A few weeks ago, I debuted the xWHIP calculator on the internet. Since, I have constructed an Excel sheet, with the invaluable help of Dave Studeman and Brian Cartwright, of the major leagues’ xWHIP leaders and losers (minimum nine games started) using the xWHIP formula. This spreadsheet omits defensive adjustments; with 156 players in the sample, inserting them would be too labor intensive. To manually make the defensive adjustment from my xWHIP calculator formula, use the following formula: ((team UZR/team total IP)*the individual pitcher’s IP)/0.49. This defensive adjustment assumes that all hits saved or allowed by a team’s defense would be singles.
The xWHIP tool is explained in detail in the link provided (see previous paragraph), but here’s a quick recap.
The xWHIP calculator is based on the notion that different batted ball in play (BIP) types each have a unique expected BABIPs (xBABIP). The four BIP types are ground balls (GB), line drives (LD), outfield fly balls (OFFB) and infield fly balls (IFFB). Per Gameday data, circa 2005-2010, xBABIP for each BIP-type is as follows:
- Popups: .008
- Ground balls: 0.237
- Outfield fly balls: 0.269
- Line drives: 0.733
This data includes home run rates, which is why the OFFB xBABIP is so high. If you take home runs out of the equation, the xBABIP for OFFBs and LDs fall to .174 and .727, respectively.
Given that line drive rates for pitchers tend to normalize around 19 percent over large enough samples, my xWHIP formula is based on a normalized BIP format. xWHIP essentially keeps GB/FB and IFFB/OFFB ratios intact and applies them to an expected BIP distribution with a normalized line drive rate. xWHIP is very similar in theory to Derek Carty’s DIPS WHIP, only the calculator itself also accords for defensive metrics.
Any questions? No? Good. Now let’s move on to the purpose of this post.
Plugging all the relevant information into an Excel spreadsheet of starting pitchers with at least nine starts in 2010, I calculated the defensive independent xWHIPs of 156 major league players. I compared these player’s xWHIPs to their actual 2010 WHIPs to determine which pitchers have the highest prospects for luck-based improvement/regression down the stretch. This data should aid all fantasy owners in need of WHIP-solidification or WHIP help down the stretch.
It should be noted that the walk rate used for my xWHIP calculator is not merely BB or BB/9. The xWHIP formula in my spreadsheet uses a modified walk rate (mBB/9), which essentially subtracts intentional walks (IBB) and adds hit batsmen (HBP) to the cumulative walks total (BB). I have long advocated that WHIP be tabulated using a pitcher’s modified walks total (BB-IBB+HBP); traditional walk rates are misleading in looking at a pitcher’s actual control and WHIPiness (a new word I invented/copyrighted).
Here, then, I present to you the (defense independent) xWHIP leaders (top 30):
The elite xWHIP guys largely consist of the major leagues’ top aces. Stephen Strasburg, Cliff Lee and Roy Halladay lead the way as the only three major league pitchers with an xWHIP below 1.10. You also see such expected names as Roy Halladay, Josh Johnson, Adam Wainwright and my boy Kris Medlen. However, there are plenty of surprising names in the xWHIP leaderboard: Vicente Padilla (1.21), Dallas Braden (1.26), and (most surprising to me) Jeff Francis (1.29 xWHIP).
And of course, there are the xWHIP losers (bottom 30):
At the bottom of this list, we find plenty of the major leagues’ struggling pitchers: Rich Harden, Scott Kazmir, Ryan Rowland-Smith, Charlie Morton, Carlos Zambrano, etc. However, we also find a few semi-surprising names (given their 2010 successes), like Fausto Carmona (1.50 xWHIP) and C.J. Wilson (1.48 xWHIP) just outside the bottom 30). More or less, however, guys at the bottom of the xWHIP list also have terrible actual WHIPs.
If you would like to see the full Excel spreadsheet of the xWHIP leaders and losers, click here. Otherwise, as always, post your love/hate in the comments.