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Page, Fellingham, and Reese: JQAS paper

 
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Mike G



Joined: 14 Jan 2005
Posts: 3612
Location: Hendersonville, NC

PostPosted: Mon Oct 22, 2007 8:16 am    Post subject: Page, Fellingham, and Reese: JQAS paper Reply with quote

From the Journal of Quantitative Analysis in Sports:

http://www.bepress.com/jqas/vol3/iss4/1/

Using Box-Scores to Determine a Position's Contribution to Winning Basketball Games

Garritt L. Page, Gilbert W. Fellingham, C. Shane Reese

The study looks at boxscore totals for a game, by position, and determines correlations with winning. These are translated into value per item for some things -- FG, FT, 3FG, Ast, Stl, TO; and into value per .01 for other quantities -- OReb%, DReb%, FG%, FT%. The value is expressed as 'points per 100 possessions'.

This seems to place an entirely different scale on those stats considered as a % , so I'm listing them separately. (I realize I may be interpreting some of this wrong, and I have no idea about the methodology.)

Data were from USAToday boxscores, from the 1996-97 season. These boxscores don't list Blocks or 3FGA. 3FG were determined by (Pts - 2*FG - FT). Only starters positions are listed, so everyone else's position is lumped in under 'Bench'.

I've hand-entered the data, dropped some digits, and ignored the uncertainty ranges. Here, it seems, are the positions and the relative importance of the stats, arranged by position.
Code:
    Center     Power Fwd    Small Fwd   Shooting Gd   Point Guard    Bench 
 .379  Stl    .309  Ast    .405  Ast    .374  3FG    .353  Ast    .132  Ast
 .325  Ast    .284  TO     .381  3FG    .326  Ast    .283  3FG    .090  Stl
 .254  TO     .150  3FG    .345  TO     .215  TO     .269  TO     .084  TO
 .135  FG     .146  Stl    .257  Stl    .144  Stl    .199  Stl    .067  3FG
 .067  FT     .086  FT     .104  FT     .099  FT     .144  FT     .053  FT
-.014  3FG   -.008  FG    -.003  FG    -.069  FG    -.007  FG    -.005  FG

Again, these are total FG, FT, and 3FG (not percents). Turnovers are negative.
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ziller



Joined: 30 Jun 2005
Posts: 126
Location: Sac Metro

PostPosted: Mon Oct 22, 2007 12:40 pm    Post subject: Reply with quote

The conclusions drawn seem a bit stretched to me. Particularly w/r/t assists: There are causality issues without doubt. The authors don't address (mention? sort-of, near the end) that assists rely on made baskets, which clearly have an impact on point spread. If PG #1 has 12 assists and PG #2 has 3 assists, and Team #1 wins by three... is it because PG #1 had nine more assists, or is it because Team #1 shot better (thus earning PG #1 more assists). We don't know based on these findings, I'd said.

This issue also screams out with steals from the center position. Is that indicative of a defensive center with good hands, or a poor offense on the other side? Has anyone ever looked to see if steal rates for centers (or even PFs) are consistent on a season-by-season basis?
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Mountain



Joined: 13 Mar 2007
Posts: 1527

PostPosted: Mon Oct 22, 2007 1:14 pm    Post subject: Reply with quote

I mentioned the original thesis that served as the basis for this slimmer, revised article 2-3 weeks ago (among others) in the New England Symposium thread.

http://tinyurl.com/235wy6

I speculated it deserved to appear in a journal, perhaps JQAS. The only comment at the time was from Mike G. joking about "the posterior means" I pulled out for ease of viewing for readers here. Now the article comes out and Mike has pulled the same set (with updated values) as I did. Kinda funny how that worked out. Good that the article is out there more clearly now to get whatever comments.
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John Hollinger



Joined: 14 Feb 2005
Posts: 175

PostPosted: Mon Oct 22, 2007 5:38 pm    Post subject: Reply with quote

I believe the rebounds from guards is another area where this is an issue. Guards' rebounds tend to be almost entirely defensive boards; defensive boards, in turn, tend to require a shot to be missed by the opponent.
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Mike G



Joined: 14 Jan 2005
Posts: 3612
Location: Hendersonville, NC

PostPosted: Tue Oct 23, 2007 4:45 am    Post subject: Reply with quote

Yeah, I thought these numbers looked familiar. Also, it's illuminating to sort and group them differently. Here they are grouped by event, sorted by position of 'importance':
Code:
 Assists    Turnovers   3-Pointers    Steals      FT made      FG made 
.405  SF    -.345  SF    .381  SF    .379  C     .144  PG     .135  C
.353  PG    -.284  PF    .374  SG    .257  SF    .104  SF    -.003  SF
.326  SG    -.269  PG    .283  PG    .199  PG    .099  SG    -.005  Be
.325  C     -.254  C     .150  PF    .146  PF    .086  PF    -.007  PG
.309  PF    -.215  SG    .067  Be    .144  SG    .067  C     -.008  PF
.132  Be    -.084  Be   -.014  C     .090  Be    .053  Be    -.069  SG

Other than turnovers, these negative correlations aren't strong; except for FG by the SG. I'm supposing that indicates that reliance on the SG for scoring isn't a good thing.
As I mentioned earlier, the following categories don't seem to be on a comparable scale (nor will they all fit into one table), so here they are separately. Points/100 possessions, for each .010 of difference:
Code:
   FG%       OffReb%      DefReb%       FT% 
.200  Be    .113  PG     .067  SG     .011  C
.168  SG    .112  PF     .061  PG    -.001  SF
.128  PG    .076  Be     .028  SF    -.001  PG
.115  SF    .070  SF    -.005  PF    -.002  PF
.078  PF    .052  SG    -.010  C     -.004  SG
.053  C     .052  C     -.016  Be    -.007  Be

And grouped by position, skills in order of importance:
Code:
   Center     Power Fwd    Small Fwd   Shooting Gd    Point Gd      Bench 
 .053  FG%    .112  OR%    .115  FG%    .168  FG%    .128  FG%    .200  FG%
 .052  OR%    .078  FG%    .070  OR%    .067  DR%    .113  OR%    .076  OR%
 .011  FT%   -.002  FT%    .028  DR%    .052  OR%    .061  DR%   -.007  FT%
-.010  DR%   -.005  DR%   -.001  FT%   -.004  FT%   -.001  FT%   -.016  DR%

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Mountain



Joined: 13 Mar 2007
Posts: 1527

PostPosted: Tue Oct 23, 2007 11:10 am    Post subject: Reply with quote

Thanks for the different data sortings. There are clues in there, though I don't know how much weight to put on the results.

Looking at these results and how specific teams were designed and operate may spur additional thoughts, new questions. Using Jorje's hoopstats.com tools in conjunction with the values on these tables, teams can be analyzed by position instead of by player, You can compare skill emphasis strategies suggested as being more favorable on paper, in aggregrate to what happens on the court with particular teams and players and ponder what to change...

A strong SF seems valuable, perhaps because it is comparatively rarer because of quantity of players that get that label and stronger correlation of skill & opportunity at other positions?
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