Welcome to the first edition of a new feature at Baseball Prospectus: Everything You Could Have Learned This Week. So much interesting baseball analysis and research comes out each week that it can be difficult to keep track of everything and pick out what's really cool, so who better than a college student working two jobs and taking 15 credit hours to walk you all through it? I've picked out a selection of pieces, ranging from complicated mathematical analyses to broad-based examinations of trends in the game, to get you caught up on the week in the sabermetric community.
Monday
Can we measure baserunning smarts? We can measure baserunning smarts! And it turns out Joe Mauer is great at still another thing: Attempting to Measure Smart Baserunning, by Ben Suissa, Banished to the Pen
In short, I operated under the assumption that if we take out the speed element of a good baserunner, what remains are the decisions he makes. Subsequently, what I tried to do was strip out the speed element of UBR. Because speed is so closely related to stolen base attempts, the best runners also have high UBR ratings. I started with UBR and subtracted a factor of SB attempts. However, since SB, CS and UBR are cumulative statistics I also scaled them to Plate Appearances. We will call the resulting value Smart UBR.
Japanese pitchers might benefit from an extra day of rest…because the baseball is different here?: The Yankees' Six-Day Solution to Keep Tanaka Healthy, by Kevin Kernan, New York Post
The MLB baseball is bigger and more slippery than that used in Japan, so Japanese pitchers tend to squeeze the ball tighter, and that can be a problem. Giving more rest between starts might help alleviate the situation. It just gives the arm another day to recover and, in theory, better protects the pitcher from injury.
Tuesday
Hitters have never wanted to fall behind in the count, but now it’s more dangerous than ever!: Counts Are Crushing Offense, by Matthew Trueblood, Baseball Prospectus
There’s no question that being ahead in the count is more important to today’s batter than it has ever been before. (In another way of viewing it, it takes a worse pitcher than ever to fall behind on a batter, so those situations simply have higher expected production.) Batters are doing more with less, because they have to do so. They’re facing two-strike counts more often than ever, and they’re more vulnerable to striking out once they get there than ever. If they don’t make hay on the rare occasions when they get the jump on an opponent, they’re lost.
Defense-independent hitting stats are coming, and we might someday be free of the shackles of outcomes altogether: On DIBS and the Future of Batting Statistics, by Rob Neyer, JABO
Ben did offer one great example, by the way. For four or five years straight, David Ortiz'€™s OPS lined up almost perfectly with his DIBS OPS. And of course he was an outstanding hitter from 2011 through '€™13. In 2014, he was somewhat less outstanding. However, according to DIBS he was almost exactly the same hitter last season — yes, despite being 38 years old — but just hit in pretty tough luck. Which obviously bodes well for his 2015. Even at 39. So we'€™ll see about that, although of course one player in one season can'€™t tell us much of anything at all.”
Wednesday
Prospects will still break your heart, but their trajectories might give you a warning sign: Prospect Trajectory as Data, by Rob Arthur, Baseball Prospectus
There’s a surprisingly smooth and continuous curve which links prospect trajectory to future performance. Guys who ascend the ladder of prospectdom, who crawl all the way from the bottom to the top, have an expected lifetime WARP of more than 10. Meanwhile, those who manage to chart the reverse course, from the top to the bottom, are doomed to lifetime production around or below replacement level.
When pitchers and hitters with extreme batted-ball profiles faces off, the hurlers tend to come out on top: Batted-Ball Trajectory: Splitting the Difference, by Shane Tourtellotte, The Hardball Times
With two flyballers facing each other, the high angles they’re both seeking could often add up to pop-ups, easy plays for fielders. With two groundballers, added downward trajectories could turn a sharp two-hopper through the infield into a little dubber the pitcher easily scoops up. And when one type faces another, they may strike a balance.
Thursday
The world finally has "the defense-adjusted wOBA statistic that we reach through a Bayesian hierarchical model" that it's been waiting for! Not entirely different, but also not the same, as the DIBS idea mentioned above: A Bayesian Approach to Expected wOBA, by Stephen Loftus, Beyond the Box Score
However, how are we going to adjust for defenses? There are a few possible ways to go about that. The first involves seeing how a player does against the average defensive starting position league wide. However, that data does not exist in the public forum, so this is not a viable option. The second option is to just regress BXwOBA to the league average. However, I don't believe this to be a viable option because it unfairly punishes the players who cannot be so easily defended. So we're going to go with a third, more directed option.
What we'll do is augment the dataset with comparable players. We'll look through the dataset and find player with a comparable hit distribution, draw individual plays from this subset, and add them to the dataset. The implicit assumption is that players with similar batted ball profiles will be defended similarly. If this is true, then a players BXwOBA will not change too much from their true wOBA. However, if a player is being defended differently from his comparable players, then his BXwOBA will change from his observed wOBA.
Does it feel like March is filled with Tommy John injuries? Well guess what you’re right: March Sadness: Understanding the True Cost of the Tommy John Surge, by Ben Lindbergh, Grantland
Without putting pitchers on Sodium Pentothal, there’s no way to know what percentage of the March spike is attributable to new injuries and what percentage is explained by an exacerbation of old injuries that we (and in some cases, the player’s team) never knew about. From a fan’s perspective, or a fantasy owner’s, though, it doesn’t much matter whether the injury is entirely new or a more aggressive form of an existing flaw. Either way, March, more than any other, is the month pitchers fall apart.
Friday
The correlation of some statistics to a team's overall offensive output has changed since baseball's offensive heyday: Further Insight About Strikeouts Learned At SABR Analytics, by Matt Eddy, Baseball America
Walks and (particularly) home runs still are valuable, but they aren’t quite as vital to a high-scoring offense as they used to be. Meanwhile, strikeouts, in the aggregate, aren’t anymore harmful to run production than they were in the high-octane era. Getting on base (OBP) and hitting for extra bases (ISO) are still the most vital characteristics of a productive offense.
The (good) long reliever is dead, long live the (good) long reliever: Death of the Long Man, by Craig Edwards, Fangraphs
Teams regularly used at least one reliever who would appear consistently and go at least an inning. The numbers dropped to roughly half the teams during the 90s, and dropped even lower over the past decade. Last season, no reliever averaged more than 1 1/3 innings and pitched at least 80 innings. The swingman role, where a pitcher might move back and forth between the bullpen and the rotation is another effective strategy to get better pitchers more innings, but it is not common either. Over the last five years, just 22 players have made at least 40 appearances,more than five starts, and totaled more than 80 innings pitched. The swingman is barely alive, and the effective long reliever appears to be dead.
Thank you for reading
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