Multiobject Optimization of National Football League Drafts: Comparison of Teams and Experts

Featured Application The proposed methodology enables researchers and sport scientists to compare and evaluate NFL drafts based on the evaluation of experts. Additionally, it offers an easy-to-use and reliable solution to solve multicriteria decision making problems, commonly found in sport sciences. Predicting the success of National Football League drafts has always been an exciting issue for the teams, fans and even for scientists. Among the numerous approaches, one of the best techniques is to ask the opinion of sport experts, who have the knowledge and past experiences to rate the drafts of the teams. When asking a set of sport experts to evaluate the performances of teams, a multicriteria decision making problem arises unavoidably. The current paper uses the draft evaluations of the 32 NFL teams given by 18 experts: a novel multicriteria decision making tool has been applied: the sum of ranking differences (SRD). We introduce a quick and easy-to-follow approach on how to evaluate the performance of the teams and the experts at the same time. Our results on the 2021 NFL draft data indicate that Green Bay Packers has the most promising drafts for 2021, while the experts have been grouped into three distinct groups based on the distance to the hypothetical best evaluation. Even the coding options can be tailored according to the experts' opinions. Statistically correct (pairwise or group) comparisons can be made using analysis of variance (ANOVA). A comparison to TOPSIS ranking revealed that SRD gives a more objective ranking due to the lack of predefined weights.

Automated summary

This study introduces a new methodology for comparing and evaluating NFL drafts through expert evaluations, providing a reliable solution to common multicriteria decision-making problems in sports science. Results show that the Green Bay Packers have the most promising draft for 2021, while experts are grouped into three distinct categories based on their distance from the hypothetical best evaluation.