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.
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.
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.
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?
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.
Yesterday, I reviewed the list of hitters who most underperformed their xwOBA marks through May 1. All but two of the 11, who had already posted actual wOBA marks over .400, improved their wOBA marks over the rest of the season, some quite dramatically. Let’s now review the hitters who most overperformed their xwOBA marks through May 2 and find out how many of them did indeed suffer a wOBA decline through the rest of the season.
Today, I continue my in-season metric lists with a review of the xwOBA underperformers through May 1. We know that xwOBA isn’t a perfect metric of what a hitter “deserved”, nor is it meant to be predictive. However, it’s certainly better to use it than wOBA itself when trying to forecast rest of season performance. This is especially true when only a small sample of games are in the books, so expected metrics play a more valuable role. So let’s find out how this group of hitters performed over the rest of the season.
Yesterday, I reviewed the starting pitchers who experienced an early season GB% surge to find out if those spikes were sustained over the rest of the season. Today, let’s now review the early season GB% decliners. Did their GB% marks rebound or remain down, increasing the risk of gopheritis? Let’s find out.
Today, let’s move over to review early starting pitcher metrics and whether the surges or declines stuck around over the rest of the season. Unlike for hitters where depending on the type of hitter they are, the optimal batted ball profile is easier to determine, it’s not as straightforward for pitchers. So this isn’t necessarily a “good” list to be on, but it could change the shape of the pitcher’s performance. More grounders should result in fewer homers, but likely more hits allowed and a higher BABIP. So let’s get to the names of those that had increased their GB% by at least 10% through Apr 19 and see whether their GB% spikes were sustained.
Yesterday, I reviewed the hitters who had raised their FB% marks by at least 10% through Apr 17 versus 2020. Let’s now switch gears to reviewing those hitters who saw their FB% marks decline by at least 10% through Apr 18. Did these early FB% declines suggest a down power year? Let’s find out.
