While we have all acknowledged that Statcast’s xBA (expected batting average) isn’t perfect, and the reason I created my hitter xBABIP equation, it’s far better than taking a hitter’s current batting average or BABIP at face value. It’s also easy to use — no calculations required, just open the Statcast leaderboard on Fangraphs and you’ll see a bevy of Statcast metrics, including xBA. So as we’re getting close to the one-third point of the season, let’s check out the biggest underperformers. These are the hitters whose batting averages are most below their xBA marks. It’s a good starting list to research further for potential trade target opportunities.

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Yesterday, I reviewed three rookie starting pitchers. Let’s keep it going and review three more today.

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Today, let’s flip over to reviewing the rookie pitchers who have faced the most number of batters so far. We remain in small sample territory, so in order for any analysis to be meaningful, I’m only sticking to those with the heaviest workloads.

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Yesterday, I reviewed three rookie hitters, all of whom happened to be top five prospects heading into the season. Let’s review another three today using the same format.

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I can’t remember the last time three of the top five prospects all debuted at the same time. Has it ever happened? I’m sure at some point in history, but it’s pretty awesome. This season, the #2 overall prospect Bobby Witt Jr., #4 prospect Julio Rodríguez, and #5 prospect Spencer Torkelson all opened the regular season in their teams’ starting lineups. Exciting! With just over a fifth of the season in the books, let’s take a gander at how these potential future stars are performing.

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Yesterday, I looked at the hitter maxEV surgers versus last year. For this comparison, 2022 sample size doesn’t matter, as the smaller number of at-bats makes it that much more impressive that the hitter has already exceeded his 2021 maxEV. Today, we flip to the maxEV decliners. This time, sample size does matter, as hitters clearly had more opportunity to launch that one ball that marked their maxEV last year than this season so far. So take these names with a larger grain of salt and don’t panic nearly as much given the sample size issues. An appearance here could prove to be meaningless, or it could be nothing, and tomorrow the hitter hits that one ball hard enough to move that maxEV up.

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Let’s talk hitter maxEV, which represents the highest exit velocity recorded by a hitter during the season. It could come on any batted ball type, though a high EV on a fly ball, or even line drive, is much more meaningful than on a grounder. Today, I’ll review the hitters whose maxEV marks have already surged versus 2021. The beauty of focusing on the surgers is that we don’t care about sample size this year. Last year, sure, sample size matters. The greater the sample size of balls in play, the better chance the hitter has of exceeding any particular EV. So the thinking goes that if a hitter has already exceeded his mark last year in just a month and change of balls in play, it could mean his power has increased. Let’s get to the names.

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Yesterday, I reviewed the hitters who have underperformed their xwOBA marks the most. Now let’s flip over to the overperformers. Once again, I acknowledge the flaws in the metric and the existence of consistent underperformers and overperformers. If one of the hitters on this list has consistently overperformed, I’ll note it.

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In the last two days, I reviewed league average hitting and pitching metrics to understand what was driving the decline in offense. Now let’s return to discussing individual players. We know that Statcast’s xwOBA has its flaws, as most (all?) estimator metrics do. But it’s the best we have and it doesn’t need to be perfect to provide us with actionable information. Furthermore, we have historical data, so when we see a hitter consistently underperforming or overperforming, we could reasonably conclude that the metric is not accounting for something and ignore the player’s placement on the list.

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Yesterday, I dove into some of the hitting metrics to determine what has been driving the decline in offense. Today, let’s over to the pitching side and review another set of metrics. Obviously, we use a lot of the same metrics on each side, so I’ll only present and discuss those that weren’t shared yesterday.

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