Last week, I shared that from 2021 to 2024, there has been about a 0.70 correlation between pitcher strikeout rate and Stuff+ grade for those who have recorded at least 100 innings. So then it follows that just like SwStk% and then years later, CSW%, Stuff+ should make for a pretty good proxy of what a pitcher’s strikeout rate should be. Remember, it’s backwards looking so don’t mistake Stuff+ to be predictive, though you would assume if a pitcher currently owns a high/low mark, he’ll continue to maintain that level. So let’s dive into the current starting pitcher Stuff+ leaders and laggards and see if we could identify any who may be in line for an increased or reduced strikeout rate if they maintain the quality of their repertoire.

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Yesterday, I reviewed the starting pitcher risers and fallers in the Stuff+ metric compared to last season. Those lists are super insightful in highlighting pitchers who have seen improvements and declines in the quality of their pitch repertoires. However, a number of pitchers failed to meet my minimum innings requirement last season and were therefore not compared. So today, let’s review 10 pitchers that missed my list due to injury last year or are rookies this season. We can compare the injury returnees to their 2023 numbers and evaluate the rookie marks on their own.

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From 2021 to 2024, there has been about a 0.70 correlation between pitcher strikeout rate and Stuff+ grade for those who have recorded at least 100 innings. That’s pretty significant! It means we have a nice proxy for strikeout rate without having to worry as much about sample size, as strikeout rate could fluctuate wildly after over just a couple of games. I would imagine Stuff+ stabilizes much more quickly. So it’s worth monitoring changes in Stuff+ or newly established marks for rookie pitchers to quickly get a rough idea of strikeout potential. So let’s check in on the starting pitchers who have experienced the greatest gains and losses in Stuff+ compared to last year.

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We’re about three and a half weeks into the season, which still qualifies as a small sample size. Even though rate stats could still fluctuate wildly, especially ERA and WHIP for pitchers given their low number of innings, it’s hard for many to ignore those marks and look at the underlying skills instead as a better predictor of future results. So it’s worth reviewing a pitcher’s ERA compared to his SIERA to get an idea of whether he’s riding strong/weak skills or is being impacted more by the luck trinity of BABIP, HR/FB, and LOB%.

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Usually this early in the season, I would share the xwOBA under/overperformers. However, a suppressed or inflated BABIP has a dramatic impact on the gap between wOBA and xwOBA and since we remain in small sample size territory for the metric, I didn’t want to include a list of obvious names. We all assume a sub-.200 BABIP is going to rebound! Likewise, a .450 BABIP isn’t sustainable. So instead, let’s review ISO versus xISO.

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While we remain in small sample territory, it’s always fun to look at the league leaders in various process-related rate metrics (though you can definitely go a layer deeper). Results are far less predictive right now, so let’s take a gander at which batters are leading in some of the statistics that we should actually care about right now, a least a little bit.

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One of the few metrics I monitor closely during spring training is pitcher velocity. Process is significantly more important than results in March and could hint at the need to update projections to account for changes we see. Of course, Spring velocity changes don’t always carry over to the regular season. Often times we see a pitcher enjoy a velocity spike and fail to hold onto it when the regular season begins, or suffer a loss of velocity, but gain it right back. So now with a couple of starts in the books, let’s find out who has actually gained and lost velocity compared to last year so far.

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It’s hard to evaluate statistics this early without sounding the small sample size alarm bells. Yesterday, I looked at hitter bat speed, which supposedly conveys meaningful information after only a few swings, but still isn’t perfect this early. Today, I’ll look at another metric that works over small sample sizes, but only one side. That’s maxEV or the highest exit velocity a batter has hit a ball over a specified time period. We can evaluate the maxEV gainers already, but given that the metric could increase as the season progresses, it doesn’t make sense to review the fallers.

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Last year, the team at Baseball Savant blessed us with a cornucopia of new bat tracking metrics. One of those shiny new numbers was hitter average bat speed, which measures the speed at which a hitter swings. Last year, there was a robust 0.70 correlation between average bat speed and HR/FB rate among qualified hitters. That’s significant! Since average bat speed requires only a few swings to become predictive, it’s a great stat to monitor early on when small sample caveats apply to nearly every other metric. This might end up being a strong power breakout predictor before the power breakout actually occurs! On the flipside, perhaps a meaningful decline suggests disappointing output.

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For the first time in many, many years, I failed to publish a bold predictions post last year. It was sad. We shall not let that happen again, so it’s time to think bold once again. I usually try to develop my bold predictions based on knowledge I don’t think is being captured by the projections. Or perhaps, it’s not being fully captured. So you won’t catch me boldly predicting that Young Player X, who hit 20 homers last year, will “break out” this season with 30 homers, because, ya know, he’s young and young hitters improve. That’s not boldly predicting, that’s just guessing based on general career trajectories. Alright, enough of the yadda yaddas, let’s get to ’em.

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