*Before reading this article, I encourage you to read my earlier xWHIP and eFIP; this is an extension of the data presented from it. All statistics are current through May 27.*

Earlier this week, I presented an updated form of my xWHIP Calculator, which, with the power of normalization, calculates a pitcher’s expected hits and expected innings based on his batted ball profile. In raw form, I presented various data points for both xWHIP and eFIP. While useful, such absolutes can be hard to interpret. What does a 1.16 xWHIP mean in isolation? Particularly with the low variance in WHIP in baseball (the range tends to be largely between 1.10 and 1.50), small differences in WHIP can make a larger difference than you might think.

To address these problems, I have calculated each player’s xWHIP Z-Score to give some sense of relativity. Because we are dealing with pitchers, where lower is better, I calculated the data so that the lowest Z-Scores equate the best impact players, while higher Z-Scores indicate the worst players with the biggest impact. I also tabulated a column of weighted Z-Scores scaled to expected innings pitched through May 27. This will give you some sense of which players *should*, in theory, have had the biggest impact on WHIP through the first two months of the season.

Because this column is weighted based on expected innings to date, which may vary in the future based on past playing time, it should not necessarily be consulted in evaluating a player’s prospects (e.g., Zack Greinke has the highest Z-Score at -1.98, but only a -1.22 weighted score due to his limited number of relative starts to begin the season). For future value, you should consult the player’s unweighted Z-Score, which should be scaled based on relative expected future innings. For example, if pitcher A is expected to pitch 20 percent more innings than the average full-time starter for the rest of the season, his Z-Score should be adjusted accordingly.

To make the data easier to interpret, particularly because I am using Z-Scores in lieu of an index (this was done because of pitcher clustering; the Z-Score calculations give a better sense of impact and relativity for xWHIP), I color-coordinated the data below.

Orange cells mean that the pitcher is in the upper echelon of the relevant column. For xWHIP, this means the pitcher has an xWHIP below 1.27. For dWHIP (the difference between actual WHIP and xWHIP), this means that the pitcher’s xWHIP is at least 0.05 points lower than his actual WHIP to date. For Z-Scores, it means the pitcher has an xWHIP Z-Score of -0.35 or lower. These are likely pitchers to target for acquisition, particularly if one or more of his xWHIP, dWHIP, or Z-Score is colored orange.

Blue cells mean that the pitcher is in the lower tier of the relevant column. For xWHIP, this means the pitcher has an xWHIP of or above 1.34. For dWHIP, this means that the pitcher’s xWHIP is at least 0.05 points higher than his actual WHIP to date. For Z-Scores, it means the pitcher has a Z-Score of +0.35 or above. These are likely pitchers to avoid or trade, particularly if one or more of his xWHIP, dWHIP, or Z-Score is colored blue.

Yellow cells are “neutral.” These are players who are unlikely to have any significant impact on your team’s future WHIP, for better or worse. The xWHIP threshold for neutrality is 1.27 to 1.33. I chose 1.27 as the lower end of the xWHIP threshold, despite the fact that the league average xWHIP is 1.33, because the sample of fantasy players in use is a subset of the starting pitching population. The worst pitchers in the league are unlikely to be on a fantasy roster, and at the same time are likely to post the highest WHIPs. In my preseason E.Y.E.S. post about how to calculate auction values, I tabulated the league average fantasy player’s WHIP at 1.265. Because starters tend to have a higher WHIPs than relievers on average (expected mean starter WHIP was 1.30), I am using 1.27 as the lower bound of neutrality.

That all noted, here is a visually organized presentation of the data. The left set of data is organized by xWHIP, while the right set of data is organized by dWHIP.

As always, leave the love/hate in the comments below.

Jeffrey Gross said...

@Tom,

Groundball pitchers tend to have lower expected ERAs, but higher expected WHIPs. This is because GBs tend to produce more hits, but less runs/hit.

As for Simple xWHIP, you’ll have to read Alex Hambrick’s article (http://www.hardballtimes.com/main/fantasy/article/evaluating-xwhip/) for more on Simple xWHIP, as I did not design that metric.

The three WHIP formulas, however, on average, tend to be relatively close to each other, and I like to use them to create an expected range of xWHIP values. If all three formulas are close, then I can say with greater confidence pitcher A’s WHIP should have been this based on what he did. Likewise, if the 3 are very divergent, more analysis must be done.

The above charts are (Expected Hits+BB-IBB+HBP)/xIP.

Keep in mind, as a predictive tool, xWHIP is best suited to multi-year data, as it assumes stability. xWHIP tells you what a pitcher HAS done and what should have happened based on that. It says nothing about what he will do if he has had anomalous numbers in small samples, such as an unsustainable K or BB rate or GB/FB rate (assuming normal LD%)

Jeffrey Gross said...

By the way, remove Dempster’s 0.1 IP disaster at AZ & his numbers look even better:

http://twitpic.com/55w46m

Will Hatheway said...

I’ll be your test case: I have D. Hudson, Norris, Stauffer, and Lilly… so if my pitching ceases to be mediocre, I will crown you a genius!

Jeffrey Gross said...

@Will,

One can only hope and have faith. Keep in mind, these above xWHIPs assume the player’s peripherals and ratios will remain constant for the rest of the year

Jeffrey Gross said...

It is important to note that some of the ZScores are different, despite “same xWHIP” (e.g., both Kennedy and Latos have 1.26 xWHIPs, but marginally different ZSCores), because the xWHIPs are rounded to the nearest 100th.

Tom said...

Have you noticed any bias, for lack of a better word, from your xWHIP calculator compared to the simple XWHIP formula based on the type of pitcher? In other words, do high K/9, low BB/9, or ground ball pitchers tend to have lower xWHIP’s with either calculator?

carter said...

Great work. Thoughts on Marco Estrada, Alex Cobb and Chris Volstad based on this work? Thanks very much.