NAME POS GP MIN WP48 PoP48 WINS PTS REB AST TO BLK STL PF
Chucky Atkins PG 81 2363 .000 -3.1 0.02 19.7 3.5 6.7 3.0 0.1 1.4 3.7
Dana Barros SG 60 1079 .097 -0.1 2.19 21.3 4.2 4.9 2.7 0.1 1.3 2.8
Jud Buechler SG 57 737 .163 2.0 2.50 12.6 6.1 2.5 1.6 0.7 1.4 5.4
Brian Cardinal PF 15 126 -.128 -7.1 -0.34 11.8 8.8 1.1 3.4 0.8 2.7 10.3
Cedric Ceballos SF 13 166 .040 -1.8 0.14 21.7 7.5 2.0 2.6 0.9 1.7 5.5
Mateen Cleaves PG 78 1268 -.051 -4.7 -1.34 16.0 5.0 7.8 5.3 0.0 1.9 5.8
Michael Curry SF 68 1485 .032 -2.1 1.00 11.5 3.9 4.3 2.0 0.1 0.9 5.6
Kornel David PF 10 69 .113 0.4 0.16 13.9 13.2 2.1 2.8 0.7 2.8 5.6
Eric Montross C 42 568 -.068 -5.2 -0.81 9.0 12.2 1.3 3.1 1.9 0.7 8.5
Mikki Moore C 81 1154 .106 0.2 2.54 14.9 13.1 1.4 3.1 2.5 1.0 8.4
Billy Owens SF 45 793 .142 1.3 2.34 12.0 12.4 3.3 2.4 0.7 1.9 5.6
Joe Smith PF 69 1941 .034 -2.0 1.36 20.9 12.1 2.0 2.2 1.2 1.2 6.4
Jerry Stackhouse SG 80 3215 .060 -1.2 3.99 35.5 4.7 6.1 4.9 0.8 1.4 2.4
John Wallace SF 40 527 -.056 -4.8 -0.62 21.6 7.6 2.1 3.3 1.5 1.2 6.6
Ben Wallace C 80 2760 .293 6.0 16.87 8.9 18.3 2.1 2.0 3.2 1.9 3.3
Jerome Williams PF 33 804 .241 4.4 4.03 14.4 16.6 1.9 2.8 0.6 2.3 4.8
Corliss Williamson SF 27 800 .188 2.8 3.14 24.7 10.1 1.7 2.7 0.5 2.1 5.2
Name FG% 2FG% 3FG% FT% eFG% TS% FGA 3FGA FTA
Chucky Atkins 39.9% 42.3% 35.7% 69.2% 46.3% 48.1% 19.3 6.9 2.6
Dana Barros 44.4% 45.3% 41.9% 85.0% 49.8% 53.4% 18.3 4.7 3.6
Jud Buechler 46.3% 50.6% 41.6% 75.0% 56.1% 57.0% 10.7 5.0 0.8
Brian Cardinal 32.3% 38.5% 0.0% 61.1% 32.3% 39.8% 11.8 1.9 6.9
Cedric Ceballos 39.4% 54.8% 27.5% 80.0% 47.2% 49.7% 20.5 11.6 2.9
Mateen Cleaves 40.0% 40.5% 29.4% 70.8% 40.6% 45.8% 15.1 0.6 5.2
Michael Curry 45.5% 45.5% 44.4% 84.9% 46.1% 50.7% 10.3 0.3 2.4
Kornel David 45.5% 45.5% 0.0% 0.0% 45.5% 45.5% 15.3 0.0 0.0
Eric Montross 41.3% 41.3% 0.0% 26.9% 41.3% 40.4% 10.2 0.0 2.2
Mikki Moore 49.3% 49.4% 0.0% 73.1% 49.3% 55.2% 11.1 0.0 5.4
Billy Owens 38.3% 40.5% 15.0% 47.5% 38.9% 40.0% 13.9 1.2 2.4
Joe Smith 40.3% 40.5% 0.0% 80.5% 40.3% 47.5% 18.9 0.1 7.1
Jerry Stackhouse 40.2% 41.8% 35.1% 82.2% 44.5% 52.1% 28.8 7.1 12.1
John Wallace 42.4% 44.3% 13.3% 77.8% 42.8% 46.3% 21.5 1.4 4.1
Ben Wallace 49.0% 49.2% 25.0% 33.6% 49.1% 47.0% 7.6 0.1 4.1
Jerome Williams 43.8% 44.2% 0.0% 72.2% 43.8% 50.1% 12.0 0.1 5.4
Corliss Williamson 53.4% 53.4% 0.0% 62.6% 53.4% 55.7% 19.3 0.0 6.4
  WP48 WINS PTS DRB ORB TRB AST TO BLK STL PF
DET 0.290 37.2 95.6 32.0 13.5 45.5 19.9 15.9 5.5 7.5 23.8
AVG 0.500 41.0 94.9 30.5 12.0 42.5 21.7 15.0 5.3 7.8 22.4
OPP 0.710 44.8 97.3 32.6 12.3 44.9 22.7 15.5 4.8 8.2 25.1
AVGOPP 0.500 41.0 94.7 30.5 12.0 42.4 21.7 15.1 5.2 7.8 22.4
  FG% 2FG% 3FG% FT% eFG% TS% FGA 3FGA FTA
DET 42.4% 43.9% 35.0% 72.1% 45.3% 49.8% 83.9 13.6 27.2
AVG 44.3% 46.1% 35.4% 74.7% 47.3% 51.8% 80.6 13.8 24.9

Wins Produced
Expected* Actual Forecast**
37.2-44.8 32-50 n/a

* The win-loss record that wins produced would have predicted based on players' WP48 so far (ignores previous seasons).

** future games only -- takes current record as given


Articles featuring the Detroit Pistons

Trade Machine Economics

So, yesterday a trade happened. I'm not going to talk a whole lot about the #BasketballReasons of the trade (other, than, perhaps to take a 5-minute break laughing myself silly at those of you who actually thought Toronto got better here) because I think Ben Gulker and Devin Digham both do a good job of that here and here. I just want to briefly talk about the economics.

Pistons: Win with Youth

From the Truehoop Bullets today:

The NBA Geek's Amnesty Guesses, Part 1

I'm calling them guesses, not predictions, because this way if I'm completely wrong, I won't look as stupid.