Markov Chain Models for the Near Real-Time Forecasting of Australian Football League Match Outcomes

2020 ◽  
pp. 111-125
Author(s):  
Casey Josman ◽  
Ritu Gupta ◽  
Sam Robertson
Keyword(s):  
Markov Chain ◽  
Real Time ◽  
Markov Chain Models ◽  
Australian Football League ◽  
Australian Football

scholarly journals Real-Time Data Center's Telemetry Reduction and Reconstruction Using Markov Chain Models

2019 ◽  
Vol 13 (4) ◽  
pp. 4039-4050 ◽  
Author(s):  
Shuja-ur-Rehman Baig ◽  
Waheed Iqbal ◽  
Josep Lluis Berral ◽  
Abdelkarim Erradi ◽  
David Carrera
Keyword(s):  
Markov Chain ◽  
Real Time ◽  
Time Data ◽  
Markov Chain Models ◽  
Real Time Data

Classifying player positions in second-tier Australian football competitions using technical skill indicators

2021 ◽  
pp. 174795412110102
Author(s):  
Adrian J Barake ◽  
Heather Mitchell ◽  
Constantino Stavros ◽  
Mark F Stewart ◽  
Preety Srivastava
Keyword(s):  
Positional Information ◽  
Multinomial Logistic Model ◽  
Sports Science ◽  
Lower Tier ◽  
Australian Football League ◽  
Australian Football ◽  
Second Tier ◽  
Performance Statistics ◽  
Player Performance ◽  
Player Positions

Efficient recruitment to Australia’s most popular professional sporting competition, the Australian Football League (AFL), requires evaluators to assess athlete performances in many lower tier leagues that serve as pathways. These competitions and their games are frequent, widespread, and challenging to track. Therefore, independent, and reliable player performance statistics from these leagues are paramount. This data, however, is only meaningful to recruiters from AFL teams if accurate player positions are known, which was not the case for the competitions from which most players were recruited. This paper explains how this problem was recently solved, demonstrating a process of knowledge translation from academia to industry, that bridged an important gap between sports science, coaching and recruiting. Positional information which is only available from the AFL competition was used to benchmark and develop scientific classification methods using only predictor variables that are also measured in lower tier competitions. Specifically, a Multinomial Logistic model was constructed to allocate players into four primary positions, followed by a Binary Logit model for further refinement. This novel technique of using more complete data from top tier competitions to help fill informational deficiencies in lower leagues could be extended to other sports that face similar issues.

A prospective study of risk factors for hamstring injury in Australian football league players

2021 ◽  
pp. 1-7
Author(s):  
Nigel A. Smith ◽  
Melinda M. Franettovich Smith ◽  
Matthew N. Bourne ◽  
Rod S. Barrett ◽  
Julie A. Hides
Keyword(s):  
Risk Factors ◽  
Prospective Study ◽  
Hamstring Injury ◽  
Australian Football League ◽  
Australian Football ◽  
A Prospective Study
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