The impact of Wheelchair Rugby classification lineup structure on possession outcome.

OBJECTIVES: To investigate the impact that the structure and combination of athlete classification within lineups has on possession outcome in Wheelchair Rugby (WR). DESIGN: Retrospective. METHODS: Analysis was conducted using data from all 18 WR matches from the 2020 Tokyo Paralympic Games. Pearson's chi-squared analysis was conducted to investigate the effect of lineup structures on performance and a nested generalised logistic mixed model (GLMM) was fitted to the data to investigate the association between independent variables and the outcome of possessions. Specifically, the impact of offensive and defensive lineup structures on possession outcome and whether the offensive and defensive lineup structures matching or not impact possession outcome. RESULTS: There was a significant relationship between the offensive lineup structure and possession outcome. Balanced lineups had greater turnovers than expected. Balanced lineups also had fewer tries than expected, whilst high-low lineups had fewer than expected turnovers. There were no significant associations between the defensive lineup structure and possession outcome. Furthermore, no significant associations were found between whether the structure of the offensive and defensive lineup matched (or not) and the possession outcome. CONCLUSIONS: The findings suggest that the offensive team's lineup structure plays a more prominent role in impacting possession outcome, compared to the defensive team's lineup structure. The present results provide valuable insights into WR performance for coaches, practitioners, and researchers.

Factors that affect handball execution in Australian Football.

A handball is one of two technical skills used to dispose of the ball in Australian Football. Previous research has only considered handball effectiveness in the analysis of team performance and there is a need to understand whether there are other more important characteristics of handball execution that explain effectiveness (i.e., performance). 1342 handballs from Australian Football League matches were analysed. Ten characteristic variables were created that represent the context and execution of each handball included in the analysis. A mixed effects generalised linear model was used to evaluate the effect of the (ten) characteristics on handball outcome. Three out of ten characteristics were associated with handball outcome, and these related to the posture of the handballer and the type of defensive pressure applied to the handballer and the receiver of the handball. These findings explain both how to increase handball effectiveness and how to reduce the handball effectiveness of an opposition team. Given the important role of handballing in passing sequences and maintaining ball possession, the practical application of these findings could enhance overall team performance.

Real time prediction of match outcomes in Australian football.

This study aimed to determine whether machine learning models based on technical performance and not score margin could be used to predict end-of-match outcome of Australian football matches in real-time. If efficacious, these models could be used to generate insights about team performance and support the decision-making of coaches during matches. A database of 168 team technical performance indicators from 829 Australian Football League matches played between 2017 and 2021 was used. Two feature sets (data-driven and data-informed) were used to train and evaluate six models (generalised linear model, random forest and adaboost) on match outcome prediction (Win/Loss) over 120 epochs (a representation of normalised time during each match). All models performed well (mean classification accuracy = 73.5-75.8%) in comparison with a benchmark score-based model (mean classification accuracy = 77.4%). Data-informed feature sets performed better than data-driven in most cases. Classification accuracy was low at the start of a match (45.7-48.8%) but increased to a peak near the end of a match (87.2-92.7%). These findings suggest that any of the employed models can be used to formulate in-match decision support. The model which is best in practice will depend on factors such as time-cost trade-off, feasibility and the perceived value of its suggestions.

A framework to explain the in-match decision-making of elite Australian football coaches.

The ability to make effective decisions is an important function of any football coach, whether during training, team selection, match-day performance or post-match player evaluation. It is not yet known how elite Australian football coaches make decisions during matches, in time-constrained but well-resourced environments. This study is the first to explore the decision-making of elite Australian football coaches during matches, in pursuit of identifying opportunities to improve the translation and implementation of research findings into the competitive match environment. Using semi-structured interviews and thematic analysis, a six-stage framework of the decision-making of elite Australian football coaches during matches was developed. The stages include (1) Opportunity trigger, (2) Understand the opportunity, (3) Determine the need for action, (4) Explore options, (5) Take action and (6) Evaluate the decision. Coaches relied on subjective and objective sources of information and consulted with assistant coaches, performance analysts, and sport scientists. The findings enable researchers to ensure future interventions to improve decision-making during matches are well integrated. They also provide an opportunity for coaches to reflect on their own decision-making process, identifying targeted areas for improvement in their own practice.