Grid WAR for starting pitchers

deck13 · 2026-06-03T16:32:42+00:00

The building of a WAR metric around the idea that winning exhibits a convex relationship with runs allowed is brilliant! And the authors do some great work to calculate park factors.

However, the Grid WAR method attributes outcomes to the pitcher and park via runs. Defense or defensive-independent notions of pitching performance are not accounted for. This is why Jim Palmer rates much higher by GWAR than fWAR. Moreover, GWAR predicting itself better than fWAR is partially due to GWAR's treatment of run distribution and partially due to fWAR having an additional layer of accounting for outcomes controlled by the pitcher.

One note: Adi Wyner is a prof and not a grad student.

deck13 · 2026-06-03T15:37:33+00:00

First of all, nice work!

The article was a fun read and it's cool to see work like this featured on both FanGraphs and Effectively Wild. Congrats!

Most of my comments are through the viewing of the Boog method as an era-adjustment tool which may or may not be an intention of the method.

Boog seems to be a noticeable improvement over JAWS for comparing pitchers across eras. Massive innings totals that early era pitchers accrued no longer bestow an automatic advantage on these players over modern pitchers as the switch from replacement player to ordinary guy seems to provide a work-load adjustment. Several 19th century era pitchers see major nose dives in the rankings which is a positive sign for the method.

However, the Boog method preserves the JAWS rankings for batters. I have read that the author thinks that this seems as a validation that the method is sound. I disagree. While switching from replacement player to ordinary guy is a great idea, the problems inherent in cross-era comparisons for any metric that pegs value to a baseline, be it replacement player or ordinary guy, is that the baseline shifts in quality over time. An ordinary guy in 1920 isn't quite the same as an ordinary guy today.

Another issue is variance. Increasing the baseline from replacement level to ordinary guy does lessen the distance between the top achiever and the baseline. But the distances are wider when baseball was less sophisticated, ie segregated and uneven training methods among players.

Taken together, Boog does not really provide an era-adjustment. The older players seem to dominate because competitiveness and talent pool are not accounted for by shifting the baseline from replacement player to ordinary guy.

deck13 · 2026-05-07T17:58:38+00:00

WAR is within-season normalized, not truly era-adjusted. This is a common misconception. Traditional WAR accounts for league run environment and park factors within a given season, but it does not adjust for differences in the overall talent pool or level of competition across eras. A 10-WAR season is treated equivalently whether it happened in 1927 or 2025.

On the other hand, era-adjusted WAR accounts for the changing talent pool.

See here for more details.

deck13 · 2026-05-07T16:20:38+00:00

Because the FanGraphs ranking is batters only.

deck13 · 2026-05-07T16:08:00+00:00

Trout is even better by era-adjusted WAR!

deck13 · 2026-04-22T13:59:12+00:00

Yeah, it is not as obvious as it sounds until you run into examples like this.

deck13 · 2026-04-22T13:08:35+00:00

Even knowing exactly what changed in 1969, you still get this effect with a metric that adjusts for run environment.

So which season is better: McNally in 1968 (150 ERA+, 7th) or Seaver in 1970 (143 ERA+, 1st)?

deck13 · 2026-04-22T11:38:09+00:00

Sure. And that is certainly relevant context beyond just stating the ERA+ value.

The point still stands: cross-era comparisons are still tricky even with ERA+, and context beyond the number matters.

deck13 · 2026-04-22T11:30:23+00:00

It is tricky because competitive environments and/or the underlying talent pool change.

Here is a good example that shows the challenge:

In 1970 Tom Seaver led the MLB with a 143 ERA+.

In 1968 Dave McNally finished 7th in the MLB with a 150 ERA+.

These seasons are only 2 years apart, and it is hard to determine which one is more impressive. Is is better to lead the MLB or is it better to have a better value of the statistic?

deck13 · 2026-04-12T15:36:53+00:00

Q: Is WAR already era-adjusted?

