There is a method to my madness here. At the top is an explanation of why traditional stats are incomplete, followed by the rate stats that I use most of the time. After that comes an explanation of the “+” stats, then a hodgepodge of my favorite saber-stats and what they mean. At the bottom are just a few terms that I use all the time that I often get asked about. Control + F is your friend on this page.
Traditional Pitching stats: The traditional stats to measure pitchers are wins and losses, saves, ERA, and maybe WHIP. As in, I’d consider WHIP traditional, but I think it only became that widely used as the internet and fantasy baseball exploded in the late 90s. The problem with every single one of these stats is that they’re widely dependent on things that the pitcher can’t control. Throw a complete game one-hitter where that one hit is a home run and your offense doesn’t score, and you’ve got yourself a loss. Pitch with a poor defense behind you, and they’re going to turn fewer balls in to outs, which is in turn going to raise both your WHIP and your ERA. Accordingly, there’s been a focus the past few years on stats that do measure what the pitcher can control. If only we could all agree on that.
AVG/OBP/SLG: The famous “triple slash” line. For almost every batter I talk about, I’ll always give batting average, on base percentage, and slugging percentage in that order. Everyone knows batting average and what it is, but it’s a very incomplete stat and it doesn’t actually give that much information. On base percentage is times on base divided by plate appearances, while slugging percentage is total bases (that is 1B+ 2*2B + 3*3B + 4*HR) divided by at-bats. Honestly, if you know on base percentage and slugging percentage, batting average is irrelevant. I mostly include it out of habit.
K/9, BB/9, HR/9, K/BB: One easy way to measure a pitcher’s performance in shorthand is to look at their rate stats. The three listed here are the three widely considered to be the ones within the realm of the pitcher’s control. If they’re not apparent, the first three stats are strikeouts per nine innings, walks per nine innings, and home runs per nine innings. The final stat is strikeout/walk ratio, which allows a comparison of strikeouts and walks. The 2009 NL leader among qualified starters in K/9 was Tim Lincecum with 10.7 and the trailer was John Lannan with 3.9, while the NL trailer was while the league average per 180 innings was 7.0. The fewest BB/9 among qualified was Joel Piniero with 1.1 and the highest belonged to Jonathan Sanchez at 4.8, while the average was 3.5. Remember while looking at the averages, though, that they’re skewed a bit by relievers. Relievers only have to throw one inning, so in many cases, both their strikeout rates and walk rates are higher. They can get away with more walks because they’re presumably giving up fewer hits. In terms of home runs, Chris Carpenter only gave up 0.3/9 innings while Braden Looper allowed 1.8. The average there, easily enough, is 1.0.
GB%, FB%, LD%, GB/FB, HR/FB, IFF%: Ground ball percentage, flyball percentage, line drive percentage, percentage of flyballs that go for home runs, and infield fly (or popup) percentage. In general, this set of stats is referred to as “batted ball data.” It’s not strictly true that pitchers have no control of what happens once the ball leaves the bat. A pitcher with a good sinker can induce a groundball upwards of 55% of the time (Joel Piniero, Derek Lowe, Jason Marquis, and Chris Carpenter all topped that mark in 2009). A high flyball rate is approximately over 45%, while any line drive percentage over 20% is high. For shorthand, about 72% of groundballs and 79% of flyballs are made into outs, while only 26% of line drives go for outs. Groundballs can’t land over the fence, though, which is why nobody wants a flyball pitcher with a low strikeout rate.
OPS/OPS+: Accordingly, one of the best known sabermetric stats is OPS, or On base Plus Slugging. Again, it is was its name suggests; it adds on base percentage and slugging percentage together. It’s a very handy stat, but it’s not nearly perfect. Most people that study this sort of thing agree that on-base percentage is more important than slugging percentage, so putting them on an equal scale devalues OBP a bit. This makes intuitive sense; a player with a .400 OBP and .400 SLG is certainly more valuable than a .300/.500 player because the huge disparity in the number of times they reach base (sixty times over six hundred plate appearances) outweights the disparity in total bases (again sixty), but they have the same OPS. In general though, it’s a much better shorthand stat to measure offensive performance than batting average. Whenever I mention OPS, I usually also try to list that player’s OPS+. OPS+ adjusts OPS by the league average for that season with a park factor (methodology at BB-REF). An OPS+ of 100 is exactly league average; anything above 100 is better than average.
