Yankees' Use of Analytics Has Backfired on Offense and Needs a Change

During the New York Yankees' post-elimination press conference, owner Hal Steinbrenner squarely placed blame on the lineup for falling short in October.

While Steinbrenner refuses to acknowledge that he is part of the Yankees' problems, alongside both Brian Cashman and Aaron Boone, he's not entirely wrong about the offense failing to meet expectations. After all, they scored just five runs in a three-game ALDS sweep at the hands of the Tampa Bay Rays.

Even though the Yankees were second in home runs this season, that didn't translate to an effective offense like it has in the past. But why is that? Perhaps that is related to how the Bronx Bombers construct their lineup and prepare for games—and the data that comes with it.

The Yankees' use of analytics is lagging behind

The word "analytics" has a rather negative stigma behind it from former players or longtime baseball fans, as they've often associated the word with "waiting around to walk or hit home runs".

In actuality, analytics is simply the use of data to optimize performance. When you look at the World Series-bound Rays and Milwaukee Brewers, they are two of the most data-driven franchises in MLB and use analytics to put the most efficient team on the field. Analytics involves finding the area where a player excels and emphasizing it.

The Yankees actually have a large analytic department under Cashman. The problem is that they're using them incorrectly—at least on the hitting side.

In contrast to the Brewers and Rays, who have a wide variety of players who excel offensively in different areas, the Yankees have a rather redundant lineup that emphasizes power over everything. It's a one-size-fits-all approach that tries to fit square pegs into round holes.

But analytics aren't just limited to roster construction. It also plays a huge role in preparing for games, as the data can be used to identify the weaknesses of a hitter/pitcher and then exploit it. That's something the Yankees' lineup hasn't been particularly good at.

It's worth mentioning how many of the Yankees' younger players, such as Ben Rice and George Lombard Jr., have diligently prepared for games and the pitchers they'd face. Rice in particular has earned a reputation among his teammates for being "data-driven more so than most."

Yet Gary Phillips of the New York Daily News, who wrote about Rice's use of analytics for game planning, noted that the Yankees' lineup isn't as receptive to analytics as the pitchers are.

One thing I touched on in here that I'd like to dive into more at some point is that the #Yankees' hitters, generally speaking, are not as receptive to analytics as their pitchers. Not saying more buy-in and data will cure their problems, but there has been stark contrast over… https://t.co/WTPYkVsfFm
— Gary Phillips (@GaryHPhillips) October 21, 2026

Granted, pitching analytics are more developed than hitting analytics. But the Yankees have a data-driven pitching coach in Matt Blake, and it shows in his work.

It's how he was able to get the best out of Clay Holmes, Luke Weaver, Brent Headrick and Paul Blackburn. It's how Cam Schlittler developed into one of the best pitchers in baseball as a second-year player. Blake identifies where these pitchers excel, get them to fully lean into that strength, and use it to attack a hitter's weakness.

Rice isn't the only Yankee hitter to use analytics. Giancarlo Stanton embraces any information he can use against a pitcher because he can fully capitalize on a mistake. It's also worth mentioning that buying into analytics doesn't guarantee success. Aaron Judge largely ignores using the underlying data, and based on his results at the plate in 2024 and 2025, he doesn't need to change anything.

However, seeing players like Jazz Chisholm Jr., Trent Grisham and Ryan McMahon constantly fall into prolonged slumps was especially frustrating to watch because their approaches to hitting never changed.

In Chisholm's case, he also wasted opportunities to take advantage of his strong baserunning; he stole 41 bases despite a mediocre .312 on-base percentage. Had he placed a greater emphasis on the improved plate discipline he's shown since joining the Yankees, he'd have more stolen base opportunities and create more chances for the lineup to drive him in.

Mentioned in here that there is a big disparity between how receptive the Yankees’ pitchers are with analytics compared to their hitters.

Been talking about this for a while on @pamsson’s and my podcast: https://t.co/NKYNxar6Wj https://t.co/KFkA2If8YA
— Chris Kirschner (@ChrisKirschner) October 21, 2026

Analytics are used to emphasize a hitter's strengths, and the Yankees aren't doing that from both a roster construction and preparation standpoint. The roster construction emphasizes one area (power) too much, while other skillsets that would be more useful against certain opponents are being ignored entirely by some players.

The Rays and Brewers use analytics for their lineups correctly because they have a variety of players to emphasize different areas and can attack opposing pitchers differently. If a pitcher doesn't hold runners well, they emphasize getting on base and moving runners along.

If a pitcher is vulnerable to fatigue from long at-bats (such as Schlittler), they emphasize driving up the pitch count with foul balls or forcing action in the field. And if a pitcher is prone to giving up home runs, they emphasize power. Analytics helped them create that flexibility, yet the Yankees used analytics to create a lack of flexibility.

Bringing in a greater variety of hitters via trades or free agency can help the Yankees immensely. Perhaps a more analytically-driven hitting coach can replace James Rowson if the Yankees desire a change there. Maybe more players buy in to how Rice and Stanton use analytics for preparation. Regardless, in order for the Yankees to fix the offense, they need to use the underlying data the right way.

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