The Future is Era-Adjusted: Welcome

Updates and Trajectory of the Era-Adjusted Baseball Statistics Project

Hi all, this entry will contain a smorgasbord of topics ranging from updates to stat snippets to a call to action from you, the reader. Welcome to those of you who are arriving here for the first time, and feel free to check out our website.

Tenure and Twitter

I am pleased to announce that I have been promoted to Associate Professor with tenure at the University of Illinois Urbana-Champaign.

With this comes the intellectual freedom to continue studying era-adjusted baseball statistics and expanding our outreach. For example, I have started using X/Twitter and will be a regular poster there through the EckSportsLab account under the handle: https://x.com/deck1388

On Twitter I will post at least once a week and will respond/argue with others about how to quantify baseball greatness and which players are truly great. Posts will range from casually informative to serious to provocative to sarcastic, much like this newsletter.

The Casually Informative:

The Provocative:

The Serious:

Boring! You can check it out here if you want some technical details.

The Sarcastic:

I am still waiting for Morbidly Obtuse to respond with his thoughts about the paper. Although I must admit that I am not expecting much from a data scientist whose response to our work on era adjustment was essentially "they were his peers; already translated."

In the meantime, we will have to refer to ASA Fellow and author of two books on era-adjustment techniques Michael Schell, who described our method in the New York Times as:

“It’s arguably the state of the art, at this point, for player evaluation over time”

See the NY Times article here.

Beyond social media outreach, we've also continued refining the statistics themselves.

Version 3.1: New Lore

In my opinion the highlight of this project is the ever expanding and evolving “digressions“ document that we post on our website. There is a new version of that document with the v3.1 release. See it here.

This new version contains two new chapters. The first is a detailed look at Shohei Ohtani and Aaron Judge — two players who have put up quite possibly the best four year runs baseball has ever seen from 2022-2025. Aaron Judge’s 2022-2025 run is the highest four year run by era-adjusted OPS (eOPS). And Shohei Ohtani’s 2022-2025 run leads everyone in era-adjusted fWAR (second only to himself in era-adjusted bWAR).

Top four year run by eOPS:

Top four year runs by ebWAR:

Top four year runs by efWAR:

The second new chapter is an expanded investigation on Babe Ruth. As you can see from the table below Babe Ruth and several other pre-integration and deceased players populate the career batting rankings.

This is somewhat to be expected, as these are great players. But what is interesting is that these players and a few other old-timers dominate in career ebWAR restricted to seasons in which a player is age 35 and older.

This suggests that modern training and medicine are hindrances to player longevity. Wait a minute, that’s not right.

Recall that our era-adjusted baseball stats are derived from what we call “Full House“ models. These models take their name from Stephen Jay Gould’s Full House book. Gould was, among many things, an evolutionary biologist and a paleontologist. In Full House, he argued that baseball with its largely consistent rules and playing conditions would follow the same rules as organisms competing under stable conditions — bad designs eventually get weeded out and what survives is much more uniform in composition.

This has happened in baseball. Yes, training methods have become more advanced. But they have become more standardized. Rewind the clock to after the 1925 season. Following a disappointing season Babe Ruth hired a personal trainer. This was an event worthy of a Wikipedia page.

Our modeling is independent across seasons. And, although our era-adjustment outputs stats that are expressed within a common context (1977-1989 NL excluding the strike-shortened 1981 season), player career trajectories are not. Thus it is plausible if not likely that era-adjusted WAR still has a bias for players from the past! Although this bias is much less than traditional WAR.

Version 3.1: New Methods

Earlier we were pleased to announce version 3.0. However, we quickly discovered some issues with the v3.0 statistics. The main change is in how we model tail probabilities, which are directly relevant for the stat leader in any given season. The plot below illustrates the issue for home run rate for both Babe Ruth and Barry Bonds. Basically, our robust modeling approach was able to capture outlying performances at the expense of modeling the bulk of the entire tail. The effect of this is that true outliers become relatively common. Thus we reinstated the legacy models.

This does produce meaningful differences in era-adjusted statistics. Per our leaderboards, Babe Ruth and Barry Bonds are among the most prolific home run hitters ever on a rate basis, as expected.

Both Ruth and Bonds fall hard when we swap out the legacy model for the robust model. Ruth was hit particularly hard with only one season in the top 100 (and it was closer to 100 than to 10). Thus we think that the legacy model is more realistic.

Interestingly, the legacy model looks worse from a practicing statistician's perspective because it yields noticeable high-leverage points. In this case the high leverage points are both explainable and interesting. And correcting them takes away the lore and the achievement in a way that is both not fun and unbelievable.

See more details in our modeling report.

Call to Action

I am grateful to you for reading what we write here, and am even more grateful to those who have created content using our era-adjusted statistics!

I invite you all to use these stats and argue with people in person and, most importantly, on the internet. Tell them that the stats you use are era-adjusted and the stats they use are not, even if they say otherwise.

Today, 06/23/26, when this Substack entry was written, Jay Jaffe is out there quoting S-JAWS to suggest that Gerrit Cole is not a Hall of Famer unless he picks it up.

Jay Jaffe has explicitly acknowledged that cross-era comparisons are complicated and that modern players compete against a deeper talent pool. Yet his Hall of Fame discussions continue to rely heavily on S-JAWS, which is not era-adjusted.

And he is definitely aware of our era-adjusted stats. He read our NY Times feature.

But, in his own words, "there's a lot of math to get through."

Through 2025, Gerrit Cole has 51.5 era-adjusted JAWS. That ranks 42nd all-time among pitchers. He has a Cy Young award and five additional top-5 finishes, including two second place finishes. He is a Hall of Famer!

(several of the above are also not in the HOF. Maybe they should be? We have an entry on Felix Hernandez’s case)

Inform him and the people who cite JAWS that our stats are available on our website. Tell them they are era-adjusted.

I’ll be right there with you doing the same thing.

Thank you for reading! You don’t actually have to become a keyboard warrior on my behalf.