What did you do this week? I determined, by a completely objective analysis, that Mexican food is the best type of food there is. It covers a wide range of flavors, textures and preferences: Sweet to savory, cool to crazy spicy, crunchy to smooth, vegetarian to carnivore. It's usually affordable, can be eaten in casual or formal settings, and is widely available all across the United States. Anyone who disagrees with me is wrong, and I have little respect for your opinion. Other thing I did this week: Learned all this stuff.
Clutch hitting: Yeah! Clutch hitters: …meh. Clutch hitting visualized, by Scott Lindholm, Beyond the Box Score
So long, statistical significance! These hitters, the best in baseball over the past seventy years or so, show a marginal increase in batting average and no difference in slugging percent in RISP situations.
What might cause the increase in average in RISP situations? Defense adjusts with runners on base–as runners are held on base, defenses may be stretched in such a manner that balls that might be reached with the bases empty slip through for a hit. Pitchers might attempt to be more precise in their pitching, thus consequently serving up a fat ball to be hit. Hitters may adjust their approach, determined to swing only at pitches that are exactly what they want.
So, does clutch hitting exist? Of course it does–it occurs every time a player delivers a hit with runners on base. What don't exist are clutch hitters–there are good hitters in baseball, and there are not-so-good (the bad are generally not represented in this sample). Adrian Gonzalez is pictured at the top of this post and labeled in the data viz because of these numbers…
Radar guns (Stalker, for example) deal with reflecting radar waves in one direction. Trackman, on the other hand, goes four ways, which is what lets it track spin: The Physics of Radar Guns, by David Kagan, The Hardball Times
TrackMan can track not only the trajectory, speed, and direction of the ball but also the spin of the ball. The radar signal reflected by the ball flickers. The frequency of the flickering matches the spin rate of the ball. There are two suggestions as to the source of the flickering.
Different parts of the ball probably reflect different amounts of the radar waves. Perhaps the seams are more or less reflective than the horsehide. Maybe the lettering or logo on the ball reflects differently.
The more likely explanation is that different parts of a spinning ball are moving at different speeds. Imagine a ball with topspin moving directly at you. The top of the ball would be moving toward you a little faster than the middle of the ball which in turn is moving a bit faster than the bottom of the ball. Since the Doppler Effect depends upon the speed of the reflecting surface, the variation in speed across the ball could be causing the flickering.
Times through the order isn't an objectively better way to keep track of a pitcher than is pitch count, but it's a better predictor of certain events: Are You Over 18?, by Russell Carleton, Baseball Prospectus
The results depended on the outcome and it was something of a mixed bag. For strikeouts, times through the order was a better predictor, and as expected they became less frequent relative to expectations. Walks, on the other hand, were more pitch count dependent, but their frequency went down as the game went on. Singles and home runs were tied more to pitch count, but extra-base hits (doubles/triples) were more tied to times through the order. All went up as the game wore on. Outs in play were more dependent on times through the order, but as the pitcher turned the order over, he became more likely to induce an out in play (but remember, less likely to induce a strikeout).
It’s not entirely clear which factor drives more of the equation. In no case did both variables enter in simultaneously. That is, once the regression decided that pitch count was the stronger predictor, there was no variance left over for times through the order. It seems like they are both picking up a lot of the same variance. But they do tell a rather interesting story combined. As the game wears on, outcomes in which the batter doesn’t hit the ball (strikeouts, walks) go down (again, relative to expectations), while outcomes where the ball is put into play go up. Expected BABIP and OBP go up (best predictor is pitch count).
Bullpen ERA tends to be lower than bullpen FIP, especially earlier in the season, but that balance flips when you look at worse-performing bullpens: The Current State of Bullpen Usage in 2015, by Craig Edwards, FanGraphs
Most of the bullpens’ ERAs outpace their FIPs so far this year, but that is not entirely surprising. Last season was the first year since 2010 that bullpen ERA was not at least one-tenth lower than bullpen FIP league-wde. This season, it is 0.18 lower, but the gap is traditionally wider early in the season and narrows as the season goes on. The Red Sox rotation has received plenty of attention, but the bullpen is not looking great either. Some of the blame should fall on the starters not pitching deep into games, leaving the Red Sox no choice but to use less desirable relievers. Here are the innings totals for teams so far this year.
A good way to kill your batting average, and steepen your aging curve, is to pull all your groundballs: The Pros And Cons Of Pulling The Baseball, by Tony Blengino, FanGraphs
It’s very difficult to hit for any sort of average overall if you’re batting .179 AVG-.179 SLG on the ground, like Ortiz did last season. We discussed Encarnacion as a successful selective fly ball puller previously. While his cohorts Cruz and Abreu were able to keep their ground ball pulling rates low enough to fend off overshifts, Encarnacion did not, posting a 7.19 grounder pull ratio. Despite hitting the ball harder in the air than just about anyone in the AL last season, while maintaining exceptional K and BB rates for a slugger, Encarnacion’s batting average was just .268 in 2014, because he’s basically an automatic out on the ground.