ERA+: Like OPS+, only for ERA. That is to say, it’s normalized for ballpark and league average. 100 is exactly average, anything above it is better than average and anything below it is below average. It’s a slightly better metric than ERA because it’s relative to league performance
BABIP: Batting Average on Balls In Play. This is the exactly what it sounds like; the batting average against a pitcher throwing out both strikeouts and home runs. Because pitchers have very little control on what happens to balls in the field, this stat should theoretically be static at around .300 and any wild deviations may indicate good luck or bad luck for the pitcher. In practice, though, a pitcher with a very high line drive percentage is going to have a higher BABIP because as mentioned above, more line drives are going to go for hits.
FIP and xFIP: FIP stands for “Fielding Independent Pitching” and was designed to be somewhat of an ERA analogue (though it’s not perfect on the high and low ends of the spectrum) calculated solely from home runs, strikeouts, and walks with a constant to make the final product roughly equivalent to ERA, meaning that what you’d traditionally think is a good ERA is a good FIP. xFIP “Expected Fielding Independent Pitching” and is the same stat as FIP, only adjusted using flyball rate and a park factor, because there’s some evidence that home run rate is largely tied to that. FIP (and I believe xFIP) were both created by Tom Tango; FIP is available at The Hardball Times and FanGraphs, xFIP is available only at THT.
tRA: This is actually a pretty newly available stat (as of 11/22, at least) and there’s a good rundown of it on FanGraphs. It combines the components of FIP (strikeouts, walks, and homers) with batted ball data included. The batted ball data uses line drive percentage, groundball rate, and flyball rate to determine the expected runs and expected outs on the balls put into play against the pitcher (not based on the actual runs and outs, but calculated from league averages) and factors that into the equation as well. It’s not a directly comparable to FIR; according to the comments on the FanGraphs post, tRA = FIP/.92. That means that a tRA of 1.08 would be about equal to an FIP of 1.00
VORP: Value Over Replacement Player. Baseball Prospectus’s measure of offensive or pitching value and one of the better known sabermetric stats. I don’t know the formula, but it measures the a mount of runs a hitter (or pitcher) is worth vs. a replacement player. Replacement level is considered to be the typical Triple-A callup. For example, VORP rated Kazuo Matsui (0.4) and Jason Kendall (-1.1) as the position players closes to replacement with 500 plate appearances. From that same sample, Albert Pujols lead the league at 98.3 (he and Joe Mauer were the only players over 80, Mauer was at 91) and Emilio Bonifacio was last with -10.3 (Ronny Cedeno’s time in Seattle was worse). I don’t usually use this stat for pitchers.
WAR: Wins Above Replacement. Found at Fangraphs, it combines batting runs (calculated from wOBA), fielding runs (from UZR), replacement runs (based on runs by a replacement player per 600 plate appearances), and a position adjustment (it’s easier to play first base than catcher, so catchers are more valuable), and combines them using the approximate shorthand that 10 runs equals a win. Because UZR is the best fielding metric available, WAR is viewed as the best of the “win” stats, and so it’s the one that I use. You can learn much, much more about win values from Dave Cameron’s long series at FanGraphs and if you have the time and are interested, I highly recommend it.
UZR: Ultimate Zone Rating. A fielding metric that improves on Bill James’ Range Factor. Created by Mitchel Lichtman (MGL), you can find the original methodology here. Basically, the field is broken into 64 zones, a run expectancy for each zone is calculated based on balls hit in that zone and the league average, then a run value is assigned to each player’s performance (this is INCREDIBLY simplified).
PMR: Probablistic Model of Range. Compiled by David Pinto at Baseball Musings every fall. It’s built on a similar concept to UZR. In short, of the balls put in play in each player’s zone, how many outs are expected? And how many are actually recorded? Methodology here and here.
Plus/Minus: Created by John Dewan. For each play made by a position player that at least one other player at that position failed to make, that player is awarded a plus. For each play a position player fails to make that’s made by at least one other player at that position, that player is awarded a minus. In the end, a positive score is above average and a negative score is below it. His explanation can be found here.
wOBA: Created by Tom Tango last year, it creates a weighted OBP that considers at walk/OBP ratio. His methodology is here, the stat is found at FanGraphs.
BS Win- Blown Save, Win. See also, Matt Capps, Jose Mesa, Mike Williams, or any other Pirate closer in the past 15 years.
LOOGY- Lefty One Out GuY. Think Damaso Marte; a guy that’s absolutely poison on lefties but not worth much else otherwise.
PA- Plate appearances, which is like at-bats, only with walks, hit-by-pitches, and sacrifices. A much more complete stat to use.
