NFL Data Analytics and Predictions Using Machine Learning

Barry Husowitz (), Mark Mixer () and Steven Morrow ()
Additional contact information
Barry Husowitz: Wentworth Institute of Technology
Mark Mixer: Wentworth Institute of Technology
Steven Morrow: Wentworth Institute of Technology

A chapter in Handbook of Visual, Experimental and Computational Mathematics, 2026, pp 1147-1167 from Springer

Abstract: Abstract This chapter explores the applications of machine learning techniques in National Football League (NFL) data analytics and predictions, providing a comprehensive overview of how these advanced methodologies are transforming sports analytics. First regression models, which are foundational tools for predicting continuous outcomes based on historical data, are discussed. These models help estimate win probabilities and player performances, offering a statistical basis for strategic decisions. Next, classification models are examined, such as decision trees and random forests, which categorize observations into predefined classes, enabling the prediction of game outcomes, individual plays, and player decisions that fall into discrete classes. Then deep learning is explored, where artificial neural networks are increasingly used to predict play types, assess player movements, and evaluate team strategies in the NFL. The chapter also introduces a novel passing statistic, developed by combining multiple features and machine learning algorithms. This metric provides a novel way of predicting a more nuanced evaluation of quarterback performance, demonstrating the potential of advanced statistical methods in creating innovative metrics. By providing detailed discussions and practical examples, this chapter aims to give readers a thorough understanding of the potential and challenges of applying machine learning techniques to NFL data.

Keywords: NFL; Football; Statistics; Analytics; Regression; Classification; Deep learning (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-032-16368-4_22

Ordering information: This item can be ordered from
http://www.springer.com/9783032163684

DOI: 10.1007/978-3-032-16368-4_22

Access Statistics for this chapter

More chapters in Springer Books from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().