Statcast vs Reality: Luck and Expected Stats
“Luck is the great stabilizer. It makes bad teams look good, and good teams look beatable — for a while.”
— Anonymous analyst quote
Baseball is a game of inches - and expected stats show just how cruel or kind those inches can be. Expected stats like xwOBA, xSLG, and xBA cut through the noise of batting average, home runs, and RBIs alone, and highlight which players are truly earning their results—and who’s living on borrowed luck.
For those who may not be familiar with advanced baseball metrics, make sure to swing by the Glossary page for definitions of the metrics I’ll use for our analysis – namely wOBA/xwOBA, SLG/xSLG, and BA/xBA. I’ve pulled the data from Baseballsavant.com.
Now that we have that out of the way, let’s get started.
Underperformers: Who deserves better?
1. Salvador Perez (C, Royals):
Let’s start by looking at the following charts detailing his 2025 actual and expected wOBA, SLG, and BA:
wOBA: .269
xwOBA: .359
Difference: -0.090
SLG: .351
xSLG: .517
Difference: -0.166
BA: .223
xBA: .291
Difference: -0.068
We can see that Perez is dramatically underperforming his expected stats in all three areas. Based on his quality of contact, Perez would be expected to have a .359 wOBA but is instead at .269. That is a 90-point underperformance. He is slugging 166 points below what is expected and is underperforming his batting average by .068 points. Perez’s barrel rate this season is 13.5% and his hard-hit rate (which is embedded in barrel rate, but interesting in isolation none-the-less) is 47.2%. These are exceptional metrics deserving of better actual production numbers.
There are a few concerns, however. Perez’s chase rate is 43.8%, meaning he swings at 43.8% pitches outside the strike zone…not so good. This could be a function of being frustrated with current numbers and trying to make things happen. None the less, it is not ideal. Additionally, his whiff rate, how often he swings and misses pitches, is 27.5%, which is high. However, the one caveat here is that he is a power hitter, and power hitters generally demonstrate higher chase and whiff rates. One main reason for this is due to the fact that power hitters often take longer, more aggressive swings, which can be harder to control, making consistent contact more difficult.
With the expected stats much higher than the current stats, we could very well see a positive regression in Perez’s hitting this season, making him an asset to fantasy managers and the team team, especially at a position, like catcher, where production can be harder to come by.
2. Dylan Crews (OF, Nationals):
Let’s, again, start by looking at the current and underlying stats:
wOBA: .277
xwOBA: .343
Difference: -0.066
SLG: .354
xSLG: .469
Difference: -0.115
BA: .196
xBA: .258
Difference: -0.062
Crews, the second overall pick in the 2023 draft, was called up to the majors in August of 2024, and was on the opening day roster with the team in 2025. He’s had a rough start in 2025, as indicated by the current stats noted above, but has been improving lately, hitting his 7th home run on May 20th, tied with the Cubs Matt Mervis to lead all rookies. However, like Perez, Crews is drastically underperforming his expected stats, especially in SLG which is currently 115 points below expectations.
Additionally, also like Perez, Crews also comes with concerns, especially in whiff rate at 31.5%, K rate at 27.7%, and the fact that he rarely walks, with a walk rate of 6.4%. Let’s keep in mind he is still a rookie and we’re only about one-third of the way through the season, so it will mostly likely take some time for him to get acclimated to big league pitching. Additionally, Crews recently left during the fifth inning in a game against the Braves with lower back discomfort found to be an oblique strain – the injury of choice by so many this year.
The underlying expected stats do point to positive regression for Crews this season, but a big determinate of whether that is realized is how he rebounds from the recent injury. So, we’ll have to evaluate and go from there.
Outperformers: Who may be receiving more than they deserve?
1. Javier Baez (OF, Tigers):
Let’s, again, start with the numbers:
wOBA: .336
xwOBA: .290
Difference: .046
SLG: .456
xSLG: .393
Difference: .063
BA: .278
xBA: .237
Difference: .041
So far in 2025, Baez is looking like the player he was pre-2020, where he consistently outperformed the expected stats. At 32 years old, he is closer to the end of his career than the beginning, but does that matter? Statistics would say that it does. There is a correlation between getting up there in age and productivity, shaped like a bell curve where players’ performance generally improves in their mid-20s, peaks around ages 27 to 29, and starts to decline into their 30s.
With a long history of outperforming expected stats, I think it makes this a little more difficult to analyze, but with age catching up to him and the last couple years not being very good, I’m going to lean towards Baez not being able to keep this up the rest of the season.
2. Tyler Fitzgerald (2B, Giants):
wOBA: .327
xwOBA: .276
Difference: .051
SLG: .403
xSLG: .323
Difference: .080
BA: .277
xBA: .223
Difference: .044
Fitzgerald has become the primary second basemen for the Giants this season, moving from his customary position at short stop following the team’s acquisition of Willy Adames. So far this year, he is outperforming wOBA, SLG, and BA compared to his expected numbers quite significantly. Interestingly enough, he finished the 2024 season by outperforming these expected stats, as well. In 2024, over 341 plate appearances his wOBA was .357 (xwOBA .292), SLG was .497 (xSLG .378), and a BA of .280 (xBA .227).
However, I also see some major red flags compared to last year. His average exit velocity so far this year is 83 mph, down 4.7 mph from last year and in the bottom one percent of the league. His barrel percentage is 4.5%, down 3.7% from last year, and his hard-hit rate is 28.1, down 3.8% from last year, and in the bottom seven percent of the league. These are telling me that luck has very much been on his side this year and negative regression could be right around the corner.
In the end, Lady Luck always plays a role—sometimes cruel, sometimes kind. But by comparing expected stats to actual results, we gain a clearer picture of a player’s true performance and value to their team. Regression isn’t guaranteed in either direction, and luck—good or bad—can linger longer than we expect. That’s why sample size matters: the larger the data set, the more likely we are to see performance settle closer to a player’s underlying skill. As we approach the halfway point of the season, expected stats help us separate true breakouts from statistical mirages. Whether you’re managing a fantasy roster or tracking your team’s future stars, don’t just trust the box score—dig deeper.