Longitudinal Strength, Power, and Push-Start Performance Changes in a Skeleton Athlete: Case Study.

OBJECTIVES: The purpose of this study was to document the longitudinal strength and power characteristic changes and race performance changes of a skeleton athlete. METHOD: Longitudinal strength and power changes were assessed with strength and power diagnostic testing over a 9-year period. Trends over 9 years for relative strength were analyzed using a linear model. Push-start time was recorded across multiple tracks. Trends over 9 years for start performance at each track were assessed using a mixed-effects linear model to account for the impact of different tracks. Lower-body strength and power changes were assessed via a 1-repetition-maximum squat and a body-weight countermovement jump. The relationship between strength and power changes was assessed over time. The relationship between strength changes and start performance was determined by assessing the fixed effect of relative strength changes on push-start time. RESULTS: Relative lower-body strength ranged from 1.6 kg per body weight to 1.9 kg per body weight and showed a significant mean improvement of 0.05 kg per body weight per year (R2 = .71, P < .01). A negative correlation (R2 = .79) between relative strength changes and push-start performance across multiple tracks was found. The mixed-effects model indicated that push-start time improved significantly year to year (0.02 s; P < .001; R2 = .74) when controlling for the effect of track. CONCLUSIONS: The longitudinal analysis of push-start time and the associations with changes in strength suggest that training this quality can have a positive effect on push-start performance.

Key performance indicators in Australian sub-elite rugby union.

OBJECTIVES: The primary aim of this study was to determine which key performance indicators (PIs) were most important to success in sub-elite rugby union, and whether the analysis of absolute or relative data sets as a method for determining match outcome was stronger than the other. METHODS: Data was taken from 17 PIs from 76 matches across the 2018 Queensland Premier Rugby Union season. A random forest classification model was created using these data sets based on win/loss outcomes. RESULTS: The randomForest model classified 53 from 73 losses (72.6%) and 53 from 73 wins for an overall percentage accuracy of 72.6%. The randomForest model based on the relative data set classified 57 from 73 losses (78.1%) and 57 from 73 wins for an overall percentage accuracy of 78.1%. McNemar's value of p=0.84 confirmed that the relative data model did not outperform the absolute data set. There were positive associations between match outcome and relative number of kicks in play, meters carried, turnovers conceded and initial clean breaks. CONCLUSIONS: Outcomes in Queensland Premier Rugby can be predicted using relative and absolute data sets, though the difference between absolute and relative set usage was not as substantial as in professional rugby. Absolute and relative data sets can be used to create match strategies and assess match performance. A game plan based around an out of hand kicking game and accumulating more metres than the opposition, whilst minimising turnovers when in possession were key to success.