It’s finally time to look toward the 2022 season by reviewing my plethora of xMetrics and where hitters finished in the various components of each. While these posts won’t include actual projections, the first step toward developing a forecast is understanding the past.
We’ll begin by diving into my xHR/FB rate equation and some of the variables driving it.
Over a month after the announcement, we’re finally getting around to covering the fantasy impact of Javier Baez’s signing with the Detroit Tigers. If you have forgotten, Baez signed a six-year, $140 million deal with the team on Dec 1, the same day a flurry of signings were announced before we went into lockout mode. Let’s now consult the park factors to see how the change in home venue might affect Baez’s results.
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.
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.
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.
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.
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.
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.
