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 Sunday 10/11/20, 10:53 AM ET Rank Team W L Rating Points BCS 1 Tampa Bay 61 34 3.79 50.81 1 2 Colorado 51 33 3.08 50.85 3 3 Philadelphia 51 34 3.06 50.75 2 4 Las Vegas 51 40 3.00 50.83 4 5 Boston 49 34 2.04 50.54 5 6 Dallas 52 44 1.73 50.37 6 7 Carolina 43 33 1.26 50.46 9 8 Calgary 41 39 1.18 50.57 11 9 Winnipeg 38 37 0.82 50.53 16 10 NY Islanders 48 42 0.76 50.13 8 Rank Team W L Rating Points BCS 11 Minnesota 36 37 0.73 50.34 12 12 Los Angeles 29 41 0.67 50.07 7 13 NY Rangers 37 36 0.55 50.29 13 14 St Louis 44 35 0.39 50.38 17 15 Vancouver 45 40 0.38 50.04 10 16 New Jersey 28 41 0.23 50.16 14 17 Edmonton 38 37 0.11 50.35 20 18 Nashville 36 37 -0.25 49.88 15 19 Chicago 36 44 -0.34 50.12 22 20 Arizona 37 42 -0.62 49.85 19 Rank Team W L Rating Points BCS 21 Montreal 36 45 -0.63 49.84 18 22 San Jose 29 41 -1.03 49.82 25 23 Anaheim 29 42 -1.39 49.46 21 24 Toronto 38 37 -1.69 49.42 24 25 Columbus 36 44 -1.71 49.53 26 26 Buffalo 30 39 -1.98 49.24 23 27 Washington 43 34 -2.26 49.45 30 28 Pittsburgh 41 32 -2.34 49.37 29 29 Florida 36 37 -2.63 49.15 28 30 Ottawa 25 46 -2.98 48.90 27 31 Detroit 17 54 -3.93 48.50 31
(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 ----------- -------------------- -------------------- ----- --------------

Current home field advantage is: 0.08 MAE for games to date: 1.86 These ratings fit to produce 0.62 of the correct winners. Pct when predicted MOV is above 0.17: 0.64 A favored away team rarely loses when favored by more than -0.68. A favored home team rarely loses when favored by more than 0.79.

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