Comments on: WAR: It Works

By: Joe R

Joe R — Fri, 30 Oct 2009 19:02:23 +0000

In the “hmmm” category for everyone:

WAR’s 2009 R^2 (linear): 68.18%
WARP-1′s R^2 (linear): 74.44%

WAR baselines out to around 47.5 wins, WARP-1 to 31. Both had slopes close to 1.

That actually surprised me a bit.

By: Jud

Jud — Thu, 15 Oct 2009 12:33:09 +0000

Hi,

I have a few reservations regarding WAR as it relates to allotting value to pitchers. As I recall, WAR for pitchers is totally based upon FIP. So I would have the following concerns

1) FIP does not take into account sequencing. So a HR, 1B, 1B, K, K, K is identical to 1B, 1B HR, K ,K, K. If we’re speaking about future predictions this might be true (I don’t know if sequencing is a repeatable skill or not), but not if we’re talking about value provided
2) FIP does not take into account leverage at all. So all relief pitchers will be at a huge inherent disadvantage. I’m not advocating going all the way and using WPA as the only measure, but I do think that some mix should be made
3) As Colin pointed out elsewhere, the range of FIP is much less than that of ERA. So there is an artificial shrinking of the differences between the best and worst pitchers
4) In cutting out the noise of fielding, FIP also removes value delivered. A hard hit ball off the Monster should not be considered a neutral event as far as the pitcher is concerned. Now disregarding FIP altogether would inject all of the fielding noise and that would be worse. Hopefully, Hit f/x would provide us with a better solution

I’m also having problems completely buying into UZR. Especially as UZR, +/-, and BP fielding stats give diverging results (we don’t see such a difference between WOBA and EQA). The BP guys claim that the raw PBP data is not that accurate, here too maybe Hit f/x will come to the rescue

By: mickeyg13

mickeyg13 — Sun, 11 Oct 2009 07:39:06 +0000

I also did a similar thing on my site using 2002-2008 data (the 2009 season wasn’t yet finished when I did my study).
http://www.sabermetrica.com/research/war-predicting-wins

I got an R^2 of about .76. I think the really amazing thing is that, despite the great importance that context plays in actual wins, WAR is very predictive despite being mostly ignorant of context.

By: B

Fri, 09 Oct 2009 13:54:51 +0000

“The two best closers in the league play in the AL East. Bullpens that are so dominant are going to screw up strict innings pitched measures”

That’s a pretty bold statement, and I’m not sure that it’s true. It certainly might be, Rivera and Papelbon are certainly great closers, but I’d throw Andrew Bailey, Brian Wilson, Joe Nathan and Jonathan Broxton into the mix, too. I don’t think you can definitively say Rivera and Papelbon are the best…

By: Colin Wyers

Colin Wyers — Fri, 09 Oct 2009 05:21:34 +0000

That would be true, Dave, if our positional adjustments were exactly correct, for instance. But a lot of the assumptions that underlie WAR are simply close approximations – to the extent that we don’t need them to determine team wins, they are a hindrance and not a help to an accurate team win projection.

(And you also getter a better fit using Pythag than a linear run-to-win estimator, for instance. And you get a better fit using BaseRuns than linear weights. WAR doesn’t do those things because you’re measuring a player’s performance in a context of an average team, and you don’t need to handle team performances substantially different from .500 – even an absurdly good 15-win player is only going to make a .592 win% team, well within the range that those assumptions hold.)

By: Dave Cameron

Dave Cameron — Fri, 09 Oct 2009 04:57:24 +0000

But how do you get get a “projected” pythag once you use the correct inputs? You run a bunch of simulations or stick the aggregate team numbers into a run estimator, right? You definitely do not carry over prior year base/out performance, assuming that teams that did well in those situations will do well again. And that’s why prior year pythag is not as good of an estimator of true talent level, because it includes that non-skill context.

So, in reality, a pythag projection with correct inputs isn’t really any different than WAR. Once you strip out the base/out context, they’re the same thing – win estimators based on linear weights.

By: Colin Wyers

Colin Wyers — Fri, 09 Oct 2009 04:29:36 +0000

Dave, that’s an arguement for using the correct inputs. Once you have those inputs, you are still going to get a more correct projection of team wins if you use straight Pythag rather than going through the manipulations of WAR first. Positional adjustments, for instance, are a necessary evil when assessing an individual player’s value, but if all you want to know is team wins they’re pointless at best.

By: Eric

Eric — Fri, 09 Oct 2009 03:15:08 +0000

I disagree about whether it is important and whether it is testable to examine if WAR can predict wins in a subsequent year. To answer this question, you could account for roster turnover by using the 2008 WAR of the individual players to measure 2008 WAR for the 2009 roster for the team. If WAR is a measure of “true talent” then the collective “true talent” of a team’s roster (measured in 2008), should correlate closely with 2009 wins.
I think it’s important to test this question, because a measure of “true talent” that is context independent should be relatively constant from year to year. Therefore, one would predict that a previous year’s WAR (accounting for roster turnover) should correlate strongly with wins in the following year.

By: Rob

Rob — Thu, 08 Oct 2009 23:35:06 +0000

The two best closers in the league play in the AL East. Bullpens that are so dominant are going to screw up strict innings pitched measures

By: Mike

Mike — Thu, 08 Oct 2009 22:19:15 +0000

http://4parl.wordpress.com/2009/08/28/team-success-in-regards-to-war/

thats what we did.

this stuff is cool.