Introduction:
Wins Above Replacement (WAR) is a statistic commonly used in baseball to quantify the amount of additional wins a team will earn over the course of a season with an exceptional player versus an average replacement player at their position. For professional football, however, this statistic has never been utilized because the NFL regular season is only 16 games; unlike baseball, it does not benefit NFL teams to ‘rest’ their starting players if they are able to compete.
In my Summer Undergraduate Research Institute (SURI) project, I will attempt to determine the correlation between wins and select NFL positions over the course of the regular season using an agent based model simulator: the newest edition of the Madden NFL video game for Xbox One. I have based this idea off the findings from ‘Positional WAR In the National Football League’ by Andrew Hughes, Cory Koedel, and Joshua Price, the very first study of its kind to construct WAR values for American football; Despite completely different methods for obtaining these numbers, I hope to draw comparisons to these Positional WAR (PWAR) values with the values that I create through the video game, which will be called ‘Madden Wins Above Replacement (MWAR)’ values.
First I will alter the video game’s settings to most accurately represent NFL player statistics in real life, and confirm its accuracy through various mathematical methods. I will then run multiple simulations with a replacement player and examine the average displacement of regular season wins with the exceptional player.
From this research, I will be able to draw conclusions about which positions in the NFL are the most influential in earning wins for their team during the regular season, according to the video game. I plan to use various forms of regression analysis and two variable t-tests to evaluate my data and generate conclusions.