Stampfl: Barrels, normative analysis, and the beauties of Statcast

From Billy Stampfl at The Hardball Times on September 29, 2016:

Statcast—MLB’s player-tracking, ball-tracking, everything-tracking tool—has improved in accuracy and volume each year since its inception. The data it provides are uniquely valuable. Thus, we need to ask an important question: How can we put these data to good use?

My purpose in writing this article is to create a set of statistics that measures how well a player should have performed based on Statcast data. I accomplished this with the creation of three new measurements: eSLG, eISO and eHR/G. We’ll go into these terms in-depth later, but for now, it’s important to know what my original intent was.

Each year, it happens that players who performed brilliantly the season before underachieve the next year. Then there’s another set of players who post career-high numbers just a summer after struggling through statistically-depressing seasons. Regression, be it positive or negative, is a staple of major league baseball. So how can we predict which players are most likely to succumb to regression? The answer lies in Statcast. Using Statcast data, I developed expected results for 407 eligible batters from 2015 and 2016. This is where eSLG, eISO and eHR/G come from.

Read the full article here:

Originally published: September 29, 2016. Last Updated: September 29, 2016.