tDIBS: A look at Defense-Independent Batting Statistics

From Ethan Bein, Max Goder-Reiser and Eric Smiley at The Hardball Times on September 30, 2015:

The Error. The Unearned Run. Fielding Independent Pitching. xFIP. These increasingly complex concepts are fundamentally about sifting through all of the noise that is present in pitching statistics and determining how a pitcher should have done by acknowledging the factors outside of his control. The notion of ascribing value to players who perform well independent of their teammates and surroundings is what sabermetrics is all about; there is a great deal of value in finding out whether good or bad luck played a role in a player or team’s season. Fortunately for baseball analysts, the sport has an incredibly large sample size to work with; in no other sport is the team with the best record more likely to have earned it rather than lucked into it.

However, sometimes the sample size of 162 games, 600 plate appearances, or 200 innings pitched is not enough for our most commonly used metrics to give an entirely accurate representation of player value. The old adage – that for every bloop that manages to fall in for a hit there is also a line drive hit right at somebody – is not always true for every batter in every season. When the batting average on balls in play against Pedro Martinez rose dramatically for a single year in 2000, it is commonly accepted that he was simply unlucky. By using FIP, it can be seen that nothing significant or predictive about him as a pitcher changed or deteriorated in that one year.

To this end, we see DIBS (Defense Independent Batting Statistics) as an equivalent metric for batters. Using batted-ball profiles in conjunction with results-based statistics may make it possible to detect when a player may have been unlucky, even when their results-based statistics may paint a bleaker picture.

DIBS is a concept we were introduced to by Ben Jedlovec’s presentation on behalf of Baseball Information Solutions in Phoenix this March at the SABR Analytics Conference. While BIS has access to proprietary data that helps fuel its calculations for its DIBS, our goal matches BIS’ – create a measurement of batting ability and offensive value separate from outcomes and past results. Ideally, this statistic will hold predictive value; much like FIP can tell us which players may have gotten lucky on batted balls in a certain year, DIBS uses batted-ball profile to predict outcomes independent of defense, park and result. tDIBS (the “t” standing for Tufts University, where we all are currently students) is an attempt to satisfy this goal using only publicly available data.

Read the full article here:

Originally published: October 1, 2015. Last Updated: October 1, 2015.