I divided up the launch angle into 50 buckets, then found the mean exit speed for each of those buckets, for both 2015 and 2016, pre ASG. And I have to admit that I am very puzzled by this plot. While there is a clear increase in mean exit speed for launch angles in the home run sweet spot, 200-350, the mean exit speeds are essentially identical for line-drive type angles, 00 to 100. This is not what I would expect to find if the ball were indeed juiced. If anything, I would expect the coefficient of restitution to have a greater effect on the line drives, which are generally more “squared up” than fly balls, as one can see from the higher average exit speed. This does not bode well for the juiced ball theory.
My conclusion that the higher exit speeds account for most of the increase in home runs still stands. However, as much as I hate to admit it, the exit speed versus launch angle plot really puzzles me. Sometimes it happens that the answers are not as clean and crisp as we would like; this seems to be one of those times. Looks like I’ll be spending more time on this one.
Not the delicious candy bar that sticks to your teeth; nor the planet that can grow Matt Damon’s potatoes. Rather, it is Multi-variate Adaptive Regression Splines, or MARS for short. MARS is a trademarked term, so the R or Python implementation is usually referred to as “earth.” Essentially, the MARS approach to regression improves upon basic multiple linear regression in three ways:
It breaks apart each regression line into multiple formulae (for example, incremental fastball velocity below 94 mph has a different value curve than velocity above 94 mph).
It prunes terms that aren’t beneficial to the model and pares it down to the important factors.
It can uncover relationships between variables (say, location and velocity).
I’ll step away here and encourage you to read the Wikipedia article linked above for a more thorough explanation.
If you are interested in defensive metrics, this is a must read. Great work!
I originally sought to better understand Inside Edge’s defensive data on my accord and articulate the context of the data here. But my exploration evolved when anecdotal evidence seemed to turn into something more. En route, I offered here limited but still quantitative evidence that WAR, as we now calculate it, fails to properly account for the difficulty of defensive seasons, at least in the tail ends of the distribution of difficulties. Further analysis may further illuminate my findings or invalidate them completely. Such is life. But I am curious to know how much farther we can take this research.
This means—and this is the really important part—that while a big-league hitter, on average, is just as likely to swing on a 3-1 count achieved after a 2-1 count as he is on the same count achieved from a 3-0 take, certain big-league hitters are far more likely to swing in the second count than the first. It’s those batters, I assume, that Zaidi and the Dodgers are targeting. Just something to watch out for, as you take in the game of baseball during this fine spring of 2016.
Let’s run through a few possible explanations for why the Pirates may have mandated this shift. The first explanation is the easiest, and it might be all we really need: maybe the Pirates just thought McCutchen and the rest of the outfield was playing way too deep, regardless of particular circumstance. McCutchen was one of the most extremely positioned outfielders in the league, and any time a player is doing something that could be perceived as an outlier, it’s worth considering the motive for that behavior. Hurdle did mention that McCutchen is more comfortable coming in than going back, but evidently not so much to prevent a change.
But the more convincing explanations are those which consider the individuals at play. Like, for example, McCutchen’s never had the reputation of a strong-armed outfielder. Quite the opposite, in fact. Could be that putting McCutchen closer to the infield is a way to help mask his deficiencies with the arm. In 2014, according to data provided by Baseball Info Solutions, McCutchen had one of the worst throwing years by a center fielder on record. In 97 instances where a base-runner was deemed to have an opportunity to take an extra base on a ball hit to McCutchen, the runner did so 70 times. That 72% advancement rate was the second-worst on record by a center fielder, dating back to 2006. Only Denard Span‘s 75% advancement rate in 2009 was worse.
Overall bunting is down throughout baseball from once every 109 plate appearances in 2004 to once every 179 this year. But that’s not the half of it. If you ignore pitchers altogether and look only at position players, batters are bunting once every 337 plate appearances this year versus once every 162.5 in 2004. That means that non-pitchers are bunting an average of about once every nine games, according to Stats, LLC.
It took baseball’s collective minds a long time to realize that men who are skilled at swinging bats should probably just swing bats. In 1927, Lou Gehrig was asked to bunt 21 times despite slugging 47 homers and knocking in 173 runs. To the delight of opposing pitchers, Babe Ruth sacrificed himself that many times in 1930 when he hammered 49 homers. The most bunts for a player with 20-plus homers this century is Derek Jeter’s 16 in 2004. But last year, Royals’ Mike Moustakas led baseball’s 20-plus-homer club with four sacrifices.
A big chunk of this is about catcher framing. But you also have run-of-the-mill luck, and plus there’s pitcher identity to take into account. Some pitchers just earn better zones than others, typically because they possess better command. Now, these numbers aren’t perfect, because the PITCHf/x system isn’t perfect. So this isn’t gospel, but below are my results. To this point, the Astros have pitched to the most generous zone. The White Sox have pitched to the least generous zone. That already tells you this isn’t the most important thing in the world, but it’s definitely a thing in the world.
