Selecting the best compositions of a wheelchair basketball team: a data-driven approach

TL;DR

This paper introduces a data-driven tool that combines Bayesian modeling and optimization techniques to select optimal wheelchair basketball team line-ups based on past performance data and player effectiveness metrics.

Contribution

It presents a novel methodology integrating Bayesian longitudinal models with integer linear programming for team composition optimization in wheelchair basketball.

Findings

Effective team line-up selection demonstrated on real Bundesliga data

Incorporates uncertainty in player performance into the optimization process

Calculates posterior probabilities of player compatibility on court

Abstract

Wheelchair basketball, regulated by the International Wheelchair Basketball Federation, is a sport designed for individuals with physical disabilities. This paper presents a data-driven tool that effectively determines optimal team line-ups based on past performance data and metrics for player effectiveness. Our proposed methodology involves combining a Bayesian longitudinal model with an integer linear problem to optimise the line-up of a wheelchair basketball team. To illustrate our approach, we use real data from a team competing in the Rollstuhlbasketball Bundesliga, namely the Doneck Dolphins Trier. We consider three distinct performance metrics for each player and incorporate uncertainty from the posterior predictive distribution of the longitudinal model into the optimisation process. The results demonstrate the tool's ability to select the most suitable team compositions and calculate posterior probabilities of compatibility or incompatibility among players on the court.