Author Archive: Mike Podhorzer

Alas, it’s the final in-season metric review of the year! It took a while, but I think it’s important to perform these reviews to be held accountable for what I share. It’s not enough to just share analysis, my opinions, and advice if it’s all wrong! So these reviews have helped prove that the in-metric analyses were worthwhile, as the majority of players moved in the direction expected. Today, we finish up by reviewing the xwOBA overperformers through June 28. Let’s find out how they performed over the rest of the season.

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Finally, we’re at the last of the in-season metric reviews! Today, we return to Statcast’s xwOBA. If you recall, earlier I reviewed the underperformers through early May, so the sample here is much larger. You would expect xwOBA to mean a little less, and actual wOBA to mean a little more over the larger sample. Let’s find out if that ended up being true for this small group.

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Yesterday, I reviewed the pitchers who had most underperformed their Statcast xHR totals through May 24. Today, let’s now review the pitchers who had most overperformed their xHR totals, meaning they allowed fewer home runs than Statcast calculated that they “should have”. Did these pitchers allow a higher HR/FB rate over the rest of the season? Let’s find out.

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We’re finally winding down the in-season metric reviews, and today, we return to Statcast’s xHR calculation, but this time for pitchers. So we’ll be reviewing the pitchers that had allowed significantly more home runs than expected according to Statcast through May 24 and find out how each performed over the rest of the season. Like I did for my hitter review, this is essentially comparing HR/FB rates through the first two months with the rest of the season, since actual fly balls are used in the calculation and Statcast is calculating how many of those should have left the park versus what actually did. Let’s find out if these pitchers did enjoy major HR/FB rate improvement over the rest of the way.

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Yesterday, I reviewed the hitters who most underperformed their Statcast xBA marks through May 17. All but one posted a stronger batting average the rest of the way. Now let’s flip over to the overperformers — those hitters who posted batting averages significantly above their xBA marks.

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Today, I move on to reviewing the list of biggest batting average underperformers through May 17, according to Statcast’s xBA. As usual, xBA isn’t perfect, as it ignores anything shift-related, but it’s good enough that if you stick with the players on the extremes (largest BA-xBA gaps), I would imagine the rest of season directional moves should be expected. Let’s find out if that was the case.

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After reviewing the early season Statcast xHR underperformers yesterday, let’s now review the early season Statcast xHR overperformers today. Once again, I’ll compare each hitter’s HR/FB rate through May 9 to his mark over the rest of the season.

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As I wind down my in-season metric reviews, I now move along to Statcast’s xHR calculation. In early May, I shared a list of hitters whose xHR total was at least one home run more than their actual totals. I wasn’t entirely sure what the best way to review how this group performed over the rest of the season, so I settled on HR/FB rate through May 8 versus the rest of the season. I ruled out AB/HR because strikeout and fly ball rates influence that rate and a homer can only be hit if the ball is put into play and isn’t on the ground.

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Yesterday, I reviewed the early season starting pitcher SIERA underperformers and discovered that the majority did indeed improve their ERA marks over the rest of the season, some significantly so. Now let’s flip over to the list of SIERA overperformers. Were these pitchers able to sustain their magic or did the force of regression prove too powerful?

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Today, I’ll review my favorite in-season ERA estimator, SIERA. This was the list of SIERA underperformers through May 3. The idea here was that absent a significant change in underlying skills (K%, BB%, batted ball profile), these pitchers should post much improved ERA marks the rest of the way as their luck metrics (BABIP, HR/FB, LOB%) normalize. Let’s find out if that did indeed happen.

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