The Maximal Intensity Period: Rationalising its Use in Team Sports Practice

Defining Worst-Case-Scenario Thresholds in Soccer: Intensity Versus Volume

PURPOSE

This study aimed to enhance the understanding of soccer match peak demands by describing worst-case scenario (WCS) and time spent above 80% and 90% of the WCS for total distance (TD) and high-speed running (HSR). The investigation considered playing level (first team vs under-19 [U19] team) and playing position (center backs, fullbacks, midfielders, and forwards) to assess how WCS and the time spent above specific thresholds vary across different populations.

METHODS

Data from 31 players in a professional Italian soccer club were collected during the 2022-23 season. Microtechnology devices tracked physical activity during matches. Players were categorized by position, and WCS was determined using rolling averages over a 1-minute period. Time spent above 80% and 90% of WCS for TD and HSR was calculated.

RESULTS

The U19 team exhibited higher HSR WCS compared with the first team (∼63 m·min-1 vs ∼56 m·min-1). Midfielders recorded the highest TD WCS (∼208 m·min-1), and forwards exhibited the highest HSR WCS (∼70 m·min-1). The first team spent significantly more time above 80% (∼6 min) and 90% (∼1 min) of TD WCS. Midfielders spent significantly more time above the 80% (∼7 min) of TD WCS, while forwards above the 80% (∼2 min) of HSR WCS.

CONCLUSIONS

The study emphasizes that WCS used alone may not sufficiently capture real match intensity. Considering the time spent above specific thresholds provides additional insights (ie, between-levels differences and position). Practitioners should consider both WCS and time spent above thresholds for individualized training prescriptions, reflecting differences in playing roles.

Analysis of peak locomotor demands in women's football-the influence of different epoch lengths

The quantification of peak locomotor demands has been gathering researchers' attention in the past years. Regardless of the different methodological approaches used, the most selected epochs are between 1-, 3-, 5- and 15-minutes time windows. However, the selection of these time frames is frequently arbitrary. The aim of this study was to analyse the peak locomotor demands of short time epochs (15, 30, 45, and 60 seconds) in women's football, with special emphasis over the high-speed metrics. During two seasons, the match physical performance of 100 female football players was collected with Global Positioning System units (STATSports Apex). Peak locomotor demands for the selected variables were calculated by using a 1-second moving average approach. For statistical procedures, linear mixed modelling was used, with total distance, high-speed running distance (>16 km∙h-1), sprint distance (>20 km∙h-1), and acceleration and deceleration distance (±2.26 m∙s-2) considered as the dependent variables and the epoch lengths (15, 30, 45, and 60 seconds) considered as the independent variables. A novel finding was the high ratio observed in the 15 seconds epochs of high-speed running distance and sprint distance (77.6% and 91.3%, respectively). The results show that most peak high-speed demands within 60 seconds are completed within just 15 seconds. Thus, intensity-related variables, such as high-speed metrics, would be better contextualised and adapted into training practices if analysed in shorter epoch lengths (15-30 seconds), while longer periods might be used for volume-related metrics (i.e., total distance), depending on the purpose of the analysis.

Characterization of Running Intensity in Canadian Football Based on Tactical Position

This study aimed to use a data-driven approach to identify individualized speed thresholds to characterize running demands and athlete workload during games and practices in skill and linemen football players. Data were recorded from wearable sensors over 28 sessions from 30 male Canadian varsity football athletes, resulting in a total of 287 performances analyzed, including 137 games and 150 practices, using a global positioning system. Speed zones were identified for each performance by fitting a 5-dimensional Gaussian mixture model (GMM) corresponding to 5 running intensity zones from minimal (zone 1) to maximal (zone 5). Skill players had significantly higher (p < 0.001) speed thresholds, percentage of time spent, and distance covered in maximal intensity zones compared to linemen. The distance covered in game settings was significantly higher (p < 0.001) compared to practices. This study highlighted the use of individualized speed thresholds to determine running intensity and athlete workloads for American and Canadian football athletes, as well as compare running performances between practice and game scenarios. This approach can be used to monitor physical workload in athletes with respect to their tactical positions during practices and games, and to ensure that athletes are adequately trained to meet in-game physical demands.

