2016-17 National Basketball Association
Computer Ratings

Introduction

This is my rough attempt at a computer rating that can, among other things, come somewhat close to predicting final game scores. The actual computer rating, which describes team performance based on games played to date, is found under the "Rating" column. To determine a hypothetical margin of victory, use the "Points" column (just subtract the teams in question) and add in the home field/court advantage, which is listed on the right. It usually is around 2.5 points for basketball. All of the games in the predictions list already have home advantage taken into account.


Ratings last updated Monday 06/05/17, 04:42 PM ET

Rank Team                   W  L   Rating   Points  BCS
   1 Golden State          79 16     5.61    12.28    1
   2 San Antonio           68 29     3.08     6.52    2
   3 Houston               62 31     2.76     5.96    3
   4 Cleveland             63 34     1.94     3.65    4
   5 LA Clippers           55 34     1.82     3.76    6
   6 Utah                  55 38     1.70     3.76    7
   7 Toronto               55 37     1.39     2.86    9
   8 Boston                62 37     1.30     1.54    5
   9 Washington            56 38     1.17     1.66    8
  10 Oklahoma City         48 39     0.66     0.94   10

Rank Team                   W  L   Rating   Points  BCS
  11 Memphis               45 43     0.39     0.99   11
  12 Miami                 41 41     0.37     1.05   12
  13 Denver                40 42     0.25     0.79   13
  14 Portland              40 46    -0.03    -0.20   15
  15 Atlanta               45 43    -0.09    -0.60   14
  16 Milwaukee             44 44    -0.16    -0.71   16
  17 Chicago               43 45    -0.19    -0.27   17
  18 Indiana               42 44    -0.31    -0.55   18
  19 Detroit               37 45    -0.65    -0.98   19
  20 Minnesota             31 51    -0.68    -0.07   23

Rank Team                   W  L   Rating   Points  BCS
  21 Charlotte             36 46    -0.69    -0.57   20
  22 New Orleans           34 48    -1.04    -2.00   21
  23 Dallas                33 49    -1.35    -3.01   22
  24 Sacramento            32 49    -1.42    -3.15   24
  25 New York              31 51    -1.75    -3.42   25
  26 Phoenix               24 58    -2.43    -4.62   29
  27 Philadelphia          27 54    -2.66    -6.07   27
  28 LA Lakers             26 56    -2.72    -5.89   28
  29 Orlando               29 53    -2.74    -6.61   26
  30 Brooklyn              20 62    -3.54    -7.02   30

(The "BCS style" ranking is one based entirely on wins and losses, similar to what is used in college football.)

 

                         PREDICTIONS FOR UPCOMING GAMES

   Date      Away Team             Home Team             Total    Prediction
-----------  --------------------  --------------------  -----  --------------
07-Jun-2017  Golden State          Cleveland              226   AWAY by  -5.49

09-Jun-2017  Golden State          Cleveland              226   AWAY by  -5.49

12-Jun-2017  Cleveland             Golden State           228   HOME by  11.76

15-Jun-2017  Golden State          Cleveland              226   AWAY by  -5.49


Current home field advantage is:  3.13

MAE for games to date:  9.97

These ratings fit to produce 0.66 of the correct winners.
Pct when predicted MOV is above 6.26:  0.80

A favored away team rarely loses when favored by more than -3.74.

A favored home team rarely loses when favored by more than 5.94.


Above are some statistics about the ratings model. Each team has its own home "field" advantage, but the average all of them is shown here. The MAE is the mean absolute error of the ratings fit to all the games played to date. This number is usually larger than you think it should be, but it's a good measure of how variable (or maybe "predictable") game outcomes can be.

Immediately below that, you can see how this best fit does in retro-predicting (there's a better word I'm sure) just the game winners. My favorite stats are the last two--when the home or away team is favored by the given margin, they only lose 30 percent of the time. This is the kind of information that people in the sports wagering world might find useful.


About the author

I have a Ph.D. in Atmospheric Science from the University of Alabama in Huntsville. I am now a faculty member at Indiana University in Bloomington, teaching courses in the broad areas of weather and climate. Don't hesitate to contact me using this email form if you have questions or non-hateful comments. shopify stats