Pitchers change their underlying talent/skill levels much more frequently and quickly than hitters do. That’s because it could be as simple as gaining/losing velocity or altering their pitch mix. One way to get ahead of the crowd in identifying changing talent levels is by comparing in-season fastball velocity. So let’s find out which starting pitchers have gained the most fastball velocity in May versus April. I included their respective strikeout rates and SwStk% marks so we could see if the added velocity has already resulted in more punchouts.

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Let’s continue our look into the hitters who have been starting regularly that you may not have even realized. Will their every day playing time continue, and if so, in which leagues, if any, are they worthy of your starting roster?

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Injury and poor performance results in a constant stream of new everyday starters in team lineups. It could be difficult to keep up and notice these changes unless you own the guy who no longer has a starting job. So let’s dive into some of the hitters who are now getting an opportunity to play every day and figure out whether that playing time will continue and whether there’s potential positive fantasy impact.

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Yesterday, I identified and discussed the hitters who have most underperformed their Statcast xHR totals. Today, let’s flip to the other end — those hitters who have most overperformed their Statcast xHR marks.

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It hasn’t received a whole lot of fanfare, but Statcast has its own xHR calculation. It defines xHR as Ballparks Gone At/30, though I am unable to find any explanation on how to calculate “ballparks gone at”. The leaderboard does tell us that “environmental variables (elevation/weather/wind/etc…) not factored into these values”, which is essentially an acknowledgement that the calculation isn’t perfect. Environmental factors are real and have an effect on home runs, so you could get most of the way there by ignoring them, you’ll never get all the way there. That said, this metric is seemingly the quickest and easiest way to determine which hitters who have luckiest and unluckiest in the home run department, which is important over a small sample of at-bats. So let’s dive into the biggest underperformers, those hitters whose actual home run total is most below their xHR total.

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Yesterday, I listed and discussed the qualified starting pitchers that have most underperformed their SIERA marks. It was a good initial list of potential acquisition targets (hopefully to be had at a discount!) in which to perform further research. Now let’s move to the SIERA overperformers. This is the group you should be thanking Lady Luck for if you own any of them as their ERAs are likely to rise, perhaps significantly, over the rest of the year. That doesn’t necessarily mean they will be bad and hurt your fantasy team, but you may very well get far more value in return for what they will provide over the rest of the way if you dangle them to your leaguemates in a trade.

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Just like we are usually better off using xwOBA over a small sample to evaluate a hitter’s performance, the same concept applies to starting pitchers and using SIERA instead of ERA. So much could happen outside a pitcher’s control, such as balls finding holes through the infield or routinely finding fielders, strong or weak bullpen support stranding baserunners or allowing them to score, etc, that ERA just isn’t a very accurate measure of performance this early. So let’s dive into the qualified starting pitchers who have most underperformed their SIERA marks. Like I cautioned with xwOBA, SIERA is not meant to be predictive, so don’t assume these pitchers will all post ERAs near their SIERA marks the rest of the season. Instead, I use it by simply substituting ERA with SIERA to determine how a pitcher has pitched so far. ERA is completely meaningless to me at this point, as SIERA does a much better job summarizing the skills I care about into an ERA-like metric.

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Yesterday, I identified and discussed the hitters who have underperformed their xwOBA marks by the most significant margin. The list made for a good initial group of potential “buy at a discount” candidates. Today, we flip to the other end, those hitters most overperforming their xwOBA marks. The gaps here are much smaller than on the underperformer list. Once again, it is important to remember that xwOBA isn’t meant to be predictive, so don’t automatically assume these hitters will soon suffer a significant decline in wOBA to meet their xwOBA marks. Instead, it’s a backwards looking metric and would be better used to evaluate how the hitter should have performed. Just like I use SIERA instead of ERA in the early going to get an idea of how the pitcher has pitched, sans luck, I would lean toward using xwOBA instead of actual wOBA to evaluate how a hitter has performed.

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We’re about one sixth of the way through the season now, so while the sample sizes of performance and advanced underlying metrics remain small, they aren’t completely meaningless like they were a week or two into the season. So let’s begin evaluating surface results on the hitting side by looking at Statcast’s xwOBA and comparing it to actual wOBA. As a reminder, xwOBA is not a predictive metric. It’s backwards looking and should be used the same way you might use SIERA for evaluate pitching performance. It’s more of a “how should the hitter have performed”, rather than a “how will the hitter perform”. That’s because it assumes all the underlying metrics driving xwOBA, like walk rate, strikeout rate, launch angle, exit velocity, etc, are all sustainable skills, which certainly isn’t true for all players and all metrics.

So don’t read this table and automatically believe these hitters will match or come close to matching their xwOBA marks over the rest of the season. That’s not how it works. However, do figure these batters have been “unlucky” and should perform significantly better the rest of the way, assuming their underlying skills don’t dramatically change. As a result, this could be your initial acquisition target list before diving deeper.

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Yesterday, I listed and discussed the starting pitchers whose GB% marks of risen by at least 10% versus last season. The caveats here were — even though batted ball profiles stabilize more quickly than most other underlying metrics, the sample size still remains small, and an increased GB% isn’t necessarily “good”, but will likely change the shape of the pitcher’s performance and results. Let’s now move on to the GB% decliners and review those whose marks have declined by at least 10%.

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