Our own director of data analysis discusses the past and present of the PITCHf/x, HITf/x and FIELDf/x technology and how it can lead to future breakthroughs.
Our own Harry Pavlidis, Baseball Prospectus’ director of data analysis, has been among the most groundbreaking voices when it comes to using the PITCHf/x, HITf/x and FIELDf/x data that Sportvision tracks in major-league and minor-league parks.
A look at the ten most likely places for a new MLB club
It seems that nearly every week, articles surrounding the potential relocation of the A’s and Rays surface. A panel looking into a potential San Jose relocation for the A’s has been gridlocked since 2009 (and remember, the A’s have been looking to move to San Jose for a heck of a lot longer than that). The Rays haven’t been far behind in their efforts to get out of Tropicana Field. Whether it’s the commute for fans to get to the domed stadium, the aesthetics, or the need to be closer to an urban core, it seems that Tampa Bay has been seeking a new ballpark for just as long. Relocation for these two clubs is crucial.
Another thing that comes up less frequently but has extra meaning going into 2013 is expansion. With the Astros moving into the AL West, the American League and National League will now be balanced at 15 clubs a piece. The problem is that 15 is an odd number, and as a result, interleague will become a daily affair. It’s unlikely that’s something that the league wanted, so getting to 32 clubs would take care of that matter. That would mean revenues spread thinner with two extra mouths to feed. Additionally, it’s no given that one or both wouldn’t be revenue-sharing takers, and trying to get ballparks built is no easy feat in this economy. So, 30 is a number that seems to suit the “Big Four” sports leagues in North America. The NBA has it. Ditto for the NHL. Currently, only the NFL—which has the advantage of being highly centralized (revenues are shared more evenly across the franchises) and exceptionally popular—is the exception at 32 clubs.
Finding out whether knuckleballs actually flutter, with the help of our friendly neighborhood physicist.
Believe it or not, most of our writers didn't enter the world sporting an @baseballprospectus.com address; with a few exceptions, they started out somewhere else. In an effort to up your reading pleasure while tipping our caps to some of the most illuminating work being done elsewhere on the internet, we'll be yielding the stage once a week to the best and brightest baseball writers, researchers and thinkers from outside of the BP umbrella. If you'd like to nominate a guest contributor (including yourself), please drop us a line.
Alan Nathan is Professor Emeritus of Physics at the University of Illinois at Urbana-Champaign. After a long career doing experimental nuclear/particle physics, he now spends his time doing research in the physics of baseball. He maintains a web site devoted to this topic athttp://webusers.npl.illinois.edu/~a-nathan/pob/. His younger colleagues at Complete Game Consulting have bestowed upon him the exalted title of Chief Scientist.
What information can we glean about a player based on "soft" and "hard" contact classifications?
In the fantasy and analytics community, we often tend to talk about players in terms of components of their production. We don’t talk about ERA; we talk about strikeouts and walks and ground balls. We don’t talk about batting average; we talk about strikeout rate and BABIP. Those who have read my work or talked with me know that I also like to blend stats and scouting. I tend to say that stats tell us the “what” and scouting tells us the “why.” Stats tell us that a player hits for power, while scouting tells us that he does so because he has strong wrists, good bat speed, loft in his swing, etc. In other words, the component stats can get broken down into component abilities via scouting.
In recent years, some of these scouting components have been quantified, much to the delight of analysts and fans of the game. PITCHf/x has been particularly influential in the analysis of pitchers, capturing things like pitch type, velocity, movement, spin, location, and release point. Unfortunately, its hitting counterpart, HITf/x, is not publicly available. Still, there is some publicly available data that can be useful for batters, such as HitTracker. MLBAM—one of the creators of PITCHf/x—also provides something interesting that will serve as today’s topic: quality of contact data.
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Mike continues his investigation of HITf/x data to glean more insights into whether pitchers can prevent hits on balls in play.
In the first part of this study, I used detailed batted ball speed information from HITf/x to examine the degree of skill that batters and pitchers had in quality of contact made or allowed. Here, I will look deeper into the question of why some batted balls fall for hits and others do not.
A few weeks ago, during the division series, Brandon McCarthyremarked on Twitter that it would be more interesting for TBS to show a diagram of the batter hot and cold zones for every batter than to show the PitchTrax strike zone and pitch location graphic. He argued that knowledge of the hot and cold zones would give viewers additional insight into the battle between the pitcher and the batter.
Recently, there has been a lot of digital ink spilled about ERA estimators—statistics that take a variety of inputs and come up with a pitcher’s expected ERA given those inputs. Swing a cat around a room, and you’ll find yourself with a dozen of the things, as well as a very agitated cat. Among those is SIERA, which has lately migrated from here to Fangraphs.com in a new form, one more complex but not necessarily more accurate. We have offered SIERA for roughly 18 months, but have had a difficult time convincing anyone, be they our readers, other practitioners of sabermetrics, or our own authors, that SIERA was a significant improvement on other ERA estimators.
The logical question was whether or not we were failing to do the job of explaining why SIERA was more useful than other stats, or if we were simply being stubborn in continuing to offer it instead of simpler, more widely adopted stats. The answer depends on knowing what the purpose of an ERA estimator is. When evaluating a pitcher’s performance, there are three questions we can ask that can be addressed by statistics: How well he has pitched, how he accomplished what he’s done, and how he will do in the future. The first can be answered by Fair RA (FRA), the third by rest-of-season PECOTA. The second can be addressed by an ERA estimator likeSIERA, but not necessarily SIERA itself, which boasts greater complexity than more established ERA estimators such as FIP but can only claim incremental gains in accuracy.
As Jose Bautista can attest, the percentage of pitches a batter sees in the strike zone tells us a good deal about his capabilities.
The pitcher begins each confrontation with a batter with the initiative. He alone controls when the baseball is thrown, how it moves, and where it is located. Thus, the batter is by nature placed in a reactive position. However, the batter, too, has a measure of control over how the plate appearance proceeds. He stands at the plate with a club, and it is within his discretion to swing his weapon or not.
What does it take for assorted pitching stats to stabilize?
Last week in this space, I updated a study originally run four years ago by former BPer Russell Carleton (then monikered “Pizza Cutter”) at the now-defunct MVN’s StatSpeak blog. The study examined how long it takes for different stats to “stabilize.” Since I looked at hitting last week, I’ll be looking at pitching this week.
Before I get started, I wanted to make one quick announcement. After my first chat was such a success, I’ll be holding my second-ever live BP chat tomorrow afternoon at 1 p.m. EST to answer all of your fantasy questions.