<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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 <bold>H</bold>euristic <bold>E</bold>valuation of <bold>A</bold>rtificially <bold>R</bold>eplaced <bold>T</bold>eammates (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 decision-making categories were found to be “very useful” in terms of the information that the methodology provided.</p></abstract>

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 decision-making categories were found to be “very useful” in terms of the information that the methodology provided.

A Team-Compatibility Decision Support System for the National Football League

Department of Analytics and Information Systems, College of Business

Department of Industrial and Systems Engineering, Russ College of Engineering and Technology

International Journal of Computer Science in Sport

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"A Team-Compatibility Decision Support System for the National Football League"}

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...

A Team-Compatibility Decision Support System for the National Football League

Publicado en línea: 29 jun 2020

Páginas: 60 - 101

DOI: https://doi.org/10.2478/ijcss-2020-0005

Palabras clave
DECISION SUPPORT SYSTEMS, LEARNING SYSTEMS, MACHINE LEARNING, PREDICTION METHODS, NEURAL NETWORKS

© 2020 W.A. Young et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

A Team-Compatibility Decision Support System for the National Football League

Publicado en línea: 29 jun 2020

Páginas: 60 - 101

DOI: https://doi.org/10.2478/ijcss-2020-0005

Palabras claveDECISION SUPPORT SYSTEMS, LEARNING SYSTEMS, MACHINE LEARNING, PREDICTION METHODS, NEURAL NETWORKS

© 2020 W.A. Young et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Palabras clave
DECISION SUPPORT SYSTEMS, LEARNING SYSTEMS, MACHINE LEARNING, PREDICTION METHODS, NEURAL NETWORKS