Top-class women's soccer performance: peak demands and distribution of the match activities relative to maximal intensities during official matches

The aims of the current study were to determine the most demanding passages of match play (MDP) and the distribution of match activities relative to maximum intensities during official matches in top-class women soccer players. Twenty-eight women players competing in European championship and international UEFA competitions were monitored during 38 official matches (277 individual samples). Maximum relative (m · min-1) total distance (TD), high-speed running (HSRD), very high-speed running (VHSRD), sprint, acceleration and deceleration distances were calculated across different durations (1-5, 10, 15, 90 min) using a rolling average analysis. Maximum intensities (1-minpeak) were used as the reference value to determine the distribution of relative intensity across the whole-match demands (90-minavg). Time and distance higher than 90-minavg (> 90-minavg) were also calculated. MDP showed moderate to very large [effect size (ES): 0.63/5.20] differences between 1-minpeak vs all durations for each parameter. The relative (m · min-1) 1-minpeak was greater than 90-minavg of about +63% for TD, +358% for HSRD, +969% for VHSRD, +2785% for sprint, +1216% for acceleration, and +768% for deceleration. The total distance covered > 90-minavg was ~66.6% of the total distance covered during the 90-minavg for TD, ~84.8% for HSRD, ~97.4% for VHSRD, ~100% for sprint, ~99.1% for acceleration and ~98.2% for deceleration. The relative distance > 90-minavg was higher (P < 0.05) than the 90-minavg for each metric (ES: 2.22 to 7.58; very large). The present results may help coaches and sport scientists to replicate the peak demands during training routine in top-class women soccer players.

Worst Case Scenarios in Soccer Training and Competition: Analysis of Playing Position, Congested Periods, and Substitutes

Aim: To understand mean (WCSmean) and peak (WCSpeak) worst case scenarios within training and game play in male professional soccer. Methods: Thirty-one (n = 31) first team players were monitored across 37 matches and 14 MD-3 sessions. Playing status was distinguished, football drills analyzed, and performance explored in long-period: >6 days, moderate-period: 5-6 days, and congested-period: ≤4 days. Relative total distance (TD), high-speed running distance (HSRD, >19.8 km·h-1), sprint distance (SD, >25.2 km·h-1), accelerations/decelerations (A+D, >3 m·s-2), accelerations (Acc, >3 m·s-2), and decelerations (Dec, >-3 m·s-2) were measured as well as Maximum acceleration (Max Acc; m·s-2) and deceleration (Max Dec; m·s-2). Results: Analysis of variance found differences between matches and training in WCSmean for TD, HSRD, SD, and Max Dec in all positions (p < .001; partial η2 > .275). Fullbacks displayed differences between match and training in Max Acc (moderate ESs; p < .001), while center backs and central midfielders in Max Dec (large ESs; p > .05). Main effects of playing status were discovered for all metrics except Max Dec (p < .001; partial η2 > .124). Analysis showed differences between long- and congested-period for A+D and Dec (large ESs; p ≤ .05). Conclusions: Findings provide more insights into short peak intensity demands of soccer showing that the maximum high velocity action of acceleration and deceleration is not being replicated in training. Nonstarters lack maximum intensity exposure in matches (WCSpeak) increasing the gap between training and competition even higher during congested fixture periods.

Machine Learning on Prediction of Relative Physical Activity Intensity Using Medical Radar Sensor and 3D Accelerometer

BACKGROUND

One of the most critical topics in sports safety today is the reduction in injury risks through controlled fatigue using non-invasive athlete monitoring. Due to the risk of injuries, it is prohibited to use accelerometer-based smart trackers, activity measurement bracelets, and smart watches for recording health parameters during performance sports activities. This study analyzes the synergy feasibility of medical radar sensors and tri-axial acceleration sensor data to predict physical activity key performance indexes in performance sports by using machine learning (ML). The novelty of this method is that it uses a 24 GHz Doppler radar sensor to detect vital signs such as the heartbeat and breathing without touching the person and to predict the intensity of physical activity, combined with the acceleration data from 3D accelerometers.

METHODS

This study is based on the data collected from professional athletes and freely available datasets created for research purposes. A combination of sensor data management was used: a medical radar sensor with no-contact remote sensing to measure the heart rate (HR) and 3D acceleration to measure the velocity of the activity. Various advanced ML methods and models were employed on the top of sensors to analyze the vital parameters and predict the health activity key performance indexes. three-axial acceleration, heart rate data, age, as well as activity level variances.

RESULTS

The ML models recognized the physical activity intensity and estimated the energy expenditure on a realistic level. Leave-one-out (LOO) cross-validation (CV), as well as out-of-sample testing (OST) methods, have been used to evaluate the level of accuracy in activity intensity prediction. The energy expenditure prediction with three-axial accelerometer sensors by using linear regression provided 97-99% accuracy on selected sports (cycling, running, and soccer). The ML-based RPE results using medical radar sensors on a time-series heart rate (HR) dataset varied between 90 and 96% accuracy. The expected level of accuracy was examined with different models. The average accuracy for all the models (RPE and METs) and setups was higher than 90%.

CONCLUSIONS

The ML models that classify the rating of the perceived exertion and the metabolic equivalent of tasks perform consistently.