A Context-Driven Framework for Fairer Ranking Across Real-World Systems

Authors

• Pawan Chaudhary, Dakota State UniversityFollow
• Justin Schroeder, Dakota State UniversityFollow

Files

Description

Classical ranking methods each rely on a single input: Massey uses point differentials, Colley uses win/loss records, and PageRank uses win proportions. None incorporates the full context of a matchup. This research develops a general-purpose ranking framework built on graph theory and linear algebra. Competitors are modeled as nodes, and each interaction produces a 10-dimensional feature vector that is mapped to an edge weight via a sigmoid function. The resulting linear system 𝑀 𝑥=−1 is solved to produce ratings with provable mathematical guarantees. The framework is validated on UEFA Champions League 2025–26 data and designed to generalize to student evaluation and recommendation systems.

Publication Date

2026

Recommended Citation

Chaudhary, Pawan and Schroeder, Justin, "A Context-Driven Framework for Fairer Ranking Across Real-World Systems" (2026). Annual Research Symposium. 88.
https://scholar.dsu.edu/research-symposium/88