Again, examples are easy. Were I properly motivated, I guarantee you I could find plenty of examples where this level of ptiching (even "high stress" pitching) did not result in injury. Interesting as they may be, examples are not useful data for understanding cause and effect. What the hell are you talking about? I just showed you 7 pitchers whose career were shortented or their effectiveness was lessoned, or were out of baseball at an early age after a) high innings and b) high pitch counts. Sonofsamiam just provided you with five more. You don't need to do regression analysis or complicated statistics to look at those data and be able to draw an inference. The obvious answer is that pitching all those IP and pitches per inning contributed to whatever results that occured. However, that doesn't rule out other factors too. Yet, it also doesn't take a Rhodes scholar to come to the conclusion that it's probably not a good idea to have a young pitcher throw when they are fatigued. No, what the hell are you talking about? You obviously have no understanding of inferential statistics. But thanks for posting the PAP data. It's nice that someone out there understands the kinds of data that are required to back a claim. Sheesh. I understand inferential statitics quite well. However, you don't use them to show a cause and effect relationship becuase in fact they cannot show a cause and effect relationship. Individual injuries after high pc over a prolonged period can show a cause and effect relationship although other factors cannot be ruled out. Using huge data sets like the study I referenced only hammer home the obvious. No, you don't understand inferential statistics. You don't seem to understand the meaning of "variable" or "effect." The analysis of the "huge data set" does much more than "hammer home the obvious." If you're convinced by a few memorable examples, that's your problem. Shall I reference peer reviewed scientific studies I've published? It's not about "being convinced by a few memorable examples". If it (whatever it happens to be) occurs once that's all the data one needs. Then one begins to ask other questions, like has it happend to anyone else? If it has, then you find out how many more. But 1 is all you really need. It's called induction and is a much more powerful method at discovering truth than hypothesis testing using inferential statistics. But like Bob Sanders just posted. In essence I agree with Mephistopheles, poor mechanics probably has a lot to do with arm injuries, but then you have to ask what causes poor mechanics. If it's fatigue born by high pitch counts then I would think that one would want to mimimize those instances.