tHoops: A Multi-Aspect Analytical Framework Spatio-Temporal Basketball Data

TL;DR

This paper introduces tHoops, a tensor decomposition framework that captures multi-aspect spatio-temporal basketball data, enabling detailed analysis of team and player tendencies beyond traditional spatial methods.

Contribution

It presents a novel tensor-based approach for extracting interpretable spatio-temporal patterns from basketball data, incorporating multiple parameters and contextual meta-data.

Findings

Identifies prototype patterns of offensive and defensive schemes.

Allows expressing entities as combinations of these patterns.

Provides a method for selecting the optimal number of components.

Abstract

During the past few years advancements in sports information systems and technology has allowed us to collect a number of detailed spatio-temporal data capturing various aspects of basketball. For example, shot charts, that is, maps capturing locations of (made or missed) shots, and spatio-temporal trajectories for all the players on the court can capture information about the offensive and defensive tendencies and schemes of a team. Characterization of these processes is important for player and team comparisons, pre-game scouting, game preparation etc. Playing tendencies among teams have traditionally been compared in a heuristic manner. Recently automated ways for similar comparisons have appeared in the sports analytics literature. However, these approaches are almost exclusively focused on the spatial distribution of the underlying actions (usually shots taken), ignoring a multitude of other parameters that can affect the action studied. In this work, we propose a framework based on tensor decomposition for obtaining a set of prototype spatio-temporal patterns based on the core spatiotemporal information and contextual meta-data. The core of our framework is a 3D tensor X, whose dimensions represent the entity under consideration (team, player, possession etc.), the location on the court and time. We make use of the PARAFAC decomposition and we decompose the tensor into several interpretable patterns, that can be thought of as prototype patterns of the process examined (e.g., shot selection, offensive schemes etc.). We also introduce an approach for choosing the number of components to be considered. Using the tensor components, we can then express every entity as a weighted combination of these components. The framework introduced in this paper can have further applications in the work-flow of the basketball operations of a franchise, which we also briefly discuss.