A Framework for Near Real-Time AFL Match Outcome Prediction

Sports analysis has always been a real talking point amongst both statisticians and sports personnel. However, the complexity of creating an efficient and accurate model coupled with the difficulties in acquiring in-game statistics have resulted in most research being focused on ex-ante result prediction. This research will present a framework for the real-time prediction of match outcomes at various strategic points within an Australian Football League (AFL) match

Automatic detection of one-on-one tackles and ruck events using microtechnology in rugby union

Objectives To automate the detection of ruck and tackle events in rugby union using a specifically-designed algorithm based on microsensor data. Design Cross-sectional study. Methods Elite rugby union players wore microtechnology devices (Catapult, S5) during match-play. Ruck (n = 125) and tackle (n = 125) event data was synchronised with video footage compiled from international rugby union match-play ruck and tackle events. A specifically-designed algorithm to detect ruck and tackle events was developed using a random forest classification model. This algorithm was then validated using 8 additional international match-play datasets and video footage, with each ruck and tackle manually coded and verified if the event was correctly identified by the algorithm. Results The classification algorithm’s results indicated that all rucks and tackles were correctly identified during match-play when 79.4 ± 9.2% and 81.0 ± 9.3% of the random forest decision trees agreed with the video-based determination of these events. Sub-group analyses of backs and forwards yielded similar optimal confidence percentages of 79.7% and 79.1% respectively for rucks. Sub-analysis revealed backs (85.3 ± 7.2%) produced a higher algorithm cut-off for tackles than forwards (77.7 ± 12.2%). Conclusions The specifically-designed algorithm was able to detect rucks and tackles for all positions involved. For optimal results, it is recommended that practitioners use the recommended cut-off (80%) to limit false positives for match-play and training. Although this algorithm provides an improved insight into the number and type of collisions in which rugby players engage, this algorithm does not provide impact forces of these events