Last week, Jonathan Villar was traded to the Marlins, which will be his third team in three years. For a guy who has posted six WAR in the last two seasons, that’s pretty surprising. He’s been quite the exciting power/speed contributor over the past four years, with double digit homers and steals galore. Will the park switch affect his offensive output? Let’s check the park factors to find out.
Last Wednesday, Dylan Bundy was traded to the Angels for a smattering of minor leaguers. As a fantasy owner who has been intrigued by his underlying skills and elite slider, I’m still waiting for that breakout season, as I’m sure many of you have been. He has now completed three full seasons and a fourth of just over 100 innings, and yet his best single season ERA is 4.02, which included lots of relief innings. Will the move from Baltimore to Los Angeles help accelerate his breakout path? Let’s check the 2018 park factors (2019 are not available yet).
It’s only the beginning of December and we’ve already seen a number of interesting trades. And Mike Moustakas has already signed a contract! What an offseason so far. On Friday, the Rays and Padres agreed to a trade, which included Tommy Pham moving to the latter and Hunter Renfroe to the former. Let’s take a look at the park factors to figure out how the moves might affect their production.
It’s always fun to look back at early season performances that surprised and check on how those players performed the rest of the way. Did they continue to surprise or regress closer to what we expected to begin with? I would say that the majority of the time, it’s the latter. In early June, I identified and discussed six starting pitchers severely underperforming their SIERA marks that I believed to make for good acquisition targets. Remember that SIERA isn’t a projection, but rather backwards looking. So if the pitcher’s skills deteriorated over the rest of the season, he obviously would not have made for a good target. Let’s see how they performed the rest of the way.
Yesterday, I reviewed the hitters who had increased their FB% by at least 10% through May 4 of the season and noted how they had performed over the rest of the season. As a group, they held onto a little bit less than half of their gains from 2018. It goes to show that regression toward historical averages are a powerful force, but that batted ball profiles are more controllable and changes could indicate a real change in approach. Will the same results show up when reviewing the hitters who “departed” the fly ball revolution through early May? Let’s find out if these guys got their FB% marks back to where they settled in 2018 or if the early marks were the first sign of an altered batted ball profile.
In early May, I identified and discussed 17 hitters who had boosted their fly ball rates by at least ten percentage points (30% to 40%, for example) through May 5th. With the fly ball revolution in full swing, these were potentially the newest members. For high HR/FB rate guys, more fly balls is probably a good thing as it will increase homers and runs scored, and probably runs batted in, which should be enough to offset a decline in batting average. Did these hitters maintain their early increased FB% marks or did they experience regression back to 2018 levels over the rest of the way?
In Early May, I dove into the world of small sample size theater to discuss some of the statistical oddities that had occurred so far on the pitching side of the ledger. Let’s review how these pitchers performed the rest of the way in the metrics highlighted.
The best part of small sample stats are the enjoyment we get from finding the weird and the extreme. This year at the beginning of May, I discussed a variety of players riding on one side of the bell curve. Let’s revisit these players and stats and find out how they performed the rest of the way.
Yesterday, I reviewed the 11 hitters I identified back in late April who my xHR/FB rate suggested had deserved dramatically better actual xHR/FB rates. Today, we flip to the overperformers, those who my equation suggested deserved significantly lower HR/FB rates over that first month. While the equation isn’t meant to be used for predictive purposes, a forecast would likely account for that apparent overperformance and project a lower HR/FB rate the rest of the way. Let’s see what ended up transpiring.
At the end of April, I calculated hitter xHR/FB rate using my equation to determine who had most underperformed at the time. I identified and discussed 11 of those hitters. Let’s see how many of these hitters actually did improve their HR/FB rates over the rest of the season and how close they came to their xHR/FB marks. Remember that xHR/FB isn’t meant to be predictive, but descriptive. The difference being that xHR/FB rate helps us determine what should have happened, rather than what will happen in the future. It attempts to strip out luck, like the majority of our expected/deserved metrics.
