@inproceedings{8f8642b58f1449a9b646b474cdd4a83e,
title = "Drafting agent-based modeling into basketball analytics",
abstract = "The growth of sports analytics (SA) has raised numerous research topics across a variety of sports, including basketball. Agent-based modeling (ABM) has great potential to assist and inform SA, but to date it has not been utilized. To support the use of ABM in SA, a model of a basketball game, which considers most fundamentals of play, is presented. Additionally, player behavior is partially predicated on assessing the length of a player{\textquoteright}s shooting streak (testing the “hot-hand” effect) and the consideration a team gives to a streak and their franchise player. The model{\textquoteright}s output is used to calibrate and validate it against statistics from the National Basketball Association (NBA). Via a set of experiments, the model indicates that an increased belief in the franchise player leads to increased scoring action, but a belief in the hot-hand a minor effect. Thereby, demonstrating the utility of ABM to SA, thus opening a new research field.",
keywords = "Agent-based modeling, Hot-hand effect, Sports analytics",
author = "Matthew Oldham and Crooks, \{Andrew T.\}",
note = "Publisher Copyright: {\textcopyright} 2019 Society for Modeling \& Simulation International (SCS).; 2019 Annual Simulation Symposium, ANSS 2019, Part of the 2019 Spring Simulation Multi-Conference, SpringSim 2019 ; Conference date: 29-04-2019 Through 02-05-2019",
year = "2019",
doi = "10.23919/SpringSim.2019.8732893",
language = "English",
series = "Simulation Series",
publisher = "The Society for Modeling and Simulation International",
number = "1",
booktitle = "Simulation Series",
address = "United States",
edition = "1",
}