FIP for Hitters? Defense Independent Offense
While writing on the “three true outcomes” (walk, strikeout, and home run) leaders and trailers from 2007-2009, I was reminded of a toy idea that I’d had earlier to create something like FIP (Fielding Independent Pitching), using the same basic components, except for hitters. I finally got around to doing it recently, and the results were interesting. I’m not saying this is any more than a junk stat. But it might be interesting, who knows?
* You want real sabermetric research? Read Matthew Carruth, Dave Allen, or one of the many other intelligent researches writers here and elsewhere. Trying to waste time at work? You came to the right place. Tom Tango may have created wOBA and FIP, but this a stat that gives me joy.
The basic formula for FIP is ((HR*13+(BB+HBP-IBB)*3-K*2)/IP) + 3.2, where “3.2″ is a season/league specific factor to put the league FIP on the same scale as the league ERA. To make it suitable for hitters, I made a couple of minor modifications: 1) I scaled it to RA rather than ERA. The RA scale for the 2009 MLB was 3.52. 2) For IP I used outs made by the hitter (divided by 3 to get on the IP scale): AB-H+SF+SH+GIDP (I left out CS because I want to deal with the pitcher/hitter matchup). Ladies and gentleman, I present the formula for Defense Independent Offense, or DIO:
((HR*13+(BB+HBP-IBB)*3-K*2)/(Outs/3)) + 3.52.
Who (among qualifying hitters) led the league in DIO for 2009? Remember that for hitters, a higher number will be better.
1. Albert Pujols, 9.18
2. Prince Fielder, 8.66
3. Adrian Gonzalez, 8.55
4. Alex Rodriguez 8.32
5. Carlos Pena 8.31
6. Adam Dunn, 8.11
So far, so good, those are some great hitters. Here are the trailers:
150. Yuniesky Betancourt, 4.26
151. Michael Bourn, 4.12
152. Randy Winn, 4.03
153. Cristian Guzman, 3.92
154. Emilio Bonifacio, 3.73
Some of these names — Betancorut, Winn, Bonifacio — aren’t surprising. But what about Michael Bourn, for example? Didn’t he have a decent season at the plate in 2009? Hold on to that thought.
Just as a player’s wOBA can be compared with league wOBA to give up the player’s runs created above average (wRAA), we can compare a players DIO with the league’s runs per game (4.61 in 2009) to produce a DRAA: =(DIO-lgR/G)*(Outs/27).* Here are the 2009 leaders in DRAA and with their wRAA figures for sake of comparison.
* One can also calculate absolute runs created (wRC) with DIO * (Outs/27).
1. Albert Pujols 69.9 DRAA, 69.7 wRAA
2. Prince Fielder 65.6 DRAA, 54.9 wRAA
3. Adrian Gonzalez 62.2 DRAA, 41.5 wRAA
4. Mark Teixeira 53.5 DRAA, 42.9 wRAA
5. Adam Dunn 53.2 DRAA, 35.9 wRAA
The Pujols figures are almost dead-on, and given the crudeness of DIO, Fielder and Teixeira aren’t that far off, but Gonzalez and Dunn seem to be quite overrated by DIO-Runs. The general “in the neighborhood-ness” isn’t that surprising, given that FIP (and thus DIO) are based on linear weights of the relevant events, and wOBA is just linear weights expressed as a rate stat. But what about the discrepancies? Does the perhaps mean we should be rethinking wOBA/wRAA in favor of my awesome new offensive metric, or at least use it more prominently, just as FIP is generally favored (around here) over ERA?
In a word: no. Going back to the origins of DIPS-theory, pitchers generally have little control over balls in play, and thus DIPS, FIP, tRA, etc. are attempts to remove the defense-dependent elements from pitcher evaluation. However, while BABIP generally has less year-to-year correlation for hitters than, say, walk rate, it does correlate far better than for pitchers. That is why traditional linear weights (like wRAA) are preferable for hitters. DIO systematically underrates hitters like Michael Bourn not only because it ignores steals, but because it assumes that the players contributions on balls in play are league-average, whereas Bourn’s contributions in those areas are well-above average. DIO’s also badly underrates hitters like Joe Mauer (40.5 DRAA vs. 54.9 wRAA in 2009) and Ichiro Suzuki (-2.2 DRAA vs. 22.6 wRAA), as well as overrating (still very good) hitters like Adrian Gonzalez and Adam Dunn.
DIO has interesting aspects. It highlights how many good hitters get most of their value from hitting home runs and walking, for example. There is also much to be said for using a rate stat baselined against outs rather than PA (I wouldn’t go so far as to make the mistake of generating a DIO-based Offensive Winning Percentage, although it was tempting). For me, it was worth it just to walk through and see how well the stat did in ranking hitters. Most of all, it was a good reminder of the difference in BABIP as a skill relative to pitchers and hitters. Without reminders like these, I’d be left on my own, like a rainbow in the dark.
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Seems like the official steroid era stat, ie barry bonds. homeruns, pitch around them, or nothing. It seems like a defense independent offense should be a lot more complex than that, and perhaps not possible yet.
change every defensive player to average range, to award things like gap power, and finding holes in the infield.
