I’m going to use all things @jeffwzimmerman for this post.
First is xBABIP. During this past offseason, Jeff found an xBABIP equation which correlated better than just BABIP year to year with the use of new Inside Edge data and player speed scores. I believe his last full updated list was posted on July 25th of this season, but he and the team provided me with an updated list this morning in order to use the data to interpret expected(x)FantasyValue vs. actual/descriptive(a)FantasyValue.
In addition to BABIP differential, I used Jeff’s (and his brother’s) Baseball Heat Maps‘ Actual vs. Expected Home Runs leaderboard for xHR. You can find this on their site by scrolling as follows: Applications > Batter > Batter Leaderboards’ tab.
xAVG and xOBP: I used the xBABIP-BABIP differential and multiplied the differential by AB-HRdifferential-K for an expected amount of +/-Hits. Naturally, adding to or subtracting from actual hit totals and dividing that by AB gets us to expected batting average, xAVG and a new xOBP. After the season, I will also incorporate Mike Podhorzer’s xK% formula for hitters to further get at xOBP.
xSLG and xOPS: xSLG was done with operational ease of use in mind: a) I subtracted/added to HR totals based on the HR differentials pulled from Baseball Heat Maps; b) I used the BABIP differential*AB for an expected amount +/-Hits and then divided that by 2: half of the additional/subtracted hits went into singles and the other half went into doubles in order to output our new xSLG. xOPS is obviously xOBP + xSLG.
For example: Anthony Rendon. Rendon’s BABIP as of 8/31 is .304, but his xBABIP is .342. Using the differential and multiplying it by AB-HRdifferential-K, we get 16.83 additional hits. I therefore upped his 1B total from 90 to 98.4 (+8.4) and his 2B total from 34 to 42.4 (+8.4). Rendon has 18 homers, but his xHR total is 16. Plugging all this in, we get the following:
Actual (a)Avg=.281; Expected (x)AVG=.313
Woop-dee-doo, What does it all mean, Basil?! It means Anthony Rendon is a stud and more of a stud from an expected BABIP perspective making him the #3 overall asset after Mike Trout and Michael Brantley this year in standard 5×5 formats. The HR and SB counting stats combo does the trick in conjunction with the xAVG of .320.
Sorting the file:
You will find the BABIP-related columns in orange, the HR-related columns in blue, and the 5×5 z-sums/fantasy values in green. You can also output your own format’s values by adding the z-sums farther to the right.
Sort away below. I’d recommend sorting by the 4th/5th column which is the BABIP/Hit differential and the 8th column (HR Differential) to understand the impact they will have on xAVG,xOBP,etc. Sort columns 9-12 depending on what stat you use in conjunction with HR,SB,R and RBI.
The main contingency is on the BABIP/hit differential column.
After the season, I may try to include shift effect and batted ball spray for a more comprehensive xFVAR (Expected Fantasy Value Above Replacement). This would give us a more realistic/less intense hit adjustment. For example, both Anthony Rendon and Brian Dozier have BABIP increase potential, but Rendon’s spray chart makes it a bit more realistic:
Dozier is very pull-heavy outside of popping the ball up. In the grid below, go ahead and sort (descending) the 5th column, Hdiff (hit differential). You will see some pull-heavy guys that get shifted on (Chris Davis, Mike Moustakas, Edwin Encarnacion, etc.). Even after EE’s success, he continues to fall well short of his expected BABIP. The shift effect/spray inclusion would have a good impact on the value of this approach.
Finally, the grid – again initially sorted by standard 5×5 value (column 9):
*Updated at 3:23pm CST on 9/23 – thanks to elkabong’s comment below.
Daniel Schwartz contributes for RotoGraphs when he's not selling industry leading thermal packaging. You can follow him on twitter @RotoBanter