CFB Rushing WAR: Which FBS RBs had the Biggest Impact on Winning Games in 2024?
Published:
By Max Rotblut, Shekhar Shah, Sam Negus, Josh Leffel
Rushing offense is one of the most heavily analyzed aspects of football. The role and impact of a run game in an offense has changed significantly over the past decade. While its role in the NFL is becoming more well defined, college football rushing attacks are utilized in different ways, and one that has yet to be thoroughly evaluated within a quantitative lense. To do this, we aim to quantify Quarterback (QB) and Running Back (RB) impact through Expected Points Added (EPA) to understand individual QB and RB impacts to their teams expected points, and furthermore, their teams wins. We started by gathering play-by-play rushing data and contextual information on all FBS RB’s and designed runs/scrambles from QB’s from the 2024 regular season. Using past information on RB’s and QB’s as well as their EPA through the regular season, we aim to predict how much expected points they would add in the future, accounting for the conference they play in, defenses they faced, and other situational factors such as redzone plays, opposing rushing defense latent ability, offensive pass strength and home advantage.
From our framework, we were able to estimate how many points a QB and RB add over an “average” player and a replacement player. To calculate the value of a replacement level RB, we first found the average number of RB’s to appear in a game across each conference. This number was between 4-6 across conferences, so we elected to use 4 for each conference to get a large enough sample size of replacement level players for each conference. We then multiplied this value across the number of teams in each conference. Using this product as a threshold, we established that all players above this threshold in terms of snap count were considered non-replacement players, and all players below the threshold were classified as replacement players. Averaging our point estimates for all replacement players in each conference, we were able to establish how many points a replacement RB would add. For QB’s, we considered the number of teams as the snap count threshold for replacement vs non replacement (i.e Big Ten has 18 teams, so the top 18 QB’s in the Big Ten by snap count were considered non-replacement, and all other QB’s were considered as replacement level players). We then translated points to wins by estimating how much points and score differential have an impact on wins. Using both points added above replacement players by conference (IPAR), as well as wins above replacement players (WAR), we can analyze how much value QB’s and RB’s add through their rushing abilities.