A Data-Driven and Modeling Study of NFL Ticket Pricing Through Team Records and Player Injury Data

open_access
Open Access
Author:
Patel, Vraj
Area of Honors:
Computer Science
Degree:
Bachelor of Science
Document Type:
Thesis
Thesis Supervisors:
Conor Ryan, Thesis Supervisor
John Morgan Sampson, Thesis Honors Advisor
Keywords:
Sports
NFL
Economics
Computer Science
Modeling
NFL Ticket Prices
Injuries
Dynamic Pricing
Algorithms
Coding
Data-Driven
Abstract:
This thesis examines the relationship between NFL ticket prices and two key variables: team injuries and team performance as measured by win-loss records. Understanding how current team conditions affect fan demand has important ramifications for revenue optimization and pricing strategy, as dynamic pricing is becoming more and more crucial in sports economics. The main question guiding this study focuses on how much do player injuries and team records impact NFL ticket pricing during a season. To explore this, I compiled a thorough dataset tracking weekly injury reports and win-loss records for all 32 NFL teams starting in Week 9 of the 2025 NFL season. Throughout this process, average ticket prices were gathered from a public, verified ticket selling platform, TickPick for each team’s home and away games. The objective was to assess whether lower ticket prices are correlated with higher injury counts and whether this effect is mitigated or enhanced by a team’s performance and the injuries to star players. Ordinary Least Squares (OLS) regression models were employed to measure the statistical significance and strength of the link between injury factors, team records, and changes in ticket prices. Python was used to create and test multiple regression models that explored combinations of features including the number of injured starters and recent win/loss streaks. To verify the robustness of the model, several statistical variables were assessed, including R-squared values, p-values, and coefficient magnitudes. The results provide data-driven knowledge of how off-field economics are impacted by on-field reality. By utilizing computer science methods to simulate and predict pricing strategy, these findings connect economic theory with actual consumer behavior more accurately. These observations can help shape future dynamic pricing plans for teams and add to more general conversations about fairness in sports ticketing.