Higher overall mean and criterion-specific tackle-related technique scores were associated with a non-injury outcome. The ability to perform well during tackle events may decrease the risk of injury and may manifest in superior performance.

Tackling is an important skill for performance in rugby, yet little has been reported on tackler characteristics and its relationship to performance in competition. The aim of this study was to identify tackler characteristics and its association with tackle performance in matches. Eighteen matches of the 2010 Super 14 were analysed, which amounted to 2092 coded tackles. Tackler characteristics such as head position - in motion, up and forward, type of tackle - shoulder tackler, arm tackle or ball-carrier fend - absent, moderate, strong and leg drive-absent, moderate, strong or shoulder usage were coded and associated with tackle outcomes. Relative risk ratio (RRR), the ratio of the probability of an outcome occurring when a characteristic was observed (versus the non-observed characteristic) was determined using multinomial logistic regression. Having the head in motion decreased the tackler's chances of a successful tackle (RRR 2.24, 95% CI 1.72-2.92, p < 0.001) relative to an up and forward head position. Ball-carriers using a fend significantly decreased the tackler's probability of success (moderate fend RRR 2.97, 95% CI 2.04-4.31, p < 0.001). Moderate leg drive (RRR 0.36, 95% CI 0.26-0.50, p < 0.001) by the tackler increased the likelihood of tackle success. Head up and forward, counter-acting the ball-carrier fend, shoulder tackles targeted at the ball-carrier mid-torso, using the arms to wrap or pull, and leg driving were key tackler characteristics associated with positive tackle outcome in matches. Based on these findings, it is recommended that these technical characteristics are emphasised and incorporated into training to effectively prepare tacklers for competition.

The high impact and physical nature of the tackle during a rugby match places the tackler(s) and ball-carrier at risk of injury. Injuries as a result of the tackle account for up to 61% of all injuries that occur during a rugby match. The purpose of this paper is to: i) review the literature on tackle injury mechanisms and events associated with increased risk of injury during the tackle; and ii) identify strategies that can be coached during training and executed by players during a match to reduce the risk of injury. Coaches equipped with a better understanding of injury mechanisms and a thorough knowledge of a safe and effective technique in the tackle have a major role in preventing or reducing tackle injuries.

Using an expert consensus-based approach, a rugby union Video Analysis Consensus (RUVAC) group was formed to develop a framework for video analysis research in rugby union. The aim of the framework is to improve the consistency of video analysis work in rugby union and help enhance the overall quality of future research in the sport. To reach consensus, a systematic review and Delphi method study design was used. After a systematic search of the literature, 17 articles were used to develop the final framework that described and defined key actions and events in rugby union (rugby). Thereafter, a group of researchers and practitioners with experience and expertise in rugby video analysis formed the RUVAC group. Each member of the group examined the framework of descriptors and definitions and rated their level of agreement on a 5-point agreement Likert scale (1: strongly disagree; 2: disagree; 3: neither agree or disagree; 4: agree; 5: strongly agree). The mean rating of agreement on the five-point scale (1: strongly disagree; 5: strongly agree) was 4.6 (4.3–4.9), 4.6 (4.4–4.9), 4.7 (4.5–4.9), 4.8 (4.6–5.0) and 4.8 (4.6–5.0) for the tackle, ruck, scrum, line-out and maul, respectively. The RUVAC group recommends using this consensus as the starting framework when conducting rugby video analysis research. Which variables to use (if not all) depends on the objectives of the study. Furthermore, the intention of this consensus is to help integrate video data with other data (eg, injury surveillance).