Hayden Croft

Virtual reality assessment of rugby lineout throw kinematics

The lineout throw in the sport of Rugby Union is a crucial component in determining whether a team wins possession of the ball. The accuracy and timing of the throw are both important factors in determining whether the opposition can intercept the ball, and these components are determined by the throwers technique and ability to identify the target. A virtual reality environment, NuView stereo-imaging device and a digital high-definition video camera were used to capture stereoscopic video footage of players during outdoor training. The video footage was presented to eight elite rugby players in life-size stereoscopic 3D as they attempted to perform a lineout as if in a real game. A custom-built laser device measured the accuracy of the virtual throws. In addition, a 12 camera motion analysis system tracked body movements, to help determine lineout throw variables associated with accurate throwing. In the trial of the virtual reality system, statistical and practically significant differences were found between accurate and inaccurate throws for the lineout throw variables; elbow separation (accurate 5.2% greater than inaccurate), front foot step (94.5 mm longer), trunk flexion (5°greater), and follow though (17°higher). These four lineout throw variables are recommended to coaches and players as important features to concentrate on when trying to enhance lineout throw accuracy.

The application of self-organising maps to performance analysis data in rugby union

With the advent of professionalism in rugby union greater volumes of information are collected about player and team performance. A typical OptaTM, Sports CodeTM timeline, for a single rugby match, can have more than 2000 instances and labels of information. Unless there is a prior understanding of an opponent much time can be spent identifying irrelevant trends and information which may not fairly represent the performance of the match. Kohonen Self-organising Maps (SOMs) are a form of artificial neural network developed for clustering and visualising high-dimensional data by reducing the output to a low-dimensional output map. These visualisations may help the analyst quickly identify important relationships among the key performance indicators describing a match. In this paper we report on the application of SOMs to discrete data summarising matches in New Zealand’s ITM Cup rugby competition. The input variables were frequencies of common performance indicators. The SOM approach was used to narrow down the input variables to those that discriminate between successful and unsuccessful outcomes as well as map regions associated with various levels of success. Since map regions indicate game patterns or styles, further analysis showed that multiple game styles tended to lead to wins and multiple different styles tended to lead to losses. SOMs represent an important method for characterising game play in rugby union, we suggest continued use of SOMs will help make coaches and analysts more familiar with their interpretation and anticipate further streamlining of key performance indicator selection.

Behind closed doors: The role of debriefing and feedback in a professional rugby team

Despite the popularity of performance analysis with high performance rugby environments, limited attention has been paid to understanding how coaches and practitioners use this information to impact on performance. The aim of this case study was to gain an in-depth understanding of how a professional rugby team – playing in New Zealand’s ITM cup competition – uses the post-match and pre-match meetings to debrief after performance and preview prior to future performance. Multiple ethnographic techniques were employed to generate a comprehensive picture of the phenomena, including participant observation, video recordings, formal and informal interviews, field notes, descriptive statistics and document analysis. Each meeting was transcribed separately and analysed using an inductive content analysis process and themes were identified. The case study explores how four key factors – the role of the coach-facilitator, player engagement, leadership and the balance between reflection and preparation – impact on the debrief/preview process. The findings reveal that while considerable emphasis was placed on the importance of player learning and engagement in team meetings, delivery was often ad hoc, coach-driven and results-focused.

Development and application of a live transmitting player-mounted head camera

The use of player-mounted cameras (PMCs) in sport is not new; however, most existing PMC devices do not have the ability to transmit footage as it is captured. This appears to be due to the size and cost of transmitters, batteries and aerials. Instead, the footage is normally stored locally (within the device) on a hard drive or a removable secure digital card. Due to this limitation, PMC devices are not often utilized for coaching feedback, as there is significant delay before being able to view the footage. This paper will describe the development of a novel, non-intrusive PMC that transmits player vantage point footage in real time, captured to a viewing laptop for immediate review by coaches and players. Applications of this technology to rugby union, karate and silat (a South-East Asian martial art) will be described, outlining how the coaches utilize the technology to demonstrate important visual cues to players for the improvement of decision-making. Finally, the strengths and limitation of this technology will be discussed with recommendations for future development.

Visualizing Rugby Game Styles Using Self-Organizing Maps

Rugby coaches and analysts often use notational data describing match events to assess their teams performance and to devise strategic plans for upcoming matches. However, given the volume and complexity of the data available, it is difficult for them to recognize high-dimensional relationships among the available performance variables. A nonlinear approach using self-organizing maps (SOM) can help visualize the performance of a team and its opponents as well as the subsequent suitability of certain game styles, given the style of the opponent.

Using performance data to identify styles of play in netball: An alternative to performance indicators

Abstract The advent of sports technology has led to large, high-dimensional, performance data-sets, which pose decision-making challenges for coaches and performance analysts. If large data-sets are managed poorly inaccurate and biased decision-making may actually be enabled. This paper outlines a process for capturing, organising and analysing a large performance data-set in professional netball. Two hundred and fifty ANZ Championship matches, from the 2012 to 2015 seasons, where analysed. Self-organising maps and a k-means clustering algorithm were used to describe seven game styles, which were used in a case study to devise a strategy for an upcoming opponent. The team implemented a centre-pass (CP) defence strategy based on the opponent’s previous successful and unsuccessful performances. This strategy involved allowing the oppositions Wing-attack to receive the CP while allowing their Goal attack to take the second pass. The strategy was monitored live by the coaches on a tablet computer via a custom-built dashboard, which tracks each component of the strategy. The process provides an alternative to use of conventional performance indicators and demonstrates a method for handling large high-dimensional performance data-sets. Further work is needed to identify an ecologically valid method for variable selection.

Relationship between physical metrics and game success with elite rugby sevens players

Abstract The purpose of this study was to use self-organising maps (SOMs) to visualise the multivariate relationship between physical metrics (PM’s) as measured by GPS and how they relate to a performance outcome in elite-level 7s rugby. The players heart rate was elevated in the high intensity zone for an average of 73% per game and covered an average distance of 1574 m. Over this distance, 22% of the time was spent high-speed running and a relative distance of 112 m/min was travelled. SOMs showed no reliable relationship between PMs and points difference while two groups of PMs demonstrated similar patterns. This included variables such as high-speed running and metabolic power in the first group and all of the body composition variables in the second group. The final group of variables, including RPE had no relationship with any of the other PM variables. High-speed running and metabolic power are related closely and provide information that can be used for the preparation of 7s specific training schedules. RPE was shown to be unreliable as it did not relate well to any of the PMs.