Let me first say that I appreciate any dive of this depth into statistics. It makes for an interesting read and an interesting discussion. I just read through both. However, there are many flaws in tying the correlations found in this study to value in players. First, it's evaluating multi-outcome measurements against single outcome results. The more outcomes you add to a statistic, the higher your correlation will be by default. OPS, for example, measures six possible outcomes (BB, 1B, 2B, 3B, HR and outs by omission - including strikeouts). Is it any surprise that it correlates better than any other statistic or outcome when it includes all of them? Hits/9 and average includes four-five outcomes (1B, 2B, 3B, HR, and the fifth again being outs) , which is pretty good, but people "savage" it (from your other response) because it does not include a key element that players can do to help their team win and add value. It's why WHIP works so well here because it essentially is a catch-all for everything -- of course would correlate. If you're giving up a ton of hits and walks per 3 outs, you're going to be in trouble. Contrast that with K, HR, and BB, which measure a single outcome each. Assigning value as you do here to a single outcome based solely on correlation is dangerous. Your placement of 3Bs proves that. Is a 3B really that worthless? Sure it doesn't correlate to a win, but to argue that it is less valuable an event than a 2B would be preposterous, right? (This is why Tom Tango's work is so valuable) So, in looking at evaluating a pitcher, we should be asking what tools we have, and which are predictive. Can we predict H/9 for pitchers? K's? Do K's correlate with H/9? If you're looking to criticize people for valuing strikeouts in pitchers because they don't correlate as well as you'd like to runs p/game, what other stats, along with HR and BB should we be using?