APBRmetrics

[Mike G]

(This thread is a spinoff from another.)

I'm feeling prompted (again) to ask a rather open-ended question. In placing individual (player) accountability for (team) wins, should individual wins add up to...
1)... actual team wins?
2)... team statistical performance, i.e., pythagorean expected wins?
3)... neither?

I think WS does #1, and PW shoots for #2. When I did eW, I didn't know if either was the better target.

If you are considering individual wins (IW, to be generic) to be a stat that players accumulate, then the best teams -- which may win 2-3 times as many games as the worst teams -- might have 2-3 times as many IW distributed among their rosters.

But an elite team may only score 15-20% more points (as differential) than a cellar-dwellar. All the things that go into point diff -- TO, Reb, Ast, Stl, Blk... -- are generally also within a similar range. If IW are a quantity of something real, and the bottom-line team quantities -- points scored -- are within 20% of one another, then is it practical to suppose players on a weak team are really only generating half (or less) of something than those on better teams?

The pat answer is, "They're generating only a fraction of the wins". Sure, but the stats they generate indicate that they're making their team more competitive, more often within striking distance, with a good rally or defensive outburst. In fact, we might define all player contributions as being stratified into levels that (1) make their team less terrible, (2) make them competitive, (3) propel them to wins.

I propose that any system forcing player IW's to sum to team wins (or PythWins) requires artificial support to do so. Players are deemed to be from excellent to terrible on defense, basically. Yet the same players don't carry over their excellence (or lack thereof) from good teams to bad teams. This doesn't seem intuitively believable to me.

The eWin system doesn't suppose that a players' defense becomes many times better or worse when he changes teams. They also don't add up to team wins. Team eW still add up hierarchically so that the best teams are best. But they aren't 2-3 times as good as the bottom teams; only some 50-60% better.

Everyone has a different intuitive sense of 'good' and 'bad'. At one time, a 40% FG rate was 'good'. At this point, I won't even get into 'individual losses'.
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40% of all statistics are wrong.

[deepak_e]

[Mike G]

Yes, if I understand what you're saying. eWins takes boxscore stats, scales them to Team/Opponent productions, and then predicts Wins non-proportionally.

Expected team wins (for an 82G season) are:

xW = eW*2 - 41

So a 61-win team typically totals 51 eW. A 21-win team totals 31 eW.

The good team isn't 3X as good as the bad one, but only 1.65 times as good. Here, 'good' is effectively defined as 'total talent above replacement level'.
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40% of all statistics are wrong.

[Mountain]

An Individual Win metric probably should compare to league average or replacement level on stats or to average or replacement level player. I'd be wary of one that didnt and didnt fully. It is the difference from these baselines that affects winning and losing not the total stat level or total player level.

EWins compares to replacement player. From your formula in the other thread it looks like a T=15+ for 2000+ minutes starts to contribute eWins. Not sure if T=15 is at or above/below mean or how closely eWins tracks with PER but does it in fact boil down to saying it takes a statistically above average player to contribute individually to wins?

In reality players are the sum of their individual stats and team effects above and beyond those individual stats. Dan's overall +/- has both (and from offense and defense) with individual generally the far heavier weight though it probably varies by type of role player. The pure +/- tries to capture the uncounted team effects. This metric appears comprehensive in scope.

Berri's Wins Produced uses league average performance on shooting. That isn't a terrible choice in itself but maybe the mistake is the mix he sets it in, one that doesnt not do the same for rebounds, etc. at least for Wins Produced. His PAWS/min uses replacement player. PAWS/min is more palatable to me as positional average performance is a main design feature and the shooting formula decision at least hits all the players at the position the same but it might be more acceptable to many here to re-run Berri's formula with shooting break-even at replacement player shooting level or some more neutral or statistically driven level. Anybody done or interested in doing this? Berri's metric does incorporate the impact of overall team defense on scoreboard but in a crude equal way (something PER is lacking, but just as crude as player W-L off of OR and DR ratings). Is it missing the offensive team effects of players or is that getting mixed in with the "team adjustment"?

[deepak_e]

I think this really depends on what we're trying to describe:

(a) Do we want to measure how much credit a player should get for his team's success? If so, then it makes sense for the IWs to add up to team wins (or expected team wins).

