APBRmetrics

[Mike G]

Expected Wins, Equivalent Wins, I don't know what to call this thing.

I took my 2004 file, which rates players, and subtracted a replacement-level value (equivalent to an average 12th-man), and apportioned eWins to every player. These do not add up to team Wins, unless it's a 41-win team. But so far, it looks like you can just about double the separation from 41 and get actual (schedule-adjusted, pythagorean-expected) team wins.

So a team whose players add to 30 wins is not really going to win 30; being 11 games under .500, just double that shortfall and expect 19 wins (41 - 22) from that lineup.

How well does this predict the success of a team that's changed it's roster from last year? Well, there are major complicating factors: Rookies, injuries, major player improvements, etc.

Let's start at the top with the defending champ Pistons. An intact starting lineup, and wholesale changes to the bench. Currently they're turning their season around; still, they've had no major injuries or midseason trades. Here's their lineup with eWins from last year, and from this year (scaled to 82 games):

2004 eWins.. 2005 eWins
9.6 RWallace 7.3
9.5 Billups ... 9.1
8.4 Hamilton 8.6
7.4 BWallace 7.3
5.9 Arroyo ... 2.9
5.2 Prince .... 6.7
1.5 Mcdyess . 5.3
1.5 Coleman 0.0
0.8 Dupree .. 0.3
0.5 Hunter ... 1.2
---------------
50.3 .. Total .. 48.2


The player minutes from last year add up almost exactly to (240x82) so don't need adjustment. The Predictor column shows 50 wins, and they're on course for 53. Part of that, at least, would be due to the fact they're in the East. It shouldn't be too hard to find average wins to add or subtract depending on the conference.

The 2nd column might suggest they're better than their record indicates.
If I double the distance from 41 wins, they should win 55 games.

Sorry in advance for any mistakes or omissions. This monster is just a baby.

[Mike G]

Well, this thread is uncrowded, to say the least. A few breakthroughs on my end, since last post:

I've (tentatively) determined the value of a replacement player. That's someone who doesn't actually contribute any wins to an average team. The only reason to play such a guy is to see what he's got, in garbage minutes. Projects and over-the-hill players.

The value of the replacement player is 11.75, in my 'system'. There have been 377 players better than this level, this year: about 13 per team. Not everyone is available at any given time (injuries, basically), so I guess that's intuitively acceptable.

Another offshoot of my revamping is a complete overhaul of Weights I'll be assigning to various conventional stat categories. These are now correlated to eWins -- which as part of their definition is "over replacement value".

For the curious, here are my before-and-after weights. "Scoring" is directly scaled by (TS%/.527), where .527 is historic average TS%. Sco, Reb, and Ast are scaled to Team and Opponent averages.

stat -- old -- new
Sco - 1.00 - 1.00
Reb - 1.00 - .895
Ast - 1.33 - 1.52
PF. - 0.25 - 0.25
Stl - 1.50 - 1.76
TO - 1.25 - 1.69
Blk - 1.50 - 2.25

All these values give the highest correlations to eWins.

Scoring is defined as 1.00, and everything else is relative to that.

Rebounds are indeed <1 in value. I cannot discern any difference in importance between OR and DR.

Assists are contentious; I always valued them >1 to justify the minutes received by high-Ast players. Plus, I like passing.

Somehow my gut guess for fouls was right-on. (PF and TO are negative.)

I upgraded steals last year due to DanR's studies. And some more just now.

Turnovers get more weight, too. They're tantalizingly close to Steals.

I'd always given blocks the 1.5 value. Even that was way short.

The free throw factor I use is .45, rather than .44. But the best correlation to wins turns out to be more like .47. This suggests perhaps that some FTA are "forced", and there's negative repercussions from "no-calls".

The standard TS% of comparison that correlates best to wins is around .540, (not the historic .527 nor the current .515). Maybe that's what TS% "should be", if every team got the ball to the best shooter every time.

I didn't attempt to correlate to actual season wins. Neither did I shoot for Pythagorean pt.-diff Expected Wins. Rather, I generated "equivalent wins" in a balanced league -- as if every team had the same strength of schedule -- from Sagarin's team ratings. I call these "Sagarin Wins", for now.

My mean error is less than 2, between sWins and eWins.

[WizardsKev]

[Mike G]

Kevin, I'm just guessing. If .44 represents a ratio of scoring-attempts/FTA, then a lower ratio suggest FT are an even better deal, while a bigger number suggests some tradeoff in efficiency.

I rather expected to see a factor lower than .44, to account for the positive effect of creating foul trouble in the opposition. It seems intuitively that teams that get to the line can often make up for rebounding deficits, shooting woes, etc. Is it fully accounted for in the points (FT) scored? And then some?

All I have come up with is that over-reliance on drawing fouls might deleteriously affect individual/team performance. A war of attrition that the aggressor gets the short end of.

Alternate explanations are warmly received.

[FrontRange]

Do blocks correlate closely to other stats?

I wouldn't be surprised that steals and blocks have such a large impact on wins, provided that what you are really capturing is above average athletic ability and basketball sense/ability.

For example Duncan, O'Neals, Camby, AK, Wallace are all very good basketball players who also get alot of blocks. However, Ostertag who tends to come up high on alot ratings isn't a very good basketball player who still gets alot of blocks. I get concerned about drawing conclusions about the values of players like that (Prizbilla, Foyle, Hunter, etc) bcs they happen to perform well in a stat that correlates well with other important basketball activities.

[FrontRange]

YOu might try running the stats excluding Utah results . . .they are so far from the norm in FT/fouling that it might make a marginal difference.

[Mike G]

I'd be leary of drawing correlations between various stats. I treat each as a free-standing attribute, for better or worse.

Ostertag was pretty good last year, but I don't mean pretty. I have him contributing 5.0 wins, which (because he was with a near-.500 team) is right between b-r.com's PW and 1/3 of WS. This year he's terrible in every way.

Utah this year puts opponents at the line more than anyone else. I've also got them "most overrated" this year. Hmm. Other high-fouling teams seem to be randomly placed on that scale.

Players that have been moved during the year I am only looking at askance. For example, Jim Jackson played big minutes in Houston and now appears with his whole year's stats in Phoenix. I'm scaling his numbers to a much higher-scoring/rebounding milieu, so it looks as if he sucks.

Overall, this year's correlations (team player-win totals to actual wins) aren't nearly as close as last year's (on which the parameters are based). Hopefully after the season (and before the postseason) I will have them sorted out, with players' part-seasons calculated separately for their various teams.