Linear_Weights
Steve does a good job by going through some various issues when using Linear Weights.?
In response to discussion of Paul Abbott on Bill James' site:
Paul Abbott: his Wins Above Replacement (WAR) as calculated by Baseball Reference (which I note as rWAR) in 2000 was 2.3 wins, while in 2001 was 1.1. That 1.2 wins difference is explained in part because Forman has calculated that his opponents in those years were stronger in 2000 than 2001 (by 0.45 runs per 9IP), as well as his fielders were better in 2001 than 2000 (by coincidentially also 0.45 runs per 9IP). After also considering park factors, Forman figured an average pitcher in Abbott's context in 2000 would allow 5.00 runs per 9IP and in 2001 would allow 4.28. Since he gave up 4.47 in 2000 (0.53 runs per 9IP better than average) and 4.36 in 2001 (0.08 runs worse than average), we have a 0.61 run per 9IP gap. Which for the seasons in question is about a 12 run gap, or 1.2 wins. This is not to suggest that I agree or can support all of Forman's assumptions in those calculations, but simply that there was a method to what seemed like madness.?
***
Paul Abbott was assigned a record of 17-4 for those historical 2001 Mariners, while a 9-7 record for the 2000 Mariners. For whatever it's worth, Sean's neutralizing function gives Abbott a 9-10 record in 2000 and 7-10 in 2001.
Note that Sean's neutralizing function actually assumes that the pitcher will receive a normal distribution of runs, and recasts his performance as if it were randomly set against that distribution. This is a bit hard to explain, but:
If a pitcher did in fact face a normal distribution of runs, in a normal park, under a normal environment, his neutralized W-L record can STILL be massively affected, because Sean ALSO presumes that the pitcher's performance (that is, his runs allowed) should have been randomly distributed.
So, he's not only neutralizing the player's environment, but he's also neutralizing any "timing" component of his runs allowed. I don't know if I necessarily agree (or disagree) with that approach.
What's weird is that he doesn't do that for hitters, with respect to runs scored and RBIs. In that case, a leadoff hitter like Rickey will maintain his massive gap in R and RBI, thereby preserving some part of that context. I didn't check to see if a guy's "clutch" performance (i.e., driving in far more runners than his seasonal lines would suggest) is preserved.
Anyway, no one ever talks about it, so I'm putting it out there.
A decent effort to lay out the discussion. First the old school, then some of the basic new school stats.
There are some errors, for example:
Lots of people like to use the statistic OPS (OBP+SLG) as a quick, shorthand way of combing all of these stats. The caveat to this is thus; is a “point” of on-base percentage equal to a “point” of slugging? No, it is not; the slugging point is worth more because of what it represents.
?Unfortunately, that's not true. As I've shown in the past, the best weighting of OBP and SLG is roughly 1.7 OBP for every 1 SLG.
?models that capture the involvement of various entities in games to estimate each of their impact toward winning
Involvement: participation, without necessarily attribution of responsibility, skill, or luck
Entity: player, manager, umpire, park, weather, loving hand of god, cruel hand of fate
Estimate: approximate calculation whose rough value can be derived in multiple ways
***
Can we all agree on this?
Dave does a good job of contrasting WAR to the other metrics, and why it has particular uses. My favorite part though is when he dismisses the Pitcher Wins as an answer, since the question is one that no one asks to begin with:
How many times did that pitcher complete at least five innings, leave the game with his team having outscored the opponent through the point at which he was removed, and then watch his relievers finish the game for him without surrendering the lead that his teammates helped create in the first place??
?Crashburn linked to Caple's sentiments on WAR.
I thought Mark Simon's objection from two years ago is sufficient. I added a couple of my thoughts at the time.
As far as I can tell, all the anti-WAR sentiment is really missing the forest for the trees.
Good stuff here. This is one of those things I like to do as well, especially if trying to see if talent is increasing or decreasing.
?
?Glenn continues his terrific work, this time focusing on wOBA. Expectedly, the coefficient for the walk regressed the least, while the single regressed the most. What is interesting is that the coefficient for the single regressed so much that it now predicted wOBA worse than the walk! This is something we talked about a long time back.
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FRV v DRS
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Runs Above Average
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Skenes v Webb: Illustrating Replacement Level in WAR
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Sacrifice Steal Attempt
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Small choices, big implications, in WAR
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Why does Baseball Reference love Erick Fedde?
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THREADS
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Component Run Values: TTO and BIP
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Blake Snell or Spencer Strider?
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Acuna and Betts, a smidge of a difference
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Strikeouts v other outs
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Who is the most fun player in MLB, outside of Ohtani?
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Lies, Damned Lies, and Batting Average
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Ryan Howard v Bobby Abreu, 2008
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W/L using IP and ER
November 07, 2021
Statcast Lab: Markov Sequences, 4-seamers on 0-1 counts
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Behind the wOBA curtain
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Statcast Lab: How much is extra speed, movement and SSW worth?
March 13, 2021
Post-introducing Core wOBA
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Run Values By Pitch Count
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When Heroes Collide
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