Drafting Agent-Based Modeling Into Basketball Analytics

Oldham, Matthew; Crooks, Andrew

doi:10.23919/springsim.2019.8732893

Cited by 3 publications

(2 citation statements)

References 18 publications

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“…Furthermore, its effectiveness has been demonstrated through myoneural activity (Ryan et al, 2018), sports performance transfer (Bock et al, 2012), and outcome prediction (Bunker & Thabtah, 2019;Oldham & Crooks, 2019). This implies that studies have examined complicated intra-and inter-system interactions between individuals rather than observing the relationship between a force and interlimb coordination (Glazier & Davids, 2010).…”

Section: Network-agent-based Evolutionmentioning

confidence: 99%

Empirical Study on Linear, Non-linear, and System Dynamics: Oriented Human Motor Behavior

Park

Han

2022

Int J Appl Sports Sci

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Human movement and motor behavior science involve various theoretical frameworks and methodologies. This study describes the force control of motor phenomenon, processes of linear or nonlinear dynamics, and proposes a system approach as an additional potential aspect. Thus far, issues regarding these motor control and learning paradigms were critically examined, and their respective mechanisms were compared using descriptive analysis. Elaborate simulations based on the transitions and development flow of each component at issue contributed to the linear approach, laid concrete emphasis on the advantages of the nonlinear approach, and empirically derived the rationale for the indispensable application of system dynamics. Sports science can benefit from system dynamics associated with human motor behavior, as demonstrated in this study.

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Section: Network-agent-based Evolutionmentioning

confidence: 99%

Empirical Study on Linear, Non-linear, and System Dynamics: Oriented Human Motor Behavior

Park

Han

2022

Int J Appl Sports Sci

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“…The flexibility that the agent-based modeling approach provides has allowed such models to be used in a diverse set of applications. These range from archeology (Axtell et al 2002), agriculture (Hailegiorgis et al 2018), basketball (Oldham and Crooks 2019), crime (Malleson et al 2013), diseases (Perez and Dragicevic 2009), disasters (Jumadi et al 2018), invasive species (Anderson and Dragićević 2018), to urban growth (Xie and Yang 2011), housing markets (Geanakoplos et al 2012), gentrification (Jackson et al 2008), slum formation (Patel et al 2018), and traffic (Manley and Cheng 2018). So, while agent-based modelers have been utilizing geographical data in their models, what has changed is the growth of data and ways of integrating such data within models (which will be discussed more in Sect.…”

Section: Application Areas For Geographically Explicit Agent-based Momentioning

confidence: 99%

Agent-Based Modeling and the City: A Gallery of Applications

et al. 2021

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Agent-based modeling is a powerful simulation technique that allows one to build artificial worlds and populate these worlds with individual agents. Each agent or actor has unique behaviors and rules which govern their interactions with each other and their environment. It is through these interactions that more macro-phenomena emerge: for example, how individual pedestrians lead to the emergence of crowds. Over the past two decades, with the growth of computational power and data, agent-based models have evolved into one of the main paradigms for urban modeling and for understanding the various processes which shape our cities. Agent-based models have been developed to explore a vast range of urban phenomena from that of micro-movement of pedestrians over seconds to that of urban growth over decades and many other issues in between. In this chapter, we introduce readers to agent-based modeling from simple abstract applications to those representing space utilizing geographical data not only for the creation of the artificial worlds but also for the validation and calibration of such models through a series of example applications. We will then discuss how big data, data mining, and machine learning techniques are advancing the field of agent-based modeling and demonstrate how such data and techniques can be leveraged into these models, giving us a new way to explore cities.

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