Predicting Hockey Game Outcomes Through Bayesian Mixed-Effects Modeling & Monte Carlo Simulation, authored by Zach Zaslow, ’27 The Pingry School, NJ, and advisor Bradford Poprik, The Pingry School, NJ.…Read More
Multi-Model Consensus: an Ensemble Approach to Basketball Predictions, authored by: Shivam Gupta, ‘26, Lambert High School, GA, and advisor Stephanie Beaulieu, Lambert High School, GA.…Read More
This study presents a novel approach to evaluating soccer team strength and calculating win probabilities given any two matchups of soccer teams in the fictional North American Soccer League (NSL), whose data is provided for the Wharton High School Data Science Competition. Traditional win-loss metrics have many limitations, like failing to account for the strength of schedule, margin of victory, and home-field advantage. Thus, we developed a Whole-History Rating (WHR) model adapted from Rémi Coulom that incorporates expected goals (xG) and accounts for home-field advantage, overcoming the sequencing issues inherent in single-pass Elo systems. Using a dataset of 476 NSL games, we implemented a multi-pass system for iterative rating adjustments and employed Bayesian inference to develop probability distributions for team ratings.…Read More
