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The year 2020 has seen the emergence of a global pandemic as a result of the disease COVID-19. This report reviews knowledge of the transmission of COVID-19 indoors, examines the evidence for mitigating measures, and considers the implications for wintertime with a focus on ventilation.

Establishing dose-response relationships between training load and fatigue can help the planning of training. The aim was to establish the relative importance of external training load measurements to relate to the musculoskeletal response on a group and individual player level. Sixteen elite male rugby league players were monitored across three seasons. Two to seven day exponential weighted averages (EWMA) were calculated for total distance, and individualised speed thresholds (via 30-15 Intermittent Fitness Test) derived from global positioning systems. The sit and reach, dorsiflexion lunge, and adductor squeeze tests represented the musculoskeletal response. Partial least squares and repeated measures correlation analyses established the relative importance of training load measures and then investigated their relationship to the collective musculoskeletal response for individual players through the construction of latent variables. On a group level, 2 and 3 day EWMA total distance had the highest relative importance to the collective musculoskeletal response (p < 0.0001). However, the magnitude of relationships on a group (r value = 0.20) and individual (r value = 0.06) level were trivial to small. The lack of variability in the musculoskeletal response over time suggest practitioners adopting such measures to understand acute musculoskeletal fatigue responses should do so with caution.

The coronavirus disease 2019 (COVID-19) pandemic has caused disruption to professional and recreational sports across the world. The SARS-CoV-2 virus can be transmitted by relatively large respiratory droplets that behave ballistically, and exhaled aerosol droplets, which potentially pose a greater risk. This review provides a summary of end-to-end SARS-CoV-2 transmission risk factors for sport and an overview of transmission mechanisms to be considered by all stakeholders. The risk of SARS-CoV-2 transmission is greatest indoors, and primarily influenced by the ventilation of the environment and the close proximity of individuals. The SARS-CoV-2 transmission risks outdoors, e.g. via water, and from fomites, appear less than initially thought. Mitigation strategies include good end-to-end scenario planning of activities to optimise physical distancing, face mask wearing and hygiene practice of individuals, the environment and equipment. The identification and removal of infectious individuals should be undertaken by means of the taking of temperature and COVID-19 symptom screening, and the use of diagnostic monitoring tests to identify asymptomatic individuals. Using adequate video footage, data from proximity technology and subject interviews, the identification and isolation of ‘close contacts’ should also be undertaken to limit SARS-CoV-2 transmission within sporting environments and into the wider community. Sports should aim to undertake activities outdoors where possible, given the lower SARS-CoV-2 transmission risk, in comparison to indoor environments.

This study aimed to identify which physical and technical-tactical performance indicators (PI) can classify between levels of rugby league match-play. Data were collected from 46 European Super League (ESL) and 36 under-19 Academy (Academy) level matches over two seasons. Thirty-one ESL players and 41 Academy players participated. Microtechnology units were used to analyse the physical PI and matches were videoed and coded for individual technical-tactical PI, resulting in 157 predictor variables. Data were split into training and testing datasets. Random forests (RF) were built to reduce the dimensionality of the data, identify variables of importance and build classification models. To aid practical interpretation, conditional inference (CI) trees were built. Nine variables were identified as most important for backs, classifying between levels with 83% (RF) and 78% (CI tree) accuracy. The combination of variables with the highest classification rate was PlayerLoad2D, PlayerLoadSLOW per Kg body mass and high-speed running distance. Four variables were identified as most important for forwards, classifying with 68% (RF) and 64% (CI tree) accuracy. Defensive play-the-ball losses alone had the highest classification rate for forwards. The identified PI and their unique combinations can be developed during training to aid in progression through the rugby league playing pathway.

