2016-17 National Hockey League
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 the home "field" advantage, which is on the right below the predictions.

All of the predictions on the right already have the home "field" added in, and also show a predicted total. Shootouts and overtimes make this tricky, and are a lot of why the average error is 1.5 goals or so.

Early-season predictions are based almost entirely on last year's games and the preseason, so use those with extreme caution.


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

Rank Team                   W  L   Rating   Points  BCS
   1 Washington            62 33     3.84     0.86    1
   2 Pittsburgh            64 39     3.15     0.67    2
   3 Columbus              51 36     2.08     0.50    3
   4 Minnesota             50 37     1.71     0.50   12
   5 Edmonton              54 41     1.66     0.33    4
   6 NY Rangers            54 40     1.57     0.41    8
   7 St Louis              52 41     1.41     0.24    6
   8 Montreal              49 39     1.32     0.33   11
   9 Chicago               50 36     1.21     0.23    7
  10 Nashville             53 47     1.18     0.26   10

Rank Team                   W  L   Rating   Points  BCS
  11 Anaheim               56 43     1.15     0.13    5
  12 San Jose              48 40     0.78     0.19   13
  13 Boston                46 42     0.78     0.20   14
  14 Ottawa                55 46     0.67     0.04    9
  15 Tampa Bay             42 40     0.56     0.13   16
  16 NY Islanders          41 41     0.24    -0.02   15
  17 Toronto               42 46    -0.01     0.09   19
  18 Calgary               45 41    -0.04    -0.11   17
  19 Winnipeg              40 42    -0.06    -0.06   18
  20 Philadelphia          39 43    -0.59    -0.14   20

Rank Team                   W  L   Rating   Points  BCS
  21 Los Angeles           39 43    -0.69    -0.11   21
  22 Carolina              36 46    -0.92    -0.19   22
  23 Detroit               33 49    -1.48    -0.27   23
  24 Florida               35 47    -1.50    -0.26   25
  25 Buffalo               33 49    -1.89    -0.41   26
  26 Dallas                34 48    -1.94    -0.46   24
  27 Arizona               30 52    -2.72    -0.64   27
  28 Vancouver             30 52    -3.15    -0.66   28
  29 New Jersey            28 54    -3.34    -0.65   29
  30 Colorado              22 60    -4.98    -1.12   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
-----------  --------------------  --------------------  -----  --------------
04-Jun-2017  Pittsburgh            Nashville               6.1  AWAY by  -0.08

06-Jun-2017  Pittsburgh            Nashville               6.1  AWAY by  -0.08

09-Jun-2017  Nashville             Pittsburgh              6.2  HOME by   0.75

12-Jun-2017  Pittsburgh            Nashville               6.1  AWAY by  -0.08

15-Jun-2017  Nashville             Pittsburgh              6.2  HOME by   0.75


Current home field advantage is:  0.33

MAE for games to date:  1.86

These ratings fit to produce 0.59 of the correct winners.
Pct when predicted MOV is above 0.67:  0.71

A favored away team rarely loses when favored by more than -0.45.

A favored home team rarely loses when favored by more than 0.72.


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