About six weeks ago, we introduced you to Deserved Run Average (DRA),1 our new metric for evaluating past pitcher performance at Baseball Prospectus. We gave you both the overview of why a new pitcher performance metric was needed and explained in detail how the metric worked and the equations we were using to get there. We even subjected one of the authors to intense questioning.
After considering the comments we received and a few additional thoughts of our own, we've made some minor revisions. Many readers also asked us for a "DRA minus" statistic that would allow them compare different pitcher seasons across different years and eras. We've done that too.
Finally, other readers asked that we break down some examples of DRA value calculations so that even if you can't (or don't want to) do the modeling yourself, you at least understand why DRA acts in the way it does, and why it does a better job than ERA and FIP in evaluating pitcher quality. We'll take these topics in order.
Before we begin, let's provide a brief reminder of what DRA is and how it works.
DRA is premised on the notion that while a pitcher is probably the player most responsible, on average, for what happens while he is on the mound, he is not responsible for everything. DRA therefore only assigns the runs a pitcher most likely deserved to be charged with.
Baseball Prospectus is excited to announce what we believe to be the best measure of pitching ever, as well as advances in measuring pitchers' effectiveness stifling basestealers and avoiding errant pitches.
Baseball Prospectus' Director of Technology Harry Pavlidis will be chatting with readers Thursday at 1 p.m. ET. If you have any questions after reading this overview of Deserved Run Average, ask them here.
Earned Run Average. Commonly abbreviated as ERA, it is the benchmark by which pitchers have been judged for a century. How many runs did the pitcher give up, on average, every nine innings that he pitched? If he gave up a bunch of runs, he was probably terrible; if he gave up very few runs, we assume he’s pretty good.
But ERA has a problem: it essentially blames (or credits) the pitcher for everything, simply because he threw the pitch that started the play. Sometimes, that is fair. If a pitcher throws a wild pitch, he can’t blame the right fielder for that. And if a pitcher grooves one down the middle of the plate, chances are that’s on him too. Not too many catchers request those.
However, most plays in baseball don’t involve wild pitches or gopher balls. Moreover, things often happen that are not the pitcher’s fault at all. Sometimes the pitcher throws strikes the umpire incorrectly calls balls. Other times they induce grounders their infielders aren’t adept enough to grab. And still other times, a routine fly ball leaves the park on a hot night at a batter-friendly stadium.
ERA doesn’t account for any of that. It just tells us, in summary fashion, how many runs were “charged” to the pitcher “of record.” And so, a starting pitcher who departs with a runner on first gets charged with that run even if the reliever walks the next three batters. The same starter would get charged if the reliever makes a good pitch, but the shortstop can’t turn a double play. And none of these runs count at all if they are “unearned”— an exclusion by which the home team’s scorer decides whether a fielder demonstrated “ordinary effort.”
The list of problems goes on. Pitchers who load the bases but escape are treated the same as pitchers who strike out the side. Pitchers with great catchers get borderline calls. Guys who can’t catch a break for months show immense “improvement.” Guys who are average one year wash out the next. ERA, in short, can be a bit of a mess, particularly when we have only a few months of data to consider.
We move ever closer to a catcher-framing metric that captures a player's true value.
Last year, Baseball Prospectus introduced our Regressed Probabilistic Model (or “RPM”) for catcher pitch-framing. RPM uses PITCHf/x data to increase the measured accuracy of the actual contributions made by catchers. But RPM also suffered from two limitations. First, because PITCHf/x data was not publicly available before 2008, RPM could only measure catcher framing from recent seasons. Second, it relied primarily on a piecemeal approach to identifying the individual contributions of pitchers, umpires and catchers.
This year, we are pleased to announce an improvement that will address both limitations. We propose to move RPM from a “With or Without You” (WOWY) comparison method to a mixed model we call “CSAA” —”Called Strikes Above Average.” This new model allows simultaneous consideration of pitcher, catcher, batter, umpire, PITCHf/x, and other data for each taken pitch over the course of a season, and by controlling for each of their respective contributions will predict how many called strikes above (or below) average each such participant was worth during a particular season. Although PITCHf/x data is preferable when available, the mixed model (in a revised, “Retro” form) will allow us to live without it when need be, permitting us to project regressed framing of catchers all the way back to 1988, when pitch counts were first officially tracked. This same technique developed for Retrosheet can also be applied to recent minor-league data to provide an even deeper view into the progression and value of this skill.
An experimental broadcast with a sabermetric slant got off to a slow start, but some 'in-game' adjustments gives us hope.
The news of a saber-oriented broadcast option for Game One of the NLCS gave me some mixed feelings. While it is always promising when a major broadcaster embraces "advanced" metrics, it's a little disheartening for it to be a separate offering, rather than something integrated with the primary broadcast.
Host Kevin Burkhardt was joined by a solid panel, including some of our friends. Padres manager Bud Black had the least broadcast experience of the group but offered the perspective of how advanced metrics are actually applied or understood by the men in the uniforms. Well known saber-scribe Rob Neyer was there, a man well-versed in communicating the subject matter at hand, along with two former big leaguers with a strong curiosity and appreciation of sabermetrics, Gabe Kapler and C.J. Nitkowski. Kapler, the former position player, has managing experience in pro ball. Nitkowski was a well traveled pitcher whose career included time in Japan.
Another BP meet-up in Chicago is fast approaching.
Join us in Chicago on Saturday, May 24 at Pizzeria Serio on the North Side of Chicago for three hours of pizza and baseball talk. Our focus will be on the upcoming MLB First-Year Player (Rule 4) Draft.
After two years on the shelf with a shoulder injury, Michael Pineda appears to have recovered his old stuff.
The Yankees got a major boost during the opening week when Michael Pineda took the mound for his first MLB game since 2011. When we last saw Pineda, he was wearing a Mariners uniform and facing a sudden dropoff in velocity, the first sign of the shoulder woes that have kept him out of the big leagues since his days in Seattle.
New pitches and pitchers we've gotten glimpses of already this season.
Spring: a season of renewal and rebirth. Also a time of new pitches and pitchers. A lack of bona fide new arms in the early going has slowed the usual flurry of new PITCHf/x data to ogle, but some established pitchers have made some notable changes.
The best receiving catchers (and the best receiving teams) of the upcoming season.
One of the benefits of our recently released catching defense metrics is they’re essentially ready-to-project, thanks to the regression feature of the model (the "R" in RPM). RPM also gives us two ways to assign value to framing, one using context (the ball-strike count) and one using a flat value (recently adjusted* to ~.155 runs).
[T]he expected runs produced from each plate appearance starting with a strike decreases by .029 runs and increases by .040 for every ball thrown on a first pitch. In other words, having as many of those 0-0 'striballs' called strikes can greatly impact the outcome of the game.