It’s been just over a month since I last reviewed the hitters that had most underperformed and overperformed their xwOBA marks. I’m not going to review how they have performed since, as the idea is for rest of season production, not just the next month.
Yesterday, I dove into the new Statcast bat tracking metrics to learn whether the metrics are any better at predicting HR/FB rate than what we already have, namely Barrel%. Sadly, the answer was no. However, that doesn’t mean these new metrics are useless, of course. As we collect more and more data and sample size issues go away, we’ll be able to investigate more with significantly greater confidence in the results.
You should all know by now that Statcast recently made their bat tracking data public. This is a big deal! Our own Ben Clemens made some early observations and also shared what the data does and doesn’t tell us, which included a correlation table between the new metrics and the familiar.
Yesterday, I reviewed the seven rookie hitters who had recorded the highest plate appearance total so far this season. Let’s continue on down the PA column for a final six.
We’re about a quarter of the way through the season (sheesh, that went fast!), so now is as good a time as any to review the performances of the rookie hitters. Today we’ll start with about half the crop I ultimately want to discuss, and we’ll do the rest of the freshmen tomorrow.
Like discussed with hitters yesterday and last week, a pitcher’s batted ball type profile is important and helps drive their BABIP and HR/9 rate. More grounders and line drives allowed usually result in a higher BABIP, while more fly balls allowed usually result in a higher HR/9 rate. So with that in mind, it follows that changes to a pitcher’s profile could change the shape of their performance. Let’s review the five starting pitchers that have raised their GB% the most, along with those who have suffered the biggest declines.
Last week, I identified and discussed the 10 hitters who have raised their fly ball percentage marks by the greatest amount compared to last year. This is important because home runs are a category in the vast majority (all?) of fantasy leagues, and all else being equal, more fly balls results in more home runs. Today, we’ll review the other side of the coin — the hitters who have suffered the most significant declines compared to last year. As a reminder, the odds are the majority of these hitters revert to previous season batted ball type distributions over the rest of the season. However, it’s still a good idea to monitor these rates moving forward as it’s always possible there’s a legit change in approach and these changes stick. If nothing else, the declines here could help explain a disappointing home run total so far.
Typically when we see a hitter break out in the power department, hitting more dingers than projected, we naturally just assume they are hitting the ball harder, which would likely result in longer fly ball distances, ultimately driving a home run spike. You would therefore see this in an increased HR/FB rate. However, that’s not always how these surges occur. Sometimes, it’s simply a batted ball distribution change. That is, the batter is now hitting a significantly higher rate of fly balls than during the previous season, and might not necessarily be hitting those flies any more optimally.
If you play in a shallow mixed league that only cares about saves and ignores holds, you rarely, if ever, have even considered picking up a non-closing reliever. However, in deep mixed, and especially mono leagues, or those that count holds in some form (as a separate category or combined with saves), non-closing relievers can accrue fantasy value. So it’s worth perusing your free agent pool and the FanGraphs leaderboards to find the next hidden gem. Ya never know, one of these names could end up finding themselves in the closer role if the incumbent hits a rough patch or the IL.
My favorite activity early in the season is to try to capitalize on underperforming and overperforming starting pitchers. I’ll dangle my pitchers with low ERAs whose skills don’t support it and target those who hold high ERAs despite strong skills. In the past, I’ve exclusively used SIERA to identify such pitcher groups. Today, I’ll use Statcast’s xERA, as it utilizes actual batted ball data, so not every ground ball and fly ball are equal, as they are in the SIERA equation. So let’s compare actual ERA to xERA to identify and discuss both the underperformers and overperformers. Don’t forget that any ERA estimator uses actual strikeout and walk rates, so if those metrics seem unsustainable (either on the high or low side), then the expected ERA equation isn’t going to be as helpful.
