Purpose: To quantify the seasonal training load completed by professional soccer players of the English Premier League. Methods: Thirty players were sampled (using GPS, heart rate and RPE) during the daily training sessions comprising the 2011-2012 pre-season and in-season period. Pre-season data were analysed across 6 x 1 week microcycles. In-season data were analysed across 6 x 6 week mesocycle blocks and 3 x 1 week microcycles at start, mid and end time points. Data were also analysed with respect to number of days prior to a match. Results: Typical daily training load (i.e. total distance, high speed distance, % HRmax, s-RPE) did not differ during each week of the pre-season phase. However, daily total distance covered was 1304 (95% CI: 434 – 2174) m greater in the first mesocycle compared with the sixth . %HRmax values were also greater (3.3 (1.3 – 5.4) %) in the third mesocycle compared with the first. Furthermore, training load was lower on the day before match (MD-1) compared with two (MD-2) to five (MD-5) days before match, though no difference was apparent between these latter time-points. Conclusions: We provide the first report of seasonal training load in elite soccer players and observed periodization of training load was typically confined to MD-1 (regardless of mesocycle) whereas no differences were apparent during MD-2 to MD-5. Future studies should evaluate whether this loading and periodization is facilitative of optimal training adaptations and match day performance

Athlete-tracking devices that include global positioning system (GPS) and microelectrical mechanical system (MEMS) components are now commonplace in sport research and practice. These devices provide large amounts of data that are used to inform decision making on athlete training and performance. However, the data obtained from these devices are often provided without clear explanation of how these metrics are obtained. At present, there is no clear consensus regarding how these data should be handled and reported in a sport context. Therefore, the aim of this review was to examine the factors that affect the data produced by these athlete-tracking devices and to provide guidelines for collecting, processing, and reporting of data. Many factors including device sampling rate, positioning and fitting of devices, satellite signal, and data-filtering methods can affect the measures obtained from GPS and MEMS devices. Therefore researchers are encouraged to report device brand/model, sampling frequency, number of satellites, horizontal dilution of precision, and software/firmware versions in any published research. In addition, details of inclusion/exclusion criteria for data obtained from these devices are also recommended. Considerations for the application of speed zones to evaluate the magnitude and distribution of different locomotor activities recorded by GPS are also presented, alongside recommendations for both industry practice and future research directions. Through a standard approach to data collection and procedure reporting, researchers and practitioners will be able to make more confident comparisons from their data, which will improve the understanding and impact these devices can have on athlete performance. Keywords: microtechnology, athlete tracking, method, MEMS, time-motion analysisGlobal positioning system (GPS) is a satellite navigation network that provides location and time information of tracking devices. Initially developed for military purposes, this system now has much wider application, including its use in athlete tracking and load quantification. GPS satellites orbit the Earth and send precise time information (from an atomic clock) to the GPS receivers (at the speed of light) to determine the duration of signal transit. 1 A minimum of four satellites are required to determine the position of the GPS receiver trigonometrically. Commercial GPS systems are now commonly used in individual-and team-sports at all levels. The development and subsequent acceptance of microtechnology in sport has led to the integration of other micro inertial sensors within GPS devices, such as triaxial accelerometers, magnetometers, and gyroscopes; collectively termed as micro electrical mechanical systems (MEMS). Thus, GPS and MEMS technology provides practitioners with a wide array of data that can be used to assess athlete physical loading and activity profile.The use of GPS in sport allows practitioners to evaluate athletic training programs, and researchers to better investigate applied research questi...

settings for detecting high-intensity efforts using Global Positioning System (GPS) data. 33Methods: Velocity and acceleration data of a professional soccer match was recorded via 10- 34Hz GPS. Velocity data was filtered using either a median or exponential filter. Acceleration and acceleration (≥2.78 m.s -2 ) efforts were then identified using minimum effort durations (0.1 38 to 0.9 s) to assess differences in the total number of efforts reported.

