Predictive Modeling of Lower-Level English Club Soccer Using Crowd-Sourced Player Valuations
Authors:
Joshua Brown
(Ursinus College)
,
Yutong Bu
(Emory University)
,
Zachary Cheesman
(Bowdoin College)
,
Benjamin Orman
(Grinnell College)
,
Iris Horng
(University of Pennsylvania)
,
Samuel Thomas
(Brown University)
,
Amanda Harsy
(Lewis University)
,
Adam Schultze
(Lewis University)
Abstract
In this research, we examine the capabilities of different mathematical models to accurately predict various levels of the English football pyramid. Existing work has largely focused on top-level play in European leagues; however, our work analyzes teams throughout the entire English Football League system. We modeled team performance using weighted Colley and Massey ranking methods which incorporate player valuations from the widely-used website Transfermarkt to predict game outcomes. Our initial analysis found that lower leagues are more difficult to forecast in general. Yet, after removing dominant outlier teams from the analysis, we found that top leagues were just as difficult to predict as lower leagues. We also extended our findings using data from multiple German and Scottish leagues. Finally, we discuss reasons to doubt attributing Transfermarkt's predictive value to wisdom of the crowd.
Keywords: linear algebra, sports, soccer, predictive modeling, forecasting, Transfermarkt
How to Cite:
Brown, J., Bu, Y., Cheesman, Z., Orman, B., Horng, I., Thomas, S., Harsy, A. & Schultze, A., (2025) “Predictive Modeling of Lower-Level English Club Soccer Using Crowd-Sourced Player Valuations”, Maths and Sports 7(1). doi: https://doi.org//ms.1327
20 Views
8 Downloads