Spatio-temporal metrics that distinguish plays in field hockey : a pilot study

MCINERNEY, Ciaran, GOODWILL, Simon, FOSTER, Leon and CHOPPIN, Simon (2016). Spatio-temporal metrics that distinguish plays in field hockey : a pilot study. In: ISPAS 2016 International Workshop, Institute of Technology, Carlow, 22-23 March 2016. [Conference or Workshop Item]

Documents
11873:37675
[thumbnail of Written paper submission]
Preview
PDF (Written paper submission)
McInerney Spatio-temporal metrics that distinguish plays in field hockey.pdf - Submitted Version
Available under License All rights reserved.

Download (261kB) | Preview
11873:37676
[thumbnail of Presentation of paper submission]
Preview
PDF (Presentation of paper submission)
McInerney Spatio-temporal metric Powerpoint.pdf - Presentation
Available under License All rights reserved.

Download (668kB) | Preview
Abstract
In team invasion sports, tactical behaviour can be examined using spatio-temporal data, i.e. the position of the players at a given time. A review of the spatio-temporal metrics used in team invasion sports performance analysis indicated that thousands of variations of metrics being used. Information about the distribution of metrics' individual effects can inform us of the best variable-selection method. The aim of this pilot study was to estimate the distribution of strong marginal effects of spatio-temporal metrics of field hockey plays. With institutional ethical approval, the Womens’ and Mens’ gold medal games from the EuroHockey 2015 field hockey tournament were recorded. Best, acceptable and worst-case outcomes for plays were described by 1,837 spatio-temporal metrics. Each metric's marginal effects were estimated using Cramér's V, Mutual Information and the I-score. Values for Cramér's V of 0.2 and 0.4 to mark the boundaries of small, moderate and large effects. Less than 1% of metrics show large effects with > 87% of all metrics showing small effects as per the Cramér's V thresholds. These large effect metrics where all within the 98th percentile of Mutual Information values and within the 96th percentile of the I-score values, which supports the Cramér's V distribution of marginal effects. Therefore, according to the recommendations of Tibshirani (1996), univariate variable-selection methods will be the most appropriate for selecting important metrics.
More Information
Statistics

Downloads

Downloads per month over past year

View more statistics

Share
Add to AnyAdd to TwitterAdd to FacebookAdd to LinkedinAdd to PinterestAdd to Email

Actions (login required)

View Item View Item