Going for Three: Predicting the Likelihood of Field Goal Success with Logistic Regression

The field goal is a critical scoring play in the National Football League. Coaches and fans alike are interested in the probability that a field goal attempt will be made or missed. Traditional analyses assume that the attempt distance is the primary factor determining success; however, we believe that other environmental and situational factors cannot be ignored. We constructed a binary logistic regression model based on data from the 2000-2011 NFL seasons to identify factors that have a significant effect on the likelihood of field goal success. Distance and most environmental factors were significant. Altitude and artificial turf improved the likelihood of a make, while cold temperatures, wind, and precipitation reduced it. Contrary to popular belief, not one situational factor (regular season vs. postseason, home vs. away, whether a timeout was called before the attempt, and situational pressure) was significant. We used our comprehensive model to evaluate kicker careers, seasons, and stadiums between 2000-2011. This evaluation is superior to pure make percentage, which is ignorant of the difficult of a kicker’s field goal attempts. By more accurately predicting the outcome of field goal attempts, coaches can make better ingame decisions and fans can gain a greater understanding of kicker ability.