2017-18 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 Tuesday 06/19/18, 12:58 PM ET Rank Team W L Rating Points BCS 1 Winnipeg 61 38 2.84 50.68 5 2 Nashville 60 35 2.81 50.60 2 3 Tampa Bay 65 34 2.77 50.55 1 4 Las Vegas 64 38 2.65 50.59 4 5 Washington 65 41 2.13 50.32 3 6 Boston 55 39 2.10 50.55 6 7 Toronto 52 37 1.59 50.37 7 8 San Jose 51 41 1.53 50.36 8 9 Minnesota 46 41 1.20 50.29 11 10 Los Angeles 45 41 1.19 50.42 15 Rank Team W L Rating Points BCS 11 Pittsburgh 53 41 1.05 50.18 10 12 Colorado 45 43 0.90 50.28 13 13 Florida 44 38 0.90 50.08 9 14 Columbus 47 41 0.58 50.09 12 15 New Jersey 45 42 0.50 50.11 14 16 Dallas 42 40 0.40 50.15 18 17 St Louis 44 38 0.39 50.08 16 18 Anaheim 44 42 0.24 50.03 17 19 Philadelphia 44 44 -0.13 49.93 19 20 Edmonton 36 46 -0.84 49.81 20 Rank Team W L Rating Points BCS 21 Carolina 36 46 -1.00 49.75 21 22 Chicago 33 49 -1.27 49.85 24 23 NY Islanders 35 47 -1.45 49.70 23 24 Calgary 37 45 -1.58 49.61 22 25 Arizona 29 53 -1.92 49.57 25 26 NY Rangers 34 48 -1.99 49.56 26 27 Vancouver 31 51 -2.28 49.49 27 28 Detroit 30 52 -2.54 49.52 28 29 Montreal 29 53 -3.20 49.30 30 30 Ottawa 28 54 -3.48 49.16 29 31 Buffalo 25 57 -4.09 49.02 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: 0.40 MAE for games to date: 1.98 These ratings fit to produce 0.64 of the correct winners. Pct when predicted MOV is above 0.81: 0.71 A favored away team rarely loses when favored by more than -0.46. A favored home team rarely loses when favored by more than 0.86.

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