The purpose of this investigation was to (1) quantify the training load practices of a professional soccer goalkeeper and (2) investigate the relationship between the training load observed and the subsequent self-reported wellness response. One male goalkeeper playing for a team in the top league of the Netherlands participated in this case study. Training load data were collected across a full season using a global positioning system device and session-RPE (rating of perceived exertion). Data were assessed in relation to the number of days to a match (MD- and MD+). In addition, self-reported wellness response was assessed using a questionnaire. Duration, total distance, average speed, PlayerLoad™, and load (derived from session-RPE) were highest on MD. The lowest values for duration, total distance, and PlayerLoad™ were observed on MD-1 and MD+1. Total wellness scores were highest on MD and MD-3 and were lowest on MD+1 and MD-4. Small to moderate correlations between training load measures (duration, total distance covered, high deceleration efforts, and load) and the self-reported wellness response scores were found. This exploratory case study provides novel data about the physical load undertaken by a goalkeeper during 1 competitive season. The data suggest that there are small to moderate relationships between training load indicators and self-reported wellness response. This weak relation indicates that the association is not meaningful. This may be due to the lack of position-specific training load parameters that practitioners can currently measure in the applied context.

Highlights Estimates of players’ maturity status should be taken every 3–4 months during an annual season, with a focus on players approaching and during peak height velocity. Key stakeholders should be educated about maturation and peak height velocity, particularly in relation to the potential use of bio-banding strategies. Clear lines of communication should be established with key stakeholders in order to identify the volume of weekly physical activity each child is engaged in. The prediction error embroiled within each maturity-estimation equation should be considered, along with the implications of additional errors imposed by spurious anthropometric measurements (i.e., self-reported birth-parent stature). Key stakeholders should be aware of the increased risk of injuries owing to inappropriate training loads across peak height velocity.

This study examined the change in countermovement jump (CMJ) performance across a microcycle of training in professional soccer players during the in-season period. Nine elite youth soccer players performed a CMJ test pre and post four consecutive soccer training sessions of an in-season weekly microcycle. Training load was quantified using global positioning systems (GPS), heart rate (HR) and rating of perceived exertion (RPE). Absolute change (pre to post training) in CMJ height across each training session was analysed using

The purpose of the study was to examine the perspectives of both academics and practitioners in relation to forming applied collaborative sport science research within team sports. Ninety-three participants who had previously engaged in collaborative research partnerships within team sports completed an online survey which focused on motivations and barriers for forming collaborations using blinded sliding scale (0-100) and rank order list. Research collaborations were mainly formed to improve the team performance (Academic: 73.6 ± 23.3; Practitioner: 84.3 ± 16.0; effect size (ES = 0.54), small). Academics ranked journal articles' importance significantly higher than practitioners did (Academic: M = 53.9; Practitioner: 36.0; z = -3.18, p = .001, p < q). However, practitioners rated one-to-one communication as more preferential (Academic: M = 41.3; Practitioner 56.1; z = -2.62, p = .009, p < q). Some potential barriers were found in terms of staff buy in (Academic: 70.0 ± 25.5; Practitioner: 56.8 ± 27.3; ES = 0.50, small) and funding (Academic: 68.0 ± 24.9; Practitioner: 67.5 ± 28.0; ES = 0.02, trivial). Both groups revealed low motivation for invasive mechanistic research (Academic: 36.3 ± 24.2; Practitioner: 36.4 ± 27.5; ES = 0.01, trivial), with practitioners have a preference towards 'fast' type research. There was a general agreement between academics and practitioners for forming research collaborations. Some potential barriers still exist (e.g. staff buy in and funding), with practitioners preferring 'fast' informal research dissemination compared to the 'slow' quality control approach of academics.

The purpose of the present study was to examine the influence of workload prior to injury on injury (tissue type and severity) in professional soccer players. Twenty-eight days of retrospective training data prior to non-contact injuries (n=264) were collated from 192 professional soccer players. Each injury tissue type (muscle, tendon and ligament) and severity (days missed) were categorised by medical staff. Training data were recorded using global positioning system (GPS) devices for total distance (TD), high speed distance (HSD,>5.5 m/s−1), and sprint distance (SPR,>7.0 m/s−1). Accumulated 1, 2, 3, 4-weekly loads and acute:chronic workload ratios (ACWR) (coupled, uncoupled and exponentially weighted moving average (EWMA) approaches) were calculated. Workload variables and injury tissue type were compared using a one-way ANOVA. The association between workload variables and injury severity were examined using a bivariate correlation. There were no differences in accumulated weekly loads and ACWR calculations between muscle, ligament, and tendon injuries (P>0.05). Correlations between each workload variable and injury severity highlighted no significant associations (P>0.05). The present findings suggest that the ability of accumulated weekly workload or ACWR methods to differentiate between injury type and injury severity are limited using the present variables.