What's Going on With Ben Rice?

By E.J. Fagan

July 23, 2025

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NOTE: The following comes from EJ Fagan's substack page and is shared with permission.

Please check out EJ's substack page for more great articles.

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Something weird is going on with Ben Rice. Even before his home run last night, his Statcast page was a thing of beauty:

But his overall batting line is just pretty good: .236/.327/.475, or a .347 wOBA.

According to Statcast, he should be slashing something like an MVP-level .290/.380/.560.

What could be causing the difference? One explanation is batted ball luck. The other is that Statcast is missing something. If the former is true, then the Yankees should be playing Ben Rice every day no matter what. If the latter is true, maybe they should trade him while the metrics are so pretty.

Let’s start by eyeballing it. Here is a scatter plot of all MLB hitters with more than 200 plate appearances in 2025. The y-axis is their wOBA and their x-axis is their predicted wOBA from Statcast. Players below the line are underperforming their xwOBA.

Rice is underperforming his xwOBA by 0.058, the fourth highest in baseball. Only Juan Soto, Bryan Reynolds and Michael Conforto Have been worse.

Overall, I don’t see much of a pattern in these names. They are some brand name hitters and some role players. Some fast and some slow. Another point in the bad luck column.

The error is pretty consistent across the distribution too, suggesting that the xwoba-woba relationship doesn’t break down as you get higher. If anything, high-xwoba players tend to outperform a hair. xwOBA explains about 60% of the variation in wOBA, so there is a lot of room for random error to reduce a player’s wOBA. Another point in the luck column.

Okay, but the gap between xwOBA and wOBA isn’t entirely random. Fast players, for example, tend to outperform it. Rice is a very average runner, so speed isn’t the cause. Let’s try and predict (xwOBA - wOBA) in a basic linear regression using the hitter stats available on Statcast:

Okay, now we might be getting somewhere. If (xwOBA - wOBA) was mostly just random noise and sprint speed, we would expect the t values and r-squared values to be near zero. They aren’t - about 19% of the variation is predicted by this model. Launch speed (exit velocity) and launch angle are the biggest predictors of underperforming xwOBA - higher exit velocities and more ground balls.

We can plug Ben Rice’s numbers into this model. It predicts that Rice’s wOBA should be something like 0.26 lower than his xwOBA, or about half the discrepancy. He’s still getting unlucky, but about half of the underperformance is real.

So I’m going to make a prediction: Ben Rice will have a 0.377 wOBA for the rest of the season, roughly equivalent to George Springer’s .276/.368/.498 batting line. That’s still pretty great, but not quite MVP-level. It’s in the range where a platoon makes a lot more sense than the Statcast page would suggest. It also feels right to me - Rice seems like a really good hitter, but I don’t get the vibe that I’m watching an MVP.