Using Hex Maps to Classify & Cluster Dribble Hand-off Variants in the NBA

Dembe Koi Stephanos; Ghaith Husari; Brian Bennett; Matthew Harrison

Using Hex Maps to Classify & Cluster Dribble Hand-off Variants in the NBA

Dembe Koi Stephanos
, Ghaith Husari
, Brian Bennett
, Matthew Harrison

Department of Computing

East Tennessee State University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

This paper introduces a novel approach to precision abstraction as it relates to the classification and analysis of play-actions in the NBA. We look specifically at dribble-hand-offs as observed from SportVU player tracking data and embed the raw coordinate data into hex map representations. We then outline the architecture for an automated pipeline capable of selecting action instances and clustering them into variants. These action variants can then be used to further differentiate players and strategies within the game context in which they are implemented.

Original language	American English
Title of host publication	MIT Sloan Sports Analytics Conference
State	Published - Mar 2022

Cite this

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title = "Using Hex Maps to Classify \& Cluster Dribble Hand-off Variants in the NBA",

abstract = "This paper introduces a novel approach to precision abstraction as it relates to the classification and analysis of play-actions in the NBA. We look specifically at dribble-hand-offs as observed from SportVU player tracking data and embed the raw coordinate data into hex map representations. We then outline the architecture for an automated pipeline capable of selecting action instances and clustering them into variants. These action variants can then be used to further differentiate players and strategies within the game context in which they are implemented. ",

author = "Stephanos, \{Dembe Koi\} and Ghaith Husari and Brian Bennett and Matthew Harrison",

year = "2022",

month = mar,

language = "American English",

booktitle = "MIT Sloan Sports Analytics Conference",

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AU - Husari, Ghaith

AU - Bennett, Brian

AU - Harrison, Matthew

PY - 2022/3

Y1 - 2022/3

N2 - This paper introduces a novel approach to precision abstraction as it relates to the classification and analysis of play-actions in the NBA. We look specifically at dribble-hand-offs as observed from SportVU player tracking data and embed the raw coordinate data into hex map representations. We then outline the architecture for an automated pipeline capable of selecting action instances and clustering them into variants. These action variants can then be used to further differentiate players and strategies within the game context in which they are implemented.

AB - This paper introduces a novel approach to precision abstraction as it relates to the classification and analysis of play-actions in the NBA. We look specifically at dribble-hand-offs as observed from SportVU player tracking data and embed the raw coordinate data into hex map representations. We then outline the architecture for an automated pipeline capable of selecting action instances and clustering them into variants. These action variants can then be used to further differentiate players and strategies within the game context in which they are implemented.

M3 - Conference contribution

BT - MIT Sloan Sports Analytics Conference

ER -