期刊名称：International Journal of Computer Science in Sport
电子版ISSN：1684-4769
出版年度：2020
卷号：19
期号：1
页码：60-101
DOI：10.2478/ijcss-2020-0005
语种：English
出版社：Sciendo
摘要：Many factors are considered when making a hiring decision in the National Football League (NFL). One difficult decision that executives must make is who they will select in the offseason. Mathematical models can be developed to aid humans in their decision-making processes because these models are able to find hidden relationships within numeric data. This research proposes the Heuristic Evaluation of Artificially Replaced Teammates (HEART) methodology,which is a mathematical model that utilizes machine learning and statistical-based methodologies to aid managers with their hiring decisions. The goal of HEART is to determine expected and theoretical contribution values for a potential candidate,which represents a player’s ability to increase or decrease a team’s forecasted winning percentage. In order to validate the usefulness of the methodology,the results of a 2007 case study were presented to subject matter experts. After analyzing the survey results statistically,five of the eight decisionmaking categories were found to be “very useful” in terms of the information that the methodology provided.
关键词：DECISION SUPPORT SYSTEMS;LEARNING SYSTEMS;MACHINE LEARNING;PREDICTION METHODS;NEURAL NETWORKS
