Posted by dahsdebater on 2/18/2012 11:38:00 PM (view original):
I'm not sure what, specifically, you're suggesting was wrong with my math. I know of several significant flaws:
1) I only looked at press and man. They looked generally the same, but I don't know for sure how anything applied to zone. I went through thousands of box scores, and there's no way I was taking extra time to average defensive ratings or go through box scores.
2) This is the big one: since I didn't go through box scores, I just assumed starters played all their minutes against one another. Obviously starters played some minutes and had some shots against bench players. All of that throws off the analysis.
3) I made no effort to differentiate between inside and outside defense, which may well be different in the engine.
Some others, but I'm really tired right now. Maybe will talk about it some more tomorrow afternoon.
not really suggesting anything wrong with the math - your math could very well have been perfectly correct. just to be safe - i am definitely not trying to criticize you or anything you did! just pointing out an important flaw. thought it might be interesting to hear what you would think of.
anyway - what i am thinking of is this. you have a model that sort of goes like this -
you have 10 variables attached to a player, call them a b c d e .... (like ath, spd, def, lp, passing all are. the name is irrelevant). then, you are looking at - really, comparing against some part of the players performance - it could be their points scored, rebounds, opponents' rebounds, etc... and saying, what impact does each individual variable have. well, players with 50-60 of variable a, maybe score 10 points, with 80-90, maybe score 15 points. the same might be true for variables b and f. so, you might conclude, all have an equal importance in how effectively a player can score.
the above conclusion could be challenged because there could be other factors impacting the results - for example, maybe a guy with 80-90 of variable a or b or f is getting treated differently by his coach - or the opponent's coach. if that was true, there is really a hidden variable - the actions of the coach. if the actions of the coach are *dependent* on one of these variables - then you cannot know what really caused the outcome. for example, is it because the player went from 50-60 in a rating to 80-90, he got that much better at scoring? or is it because the coach played him more, so he got more opportunities? or did the coach play him the same amount of time, but let him take more shots?
well, the above principle doesn't just apply to the numerous hidden variables present... it also applies to the named ones we are studying, a, b, c, etc... what if a is always half of b? then when you compare a player's performance at 30a vs 40a, you are also getting the impact of 60b vs 80b. a might not mean a damn, but if b does, then it makes a look important. this kind of thing can happen no matter what the relationship of a and b - if there is a relationship. the only way the model makes sense is if all the variables are *independent*. i am trying to explain in a way people not trained in statistics may follow...
so yeah - it really comes down to, are the ratings you are considering independent of each other? of course, we KNOW there are "hidden" variables that they are dependent on - im sure everybody reading can think of a dozen ways a coach might adjust his behavior as his players' ratings change. well, in HD - the answer is no.
people talk about height and weight not mattering. that is true in games - but it matters in recruit generation. for example, taller players generally have better sb and reb. its very significant - if you've never seen it, you can look through big men recruits for about 2 minutes and you should be able to see it. well, along those lines - sb definitely impacts reb. a tall player will generally have high reb and high sb - therefore, to some extent (and it is significant, but possibly lesser), a player with high sb is likely to have high reb. there are quite a few other correlations as well, in recruit generation. (if you struggle to think of any, check out def vs ath/spd - see anything?). of course, its not just height and weight that don't matter in game, but impact recruit generation - there is also the position. as a result, there are tons of little relationships in the variables. think about it for a second - a guy with 80 ball handling is BOUND to have better passing, on average, than a guy with 1 ball handling. would anybody dispute that? so, how do you know, when you look at the impact of bh on fg% - if it is the bh or the passing that is causing that?