This study investigated sources of variability in the overall and phase-specific running match characteristics in elite rugby league. Microtechnology data were collected from 11 Super League (SL) teams, across 322 competitive matches within the 2018 and 2019 seasons. Total distance, high-speed running (HSR) distance (>5·5 m·s−1), average speed, and average acceleration were assessed. Variability was determined using linear mixed models, with random intercepts specified for player, position, match, and club. Large within-player coefficients of variation (CV) were found across whole match, ball-in-play, attack and defence for total distance (CV range = 24% to 35%) and HSR distance (37% to 96%), whereas small to moderate CVs (≤10%) were found for average speed and average acceleration. Similarly, there was higher between-player, -position, and -match variability in total distance and HSR distance when compared with average speed and average acceleration across all periods. All metrics were stable between-teams (≤5%), except HSR distance (16% to 18%). The transition period displayed the largest variability of all phases, especially for distance (up to 42%) and HSR distance (up to 165%). Absolute measures of displacement display large within-player and between-player, -position, and -match variability, yet average acceleration and average speed remain relatively stable across all match-periods.

Objectives Soccer leagues reflect the partial standings of the teams involved after each round of competition. However, the ability of partial league standings to predict end-of-season position has largely been ignored. Here we analyze historical partial standings from English soccer to understand the mathematics underpinning league performance and evaluate the predictive ‘power’ of partial standings. Methods Match data (1995-2017) from the four senior English leagues was analyzed, together with random match scores generated for hypothetical leagues of equivalent size. For each season the partial standings were computed and Kendall’s normalized tau-distance and Spearman r-values determined. Best-fit power-law and logarithmic functions were applied to the respective tau-distance and Spearman curves, with the ‘goodness-of-fit’ assessed using the R2 value. The predictive ability of the partial standings was evaluated by computing the transition probabilities between the standings at rounds 10, 20 and 30 and the final end-of-season standings for the 22 seasons. The impact of reordering match fixtures was also evaluated. Results All four English leagues behaved similarly, irrespective of the teams involved, with the tau-distance conforming closely to a power law (R2>0.80) and the Spearman r-value obeying a logarithmic function (R2>0.87). The randomized leagues also conformed to a power-law, but had a different shape. In the English leagues, team position relative to end-of-season standing became ‘fixed’ much earlier in the season than was the case with the randomized leagues. In the Premier League, 76.9% of the variance in the final standings was explained by round-10, 87.0% by round-20, and 93.9% by round-30. Reordering of match fixtures appeared to alter the shape of the tau-distance curves. Conclusions All soccer leagues appear to conform to mathematical laws, which constrain the league standings as the season progresses. This means that partial standings can be used

BACKGROUND AND AIM:Age-related declines in muscle strength have been associated with reduced functional capacity, postural instability and increased risk of falls in older adults. Studies have generally focussed on the significance of lower limb strength, however more recently the importance of trunk muscle strength has been explored, which may have been previously overlooked (Granacher et al., 2013). A limited number of studies have investigated the relationship between trunk strength and function, but this was tested under isometric contractions(Shahtahmassebi et al., 2017). Therefore, the purpose of this study was to investigate the relationship between dynamic (concentric) trunk strength and functional mobility in older women. METHODS:A sample of 40 older women (age: 68.9±3.73 years) participated in this study. To assess functional mobility, participants completed the timed up and go (TUG), 30 second chair stand test (CST), and a timed stair climb task (ascent and descent). Both self-selected normal and fast gait speed (GS) were also recorded. Concentric trunk flexion and extension strength data were collected using an isokinetic dynamometer (Biodex, USA). Measurements were conducted in the seated-compressed position in line with the manufacturer's guidelines. Following five sub-maximal trials, peak torque was recorded during three maximal flexion and extension trials at two testing speeds (20°/s and 45°/s). Peak torque was then normalised to body mass. Pearson's correlations were performed to investigate relationships between trunk strength and functional mobility measures. The level of significance was set at p≤0.05. RESULTS:A number of associations between trunk strength and functional mobility measures were revealed. Greater trunk extension strength was correlated with superior performance in the CST (45°/s: r=0.33, p=0.037), stair ascent (20°/s: r=-0.32, p=0.043; 45°/s: r=-0.56, p=<.001) and stair descent tasks (45°/s: r=-0.47, p=0.002). Trunk extension str

Objectives Professional sporting organisations invest considerable resources collecting and analysing data in order to better understand the factors that influence performance. Recent advances in non-invasive technologies, such as global positioning systems (GPS), mean that large volumes of data are now readily available to coaches and sport scientists. However analysing such data can be challenging, particularly when sample sizes are small and data sets contain multiple highly correlated variables, as is often the case in a sporting context.