1. Measuring NFL team performance by quarterback stats A neural networks approach By David Michlig EC 539

2. Teams (and fans) want victories!  The quarterback is the leader of the team  Typically, the quarterback stays with a team to play if they have a good season  A neural network approach to determining how the performance of the quarterback affected the overall team performance can make the decision of who to play easier  How important is the quarterback, anyway?

3. Data used  Collected stats from 46 active quarterbacks in the league  Game played, completions, attempts, completion, yards, yards per play, touchdowns, interceptions, fumbles, passser rating, rush attempts, rushing yards, rushing average, rushing touchdowns, and first downs.  Number of wins was used as the classes  Used seasons where quarterback played in 10 or more games  In total, over 200 individual seasons were used

4. Project procedure  I plan to use pattern classification methods to determine relationship between quarterback performance and team wins  Nearest Neighbor algorithm  Maximum Likelihood  Is the quarterback truly the leader of the team, or just another factor?

5. Initial results  Very low classification rate, which doesn’t look promising.  The confusion matrix, however, shows that although it’s not usually an exact prediction, it is often within a few games of the true record

6. Future results  There are many outside factors that need to be considered  Injuries  Quality of the team overall  Defense  Protection  The quarterback does not have a say in these things!  Based on initial results, however, it seems that although exact classification may not be possible, estimates may be made.