Predicting NHL Player Contracts
Teams use statistical models to predict NHL player contracts, helping them plan for salary cap constraints. The models consider player statistics, contract history, and free agent status to estimate player salaries accurately. The new model improves accuracy by predicting both contract term and salary, with enhanced performance metrics compared to previous models.
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Predicting NHL Player Contracts Matt Cane @Cane_Matt Hockey Graphs | Puck++
Predicting Contracts: Why? Long Term salary cap planning Teams have limited resources to identify and pursue free agents Useful for players to know what statistically similar players have been paid Don t want to leave money on the table Can help identify teams who overpay vs. market, or find cheap free agent talent Can help identify players whose price price (dollars) is different from their value value (wins) Bonus: Provides something for strangers on the internet to yell at you about when you suggest that their favourite player is overpaid
Past work Run models over the past 3 off-seasons 2014-15: Linear Regression, UFA Skaters only 2015-16: Beta Regression, UFA + RFA Skaters 2016-17: Random Forest, UFA + RFA + Goalies Other models: Manny Perry: k-Nearest Neighbours Luke Solberg (@evolvingwild): Regression w/ WAR/Game Score/TOI Chris Watkins (@yolopinato): Regression Carolyn Wilke: Salary Cap Bands
The 2016-17 Model Uses Random Forest to predict adjusted cap hit percent at time of signing: Adj. Cap Hit Percent = (Cap Hit Min. Cap Hit)/(Max Cap Hit Min Cap Hit) Model player salary using: Prior Year and 3-Year Total Stats from NHL.com Past Contract History (Last Cap Hit) Contract Timing (Before a player hits FA or after) Free Agent Status (UFA vs RFA, Buyout)
Whats new? Predict both term and salary Include term as a predictor in the salary model Use Z-scores by season rather than absolute totals as predictors Model buyouts separate from other players, assume buyout contracts are for 1 year
Is the new model any good? For all Free Agents signed after July 1st, 2017 (excluding extensions before FA): Model Model Mean Absolute Mean Absolute Error Error Mean Absolute Mean Absolute Percentage Error Percentage Error Old Model $582K 38.0% New Model w/ Predicted Term $529K 30.3% New Model w/ Weighted Average Term $529K 33.1% New Model w/ Actual Term $429K 28.5% Term model predicts 49.4% of contract lengths correctly
What does the new model struggle with? Many things! Predicting the super high-end players Players like Connor McDavid and Auston Matthews tend to be undervalued For most players, as term increases predicted salary increases Some cases this makes sense (bridge vs. long-term deals) Other cases we d expect more term would mean less money The Olds Harder to model older players since many retire
How can we use this data?
Who were the most overpaid players this off-season? (aka GM errors) Overpaid Overpaid Cap Hit Cap Hit Expected Cap Hit Expected Cap Hit $ $ Above Expected Above Expected Jack Eichel 10.0M 7.4M 2.6M Leon Draisaitl 8.5M 6.5M 2.0M Marc-Edouard Vlasic 7.0M 5.1M 1.9M Evgeny Kuznetsov 7.8M 6.2M 1.6M Erik Gudbranson 3.5M 1.9M 1.6M
Who were the most underpaid players this off-season? (aka model errors) Underpaid Underpaid Cap Hit Cap Hit Expected Expected Cap Hit Cap Hit $ $ Below Expected Below Expected Adam Pelech 1.6M 3.2M 1.6M Radim Vrbata 2.5M 3.9M 1.4M Patrick Sharp 0.8M 2.1M 1.3M Cody Franson 1.0M 2.2M 1.2M Connor Brown 2.1M 3.3M 1.2M
Predicting Next Contract Term The model is fairly certain Matthews will get a long-term deal, but Marner s prediction is divided.
Long-Term Cap Planning (Or are the Sens at risk of losing Erik Karlsson?) Karlsson is a Free Agent in 2019-20 Projected Contract:8 years, 7.6M Sens Sens Key Key Free Agent Projections Free Agent Projections 2018 2018 Term Term $ $ Turris 8 6.4 Current Cap Commitments: 2019-20: $39.7M (6F, 2D, 2G) Stone 5 6.3 Ceci 6 4.7 Other Key Free Agents: ~21.3M to retain 2019 2019 Term Term $ $ Brassard 2 3.9 Assuming 80M Cap in 2019-20: 11.5M for ~4 forwards and ~3 defencemen
Future Work Improving Term Model Tends to default to 1 year deals in too many cases More subjective variables: 3-Star Selections NHL Award Voting Captaincy Term with Team Better Adjustments for Injuries and Early Career players Second year players don t have the same three year stat profiles Connor McDavid s injury changes his stat profile Including advanced metrics and one number metrics for player value Corsi, WAR, K, etc.
