Physical Demands during the Game and Compensatory Training Session (MD + 1) in Elite Football Players Using Global Positioning System Device

The burdens of sitting on the bench - comparison of absolute and relative match physical load between handball players with high and low court time and implications for compensatory training

Wearables quantify the activity in team sports and indicate that players experience peak physical loads during competitions. Accordingly, players with limited court time in competitions will miss important training stimuli. The present study aimed to quantify these gaps in physical load in professional handball players. Activity of all players competing in the 2021/2022 Bundesliga (Germany) was tracked using Kinexon LPS sensors. Gaps in physical load were quantified comparing the 25% of appearances with the highest (HIGH; 51.8 ± 5.2 mins) and lowest court times (LOW; 10.1 ± 4.3 mins). Distances, accumulated acceleration, jumps, sprints, impacts, accelerations, and decelerations were analysed as absolute and relative (per minute) outcomes. Players were grouped into wings, backcourts, and pivots. Unpaired t-tests between HIGH and LOW were performed (p < .05), and effect sizes were calculated (Cohen´s d). Analyses revealed significant effects of court time on activity. While absolute activity increased for HIGH, relative activity increased for LOW (p < .05). In addition, effect sizes revealed position-specific gaps in physical load, particularly for acyclic activities (jumps, accelerations). Gaps in physical load resulting from limited court time are highly position-specific. Our observations may provide benchmarks for the position-specific calibration of compensatory training.

Engineering Features from Raw Sensor Data to Analyse Player Movements during Competition

Research in field sports often involves analysis of running performance profiles of players during competitive games with individual, per-position, and time-related descriptive statistics. Data are acquired through wearable technologies, which generally capture simple data points, which in the case of many team-based sports are times, latitudes, and longitudes. While the data capture is simple and in relatively high volumes, the raw data are unsuited to any form of analysis or machine learning functions. The main goal of this research is to develop a multistep feature engineering framework that delivers the transformation of sequential data into feature sets more suited to machine learning applications.

Weekly Training Load Differences between Starting and Non-Starting Soccer Players

The aim of this study was to examine the differences in the weekly training load between starters and non-starters classified based on the match starting line-up, with respect to the playing position and a training day. Notably, 31 young adult soccer players (age: 18.79 ± 1.04 years) competing in the 3rd Czech division were monitored across the season. The weekly training load was measured using a GPS system as follows: total distance covered (TD), high-speed running distance (HSR), sprint running distance (SR), and acceleration and deceleration distance (ACDC). We found higher values in three out of four observed variables (HSR, SR, and ACDC, excluding TD) for starters compared to non-starters (p < 0.05), with small to moderate effect sizes (d = 0.40-0.49). Differences were observed especially in players who were fullbacks, offensive midfielders, and forwards. Moreover, the largest differences were found in training prior to a match day for HSR, SR, and ACDC (p < 0.05). Non-starters experienced lower weekly external loads in offensive player positions, predominantly in high-intensity variables, which are essential for their physical performance. It seems that non-starters may experience potential under-loading in the training process. Coaches and practitioners should be aware of this potential risk and find an appropriate method to compensate for load discrepancies, particularly in terms of high-intensity activities.

Sensor Technology for Sports Monitoring

Over the past decades, huge steps have been made in the development of sensor technology related to sports monitoring [...].

Weekly Training Load across a Standard Microcycle in a Sub-Elite Youth Football Academy: A Comparison between Starters and Non-Starters

Compensatory training sessions have been highlighted as useful strategies to solve the differential weekly training load between the players' starting status. However, the influence of the players' starting status is still understudied in sub-elite youth football. Thus, the aim of this study was to compare the weekly training load on a standard microcycle in starters and non-starters of a sub-elite youth football academy. The weekly training load of 60 young sub-elite football players was monitored during a 6-week period using an 18 Hz global positioning system (GPS), 1 Hz telemetry heart rate, rating of perceived exertion (RPE), and total quality recovery (TQR). The total distance (TD) covered presented a significant difference between starters and non-starters with a moderate effect (t = -2.38, Δ = -428.03 m, p = 0.018, d = 0.26). Training volume was higher in non-starters than in starter players (TD_Starters = 5105.53 ± 1684.22 vs. TD_Non-starters = 5533.56 ± 1549.26 m). Significant interactive effects were found between a player's starting status, playing time, and session duration in overall training load variables for within (F = 140.46; η² = 0.85; p < 0.001) and between-subjects (F = 11.63 to 160.70; η² = 0.05 to 0.76; p < 0.001). The player's starting status seems to only influence the training volume in sub-elite youth football, unless one considers the covariance of the playing time and session duration. Consequently, coaches should prioritize complementary training to equalize training volume and emphasize similar practice opportunities for non-starters. Future studies should evaluate the gap between training and match load, measuring the impact of recovery and compensatory sessions.