Previous research on performance indicators in rugby league has suggested that dimension reduction techniques should be utilised when analysing sporting data sets with a large number of variables. Forty-five rugby league team performance indicators, from all 27 rounds of the 2012, 2013 and 2014 European Super League seasons, collected by Opta, were reduced to 10 orthogonal principal components with standardised team scores produced for each component. Forced-entry logistic (match outcome) and linear (point’s difference) regression models were used alongside exhaustive chi-square automatic interaction detection decision trees to determine how well each principle component predicted success. The 10 principal components explained 81.8% of the variance in point’s difference and classified match outcome correctly ~90% of the time. Results suggested that if a team increased “amount of possession” and “making quick ground” component scores, they were more likely to win (β = 15.6, OR = 10.1 and β = 7.8, OR = 13.3) respectively. Decision trees revealed that “making quick ground” was an important predictor of match outcome followed by “quick play” and “amount of possession”. The use of PCA provided a useful guide on how teams can increase their chances of success by improving performances on a collection of variables, instead of analysing variables in isolation.

Performance indicators allow for the objective quantification of performance, however, limited PI research for professional rugby league exists. Therefore, this paper assessed 24 relative PIs (home value minus away) from all 27 rounds of the 2012, 2013 and 2014 European Super League seasons, collected by Opta, amounting to 567 matches. Backwards logistic (match outcome) and linear (points difference) regression models were used alongside exhaustive Chi-Square Automatic Interaction Detection decision trees to identify performance indicators (PIs) and key performance indicators. Teams had a higher chance of winning and would gain more points when they scored first (OR = 1.6, β = 2.4) and increased completed sets (OR = 1.2, β = 1.2) by one unit. Conversely, teams had a lower chance of winning when they increased scoots (OR = 0.9, β = −0.2). However, some PIs which were thought to be important (as identified by previous literature) were removed from the analysis thus calling into question the appropriateness of stepwise methods. Future research may consider utilising dimension reduction techniques when analysing large data-sets that encompass multiple variables.

The study considered performance analysis and feedback from the perspective of the performance analyst through the investigation of the ‘what’, ‘how’ and ‘when’ of practice within a selection of Olympic sports. Twenty-three performance analysts (experience 6.4 ± 4.1 years) engaged in a structured interview (85 ± 15 min) regarding their processes within applied practice. Likert scales (All the time, Often, Sometimes, Rarely and Never) were used to facilitate cross-sport and environment comparison. The performance analysts highlighted the experience of their coaches as the most prominent feature influencing analysis direction, and time had the greatest impact upon feedback provision. The main analysis techniques used were video, profiling and performance reports. Feedback was delivered primarily either, (1) < 1-h post-performance within sessions lasting < 10-min or (2) the following day within sessions lasting 25 + min. Video feedback was usually coach led; however, data delivery was more evenly distributed between coach and analyst. Very similar processes across the participants were identified, despite a wide variety of sports and participant experience levels. The findings have begun to illustrate practice within elite sport whilst highlighting the importance and need for further practitioner-based investigation regarding the use of performance analysis and feedback within applied contexts.

The use and implementation of performance analysis and feedback by 18 elite Olympic/Paralympic coaches (coaching experience 16.1 ± 7.4; experience using performance analysis 8.3 ± 4.8 years) was explored via an online questionnaire (mean time to complete = 29 min). Likert scales were used to facilitate cross-sport comparison. Comment boxes were included to enable providing additional information if deemed necessary. Training goals, athlete discussion and coaching philosophy were the most prominent features influencing analysis direction. Time available had the greatest impact upon feedback provision. The main analysis techniques used were video, performance reports and trend analysis. Coaches with greater experience delivered significantly more feedback sessions within 1 h of performance. Feedback sessions were < 20 min in duration and delivered in a balanced (experienced) or mostly positive (inexperienced) approach. Feedback was delivered consistently according to a preferred schedule, face-to-face and within an individual format. Sessions were usually coach led; however, considerable value in a combined or analyst-led approach was demonstrated. The findings have begun to illustrate practice within elite sport from the perspective of a key user of performance analysis, that is, the coach, and have clear implications for practitioners by identifying the key areas coaches’ value from performance analysis.

This study utilized a process tracing methodology to analyse the goals and strategies of control room teams in dealing with an unpredicted plant disturbance. The human processes of control used by operators and their supervisors, and interactions between them, were analysed during phases of detection, diagnosis, and control of a small plant leak. Five control room teams were videotaped tackling this simulated scenario on a full-scale simulator. The results found substantial differences both within and between teams in how the goals of monitoring and implementing procedures during the detection phase, and problem-solving and plant control during the diagnosis phase were achieved. The temporal patterning of the activities associated with these goals revealed that the teams used different strategies. The training implications of these findings are discussed, in particular with respect to the control room supervisor who had a pivotal role.

In spite of the extensive research on incidence site and type of injury in ballet and modern dancers, limited studies on injury in DanceSport have been reported. Therefore, this study determined retrospectively (within last 12 months) incidence, severity, site and type of injury, between gender and age-class in DanceSport. Participants were 97 international sport-dancers (female, 41; male, 56). Sixty-six (69%) dancers reported 96 injuries (1.00 (range = 4)) injuries per dancer) and an injury incidence of 1.7 (range = 14) per 1000 h. Females revealed significantly higher median injury incidence (females, 2.6 (range = 14); males, 1.9 (range = 9), p < 0.05) than males. A total of 61.5% of all injuries recorded were traumatic with a significant gender difference (Wald chi-square = 11.616, df = 1, p < 0.01). Injury severity was 3 (range 240) days with an interaction effect between gender and age-class (Wald chi-square = 251.374, df = 3, p < 0.001). Meanwhile, 72.3% of the dancers reported not including sport specific exercises besides dancing. These findings show gender and age-class differences in injury incidence, type and severity. Therefore, to reduce the likelihood of injuries, the implementation of supplemental DanceSport specific exercises that also considers the gender and age-class anatomical, functional, and choreographic demand differences in the training program should be recognized.