added up over the course of the season, you can get an idea of which pitch typically yields good results to a pitcher. If one pitch is hit especially hard or a pitcher can’t locate one pitch for a strike, these problems will show up using Pitch Type Linear Weights. Also, if a pitcher gets lots of strikes and outs with a specific pitch, this success will show up.

You’ll notice that there are two different types of Pitch Type Linear Weights: total runs by pitch (which is shown as wFB, wSL, wCB, etc.) and standardized runs by pitch (shown as wFB/C, wSL/C, wCB/C, etc.). The first category is the total runs that a pitcher has saved using that pitch. However, it is tough to compare these total numbers since pitchers throw different amounts of each pitch. The second category corrects for this, standardizing the values on a “per 100 pitch” basis. In other words, when you see wFB/C, that represents the amount of runs that pitcher saved with their fastball over the course of 100 fastballs thrown.

ERA-, FIP-, xFIP- – Also known as “the minus stats,” per Fangraphs, these are essentially the pitching version of OPS+ and wRC+: a simple way to tell how well a player performed in relation to league average. All of these statistics have a similar scale: 100 is league average, and each point above or below 100 represents a percent above or below league average. However, as lower is better for (almost) all pitching stats, a lower ERA- and FIP- is better. These statistics are all park and league adjusted, so it accounts for the fact that some pitchers throw in Fenway Park in the AL, while others throw in PETCO Park against the NL.

Value

WAR – Wins Above Replacement. Per Fangraphs, WAR is an attempt by the sabermetric community to summarize a player’s total contributions to their team in one statistic. You should always use more than one metric at a time when evaluating players, but WAR is all-inclusive — the calculation takes both offense and defense into account– and provides a handy reference point. WAR basically looks at a player and asks the question, “If this player got injured and their team had to replace them with a minor leaguer or someone from their bench, how much value would the team be losing?” This value is expressed in a wins format, so we could say that Player X is worth 6.3 wins to their team while Player Y is only worth 3.5 wins.

WAR is, at the moment, the best single number we have when evaluating a player’s overall contributions on the diamond. That’s not to say it’s without its shortcomings — for one, WAR isn’t terribly useful for relief pitchers, because the calculation takes playing time into account. There are also currently two competing WAR calculations — Fangraphs’ WAR (fWAR) and Baseball-Reference’s WAR (bWAR). I’d say the majority of us at TYA favor fWAR, but sometimes it can be useful to take a look at both, as the two systems sometimes wildly disagree on a player’s value (due to the fact that the statistics use the same framework, but are calculated slightly differently).

The other thing that’s great about WAR is that it also tells you what a player would be worth on the open market in dollars. While the marginal value of a win (or 1.0 fWAR) changes from year to year, it’s been somewhere in the neighborhood of $4.5-$5 million during the last few seasons. For the 2011 season, based on the Fangraphs’ leaderboard it looks like it’s back down to $4.5 million. So for example, if a given player puts up a 5 fWAR season, you can say they provided $22.5 million of value to their team. You’ll see us citing this data at lot more frequently come the offseason when assessing and analyzing players that may or may not make sense for the Yankees to pursue.