Older article, going back to the John Henry announcement six weeks ago that the Red Sox would be placing less emphasis on analytics (which was discussed pretty thoroughly at the time). I think the biggest insight here is that organizations can get down in the weeds with the data and lose sight of the context to which the data applies.
But once organizations get zealous about data, such as the Red Sox, they can go too far in the other direction. Rudin calls these organizations “groundhogs” because they are too focused on the data to see the bigger story. In other words, they can’t see the forest through the trees. And a myopic emphasis on data to the exclusion of common sense leads to rather comical, if not tragic, outcomes.
For home run rate (HR/PA) to stabilize, you need 170 plate appearances. Through six games, Story has only 28. Plainly, Story’s 25 percent rate of home runs per plate appearance is not “stable.” If it were, we’d expect him to hit 150-plus home runs this year. Were Story to have 43 HR 170 plate appearances into the year, we’d have to take the possibility seriously. So Story’s seven-homer week is mostly luck and not remotely indicative he’s the greatest player of all time by a wide margin. But that information isn’t especially useful. No serious fantasy owner even in a 10-team mixed league would leave Story on waivers until the 170-plate-appearance threshold before picking him up. What we need more than the .70 correlation standard is a sliding scale where 25 plate appearances means it’s 10 percent likely to be skill/90 percent luck, 50 means 20/80 and so on until we get to 170 when it’s greater than 50/50. Because players on waivers (or those you draft in the middle and late rounds) are like stock options. You’re buying once the threshold hits five or 10 or 15 percent (ownership styles vary), hoping the player’s performance leaves you in the money.
So we need to have an idea of what a small performance sample says about the possibilities – not the likelihood – regarding a player’s skill set. The best fantasy owners live in that sweet spot – not picking up every random player who hits an odd home run or two, and also not waiting forever for proof.
It’s just — for one thing, you notice when a pitcher is down a few miles. It’s of particular significance when you’re talking about an ace, who also had an under-powered spring. Felix wasn’t very effective down the stretch a year ago, and the Mariners need for him to bounce back. It looks like Felix’s arm slot has dropped. And I might as well acknowledge what Andy Van Slyke said, when he claimed that Felix has been pitching through a partially torn UCL. Every pitcher has some amount of wear and tear, and Van Slyke has been largely discredited for other remarks, but there’s no ignoring that Felix’s long-term contract with the Mariners includes the John Lackey busted-elbow clause. That’s not normal, and given that actions speak louder than words, it’s evidence the Mariners think something could be up. I’m not trying to be an alarmist. I’m just trying to put the pieces together as best I can.
Posting this because of some of the factors related to his resignation are relevant to MLB front offices.
I haven’t had time to read the full thirteen pages of his long-winded resignation. With that in mind…as much as I buy into having an objective “process” in running a sports organization, you need people skills and ownership backing to get buy in or your franchise will be a muddled mess. Ultimately, though, you either find success or you end up unemployed.
I’ve never been one to accept the broad sabermetric pronouncements. In this case, I’ve always kept my eye on spring training and when I notice someone with a great stat line, I wonder, “Why?” Is there something different? Did the pitcher learn a new pitch? Has a batter made some kind of discernible adjustment? If I can find something I’ll take a flyer on the player. If I can’t, I ignore it. Not every guy I’ve picked up has panned out, but enough of them did to make the effort worthwhile.
Yet in spite of all these caveats, the claim that spring-training numbers are useless is wrong. Not a little bit wrong, not debatably wrong—demonstrably and conclusively wrong. To be sure, the figures are noisy. But they still contain a signal. At the MIT Sloan Sports Analytics Conference held in Boston on February 27th-28th, I presented a study (see slides) that explained how to extract the statistical golden nuggets buried in this troublesome dataset, and offered some lessons this example provides for the practice of quantitative sports research more broadly.
“I’ve got nothing against the bunt, in its place. But most of the time that place is at the bottom of a long forgotten closet.” ~ Earl Weaver
This essentially means a manager must pick and choose his sacrifice bunting opportunities very carefully. The Red Sox have done just that, executing only 20 sacrifice bunts in 2014 and 30 in 2015.
“I think there’s a place and a time for it,” said Red Sox bench coach Torey Lovullo. “Never would we ask Mookie Betts or Travis Shaw or Hanley Ramirez or David Ortiz to lay down a bunt. They’re run producers. And we never stop players from making a baseball play. I would say over half the bunt attempts we make are players doing it on their own.
There are still a lot of smart guys on the Internet.
And, for the younger generation, there is Brad Pitt.
—‰‘Moneyball’ was the moment,” said Sam Miller, the editor-in-chief of Baseball Prospectus. “Before that, there were smart guys who knew each other on the Internet. But that was about it. And they clearly felt like an outsider culture.”