Integrating machine learning and decision support in tactical decision-making in rugby union

Funding: National Research Foundation of South Africa andthe Department of Higher Education and Training via the Teaching and Development Grant (IRMA:29113).Rugby union, like many sports, is based around sequences of play, yet this sequential nature is often overlooked, for example in analyses that aggregate performance measures over a fixed time interval. We use recent developments in convolutional and recurrent neural networks to predict the outcomes of sequences of play, based on the ordered sequence of actions they contain and where on the field these actions occur. The outcomes considered are gaining territory, retaining possession, scoring a try, and being awarded or conceding a penalty. We consider several artificial neural network architectures and compare their performance against baseline models. Accounting for sequential data and using field location improved classification accuracy over the baseline for some outcomes. We then investigate how these prediction models can provide tactical decision support to coaches. We demonstrate that tactical insight can be gained by conducting scenario analyses with data visualisations to investigate which strategies yield the highest probability of achieving the desired outcome.PostprintPeer reviewe

Team Performance Indicators That Predict Match Outcome in Rugby Union

The aim of the study is to identify the most significant indicators of the national team's performance at the European Rugby Championships 15 and to design a model for predicting the outcomes of matches. Data was collected from teams’ performance at the European Rugby 15 Championships 2021, 2022 and 2023 for the analysis. The total number of matches was 41. All indicators presented in the official reports were taken: 22 for the home and away teams. The analysis of the team results was carried out according to all indicators: mean value, standard deviation, and test were used to compare the performance indicators of the winning and losing teams. Machine learning techniques were utilized to develop a predictive model for match outcomes. On one hand, 15 indicators (68.2%) are higher for teams that won (winning teams). On the other hand, 7 (31.8%) indicators are higher for teams that lost. The difference between the teams' means varies from -56.46% (the minus indicates that this indicator is higher for the teams that lost) to 273.68%. Based on the results, the Random Forest Classifier and Extra Trees Classifier algorithms have the best prediction accuracy (0.92). The most significant indicators of team performance that affect the final result of the match are tries (196.3% – the difference between the average values of winning and losing teams), conversions (176.7%), missed tackles (- 56.46%), offload (126.3%). Based on the data obtained, refining the team training process in Rugby 15 is possible

“The Best or the Rest”: An exploration of UK Rugby Union coaches’ team selection decisions

Coaches play a crucial yet complex role in sport, including selecting players for games - a key decision many coaches regularly make. Despite this, little is known about why or how coaches make team selection decisions. The purpose of this thesis, therefore, is to investigate rugby union coaches’ team selection decisions, with specific reference to the cues (pieces of information) they use. Chapter 1 provides the context and rationale for this thesis. Chapter 2 comprises a systematic review which reveals the only study that has investigated coaches’ team selection decisions directly (by asking coaches), and the 15 studies that examined the differences between selected and non-selected players after selection had occurred. Given the small number of studies found in the systematic review, Chapter 3 contains a narrative literature review which summarises the cues that could influence coaches’ judgements and decisions made on their athletes while viewing them. Through a longitudinal interview study, Chapter 4 portrays the large number of diverse cues six rugby union coaches reported using to make team selection decisions and how this information changed dramatically from pre-season to post-season interviews. In Chapter 5, a case study of five rugby union coaches working within the same coaching team revealed the breadth and variety of the cues the coaches reportedly used to make team selection decisions, the processes these coaches went through (“the best or the rest” selection strategy), and how the power relationships among the coaching team impacted their selection decisions. This study also found through visual and audio observations of the head coach that most selection cues were only stated in one training session, suggesting an absence of a clear, long-term selection strategy. Chapter 6 provides coaches with a practical overview of the key results of this thesis and the implications for their coaching practices. Finally, Chapter 7 concludes this thesis by summarising the key findings and making several future recommendations for researchers and coaches

ImageNet Large Scale Visual Recognition Challenge

The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images. The challenge has been run annually from 2010 to present, attracting participation from more than fifty institutions. This paper describes the creation of this benchmark dataset and the advances in object recognition that have been possible as a result. We discuss the challenges of collecting large-scale ground truth annotation, highlight key breakthroughs in categorical object recognition, provide a detailed analysis of the current state of the field of large-scale image classification and object detection, and compare the state-of-the-art computer vision accuracy with human accuracy. We conclude with lessons learned in the five years of the challenge, and propose future directions and improvements.Comment: 43 pages, 16 figures. v3 includes additional comparisons with PASCAL VOC (per-category comparisons in Table 3, distribution of localization difficulty in Fig 16), a list of queries used for obtaining object detection images (Appendix C), and some additional reference

Assessing the determinants and impacts of and relationships between, sports club and sports event volunteers'behaviour: The Case of Women's rugby in England

The purpose of this study is to examine if the experiences of volunteers within women s rugby at both rugby clubs and at the 2010 Women s Rugby World Cup (WRWC) in England provide the basis for the continuation of such activities as well as the transfer of volunteer effort to event-based or club-based activity within the specific sports concerned or across sports to contribute towards society s broader sporting needs. Sport volunteering in the UK accounts for 26% of the total formal voluntary activity, and largely takes place within the Voluntary Sport Club (VSC) system (Sport England, 2003). It provides the basis for the development of grassroots sports. Sport volunteering also takes place at sport events which provide the foundation for elite level sport development. It is known, however, that if the volunteering experience is satisfying then this may lead to higher levels of commitment with the sports organization, the event or the voluntary cause, which may affect volunteers longevity and intentions to continue volunteering (Doherty, 2009). Women s rugby was selected as a case study, as the 2010 Women s Rugby World Cup was held in England. This facilitated comparisons between club and event volunteers. With the cooperation of the Rugby Football Union for Women (RFUW), research participants were identified and recruited via an email invitation including a link to an internet-administered questionnaire. A total of 70 individuals that volunteered for the 2010 WRWC and 168 volunteers involved in the women s rugby clubs completed the online survey. The results indicated that overall and despite some variation in the emphasis of the findings there is evidence in support of the relevance of the widely known determinants of volunteering such as motivation to volunteer, socio-demographic characteristics, satisfaction with the volunteering experience, engagement to sport and volunteering at to the continuation of future club or event volunteering as well as its transfer to other rugby and other sport events. Consequently, event organisers should work closely with club authorities to help volunteers to make a better connection from their club to the sport more widely and with the role of clubs and events to support the sport generally, to develop a shared identity in both clubs and events, that is across the whole sporting experience and to increase volunteers development opportunities through deploying their efforts in more than one setting which may then lead to the development of social capital