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APBRmetrics The statistical revolution will not be televised.
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cherokee_ACB
Joined: 22 Mar 2006 Posts: 157
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Posted: Fri Mar 07, 2008 4:01 am Post subject: |
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One quick calculation. If the usage coefficient is 0.25, then the marginal efficiency is roughly 106-0.25*100 = 81, which corresponds to 33% eFG. |
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Guy
Joined: 02 May 2007 Posts: 128
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Posted: Fri Mar 07, 2008 6:55 am Post subject: |
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Eli: Could you say more about why you think weighting lineups by possessions is the better approach? I see the advantage in terms of sample size and reliability. But this will tend to give much more weight to lineups with usage rates close to 1, and minimize impact of very high/low usage lineups. If we only cared about the lineup overall, that would probably be fine. But you're using this to try to understand players, whose usage rates vary far more. So getting a good read on extreme lineups is arguably where you'll get important insights.
It's also interesting that the coefficients are so different depending on how you weight the sample. Suggests the possibility that you have a non-linear relationship here, in which the impact on efficiency grows the more a player departs from his normal usage level (at least for low-usage players, less clear re: high-usage players). That certainly seems plausible. When you have the expanded dataset, you may want to look at non-linear models. |
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HoopStudies
Joined: 30 Dec 2004 Posts: 705 Location: Near Philadelphia, PA
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Posted: Fri Mar 07, 2008 10:47 am Post subject: |
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Good piece.
Obviously, I come from the bias that this effect must exist. I think your work supports this and illustrates that it is statistically significant. There are some ways to poke small holes in this, but my sense is that the burden of proof goes further towards those saying that this effect doesn't exist.
If you do go to multiple years, you have to consider Ortg/usage being from career, multiyear, or single year data.
As to the magnitude, that ends up being an argument for the linear weights people to deal with. I haven't spent a lot of time trying to figure out the assumed importance those methods make on shot creation or how that relates to the baseline shooting percentage. _________________ Dean Oliver
Author, Basketball on Paper
The postings are my own & don't necess represent positions, strategies or opinions of employers. |
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Eli W
Joined: 01 Feb 2005 Posts: 402
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Posted: Fri Mar 07, 2008 3:15 pm Post subject: |
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Guy wrote: | Eli: Could you say more about why you think weighting lineups by possessions is the better approach? I see the advantage in terms of sample size and reliability. But this will tend to give much more weight to lineups with usage rates close to 1, and minimize impact of very high/low usage lineups. If we only cared about the lineup overall, that would probably be fine. But you're using this to try to understand players, whose usage rates vary far more. So getting a good read on extreme lineups is arguably where you'll get important insights. |
Yeah, I'm open to any suggestions in this area. With no weights, the high uncertainty actORtg's of lineups that played together for only a few possessions count equally with the more reliable actORtg's of lineups that played together a lot. Weighting by possessions controls for that but it may go too far in the other direction in terms of over-weighting the lineups that played together a lot. A compromise is to use a minimum possession cutoff, but that makes the sample sizes for each bin pretty small. Using more data should help things.
That's all just for the binned chart values. For running regressions on the data as a whole it's not as big an issue because the sample is larger. _________________ Eli W. (formerly John Quincy)
CountTheBasket.com |
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Mountain
Joined: 13 Mar 2007 Posts: 1527
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Posted: Sat Mar 08, 2008 1:46 am Post subject: |
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Trying to get back to the challenge of a coach I wonder if some data separation could be done to look at relationship between offensive usage/offensive efficiency and defensive efficency in 5 man lineups?
Does that help explain use of low usage and / or efficency lineups better than the offensive data does alone?
Do high defensive efficency lineups within the other 3 groups besides high usage-high efficiency make up for their offensive shortfall on the defensive side of the ball?
Do coaches do better or worse on this net efficiency challenge with lineups used 50+ possessions compared to those below 50?
Would game to game recalculation of this show on average NBA coaches learning the relative strength or weakness of a lineup and either increasing or decreasing use or effectively administering instruction to improve the net results? Or are they guessing in the dark and / or not demonstratig learning or improvement on average? How many coaches/ teams show good results on this?
Strength of opponent defense matters as does strength of defense of your lineup. |
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Mike G
Joined: 14 Jan 2005 Posts: 3601 Location: Hendersonville, NC
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Posted: Sun Mar 09, 2008 8:02 am Post subject: |
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Guy wrote: | Quote: | I tried two separate regressions - one with high-usage lineups and one with low-usage lineups, and unfortunately the sample size was a problem. |
Starting with 4 simple bins, usage X projected efficiency, might be instructive:
low-usage/low-projected-efficiency
low-usage/high-projected-efficiency
high-usage/low-projected-efficiency
high-usage/high-projected-efficiency |
I like Guy's idea; of course, then you have even smaller sample sizes. But don't we all wonder how high-eff/low-usage lineups fare? Again, opponent response to such unbalanced lineups becomes an issue. Efficiency, in scoring or rebounding, is really about gains vs the opponent.
HoopStudies wrote: | ... I come from the bias that this effect must exist.
...
As to the magnitude, that ends up being an argument for the linear weights people to deal with. I haven't spent a lot of time trying to figure out the assumed importance those methods make on shot creation or how that relates to the baseline shooting percentage. |
One could assume that a high-% shooter can shoot more, and his % can drop a ways before it's not good enough to shoot more. Conversely, a low-% shooter generally has an inflated scoring rate; and he might be a 'better' scorer by shooting less.
The low-% shooter's scoring rate can be deflated by multiplying by (his TS%, divided by a standard TS%), raised to some power. Playing with that exponent, and supposing a TS% of comparison is .525, then we can standardize the 'effective' scoring of guys who score 15 pts/36min with disparate TS%:
Sco = Pts/36 * (TS%/.525)^E
Code: | 15pts/36 'equivalent' pts/36
TS% E= .00 .50 1.00 2.00
.625 15.0 16.4 17.9 21.3
.525 15.0 15.0 15.0 15.0
.425 15.0 13.5 12.1 9.8
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The 'average' shooter, .525, is a 'true' 15 pts/36 scorer. The terrible one, .425, may be 'really' a 12 or a 10 scorer. _________________ `
36% of all statistics are wrong |
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Eli W
Joined: 01 Feb 2005 Posts: 402
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Posted: Tue Mar 11, 2008 3:10 pm Post subject: |
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Eli W wrote: | DLew wrote: | Could you do the same thing with multiple seasons worth of data to determine how robust the findings are? |
Yeah, I forgot that BasketballValue had lineup data from the last two seasons that could work for this study. For the rebounding study only this year's data worked (because that's when Aaron added lineup rebounding numbers), so when I started this study I unthinkingly again just used this season's data. I'll try to re-run things with the larger data set in the next few days. |
I spoke too soon. The BasketballValue data from 05-06 and 06-07 overestimated possessions (there's been some discussion of it on the forum), and that's causing a lot difficulties. Team possession figures being too high makes player %TmPoss and lineup actual ORtg's too low. I can make some estimated adjustments, but I'm afraid that will throw things off too much for the kind of sensitive analysis I'd like to do on the larger data set. I'm going to keep working on it, but at least for now I'm not going to be able to supplement the current season regressions that I ran. _________________ Eli W. (formerly John Quincy)
CountTheBasket.com |
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