REVIEW 1 cited by
A Continuous-Time Stochastic Process for High-Resolution Network Data in Sports
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original 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.
Forward citations
Cited by 1 Pith paper
-
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.
Discussion (0). Continue with ORCID to comment.