Yesterday, I discussed the hitter average bat speed risers and fallers. Thanks to frequent commenter Anon, I discovered that I must have kept the default “Qualified” filter on, which resulted in lots of interesting names being missed. So in today’s review, I’ll discuss the names that would have appeared in yesterday’s list, but were left off. Note that there are lots of bat speed changes, so I’ve kept these posts to changes of at least two miles per hour. Also note that since I relaxed the swing minimum, the lists are now more hand-picked based on fantasy relevance.

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Only a couple of days into the season, I shared the super early hitter average bat speed risers and fallers. Though bat speed does stabilize quickly, it’s still subject to small sample caveats. Now nearly a month and a half later, let’s revisit the metric, as the numbers now will give us a much better idea of which hitters have enjoyed sustainable bat speed increases or suffered from losses.

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We’re just over a fifth of the way through the season, which feels like prime time to start making trade offers. I typically use this time to identify my team’s overperformers and underperforming targets on my leaguemates’ teams and then send out a flurry of offers. You never know how other owners value your overperforming players or whether they have become impatient with their own underperformers. So it’s worth finding out by engaging in trade talks. Today, I’ll review the top underperformers and overperformers by comparing wOBA versus Statcast’s xwOBA. We know the latter isn’t perfect, but it’s the best metric we currently have to evaluate expected performance to-date.

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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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