Saturday, November 08, 2014

## ESPO - Introduction

ESPO - "Expected Shot Probability Outcome"

Chess

• Chess players are rated
• Compare ratings to generate probability of winning
• Ratings are adjusted after a tournament or a match

Elo Rating System

• Value used in determining score adjustment is constant

Glicko Rating System

• Value used in determining score adjustment evolves with player

## ESPO - Applying Elo

Why Is Elo an Appropriate System?

• Many parallels between Chess and Ice Hockey
• Two opposing parties playing to win a match
• Evolving skill levels
• Binary outcomes
• Adding effects (black-white effect is similar to home-ice effect)

## ESPO - Impact

Why Is Team Evaluation Important and What Can ESPO Predict?

• Tracking which teams are "truly" the best, if the standings don't show it
• Predicting outcomes of games, shot ratios, and goal ratios

Why Is This Model Interesting?

• ESPO ratings evolve, so as a team's skill changes, the ratings should change
• Since the ratings evolve, it can be used to predict an outcome at any specified point in time.
• Effects can be added, such as home ice, score effects, etc.

## ESPO - Calculating Probabilities

• Ratings are initialized at 1500, Value used in determining score adjustment was optimized through testing
• Expected Outcome for Team A: $$\frac{1}{1+10^{(Rating_B - Rating_A)/400}}$$
• 80% chance of event occurring requires 240 point differential
• 70% chance of event occurring requires 149 point differential
• 60% chance of event occurring requires 72 point differential
• Update ratings after each outcome: $$R_A' = R_A + K* (Outcome - Expected Outcome)$$
• An underdog team gains more points from an upset than a favorite that wins

## ESPO - Findings

Confirmation

• The best teams of each season had the highest ratings (in recent years, CHI, LAK)

Predictability

• The team that shoots the puck next can be predicted with 54% accuracy
• The team that scores the next goal can be predicted with 55% accuracy
• Game outcomes can be predicted with 58% accuracy
• Room for improvement, perhaps use other systems

## ESPO - Conclusions & Further Development

Conclusions

• There is room for improvement, especially in determining the uncertainty associated with the rating itself

Further Development

• Kalman filters use to rate teams in NBA (Poropudas, 2011)
• Elo ratings in the NFL (Nate Silver, FiveThirtyEight.com)