For those who have been hanging around these parts since this past off-season, you’ll surely be familiar with Chris Dutton and Peter Bendix’s work on creating an expected Batting Average on Balls in Play metric (xBABIP). This was terrific work, which I later examined a little closer to find that xBABIP was indeed a very strong predictor of future performance.
Today, I’d like to announce that I’ll be working with Chris Dutton to develop an even more advanced version of xBABIP. This is something that I’ve been thinking about for quite some time, and when I heard that Peter Bendix had taken a job with the Rays, I thought it made perfect sense to team up with Chris myself. We don’t currently have an estimate for when the new xBABIP will be ready, but hopefully the payoff will be a good one.
To wet your whistles while you wait, Chris has put together a very nice Excel tool for calculating a simplified version of xBABIP. This is almost identical to the version that I tested in my article that I linked to above, which turned out to be quite predictive itself. The tool also does a number of other cool things, so Chris took the liberty of putting together a quick explanation/tutorial for everyone.
Simple xBABIP tool download
Here is the link to download Chris’s simple xBABIP tool (the password to use it is “tuftsbat”), and here is a screenshot of what you’ll see (click for a larger version):
Now for the explanation provided by Chris.
Simple xBABIP tool explanation by Chris Dutton
Begin by choosing any player/year combination from the database (note: cut-off is 300 PA in any given season)
For each player, key performance stats are displayed for the given year, as well as the MLB average, the percent above/below average for that particular player (green = significantly better, yellow = comparable, red = significantly worse), and the maximum/minimum values for that particular year. The key stats shown here are the “Luck Factor”, which is the difference between a player’s BABIP and xBABIP, and the predicted batting line, which is an estimation of AVG, OBP, and SLG based on the predicted (rather than true) batting average on balls in play. In other words, this is the performance that we might expect to see in a luck-neutral environment.
Trended Performance Graph
This graph allows you to select one or two metrics and trend them either alone or against each other over time. The list includes 28 different metrics, ranging from runs and stolen bases to xBABIP, line drive percentage, and pitches per plate appearance, to name a few. This can be especially useful as a forecasting tool, as it allows you to clearly observe trends across a variety of core statistics.
Player Comparison Graph
This graph provides the same selection of metrics, and allows you to compare the performance of one player against another. In the screenshot above Manny Ramirez and Aramis Ramirez are trending against one another on the basis of RBI.
xBABIP Quick Calculator
Perhaps the most useful section of the dashboard, the xBABIP quick calculator uses a slightly simplified predictive model using more readily available statistics. By simply plugging in values for each variable, you can calculate the expected BABIP on the spot and see who is out-performing or under-performing to this point in the season. If you’re wondering whether to sell high on Jermaine Dye or buy low on Magglio Ordonez, this tool can certainly help to guide your fantasy decisions.
2009 data hasn’t been incorporated into the tool since it is constantly changing, but you should still be able to input the simple xBABIP variables and compare to the BABIP listed on our player pages. Hopefully this ends up being a useful tool for everyone as we enter the second half of the season, and hopefully we’ll have the brand new xBABIP ready to debut shortly.
If you guys have any questions for me or Chris, feel free to send either of us an e-mail or comment below.