DYNAMO uses kernel-weighted local M-estimators to learn time-varying causal graphs from non-stationary time series, and applies them to EPL data to claim time-varying home field advantage driven partly by referee bias.
A Continuous-Time Stochastic Process for High-Resolution Network Data in Sports
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abstract
Technological advances have paved the way for collecting high-resolution network data in basketball, football, and other team-based sports. Such data consist of interactions among players of competing teams indexed by space and time. High-resolution network data are vital to understanding and predicting the performance of teams, because the performance of a team is more than the sum of the strengths of its individual players: Whether a collection of players forms a strong team depends on the strength of the individual players as well as the interactions among the players. We introduce a continuous-time stochastic process as a model of interactions among players of competing teams indexed by space and time, discuss basic properties of the continuous-time stochastic process, and learn the stochastic process from high-resolution network data by pursuing a Bayesian approach. We present simulation results along with an application to Juventus Turin, Inter Milan, and other football clubs in the premier Italian soccer league.
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Time-Varying Home Field Advantage in Football: Learning from a Non-Stationary Causal Process
DYNAMO uses kernel-weighted local M-estimators to learn time-varying causal graphs from non-stationary time series, and applies them to EPL data to claim time-varying home field advantage driven partly by referee bias.