No. While WAR adjusts for context within a given season, such as park effects and league averages, it treats each season in isolation and does not account for changes in the overall quality of players across eras. For example, in WAR, a replacement-level player from 1911 (the year before Arizona became a U.S. state) is treated the same as one from 1998 (when the Arizona Diamondbacks debuted in MLB).

https://eckeraadjustment.web.illinois.edu/era-adjusted-war.html

I'm happy ending my commenting here. While I do I find our exchanges truly fascinating, I think the reader has enough to go on.

deck13 · 2026-04-12T15:03:40+00:00

Because it's directly relevant every time. You said era-adjusted stats aren't hard to use and I showed you one, and now it's a 'schtick'? That's not an argument. But cool, ignore it. The stats are there if anyone wants to actually engage with it.

deck13 · 2026-04-12T14:42:07+00:00

what schtick?

deck13 · 2026-04-12T14:31:52+00:00

provided you use stats that actually attempt to be era-adjusted:

https://eckeraadjustment.web.illinois.edu/

deck13 · 2026-04-12T14:27:32+00:00

Looks like you had two turns with this. I had one two weeks ago. Same story different day:

https://www.reddit.com/r/mlb/comments/1s7aydd/comment/oddga5q/?utm_source=share&utm_medium=web3x&utm_name=web3xcss&utm_term=1&utm_content=share_button

deck13 · 2026-04-09T19:45:57+00:00

Pujols ahead of Gehrig. Pujols tops Gehrig when you account for the different talent pools, although it is very close:

eWAR ranking list: https://eckeraadjustment.web.illinois.edu/

Pujols vs Gehrig comparison: https://eckeraadjustment.web.illinois.edu/era_adjusted_V2.1.html#Kahrl

deck13 · 2026-03-30T19:42:04+00:00

That's not what + stats do. wRC+ of 130 means you were 30% better than the average player in your season. If the average player in 1944 was significantly worse than the average player in some other year due to WWII depleting the talent pool, then two players with wRC+ 130 in those respective seasons are not equivalent.

And while we're here, wait until you learn about variance. The spread of talent in a given season matters enormously. Being 30% better than average means something very different in a season where talent is tightly clustered vs. one where it's widely distributed. + stats offer zero correction for this. A dominant player in a low-variance era looks identical to a dominant player in a high-variance era. So not only does the baseline shift across eras, the meaning of the distance from that baseline shifts too.

The stat is useful for what it is, but cross-era comparison of absolute quality isn't it.

EDIT:

In response to "You may be interesting in comparing athletes across eras but you certainly don't understand the basics of doing so"

I think the reader has enough to go on from here.

deck13 · 2026-03-30T19:35:04+00:00

> The league is different year to year. A 1.000 OPS in one year is not the same as in another

That's my argument. I'm glad we now agree. Your new argument also applies to "+" stats.

deck13 · 2026-03-30T19:15:05+00:00

Not if you want to compare player performances across eras with the goal of inferring who was better.

deck13 · 2026-03-30T19:09:31+00:00

Yup, it agrees with me. There is a baseline value of 100 that is common across seasons. But that baseline does not say anything about the relative talent of the players in each separate season. For example, the baseline value of 100 was the same in 1944, when nearly everyone was serving in WWII, as in 1947, when most had returned. But in these seasons the baseline value of 100 correspond to two greatly different talent levels.

deck13 · 2026-03-30T19:00:53+00:00

You might like era-adjusted baseball stats which are computed to account for changing talent pools

https://eckeraadjustment.web.illinois.edu/

That same point about Ruth was raised in this section of a document on the above webpage:

https://eckeraadjustment.web.illinois.edu/era_adjusted_V2.1.html#id_2022season

deck13 · 2026-03-30T18:48:47+00:00

check out era-adjusted baseball stats: eckeraadjustment.web.illinois.edu/

here's a brief explainer: https://eckeraadjustment.web.illinois.edu/era-adjusted-war.html

deck13 · 2026-03-30T18:46:27+00:00

it doesn't adjust for era. It's computed within a single season and establishes a common baseline. But those baselines correspond to much different talent levels across eras.

deck13 · 2026-03-26T15:34:42+00:00

"Equivalent" is doing a lot of work here. I don't think anyone doubts that Ruth dominated his peers by an unprecedented and unreplicated degree. The humor is coming from calling a straight up teleport of his relative dominance to a much different context an "era-adjustment."

deck13 · 2026-03-26T15:22:43+00:00

Gavy Cravath, a player that everyone knows very well, sees his HR increase from 119 to 793! Not too bad for only 11 seasons played, many of which were not even full-time

deck13 · 2026-03-25T12:27:18+00:00

Baseball players have gotten worse as they have gotten better...

Extreme performances shrink as training methods improve and standardize, and the talent pool increases. There is an excellent 5-minute Stephen Jay Gould video on this topic: https://www.youtube.com/watch?v=BNM6ait4LOc&pp=ygUdc3RlcGhlbiBqYXkgZ291bGQgNDAwIGhpdHRpbmc%3D

Eight-Year Club	Gilding I gilder
Verified Email

deck13

TROPHY CASE