When I was looking at the 2026 New York Mets Media Guide for a recent review, I noted how the players’ pages highlighted specific OG statistics like home runs, RBI, batting average, wins, strikeouts, and ERA. Of course, nowadays we have pitch rotation, bat speed, exit velocity, launch angles, WAR, BABIP, and more than I can keep track off. (Besides showing how strong a player is, what does exit velocity really tell us?)
I’ve posted before on books about stats but here’s a new one for ya: Game Theory, Machine Learning, and Production in Sports: The Fair-Credit Baseball Statistics.
According to this piece in The Orange County Register, author Michael McBride — an economics professor at UC Irvine — “developed a couple of statistical tools — Shapley Run Credits and Offensive Shapley Win Credits — to help track and recognize all the responsible parties when any run [other than a solo homer] scores.”
The article notes
Line drive outs exist. Swinging bunts exist. Foul ball homers, seeing-eye singles, blown calls, sunny sky pop-ups. Hundreds of unfair things can happen in baseball games, and those unfair things often affect everything from how we regard a player’s performance to the final score.
So why this new theory?
A few years ago, when he was coaching his children’s baseball teams, McBride realized game theory could help explain what really happens during a baseball game, specifically, when a run scores. It’s pretty obvious who deserves the credit when a batter hits a solo home run, but every other kind of run has several contributors. And those contributors — in McBride’s view — haven’t always been given a fair level of credit.
Sounds nice, but we’ll see how well — or even if — this takes off.
You can learn more at the Fair-Credit Baseball website. Here’s a brief synopsis from the author. Goodness knows I not a math whiz, but I think this might actually be one of the better “accounting methods” in recent years.