This highlights the slipperiness of the slope of his offensive game, and here we are, watching him struggle thus far in 2015. It’s very rare for a pull hitter to reverse course and use the opposite field to a significant extent; extreme pulling often represents the “harvesting” phase of a player’s career, the storm before the calm, the beginning of the end.
Want to analyze a player's skill based on batted-ball data? Well, first, get a real hobby, ya loser, and second, trust BIS over Inside Edge or Statcast: What can be learned from batted-ball data? by Henry Druschel, Beyond the Box Score
What can be taken from all this? The BIS data from 2014 conveyed useful information about BABIP and will probably do the same for 2015. Identifying players over- or underperforming their expected BABIP based on their speed and hard-hit rate seems like it wouldn't be statistically irresponsible, though there are obviously unanswered questions of sample size. The same cannot be said about the limited glimpses of Inside Edge data – without a comprehensive, league-wide dataset, the rates just don't convey enough information to have any analytical power. Finally, the Statcast data (unsurprisingly) does not appear comprehensive or meaningful enough to draw any conclusions.
It's possible that a different model might add even more explanatory power. Alan Nathan (@POBGuy) tweeted this fascinating plot of the linear weights of the different batted ball velocities, showing that medium-speed balls are actually worse than softly-hit balls, since a speedy batter can sometimes beat a squibber out for a hit, while the same isn't true of a firm grounder. While three pieces of contact at 80MPH have the same average as three at 50MPH, 80MPH, and 110MPH, the second group would probably generate more hits. For that level of analysis, though, the current Statcast data is woefully inadequate. That may change in the near future, but until then, I suspect simple models will have to suffice.
What we call "cutters" can be grouped more like two different pitches: One closer to a fastball and another closer to a slider: We've Been Getting Cutters All Wrong, by Dan Rozensen, Baseball Prospectus
Cutters tend to be more nebulously defined because they are picked up by pitchers usually after they already have the core elements of a repertoire: a primary fastball (two-seam or four-seam), a curveball or slider as their breaking ball, and a changeup. That’s all, at a minimum, you’re expected to have by the time you’re called up to the show.
By contrast, a cutter is usually a later-career addition. Pitchers often start throwing cutters because they think there’s a hole in their repertoire, or one of their current offerings—either their fastball or their slider—isn’t up to par. Dan Haren famously added the cutter because he was losing fastball velocity and wanted a new pitch to keep hitters off balance.Rick Porcello began messing around with a cutter in 2012 because his slider was awful. Brandon McCarthy used it to balance out the sinker he began throwing in 2011 as part of his transformation into a groundball pitcher. There’s also the group of pitchers—populated notably by Rivera, David Robertson, Jansen, and recently Michael Pineda—for whom cutters are just their default fastball. The quirk of the cutting action on their heater is part of their signature.
In other words, cutters play a more versatile role than most other pitch types. They are often pitches by design, individually tailored for a need and purpose.
There are quite a lot of MLB fans, compared to other sports, and how you feel about the designated hitter depends quite a lot on how old you are: Sports Poll, by Tom Jensen, Public Policy Polling (complete poll here)
We asked baseball fans whether they prefer pitchers hitting or the designated hitter, and pitchers hitting won out by a pretty substantial margin at 55/33. There's a fair amount of consensus about letting pitchers bat both across party and generational lines.
Even if you're doing well at keeping pitches inside the zone strikes, failing to steal a bunch of pitches outside the zone can tank your framing rating: A Theory on Russell Martin's Framing Numbers, by Miles Wray, FanGraphs
Does a difference of two percentage points really turn an elite framer into a framer that’s costing his team value? Well, yeah. Now, a difference of two hundredths of a point in batting average — like from .240 to .260 — hardly has any influence on a hitter’s overall value. That’s because a hitter is getting, at the very uppermost, 700 plate appearances a year, meaning that a change by those two hundredths is affecting, at the most, the outcome of 14 plate appearances on the whole season. Catchers, meanwhile, are receiving thousands of pitches a year, with the league’s most prolific one or two catchers getting around 10,000 pitches caught (that is: pitches that actually make it into the catcher’s glove). Martin is already at 2,264 pitches caught this season: by losing two points of his stolen strikes, that means he’s already lost 45 strikes that he usually secures for his pitchers. Hey, that’s a few good innings of nothing but balls that Martin has typically converted into nothing but strikes! If Martin gets around his full-season average of 8,000 pitches received, that’s a difference of 160 pitches — more than a whole game of could-be strikes that are going down as balls. Expressed in terms of total pitch count like this, it’s a lot clearer (at least for me) how framing really does add up to full wins and losses.
Nope, Dee Gordon isn't going to hit .420 this year. Batted ball velocity is giving us heretofore impossible insight into regression candidates: Chase Utley Is The Unluckiest Man In Baseball, by Robert Arthur, 538
Each additional mile per hour of batted ball velocity equates to an 18-point increase in OPS (on-base plus slugging). In other words, a hitter who smokes the ball tends to be better.
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