He took the data for players with 100+ shots from the years 2012, 2011, and 2010. Next, He ran a regression with a constant of 0 for 2010 and 2011 eS% as the independent variables and 2012 as the dependent. This basically means that it weights the 2010 and 2011 stats to find the best weights to get 2012 results.
The formula would look like this: 2012_eS% = 2011_eS% * w1 + 2010_eS% * w2 + c where year_eS% is the effective shooting percentage for that year and w1/w2 are the weights from the regression and c is the linear constant (0 in our case). So, with simplified numbers plugged in, we get 2012_eS% = 2011_eS% * 0.71 + 2010_eS% * 0.31.
As an aside, the p-values (for alpha = 0.05) for the weights are 2011: 0.0288 and 2010: 0.3117. So it's technically not a super strong fit, but who cares. I ran a regression for just one year predicting the next (ie 2011 -> 2012 and 2010 -> 2011) and came up with statistically significant coefficients but that's more regression to the mean instead of prediction. The main problem with that is it really hurts players like JT who shoot REALLY well and it treats that as an outlier and penalizes them the following year.
Anyways...I then took players that had 100+ shots in 2011 and 2012 and calculated their 2013 eS%. I could do more, but players with over 100 shots are the ones that are getting the majority of floor time and are the real difference makers on offense.
Here's the results with the columns being: Player name, 2012 eS%, 2011 eS%, Expected 2013 eS%, and change from 2012 (sorted from largest increase (+) to largest decrease (-))
Player
|
2012
|
2011
|
E(x)
|
Delta
2012
|
Kevin
Buchanan
|
0.113
|
0.236
|
0.154
|
0.040
|
Aaron
Wilson
|
0.165
|
0.271
|
0.202
|
0.036
|
Chad Culp
|
0.245
|
0.323
|
0.274
|
0.029
|
Scott
Ranger
|
0.263
|
0.339
|
0.292
|
0.029
|
Stephan
Leblanc
|
0.246
|
0.315
|
0.272
|
0.026
|
Shawn
Williams
|
0.209
|
0.270
|
0.232
|
0.023
|
Cody
Jamieson
|
0.245
|
0.296
|
0.266
|
0.021
|
Dan
Dawson
|
0.263
|
0.312
|
0.284
|
0.021
|
Lewis
Ratcliff
|
0.242
|
0.288
|
0.261
|
0.019
|
Callum
Crawford
|
0.243
|
0.284
|
0.261
|
0.018
|
Rhys
Duch
|
0.282
|
0.315
|
0.298
|
0.016
|
Jeff
Shattler
|
0.286
|
0.316
|
0.301
|
0.015
|
Mike
Accursi
|
0.265
|
0.292
|
0.279
|
0.014
|
Ryan
Benesch
|
0.334
|
0.348
|
0.345
|
0.011
|
Ryan
Ward
|
0.276
|
0.287
|
0.285
|
0.009
|
Curtis
Dickson
|
0.318
|
0.323
|
0.326
|
0.008
|
Scott
Evans
|
0.238
|
0.247
|
0.245
|
0.008
|
Corey
Small
|
0.206
|
0.217
|
0.214
|
0.007
|
Drew
Westervelt
|
0.307
|
0.307
|
0.313
|
0.006
|
Dane
Dobbie
|
0.254
|
0.247
|
0.257
|
0.003
|
Luke
Wiles
|
0.321
|
0.307
|
0.323
|
0.002
|
Athan
Iannucci
|
0.263
|
0.247
|
0.263
|
0.000
|
Garrett
Billings
|
0.247
|
0.226
|
0.246
|
-0.002
|
Shawn
Evans
|
0.279
|
0.254
|
0.277
|
-0.002
|
Colin
Doyle
|
0.284
|
0.244
|
0.277
|
-0.007
|
Brendan
Mundorf
|
0.236
|
0.177
|
0.222
|
-0.014
|
John
Grant
|
0.290
|
0.211
|
0.271
|
-0.019
|
Josh
Sanderson
|
0.290
|
0.206
|
0.270
|
-0.021
|
John
Tavares
|
0.408
|
0.292
|
0.380
|
-0.028
|
Mark
Steenhuis
|
0.382
|
0.204
|
0.334
|
-0.048
|
You’ll see players like Kevin Buchanan, Aaron Wilson, Chad Culp, Scott Ranger, and Stephan Leblanc score 4-6 more goals than last year and on the other end John Grant, Josh Sanderson, John Tavares, and Mark Steenhuis score 3-7 fewer goals (based on if they take 150 shots.)