Contrary to what you might assume given my status as a veteran RotoGraphs writer and member of the Tout Wars and LABR leagues, I don’t pay a whole lot of attention to players on none of my fantasy teams. So every so often, I express surprise, or perhaps even shock, when I peruse the day’s box scores and notice a batting average or counting stat from a player at a level far off from our expectations. “Player X has 15 homers already?!?!” is how I will frequently react. Let’s discuss some of these players.

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About a week and a half into this season, I decided to have a little fun with small samples and statistical anomalies. Anything could happen over a tiny number of games, and indeed anything did. We stress here not to overreact to performances over the first couple of weeks, so let’s see where things stand for the players I highlighted. Did that early start hint at things to come?

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Last week, I took Alex Chamberlain’s xBABIP equation for the first drive of the 2016 season, identifying those hitters whose xBABIPs most exceeded their actual BABIP marks. That was your potential BABIP surger list. Today I’ll check in on the flip side, those hitters whose BABIP marks greatly exceed their xBABIP marks. These hitters are at serious risk for BABIP, and resulting batting average, regression.

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It’s hard to believe that we’re now about two months into the season and this has been my first post on xBABIP all year. Well, here it is. While I developed my own xBABIP equation back in early 2015, the availability of new data since my formula was published allowed Alex Chamberlain to create an equation I prefer and use instead. Not only is the r-squared slightly better, but all the metrics are available here on FanGraphs. When I’m looking to buy low, the most attractive targets are hitters whose power is fine, but are simply suffering from an unfortunate BABIP. Below is that list of hitters.

For the first time, I have included all the components of the xBABIP equation so we can get a better sense of which component(s) each hitter is excelling in and where he could improve.

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Last year, I determined that pitcher batted ball distance allowed was correlated with HR/FB rate to a reasonable degree. This was fairly obvious, though the correlation was much lower than I expected, and less significant than that of hitters. It’s why I rarely discuss pitcher batted ball distance, along with the fact it doesn’t correlate from year to year all that well. But the correlation is there and it does convey meaning. We could run the same analysis for pitchers, comparing their distances with HR/FB rates to identify those due for improvement or regression. So today I’ll look at American League starting pitchers with highly inflated HR/FB rates, but distances that suggest major improvement is on the horizon.

While some of these players will obviously improve due to how high their HR/FB rates are, you might wonder if that improvement means a 10% mark going forward or a 14% mark the rest of the way. This analysis should help form those expectations.

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Last week, I identified five hitters whose batted ball distance suggested better HR/FB rate days may be ahead and another five hitters who may be in for a sudden HR/FB rate decline. That’s one way to use a hitter’s average home run and fly ball distance — as a predictive metric to speculate on improvers and regressers moving forward. The other use is to validate as a backwards looking tool. Player X’s HR/FB rate has declined, has his batted ball distance plummeted as well? The batter is seemingly at greater risk of maintaining that decreased HR/FB rate if it came with a similar trend in his distance. Of course, he may improve the distance, in which case the HR/FB rate would rebound. But we still want to see the two match up.

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Updated Feb. 25, 2017

Aug. 16, 2016: Updated Alex’s xBABIP equation and added Andrew Dominijanni’s xISO equation.
May 23, 2016: Published.

Jump around in this post:
Hitter metrics: xBABIP | xISO | xHR/FB | xOBA | xK%
Pitcher metrics: xHR/FB | xLOB% | xK% | xBB%

* * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * * *Do you frequently use RotoGraphs’ “X” (expected) metrics? Do you wish they were easier to find? Have you ever commented to ask if they could be added to the leaderboards or at least wished they were all located in one spot? If so, you may want to…

BOOKMARK THIS PAGE!

I don’t know if there will ever be a time when FanGraphs has a leaderboard devoted to “X” metrics. The fantasy analysts at RotoGraphs have taken a largely vigilante approach to creating descriptive and predictive expected metrics over the years. Moreover, each metric typically undergoes an iterative process by which we improve it when new data is made publicly available to the authors.

So, this is it. This is my best attempt, on behalf of RotoGraphs’ staff and at the polite and enthusiastic behest of its readers, to centralize the freshest versions of the relevant metrics the RotoGraphs staff most frequently cites. I have also built primitive Microsoft Excel-based calculators for some (but not all) of the metrics that crunch the numbers as long as you provide the appropriate inputs. It should save us all an extra minute or two and preserve our sanity a little bit.

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Last week, I identified five hitters whose batted ball distance suggested better HR/FB rate days may be ahead and another five hitters who may be in for a sudden HR/FB rate decline. That’s one way to use a hitter’s average home run and fly ball distance — as a predictive metric to speculate on improvers and regressers moving forward. The other use is to validate as a backwards looking tool. Player X’s HR/FB rate has spiked, has his batted ball distance surged as well? The batter would seemingly have a better shot at sustaining that increased HR/FB rate if it came with a similar trend in his distance. Of course, he may not sustain the distance, in which case the HR/FB rate would fall. But we still want to see the two match up.

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So we all know by now that batted ball distance correlates pretty well with HR/FB rate. It’s fairly obvious, as the further the batter hits the ball, the better chance it has of landing on the other side of the fence for a dinger. Adding in the two additional components that compose my xHR/FB rate equation increases the usefulness, but sadly that data is not available just yet. Yesterday, I discussed five hitters whose distances suggest dramatically higher HR/FB rates than have been posted. Today, I’ll look at the bottom of the distance boards and identify five hitters whose HR/FB rates are out of place, and should be lower, perhaps significantly so.

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So we all know by now that batted ball distance correlates pretty well with HR/FB rate. It’s fairly obvious, as the further the batter hits the ball, the better chance it has of landing on the other side of the fence for a dinger. Adding in the two additional components that compose my xHR/FB rate equation increases the usefulness, but sadly that data is not available just yet. So today, we’ll look at the top of the distance boards and I’ll identify five hitters whose HR/FB rates are out of place, and should be higher, perhaps significantly so.

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