Level of Education of Students Involved
Undergraduate
Faculty Sponsor
Jon Beagley
College
College of Arts & Sciences (CAS)
Discipline(s)
Data Science
Presentation Type
Poster Presentation
Symposium Date
Spring 4-24-2025
Abstract
The financial valuation of NFL running backs has become a critical aspect of team management, with contracts often sparking debate over player worth. This study examines the relationship between in-game performance metrics and salary outcomes to develop a predictive model for running back compensation. Using historical data, I analyze key performance indicators—including rushing yards, touchdowns, carries, and yards per attempt—alongside financial factors such as contract value, average annual salary, and guaranteed money.
Through machine learning techniques, specifically Linear Regression and Random Forest models, I identify the most significant predictors of running back salary. These results will hopefully help give a better understanding as to why teams are both undervaluing and overvaluing certain players based on on-field performance
Recommended Citation
Simakoski, Kristian, "Predicting Running Backs Yearly Salary Based on On-Field Performance" (2025). Symposium on Undergraduate Research and Creative Expression (SOURCE). 1485.
https://scholar.valpo.edu/cus/1485
Biographical Information about Author(s)
Kristian Simakoski is a data scientist with experience in financial analysis, statistical modeling, and business intelligence. A senior at Valparaiso University, he has worked on projects analyzing NFL player performance, business cost evaluations, and data-driven decision-making. Proficient in Python, SQL, and data visualization, he is passionate about leveraging analytics to drive strategic insights. Kristian aims to apply his skills in finance, sports analytics, and business operations to make a meaningful impact.