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Oliver Reimer Matthew Crites Brian Jones.  Determine the winner of a NFL game between two teams.  How? ◦ What aspects of a team are most important in.

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Presentation on theme: "Oliver Reimer Matthew Crites Brian Jones.  Determine the winner of a NFL game between two teams.  How? ◦ What aspects of a team are most important in."— Presentation transcript:

1 Oliver Reimer Matthew Crites Brian Jones

2  Determine the winner of a NFL game between two teams.  How? ◦ What aspects of a team are most important in a game’s outcome? ◦ How to represent match-up exploitations?  (i.e. a top-tier defensive team vs. a mediocre offensive ball club)

3  Given 2 teams, teamH and teamA  based upon the comparison between offensive and defensive factors, O and H ◦ O H, D H ◦ O A, D A  Each team’s “edge” in the matchup will be computed as  The team with the larger edge value is the predicted winner

4  Given a set of n team normalized, offensive attributes, P= {0, 1, … n-1}  And a set of n team normalized, defensive attributes Q= {0, 1, … n-1}  And a set of n weights W = {0, 1, … n-1}  The offensive factor is calculated  The defensive factor is calculated

5  Statistics were taken as a per game average over the 16 games of the 2014 NFL season.  Offensive statistics were normalized  Defensive statistics were normalized

6  Passing Yards per game  Rushing Yards per game  Points per game  First Downs per game  Fumbles (lost and recovered)  Interceptions

7  What set of weights will give optimal results?  What team attributes are more important in the game outcome?  The nature of these questions support a genetic approach

8  Four step process ◦ Determine fitness of the members of the population ◦ Generate offspring ◦ Mutate ◦ Update the population

9  Start with a population of n randomly generated solutions ◦ Weights had values ranging from 0 – 1 exclusive  Determine Fitness  Generate Offspring from the most fit  Mutate  Update Population  Repeat for m generations

10  Four Solution Fitness categories ◦ Edge differences ascending ◦ Edge differences descending ◦ Simulation ascending ◦ Simulation descending  The four categories produce vastly different results

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12  Accuracy in predicting the 2014 NFL Season ◦ Edge Difference Ascending  50.4 % ◦ Edge Difference Descending  70.7 % ◦ Simulation Ascending  70.7 % ◦ Simulation Descending  48.8 %

13  Most Important ◦ Forcing Turnovers  Fumbles  Interceptions  Least Important ◦ Yardage Allowed  Rushing  Passing ◦ First Downs Allowed

14  Hot Streaks  Past Match-up Experience  Injuries  Home-field/Weather advantage

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