2018-19 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 Thursday 06/13/19, 07:46 PM ET Rank Team W L Rating Points BCS 1 Tampa Bay 62 24 4.52 51.04 1 2 Boston 64 42 3.00 50.71 2 3 St Louis 61 47 2.29 50.42 3 4 Columbus 53 39 2.03 50.46 4 5 Calgary 51 36 1.98 50.60 8 6 NY Islanders 52 38 1.74 50.38 7 7 Washington 51 38 1.63 50.32 6 8 Carolina 54 43 1.45 50.23 5 9 San Jose 56 46 1.24 50.27 9 10 Winnipeg 49 39 1.18 50.35 11 Rank Team W L Rating Points BCS 11 Toronto 49 40 0.96 50.33 14 12 Las Vegas 46 43 0.89 50.33 15 13 Pittsburgh 44 42 0.82 50.32 16 14 Nashville 49 39 0.79 50.18 13 15 Montreal 44 38 0.78 50.16 10 16 Dallas 50 45 0.72 50.13 12 17 Colorado 45 49 0.28 50.18 17 18 Arizona 39 43 -0.28 49.94 18 19 Florida 36 46 -0.72 49.88 20 20 Chicago 36 46 -1.08 49.72 21 Rank Team W L Rating Points BCS 21 Minnesota 37 45 -1.14 49.74 22 22 Philadelphia 37 45 -1.30 49.59 19 23 Detroit 32 50 -1.88 49.54 24 24 Edmonton 35 47 -1.89 49.53 23 25 Vancouver 35 47 -1.91 49.56 26 26 NY Rangers 32 50 -2.15 49.53 27 27 Anaheim 35 47 -2.45 49.28 25 28 Buffalo 33 49 -2.53 49.48 30 29 Los Angeles 31 51 -2.90 49.24 28 30 New Jersey 31 51 -2.99 49.26 29 31 Ottawa 29 53 -3.10 49.29 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: 0.30 MAE for games to date: 2.06 These ratings fit to produce 0.60 of the correct winners. Pct when predicted MOV is above 0.60: 0.66 A favored away team rarely loses when favored by more than -0.60. A favored home team rarely loses when favored by more than 0.87.

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