(b) Or do we want to measure player performance, irrespective of his team's success? If so, then the eWins system makes more sense to me.

More practically, I think we'd like to know how much a player would contribute to the success of a future team. That is, if I have a new roster I put together, how successful can I expect this team to be, and how much would each player contribute? For this purpose, which type of metric would be more useful -- (a) or (b)?

[Mountain]

The 2:1 ratio of wins / statistical performance difference you observe with EWins Mike, I have observed with team minutes weighted tendex and team PER too so this appears to a rough universal rather than system specific. And caused by crediting possession affecting actions as well as scoring actions. I think Nick S spent a good of time talking about accounting and touches on this.

[Mike G]

Mountain:
Of course average players contribute to wins. Replacement T was figured at 11.7 last year; over 400 players met this level, 13-14 per team. All contribute a net positive.
The 30 top 'sub-replacement' types (in minutes played) averaged 37%TS. 30 guys around 11.7 (15+ and 15-) averaged .433.
Last I checked, a PER of 15 was roughly equal to a T of 26. This varies from team to team, as T rates actually correspond to team strength.

Deepak:
eWins ranks players on a given team; thus gives due credit for that team's success. It also gives a Kevin Garnett (great player/terrible team) with more 'credit for success' than a Jason Terry (good player/great team) -- since KG takes an otherwise-NBDL lineup and makes them competitive -- unlike some other IW systems.

If you are building a team and adding last year's player eW rates (properly allotting minutes/positions), you will do very well in predicting this year's wins. PW and WS, as far as I know, make no claim along these lines; much of their credits are tied up in 'team defensive' schemes.

Remember, eWins is very cognizant of team success. The factors of Tm/Opp Pts and Reb are what distinguish it from others. But the scaling is much less drastic than what is found in W-L records.
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40% of all statistics are wrong.

[Mountain]

Mike, my bad, I was thinking of wins against an average team level, 41, (as in some metrics I have worked on conceptually) rather than wins period. Certainly players below average will contribute to wins period. Different scale. So a TS+26 playing starter minutes would contribute somewhere in the range of 6-8 wins. that makes sense.

I like the innovative features of EWins, hence supported scoring it against other more widely publicized measures.Tm/Opp Pts does incorporate shot defense (with everything that affects opponent points), though the team defensive result is assigned to all equally, correct (as in some other systems)? Would you have any interest in modifying to base it off of say a 50% weight of team opponent scoring and 50% estimated counterpart scoring? Giving EWins another distinction, that perhaps puts it closer to what is happening by recognizing at least some- call it half to start- of shot defense is local?

[Mike G]

Mtn, I greatly appreciate the shout-out, as I don't really think eWins is 'well known', unless you are a thorough reader of these forums. Also thanks for reminding of Nick S's work a few weeks back; I was heavily curious where he would take that. Dan D (Statman) is another to watch.

But I'm very skeptical of 'counterpart' effects. The Spurs don't match up with others' positions, and they're the flagship franchise of the NBA. Coaching quirks are unpredictable. Players don't have positions, on O and on D, in my view, and the best players will always be just Players.

Fixing things at the 90% level is a lot closer to 100 than to 50. That's how I think of team effects: You can go to many times more work for the next few % of accuracy. You still win by outscoring your opponent, and the points-ratio is what determines wins.
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40% of all statistics are wrong.

[NickS]

[Mountain]

Ok Mike, I understand your perspective. I've shared my perspective about adding individual shot defense enough. DCS has it. I will look to that more and maybe create my own version of it later.

[Mountain]

Well Mike, EWins goes primetime with Henry Abbott's mention of it and link to this board about it this morning. If folks haven't seen it yet: http://myespn.go.com/nba/truehoop

Dan and David's presentation gets more publicity and discussion too.

(And in a bankshot WARP as presented by DW might get some exposure.)


P.S. I found this formula in the mentioned player ranking thread that covers EWins

T = Sco + .98*Reb + 1.335*Ast - .25*PF + 1.5*Stl - 1.5*TO + 1.75*Blk

Still accurate?

[mateo82]

Well, I would think it would have to measure expected wins, since the difference between expected wins and actual wins is essential chance

[Mike G]

[Mountain]

Thanks for sharing Mike.
It will take some time to look at. If I have any observations or questions that might be worthwhile I'll pass them along later.