Archive for Hitters

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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Let’s be honest, most sleepers don’t come through in fantasy baseball. At least not the true sleepers, guys picked beyond the 250 mark. There have definitely been some big hits beyond 250 this year: Trevor Story (252), Marco Estrada (273), Rich Hill (288) – although he did just hit the DL, Jackie Bradley Jr. (320), and Carlos Beltran (326) to name a few.

I was reminded of the failure of late sleepers when news of the Chris Coghlan trade came across the wire yesterday during my chat. He was someone I really liked to keep up what he did last year (16 HR, 11 SB) with plenty of playing time available in Oakland. Let’s see what’s going on with him and two other deep sleepers (pick 250 or later) I liked. They’re still asleep, but let’s see if there is hope.

Chris Coghlan

He’s hitting .146/.215/.272 in 172 PA so far. It’s been really rough. Sure, a .170 BABIP is unfavorable and probably due for some regression, but it didn’t just happen to him. His strikeout rate has surged from 19% to 27% with his walk rate dipping from 12% to 8% at the same time. Last year, he made a concerted effort to pull more and untap his power, but his 32% pull rate is his lowest since 2010 (30%) and well below last year’s 42%. He’s also reaching a lot more. His O-swing% is way up at 30% after a 23% rate last year that was 11th-lowest in baseball. He’s also seeing the fewest fastballs of his career at 54%, down from a consistent 57-58% from 2012-15.

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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 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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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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It was suggested that we should celebrate the day that stats stabilize. Today is Grounder Day! Eat a sloppy joe while sitting on a blanket. Drink one of these, or some of this, but I don’t know about having any of this. Grounder Day!

Well, we actually aren’t all the way there. Only about twenty players have officially reached the stabilization point for ground ball rate. But that’s fine. It actually serves as a reminder that stabilization is not something that magically happens at one point. Stabilization happens over a spectrum, and today we know a little more than we did yesterday, and tomorrow we’ll know a little more.

But! Relative to *other* stats on our leaderboards, we know a good deal about a player’s ground ball rate by now. And the beauty of that news is that just knowing a player’s change in ground ball rate can tell us a good deal about what sort of power we should expect from them going forward.

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Welcome to an all Astros edition of the deep league waiver wire! With a series of roster moves, opportunities abound.

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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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