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.

Oftentimes, the various coaching staff, sport science, and medical practitioners of a sports club require a single, holistic indication of an athlete's athleticism. Currently, there is no consensus on how this is best defined, and thus, a total score of athleticism (TSA) may provide one such method. The TSA is derived from the average of Z-scores (or T-scores in the case of small samples) from a sport-specific testing battery, ensuring athletes are judged across all the relevant fitness capacities that best define the physical demands of competition. To aid readers in using the TSA, this article also details how it is computed in EXCEL.

Effective playing time in soccer is typically < 60 min per game and while players may reposition themselves when the ball is out of play, it is likely the physical demand decreases during this period. Therefore, if this period is included in data when quantifying match demands, it may under-report the physical requirements of soccer players. This study investigated an alternative method for quantifying external workload called ball in play (BiP), which analyses the data excluding stoppages, and thus potentially offers a more insightful analysis of match demands. Whole match demands as typically recorded via GPS, were compared to those based on BiP, and maximum BiP, with the latter representing worst case scenario phases of play. The 25-elite male youth soccer players (age: 17.9 ± 0.6 years; height: 174.8 ± 6.2 cm; body mass: 66.3 ± 8.1 kg) who participated in this study were also categorised in to positional groups (defender, midfielder, and forward) to assess differences in positional demands. While no differences were noted based on position, whole match metrics were significantly lower than mean and maximum BiP metrics (p < 0.05). There was also a significant difference for maximum BiP outputs across different in-play durations, when comparing 30-60 seconds, 60-90 seconds, and > 90 seconds. This data allows practitioners to gain a deeper understanding of the physical demands imposed on players and plan sessions using targets that better represent match demands.

This is the first study to report the whole match, ball-in-play (BiP), ball-out-of-play (BoP), and Max BiP (worst case scenario phases of play) demands of professional soccer players competing in the English Championship. Effective playing time per soccer game is typically 90 s), providing peak match demands. Whole match demands recorded were compared to BiP and Max BiP, with BiP data excluding all match stoppages, providing a more precise analysis of match demands. Whole match metrics were significantly lower than BiP metrics (p < 0.05), and Max BiP for 30–60 s was significantly higher than periods between 60–90 s and >90 s. No significant differences were found between positions. BiP analysis allows for a more accurate representation of the game and physical demands imposed on professional soccer players. Through having a clearer understanding of maximum game demands in professional soccer, practitioners can design more specific training methods to better prepare players for worst case scenario passages of play.

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.