<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In elite Australian football (AF) many studies have investigated individual player performance using a variety of outcomes (e.g. team selection, game running, game rating etc.), however, none have attempted to predict a player’s performance using combinations of pre-game factors. Therefore, our aim was to investigate the ability of commonly reported individual player and team characteristics to predict individual Australian Football League (AFL) player performance, as measured through the official AFL player rating (AFLPR) (Champion Data). A total of 158 variables were derived for players (n = 64) from one AFL team using data collected during the 2014-2019 AFL seasons. Various machine learning models were trained (cross-validation) on the 2014-2018 seasons, with the 2019 season used as an independent test set. Model performance, assessed using root mean square error (RMSE), varied (4.69-5.03 test set RMSE) but was generally poor when compared to a singular variable prediction (AFLPR pre-game rating: 4.72 test set RMSE). Variation in model performance (range RMSE: 0.14 excusing worst model) was low, indicating different approaches produced similar results, however, glmnet models were marginally superior (4.69 RMSE test set). This research highlights the limited utility of currently collected pre-game variables to predict week-to-week game performance more accurately than simple singular variable baseline models.</p>
</abstract>

In elite Australian football (AF) many studies have investigated individual player performance using a variety of outcomes (e.g. team selection, game running, game rating etc.), however, none have attempted to predict a player’s performance using combinations of pre-game factors. Therefore, our aim was to investigate the ability of commonly reported individual player and team characteristics to predict individual Australian Football League (AFL) player performance, as measured through the official AFL player rating (AFLPR) (Champion Data). A total of 158 variables were derived for players (n = 64) from one AFL team using data collected during the 2014-2019 AFL seasons. Various machine learning models were trained (cross-validation) on the 2014-2018 seasons, with the 2019 season used as an independent test set. Model performance, assessed using root mean square error (RMSE), varied (4.69-5.03 test set RMSE) but was generally poor when compared to a singular variable prediction (AFLPR pre-game rating: 4.72 test set RMSE). Variation in model performance (range RMSE: 0.14 excusing worst model) was low, indicating different approaches produced similar results, however, glmnet models were marginally superior (4.69 RMSE test set). This research highlights the limited utility of currently collected pre-game variables to predict week-to-week game performance more accurately than simple singular variable baseline models.

Can Elite Australian Football Player’s Game Performance Be Predicted?

International Journal of Computer Science in Sport

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Can Elite Australian Football Player\u2019s Game Performance Be Predicted?"}

In elite Australian football (AF) many studies have investigated individual player performance using a variety of outcomes (e.g. team selection, game running,...

Can Elite Australian Football Player’s Game Performance Be Predicted?

Pubblicato online: 10 ago 2021

Pagine: 55 - 78

DOI: https://doi.org/10.2478/ijcss-2021-0004

Parole chiave
PLAYER RATING, AUSTRALIAN FOOTBALL LEAGUE, MACHINE LEARNING

© 2021 J. Fahey-Gilmour et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Can Elite Australian Football Player’s Game Performance Be Predicted?

Pubblicato online: 10 ago 2021

Pagine: 55 - 78

DOI: https://doi.org/10.2478/ijcss-2021-0004

Parole chiavePLAYER RATING, AUSTRALIAN FOOTBALL LEAGUE, MACHINE LEARNING

© 2021 J. Fahey-Gilmour et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Parole chiave
PLAYER RATING, AUSTRALIAN FOOTBALL LEAGUE, MACHINE LEARNING