Discovering patterns of play in netball with network motifs and association rules
Autor: | Sam Robertson, Peter Browne, J Bahnisch, Stuart Morgan |
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Rok vydání: | 2019 |
Předmět: |
General Computer Science
Association rule learning business.industry Computer science Biomedical Engineering 030229 sport sciences computer.software_genre 01 natural sciences 0104 chemical sciences Physical education 010404 medicinal & biomolecular chemistry 03 medical and health sciences 0302 clinical medicine Pattern recognition (psychology) Artificial intelligence business computer Natural language processing |
Zdroj: | International Journal of Computer Science in Sport. 18:64-79 |
ISSN: | 1684-4769 |
DOI: | 10.2478/ijcss-2019-0004 |
Popis: | In netball, analysis of the movement of players and the ball across different court locations can provide information about trends otherwise hidden. This study aimed to develop a method to discover latent passing patterns in women’s netball. Data for both pass location and playing position were collected from centre passes during selected games in the 2016 Trans-Tasman Netball Championship season and 2017 Australian National Netball League. A motif analysis was used to characterise passing-sequence observations. This revealed that the most frequent, sequential passing style from a centre pass was the “ABCD” motif in an alphabetical system, or in a positional system “Centre–Goal Attack–Wing Attack–Goal Shooter” and rarely was the ball passed back to the player it was received from. An association rule mining was used to identify frequent ball movement sequences from a centre pass play. The most confident rule flowed down the right-hand side of the court, however seven of the ten most confident rules demonstrated a preference for ball movement down the left-hand side of the court. These results can offer objective insight into passing sequences, and potentially inform team strategy and tactics. This method can also be generalised to other invasion sports. |
Databáze: | OpenAIRE |
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