1. School of Computing Science
Simon Fraser University
Vancouver, Canada
What is the value of an action in ice hockey? Deep Learning for The National Hockey League
Oliver Schulte
Guiliang Liu
If you use “insert slide number” under “Footer”, that text box only displays the slide number, not the total number of slides. So I use a new textbox for the slide number in the master.
This is a version of “Equity”.

2. Overview
Evaluate NHL players by the total aggregate value of their actions (decisions)
Evaluate values of actions by how they impact a teams’ chance of scoring the next goal
Use a neural net to estimate the chance that a team scores the next goal

3. Evaluating Actions by Expected Future Success

4. Expected vs. Actual Success
Important insight: players should create chances to score
but actual scoring success depends on other factors beyond their control (e.g. goalie, teammates, luck)
for shots, look at the expected goals, not the actual goals
So far has been done for the immediate effects of actions (e.g. chance of a goal occuring within 30 sec, THOR Shuckers and Curro 2013)
What about actions with indirect and delayed effects (e.g. drawing penalties, strong passes)?
Consider the chance of success in the future, down the line.

5. Examples Basketball Expected Point Value (Cervone, Bornn et al. 2014):
The Spurs have the ball with 15 sec left on the shot clock. What is the chance that they will score 3 points?
In-game win probabilities (Pettigrew 2015)
big impact
smaller impact

6. Our Version: Scoring The Next Goal
Our model estimates the probability that a team scores the next goal (within any amount of time)
big impact
but no goal
Q = chance of scoring the next goal
Blue Jackets vs. Penguins Nov 17, 2015
- more fine-grained information than in-game win probabilities
shouldn’t this be “blue jacket advantages”? And Penguins passed around the goal?
the three numbers add up

7. Take-Aways
If you can estimate the chance of future success (winning, scoring), you can assign values to actions
Value of an action = impact on expected success
The impact can be measured for all actions, offensive and defensive
Action Type
Impact
Offensive
(e.g. pass)
increases chance that my team scores the next goal
Defensive (e.g. block)
decreases chance that the other team scores the next goal 

8. Estimating Expected Future Success
Deep Learning for the NHL

9. Function Estimation
Q = 0.2
Think of a function that maps a game state to the chance of scoring/winning
This is called the value function or the Q-function in machine learning
What is the mathematical form of this function (linear, polynomial,...)?
Beats me!

10. Neural Networks To The Rescue
No reason to think that the Q-function for hockey has a nice mathematical form
Use a universal function approximator (non-parametric statistics)
Neural networks are a well-developed type of universal function approximator

11. Neural Network Training
Applies reinforcement learning
Reinforcement learning builds models for estimating expected future success from actions
Google Deepmind uses RL to build artificial intelligence
SportLogiq dataset with >3M NHL events
We’ve also used nhl.com play-by-play data

12. From Action Values to Player Evaluation

13. Total Goal Impact Metric (GIM)
Traditional Approach: Add up the impact values for each player to get a total score
We are looking at every action can attribute to specific player
rather than all players on the ice

14. Player Ranking Top-20 highest impact players
Identify undervalued players Johnny Gaudreau and Mark Scheifele
Later they received a $5M+ contract for the season.

15. Impact Metric Correlates Well With Standard Stats
Looked at 14 standard stats, also auto-correlation
Table shows correlation with season points only
Performance Metric
Correlation With Points
+/-
0.237
Goals Above Replacement (GAR)
0.622
Wins Above Replacement (WAR)
0.612
Expected Goals
0.854
Scoring Impact
0.87
Goal Impact
0.93

16. Goal Impact Predicts Salary
For worth of new contracts following the season
Correlation increases for contracts 2 seasons away

17. Visualization
In the season (left), we find many underestimated players (with high GIM but low salary).
But the percentage of players who are undervalued decreases in the next season.
This suggests that the Goal Impact Metric provides an early signal of a player's value.

18. Conclusions
If you can estimate the chance of future success (winning, scoring), you can assign values to all actions
Value of an action = impact on expected success
Estimating chance of future success can be done with neural nets (deep reinforcement learning)
Total action value = goal impact metric for each player
Powerful new performance metric:
Correlates well with points, goals, games played, etc.
predicts worth of new contracts