Dietary Factors May Be Associated With Measures of Ultrasound-derived Skeletal Muscle Echo Intensity.

Skeletal muscle echo intensity (EI) is affected by ageing and physical activity; however, the effects of nutrition are less understood. The aim of this study was to explore whether habitual nutrient intake may be associated with ultrasound-derived EI. Partial least squares regression (PLSR) models were trained on an initial sample (n=100, M=45; F=55; 38±15 years) to predict EI of two quadriceps muscles from 19 variables, using the 'jack-knife' function within the 'pls' package (RStudio), which was then tested in an additional dataset (n= 30, M=13; F=17; 38±16 years). EI was determined using B-mode ultrasonography of the rectus femoris (RF) and vastus lateralis (VL) and nutritional intake determined via three-day weighed food diaries. Mean daily intake of specific nutrients were included as predictor variables with age, sex and self-reported physical activity. PLSR training model 1 explained ~52% and model 2 ~46% of the variance in RF and VL EI, respectively. Model 1 also explained ~35% and model 2 ~30% of the variance in RF and VL EI in the additional testing dataset. Age and biological sex were associated with EI in both models (P<0.025). Dietary protein (RF: ß=-7.617,VL: ß=-7.480), and selenium (RF: ß=-7.144,VL: ß=-4.775) were associated with EI in both muscles (P<0.05), whereas fibre intake (RF: ß=-5.215) was associated with RF EI only and omega-3 fatty acids (n-3/ω-3 FAs, RF: ß=3.145) with VL EI only (P<0.05). Therefore, absolute protein, selenium, fibre and n-3 FAs may be associated with skeletal muscle EI, although further mechanistic work is required before claiming causal inference.

The importance of place-kicking in Women's International Rugby Union.

Despite the growing popularity of women's rugby, there is a lack of research understanding the contribution of place-kicking to match outcomes. This study aims to establish the characteristics and contribution of place-kicking to women's international Rugby Union and evaluate the performance of place-kickers while accounting for factors that contribute to kick difficulty. Data from 674 place-kicks across 80 matches were analysed. A binomial generalised linear mixed model (GLMM) was used to predict the probability of kick success. 60.5% of place-kicks were successful, and they contributed 23.9% of all points scored; conversions accounted for 16.8% and penalties 7.1%. Kick success percentages for conversions (56.9%) and penalties (78.3%) significantly differed (p < 0.01). Kick distance and angle were significant (p < 0.01) predictors of kick success and the GLMM had a prediction accuracy of 73.6%. The performance rankings of kickers changed when comparing observed and expected success, highlighting the need to consider contextual factors contributing to kick difficulty when evaluating performance. The GLMM results provide valuable insights for coaches and players to make informed decisions, for example, whether to attempt a place-kick when a penalty is awarded, by enabling predictions of place-kick success. This could enhance a team's chances of winning matches.

Classifying Winning Performances in International Women's Rugby Union.

PURPOSE: The efficacy of isolated and relative performance indicators (PIs) has been compared in rugby union; the latter more effective at discerning match outcomes. However, this methodology has not been applied in women's rugby. The aim of this study was to identify PIs that maximize prediction accuracy of match outcome, from isolated and relative data sets, in women's rugby union. METHODS: Twenty-six PIs were selected from 110 women's international rugby matches between 2017 and 2022 to form an isolated data set, with relative data sets determined by subtracting corresponding opposition PIs. Random forest classification was completed on both data sets, and feature selection and importance were used to simplify models and interpret key PIs. Models were used in prediction on the 2021 World Cup to evaluate performance on unseen data. RESULTS: The isolated full model correctly classified 75% of outcomes (CI, 65%-82%), whereas the relative full model correctly classified 78% (CI, 69%-86%). Reduced respective models correctly classified 74% (CI, 65%-82%) and 76% (CI, 67%-84%). Reduced models correctly predicted 100% and 96% of outcomes for isolated and relative test data sets, respectively. No significant difference in accuracy was found between data sets. In the relative reduced model, meters made, clean breaks, missed tackles, lineouts lost, carries, and kicks from hand were significant. CONCLUSIONS: Increased relative meters made, clean breaks, carries, and kicks from hand and decreased relative missed tackles and lineouts lost were associated with success. This information can be utilized to inform physical and tactical preparation and direct physiological studies in women's rugby.

Performance indicators associated with match outcome within the United Rugby Championship.

OBJECTIVES: The aims of this study were to: i) identify performance indicators associated with match outcomes in the United Rugby Championship; ii) compare the efficacy of isolated and relative datasets to predict match outcome; and iii) investigate whether reduced statistical models can reproduce predictive accuracy. DESIGN: Retrospective analysis of key performance indicators in the United Rugby Championship. METHODS: Twenty-seven performance indicators were selected from 96 matches (2020-21 United Rugby Championship). Random forest classification was completed on isolated and relative datasets, using a binary match outcome (win/lose). Maximum relevance and minimum redundancy performance indicator selection was utilised to reduce models. In addition, models were tested on 53 matches from the 2021-22 season to ascertain prediction accuracy. RESULTS: Within the 2020-21 datasets, the full models correctly classified 83% of match performances for the relative dataset and 64% for isolated data, the equivalent reduced models classified 85% and 66% respectively. The reduced relative model successfully predicted 90% of match performances in the 21-22 season, highlighting that five performance indicators were significant: kicks from hand, metres made, clean breaks, turnovers conceded and scrum penalties. CONCLUSIONS: Relative performance indicators were more effective in predicting match outcomes than isolated data. Reducing features used in random forest classification did not degrade prediction accuracy, whilst also simplifying interpretation for practitioners. Increased kicks from hand, metres made, and clean breaks compared to the opposition, as well as fewer scrum penalties and turnovers conceded were all indicators of winning match outcomes within the United Rugby Championship.