I semi-recently had the honor of presenting at PitcherList’s PitchCon online conference to help raise money for Feeding America. My presentation, “ERA Estimators: Past, Present, and Future,” discussed, well, exactly what it sounds like it discussed. Over three posts, I will recap and elaborate upon points made in my presentation.

In the first two parts of this series (1) (2), I reviewed every manner of estimator, from the classics (FIP, xFIP, SIERA) to new-fangled doohickeys (Baseball Prospectus’ DRA, Statcast’s xERA, Connor Kurcon’s pCRA, Dan Richards‘ FRA). Today, we march forward, envisioning a future that may already be upon us.

ERA Estimators, Part III: Future

Read the rest of this entry »

I semi-recently had the honor of presenting at PitcherList’s PitchCon online conference to help raise money for Feeding America. My presentation, “ERA Estimators: Past, Present, and Future,” discussed, well, exactly what it sounds like. Over three posts, I will recap and elaborate upon various talking points from the presentation.

If the previous post was an elementary look at the “big three” estimators (FIP, xFIP, and SIERA), I hope this one is a little more illuminating.

ERA Estimators, Part II: Present

Read the rest of this entry »

I semi-recently had the honor of presenting at PitcherList’s PitchCon online conference, which raised a good chunk of money for Feeding America. My presentation, “ERA Estimators: Past, Present, and Future,” discussed, well, exactly what it sounds like. Over three posts, I will recap and elaborate upon various talking points from the presentation.

However, if this does happen to be your first exposure to ERA estimators or you are familiar with them but don’t necessarily understand their innards, then I hope you find this launching-off point beneficial.

ERA Estimators, Part I: Past

Read the rest of this entry »

I use xwOBA as a leading indicator of good or bad things to come mid-season, for better or for worse. It’d be good to know if such reliance is truly warranted. I further talked myself into the idea when I wrote about several underperforming hitters in early June. Many of the names therein went on some serious heaters afterward, too. It wasn’t as prescient as it was playing the odds: the hitters underperforming xwOBA most extremely through two months always, always (in the Statcast Era^TM) bounce back to some degree.

It’s “predictive,” but not universally so, and only by virtue of common sense, in the same way a pitcher who allows a sub-.200 batting average on balls in play (BABIP) through two months could not reasonably sustain this high level of contact management. (There’s a discussion to be had here about the gambler’s fallacy, but I don’t think it necessarily applies to baseball. For another day.)

In terms of prior work, it’s all Baseball Prospectus‘ Jonathan Judge (only a slight exaggeration): he compared xwOBA to BP’s DRA metric as well as FIP (fielding independent pitching), a much simpler ERA estimator, and showed xwOBA is hardly superior to the field, at least for pitching. However, the article only covered year-to-year, not in-season, correlations.

After our dear and departed (but not dead) Eno Sarris asked Judge if he had looked at in-season correlations specifically, and after our dear and departed (and also not dead) Mike Petriello reinforced the notion that xwOBA could serve as an in-season predictor of regression under certain circumstances, I figured it’s high time I just tackle the question.

So: How predictive is xwOBA of wOBA in-season? For hitters and for pitchers?

Read the rest of this entry »

Edit (3/29/17, 7:55 pm EDT): Brent Hershey of BaseballHQ and Ron Shandler’s Baseball Forecaster (very politely) brought to my attention that this has been done before! By Bill Macey back in 2012. Formerly behind a paywall, it has now been made public for your reading pleasure. I didn’t even know this research existed (so I’m really glad Murphy brought it to my attention); I am always reluctant to ever claim to break ground in this field that progresses so quickly but also has such a rich history of research. Please consider the following research a companion to and external validation of Macey’s work.

* * *

I welcome all constructive criticism. This research is not especially rigorous, but given the nature of the claim — a legitimately significant spring training statistic! — it merits the disclaimer.

I found a statistically significant spring training statistic.

I’d rather not rehash the history of research and speculation regarding The Spring Training Stat(s) That Matter. Just know that, outside the modest results from this Dan Rosenheck piece in The Economist, it’s generally accepted that Spring Training statistics mean virtually nothing, and you’ll read all manners of baseball writers bashing this notion.

The big caveat is most of this research concerns individual players. Mine: team-level statistics. Alas, it’s an inherently different beast with which I’m dealing. Despite small within-year populations (30 teams rather than hundreds of players), the observation-level sample sizes are much larger (hundreds of plate appearances rather than dozens), making the odds of finding meaningful correlations much better despite fewer data points.

Per usual, I buried the lede: a team’s rate of stolen base attempts (calculated from stolen bases [SB] plus caught stealing [CS]) during spring training is actually meaningful. I’ll get to the implications of this later because there are many. First, let’s dig into the guts of the research. I gathered team-level spring training statistics from 2006 through 2016 and paired it with regular season statistics from the same span plus 2005.

Read the rest of this entry »