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It’s easy to log complaints about his ratings when you don’t fully appreciate what Hollinger is measuring.</p>
Part of that is his fault. He refuses to set forth the formulae he is using, stating only that we can find them in a book of his–so, it’s hard to take an objectively critical view of his methods. That said, there’s something intuitive about trying to measure a player’s “good” contributions (points, rebounds, assists, blocks, steals) minus his “negative” contributions (missed shots, turnovers). I assume that he weights each one of these measurement in some way--for example, I wouldn't be surprised if he discounts assists somewhat, because there isn't a strong correlation between number of assists and which team wins a game. The problem is, if you take and miss more shots, you’re just going to have a lower rating than someone who is a “black hole”–someone who only gets the ball in the post and never passes to a teammate. I'm not sure that the second guy is more valuable from a basketball sense. For that reason, I’d like to know whether there is a correlation between a teams’ overall PER and its record. It doesn’t seem that I’ll get that, though–maybe it is in the book?–so I’ll settle for pointing out that he’s not a trainied statistician, and many of these ideas have been lifted from baseball analysts. For example, do you realise that he is NOT ranking each player’s PER from last season–he is PROJECTING their PER for the upcoming year, by comparing each player to how some number of similar players from previous seasons did at the same age? This is actually a concept used by the PECOTA baseball player rating system, that appears to be slightly–very slightly–better than other projection systems. The idea is that you find other players with similar skills and similar heights (haha, obviously not in baseball) and see what their career arcs looked like. I see a couple of problems: first, PECOTA actually breaks up each player into categories based upon the SHAPE of their arc–some players have a sudden dropoff at a certain age, while others suffer a more gradual decline. Hollinger doesn’t seem to do this. Second, there is limited data for players with unusual skills. In baseball, this is less of a problem for a variety of reasons, but in basketball, how many analogs do you think he found for Jason Kidd? One? Three? And if that player or small group of players declined rapidly at age 35, he’ll predict that Kidd will, too, without looking closer at the numbers to see if they have validity. With regard to players with very common skill sets, who have already established their individual arc by being in the league between say, three and ten years, he probably gets pretty good results. For very young players who haven't really figured out how to play, or those with unusual talents, it wouldn't work as well, obviously. He also can't incorporate how each player in his system has been USED in generating those numbers--both the players he is rating, and their analogs. Maybe players have gone from the third option to the second option, who knows. Again, this is not a problem in baseball, where everyone has the SAME opportunities--a series of one-on-one interactions with the opposing players. That is, interaction with TEAMMATES is irrelevant, unlike in basketball. I think what he needs to do is calculate what PER each player actually earned last season, compare that number to the projections, and take a close look at the players with the largest variability to see if it is reasonable. If he’s projecting that Kidd will suffer a fall-off because, for instance, Michael Ray Richardson entered drug rehab as a 35-year-old, obviously it wouldn’t make too much sense.</p>
Maybe he does that in his book.</p>
I guess I'll never know.</div>