1. Contextualized Goals in Soccer Frank Silva

2. 2

3. 3

4. 4

5. 5

6. Methodology Applications Future Work 6

7. Win-Probability Model Score DifferentialInstances Home Wins+1399 Home Loss+175 Home Ties+193 Total+1567 15 th Minute: 7

8. Win-Probability Model Score DifferentialProbability P H (W)+10.704 P H (L)+10.132 P H (T)+10.164 Total+11 15 th Minute: 8

9. Win-Probability Model 9

10. 15 th Minute: Score Differential Probability actual Probability calculated P H (W)+10.7040.719 P H (L)+10.1320.115 P H (T)+10.1640.166 *New Probabilities based on Regressions Valuing Goals 10

11. . 719 P H (W) = P H (W) * 3 + P H (T) * 1= 2.328 P H (T) =. 171 15 th Minute, (+1) Score Differential: Expected Value of Points 11

12. Exp. Value (+2) – Exp. Value (+1) ExPA = 2.750 – 2.328 = 0.422 Expected Points Added (ExPA) 12 15 th Minute:

13. PlayerGoals 1. Sergio Aguero26 2. Harry Kane21 3. Diego Costa20 4. Charlie Austin18 5. Alexis Sanchez16 Top EPL Scorers vs. Top ExPA PlayerExPA 1. Harry Kane19.24 2. Alexis Sanchez13.19 3. Sergio Aguero12.30 4. Diego Costa12.08 5. Charlie Austin10.35 13 Goal count does not tell the entire story

14. 1. Account for team quality 2. Account for shot difficulty Future Applications 14 Betting lines and Expected Goal metric can further contextualize goals

15. Daniel Sturridge 22 goals Yaya Touré 20 goals 15

16. Daniel Sturridge 22 goals Up by 3 or more: 1 Penalty kicks: 0 16

17. Yaya Touré Up by 3 or more: 3 Penalty kicks: 5 20 goals 17

18. Applications 18