With more than a quarter of the season in the books, buy low and sell high time is in full swing. Between X posts, website articles, and podcasts, everyone is dropping names of players off to cold or hot starts that are candidates to be involved in a trade to extract maximum value. Unfortunately, it’s far easier said than done, particularly if you’re in a competitive league (like one filled with FanGraphs and RotoGraphs readers, of course!). So while it’s easy to tell your readers to buy Gunnar Henderson, Ketel Marte, Fernando Tatis Jr., and Shohei Ohtani, actually executing on such recommendations ain’t.

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Out of all the ERA estimator equations we have, SIERA does the best job at predicting future ERA. That said, they all kind of stink because luck is a meaningful factor here and it’s obviously impossible to predict the type of fortune a pitcher is going to have. Furthermore, underlying skills often change, whether it’s added or reduced velocity driving strikeout rate changes or a pitcher suddenly learning control and improving his walk rate dramatically. So for the ERA equation to be right, it requires the common “all else being equal” caveat that demands that the underlying skills to the same, and that doesn’t always happen.

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I probably wait longer than most to evaluate my team and perform research on potential trades, but with just over a month in the books, it’s definitely trading season for the vast majority. This time of year is when we dive into the underlying skills to determine what’s real and what isn’t, identify trade targets, and review which of your own players should be put on the block.

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If I could choose just one underlying skill-based metric to evaluate a starting pitcher on, it might very well be SwStr%, especially for fantasy purposes, assuming your league uses strikeouts in some shape or form. Obviously, there’s more to effective pitching than just generating whiffs. However, with a -0.42 correlation with ERA and 0.85 correlation with strikeout rate among qualified pitchers from 2021 to 2025, it tells you a whole lot all by itself. So with that in mind, let’s peruse the starting pitchers that have experienced the greatest gains and declines in SwStr%.

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Among qualified batters between 2021 and 2025, there was a 0.86 correlation between Barrel% and HR/FB rate. That’s huge! It means we have a quick and easy way to determine whether a batter’s current HR/FB rate is real or not, and who might be expected to improve their mark or suffer a decline. So let’s dive into the batters that have enjoyed the largest surges and suffered the greatest declines in Barrel% this season.

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From 2021 to 2025 among pitchers with at least 100 innings recorded, Stuff+ has a 0.708 correlation with strikeout rate. That’s quite significant. Although I don’t have the stabilization point of Stuff+, I have to imagine it becomes meaningful far more quickly than strikeout rate. Assuming that’s the case, it follows that monitoring Stuff+ changes is key to predicting future strikeout rate direction.

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Among qualified batters between 2023 and 2025, Statcast’s BatSpd has a 0.662 correlation with HR/FB rate, the highest among all the metrics in our Statcast – Bat Tracking section. So it follows that we should constantly be monitoring the biggest gainers and decliners as names on either list could either validate current performance or predict future performance. Let’s now discuss the hitters on each end of that spectrum.

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Since we remain in tiny sample size territory, I’m still hesitant to perform a deep dive into any batter metrics. However, there’s one metric that could be worth reviewing almost immediately — maxEV. At this point, we don’t care about the decliners and probably won’t until the end of the season. Batters have all season to post their peak velocity marks. What we are interested in are those batters who have already increased their maxEV marks compared to last season. If you’ve already recorded a higher maxEV this year over just 5%-7% the number of events than last year, that could be meaningful.

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Since the majority of stats remain in small sample size territory, especially for starting pitchers who may have only made one start so far, I like to dig into the underlying metrics that could ultimately drive a change in results. One of those drivers is pitcher fastball velocity. We know that velocity is highly correlated with strikeout rate and ERA, so all else being equal, higher fastball velocity should result in a higher strikeout rate and lower ERA. Of course, each individual pitcher start is a small sample, so you’re not always going to see the increased strikeout rate or lower ERA immediately, or vice versa. That’s why it’s prudent to track velocity changes early on to better predict where a pitcher’s strikeout rate and ERA may be headed.

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Yesterday, I shared some of the fun tiny sample size performances among the batter population. Now let’s flip over to pitchers. Unfortunately, the pitching side is going to be a little less fun because not every starter has even made a start yet. Let’s see what we can come up with.

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