This week, let’s move on to another important variable in my hitter xHR/FB rate equation, average distance of fly balls and line drives (ADFBLD). This metric is pretty straightforward to understand, as it’s simply calculating the average distance a hitter’s fly balls and line drives traveled during the season. Obviously, if you want power, the higher the ADFBLD, the better. So let’s review the leaders in the metric and discuss any surprising names. I used a minimum of 30 FB+LD to get my list.

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Yesterday, I reviewed the hitters whose Barrel FB% (BFB%) surged the most in 2021 vs 2020. Today, let’s flip on over to the decliners. As a reminder, due to a potential batted ball classification issue between flies and line drives in 2020, BFB% looked suppressed in 2020 (while Barrel LD% was inflated), so there were far more surgers in 2021 as a result, as the two metrics returned back to their normal levels. So that also means that there are fewer decliners and the magnitude is smaller. It’s all relative though, so these declines are more significant than the raw differences suggest.

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Today, I’ll continue my 2021 reviews of hitter Barrel FB% (BFB%). This time, we’ll take a look at the BFB% surgers compared to 2020. Remember that not only was 2020 a short season, but it was marred by irregular preseason training and interrupted by COVID-19 infections. So this is going to be a weirder list than usual with small sample marks in 2020 as hitters had less time for regression toward their career means.

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Yesterday, I began my 2021 review of my xMetrics and its variables, by reviewing the hitter Barrel FB% (BFB%) leaders and discussing the more interesting names. Today, we’ll flip over to the BFB% laggards. As you could guess, this list is going to include more obvious names then the leader list. Hopefully there are at least a couple worth highlighting.

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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.

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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.

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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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