Its a three true outcome stat. Unless you think roids have something to do with, I think thats an over simplification.
BTW Devil, thanks for doing this.
No prob… For everyone else — J.D. and I went back and forth about this on Twitter last week (I think), and it also reminded me that I had planned on doing it for a while
I think the only reason why this stat is not meaningful is that hitters have control on BABIP, and formula is not crediting the skill. Hitters also have control on the inside-the-park extra base hit, and the stat ignores it.
yeah, that’s exactly what I was trying to say in my conclusion
Have you considered trying to set it up so it normalizes a player’s BABIP to their xBABIP, instead of LA?
xDIO!
It briefly crossed my mind… then it sounded like a whole lotta work. I might do it — for individual players it might not be so bad, but setting it up to generate the figures for the whole league seems brutal.
Yeah, but… it’ll have an ‘x’ at the front! Everybody knows that makes a stat better!
In all seriousness, I understand the “whole lotta work” factor. I’m literally trying to talk my fantasy commissioner out of using OPS as a stat this year because it’s a pain in the ass to generate the formula I use to control AB/PA/IP for players’ projected rate stats when dealing with OPS.
hey mr. klaassen,
you basicly stole my idea xD. I did this a week ago or so http://www.fangraphs.com/forums/topic.php?id=3416
well i have a slight different way of interpretation on this stat (see the link).
i am not complaining… i actually think it is really cool that you bring this up as a post.
best wishes
AC_Butcha_AC
Sorry AC, but I honestly did not see your forum post until just now!
hey i do not complain xD
i think it is a fun stat and nice to see this brought up on the front page^^
Maybe he’ll my pitcher’s wOBA now too….
*do* my pitcher’s wOBA
I love me some junk stats.
How about the Max Bishop award for most walks relative to HR’s (in 2009 for league eligibles, it was about BB = .9289*HR + 40.183). 2009′s Maxie award went to Chone Figgins. This year’s Bengie Molina award went to…Bengie Molina.
Is there a way to incorporate weighted speed scores so that hitters like Dunn (down) and Bourn (up) end up more where wOBA suggests they should be? It’s not completely defense-independent but would have greater real-world applicability. Or does this defeat the purpose?
Thanks for the Ronnie James Dio reference! You don’t see too many of those.
If batters can effect in-play outcomes, don’t DIPS need to be adjusted for quality of opposition?
The idea is that this smooths out over the course of a season, thus leaving only league adjustments to be made.
That’s what you call an assumption of convenience. With the unbalanced schedule, the intradivisional matchups are >10% of the schedule. So, the Orioles pitchers face the Yankees hitters for 10% of the schedule and vice-versa. Add to that the fact that you may have an Oriole pitcher catch the Yankees disproportionately on his rotation turns and you could have a significant distortion there. For example, last year, Jeremy Guthrie made 9 of his 33 starts (27%) against the Yankees and RedSox (5 & 4). Jason Berken drew them in 6 of his 24 starts (25%). Bergesen in 3 of 19 (16%). Matusz 1/8.
And, of course, the Yankee pitchers face the Yankees never.
This is the HOLY DIVER of new metrics…kudos. It may not be much more than a “junk stat” as you call it but it gives more of a Jamesian vibe than the BP vibe where the new metric “will change the face of the world”….Thanks.
Too bad we’re not on SBN, because I’d rec this just for “this is a stat that gives me joy.”
Really cool. This makes me think one could come up with a “secret sauce” type stat for fantasy. Like could one make a useful stat that only weighs skills that highly correlate from year to year and contribute to traditional metrics, i.e. high LD% leads to high BABIP leads to high Avg.
So this imagistat (TM) would include LD% (for Avg.), SB attempts (prob with a mild penalty for CS), ISO and team wOBA (for stats like runs and RBI).
I can already see some major problems with this, but does this make sense to anyone else? Is wOBA good enough for this purpose?
Yeah, you could do that…
or, you could just use good projection systems that incorporate this stuff properly, like CHONE, ZiPS, etc.
Wow! It means that opposing pitchers pitch to a 9.18 “ERA” against Pujols or an 8.66 “ERA” against Fielder? That’s heavy.
Can’t be comparable like that….considering Betancourt would yield a 4.26 era…which I find hard to believe. That is being a bit generous to the batter!
Betancourt only struck out 9.4% of his PA, which converts to between 3.5-4 K/9. If Betancourt would have had a normal BABIP, a 3.86 ERA against would seem reasonable.
It’s an RA scale, not ERA. So basically minus about .4 from these to get the ERA scale.
What *would* be useful would be a stat that subtracts out the effect of opposing fielders on a hitters’ stats–crediting him with 99% of a HR if a fielder makes a HR-saving over-the-fence catch, only giving him 15% of a hit if a fielder misses a play but isn’t charged with an error, etc. The databases used for PBP defense metrics would have all the info to generate this. You’d just have to be careful to retain credit for speedy players who beat out infield hits.
While pitchers pitch in front of the same defense day in and day out, hitters hit into different defenses every third or fourth day. While I agree it would be interesting to remove defense from the idea of how good hitters can be, I’m guessing the stats we have already account for this, given the relative distribution of defensive skill. The