2016
DOI: 10.1080/02640414.2016.1142106
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A method to assess the influence of individual player performance distribution on match outcome in team sports

Abstract: This study developed a method to determine whether the distribution of individual player performances can be modelled to explain match outcome in team sports, using Australian Rules football as an example. Player-recorded values (converted to a percentage of team total) in 11 commonly reported performance indicators were obtained for all regular season matches played during the 2014 Australian Football League season, with team totals also recorded. Multiple features relating to heuristically determined percent… Show more

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Cited by 55 publications
(49 citation statements)
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“…For the calculation of team characteristic variables, individual player game data was sourced from Champion Data (CD, Pty Ltd, Melbourne, Australia), the official statistics provider of the AFL, which is a valid and reliable source (Robertson, Gupta, & McIntosh, 2016). For every season (2013-2018) each team's official team list was collated, this included a player's height, mass and date of birth.…”
Section: Data Collectionmentioning
confidence: 99%
See 1 more Smart Citation
“…For the calculation of team characteristic variables, individual player game data was sourced from Champion Data (CD, Pty Ltd, Melbourne, Australia), the official statistics provider of the AFL, which is a valid and reliable source (Robertson, Gupta, & McIntosh, 2016). For every season (2013-2018) each team's official team list was collated, this included a player's height, mass and date of birth.…”
Section: Data Collectionmentioning
confidence: 99%
“…A general solution is to exclude opposition data in the analysis in a long format with each game (and variable) being represented from the perspective of both teams (Leicht, Gomez, & Woods, 2017;Robertson, Gupta, & McIntosh, 2016;Robertson & Joyce, 2015;Woods, Sinclair, & Robertson, 2017). Nevertheless, this approach still presents challenges, as for any given game two different predictions can be made from the analysis (i.e., one for each team).…”
Section: Data Structurementioning
confidence: 99%
“…Robertson, Back and Bartlett (2016) advocated this method for preparing for matches by including the opposition in the analysis; although it is more common for research papers to use absolute values (e.g. Casamichana, 2015;Castellano, Casamichana & Lago 2012;Graham & Mayberry, 2014;Higham, Hopkins, Pyne & Anson, 2014a;Higham, Hopkins, Anson & Pyne, 2014b;Lago-Penas, Lago-Ballesteros & Rey, 2011;Meletakos, Vagenas & Bavios, 2011;Najdan, Robins & Glazier, 2014;Vahed, Kraak & Venter, 2014) or correctly labelled (Robertson, Back & Bartlett, 2016;Robertson, Gupta & McIntosh, 2016). O'Donoghue (2008) suggested key performance indicators had higher correlations with principal components; Bremner, Robinson & Williams (2013) suggested they were more significantly related to success and Shafizadeh, Taylor & Penas (2013) provided no definition or evidence.…”
Section: Introductionmentioning
confidence: 99%
“…Quantifying the individual performance of football players within teams is always a challenging task due to the variability and complexity of game dynamics. Nevertheless, understanding the impact of the individual contribution of players to the overall team performance 1 , is paramount for aiding performance analysts and coaches to better optimize teams' training and selection, or even to enhance scouting methods 2,3,4 .…”
Section: Introductionmentioning
confidence: 99%