Predicting Soccer Players' Fitness Status Through a Machine-Learning Approach.

PURPOSE: The study had 3 purposes: (1) to develop an index using machine-learning techniques to predict the fitness status of soccer players, (2) to explore the index's validity and its relationship with a submaximal run test (SMFT), and (3) to analyze the impact of weekly training load on the index and SMFT outcomes. METHODS: The study involved 50 players from an Italian professional soccer club. External and internal loads were collected during training sessions. Various machine-learning algorithms were assessed for their ability to predict heart-rate responses during the training drills based on external load data. The fitness index, calculated as the difference between actual and predicted heart rates, was correlated with SMFT outcomes. RESULTS: Random forest regression (mean absolute error = 3.8 [0.05]) outperformed the other machine-learning algorithms (extreme gradient boosting and linear regression). Average speed, minutes from the start of the training session, and the work:rest ratio were identified as the most important features. The fitness index displayed a very large correlation (r = .70) with SMFT outcomes, with the highest result observed during possession games and physical conditioning exercises. The study revealed that heart-rate responses from SMFT and the fitness index could diverge throughout the season, suggesting different aspects of fitness. CONCLUSIONS: This study introduces an "invisible monitoring" approach to assess soccer player fitness in the training environment. The developed fitness index, in conjunction with traditional fitness tests, provides a comprehensive understanding of player readiness. This research paves the way for practical applications in soccer, enabling personalized training adjustments and injury prevention.

Monitoring Readiness to Train and Perform in Female Football: Current Evidence and Recommendations for Practitioners.

PURPOSE: Monitoring player readiness to train and perform is an important practical concept in football. Despite an abundance of research in this area in the male game, to date, research is limited in female football. The aims of this study were, first, to summarize the current literature on the monitoring of readiness in female football; second, to summarize the current evidence regarding the monitoring of the menstrual cycle and its potential impact on physical preparation and performance in female footballers; and third, to offer practical recommendations based on the current evidence for practitioners working with female football players. CONCLUSIONS: Practitioners should include both objective (eg, heart rate and countermovement jump) and subjective measures (eg, athlete-reported outcome measures) in their monitoring practices. This would allow them to have a better picture of female players' readiness. Practitioners should assess the reliability of their monitoring (objective and subjective) tools before adopting them with their players. The use of athlete-reported outcome measures could play a key role in contexts where technology is not available (eg, in semiprofessional and amateur clubs); however, practitioners need to be aware that many single-item athlete-reported outcome measures instruments have not been properly validated. Finally, tracking the menstrual cycle can identify menstrual dysfunction (eg, infrequent or irregular menstruation) that can indicate a state of low energy availability or an underlying gynecological issue, both of which warrant further investigation by medical practitioners.

Measurement properties of external training load variables during standardised games in soccer: Implications for training and monitoring strategies.

The aim of this study was to assess the measurement properties of external training load measures across three formats of standardised training games. Eighty-eight players from two English professional soccer clubs participated in the study spanning three consecutive seasons. External training load data was collected from three types of standardised game format drills (11v11, 10v10, 7v7+6) using Global Positioning Systems. For each external training load metric in each game format, the following measurement properties were calculated; coefficient of variation (CV%) to determine between- and within-subject reliability, intraclass coefficient correlation (ICC) to determine test-retest reliability, and signal-to-noise ratio (SNR) to determine sensitivity. Total distance (TD) and PlayerLoad™ (PL) demonstrated good sensitivity (TD SNR = 1.6-4.6; PL SNR = 1.2-4.3) on a group level. However, a wide variety of within-subject reliability was demonstrated for these variables (TD CV% = 1.7-36.3%; PL CV% = 4.3-39.5%) and corresponding intensity measures calculated per minute. The percentage contribution of individual planes to PL showed the lowest between-subject CV% (CV% = 2-7%), although sensitivity varied across formats (SNR = 0.3-1.4). High speed running demonstrated poor reliability across all three formats of SSG (CV% = 51-103%, ICC = 0.03-0.53). Given the measurement properties of external training load measures observed in this study, specifically the within-subject variation, reliability across trials of standardised training games should be calculated on an individual level. This will allow practitioners to detect worthwhile changes across trials of standardised game format drills. Such information is important for the appropriate implementation of training and monitoring strategies in soccer.

Tracking Systems in Team Sports: A Narrative Review of Applications of the Data and Sport Specific Analysis.

Seeking to obtain a competitive advantage and manage the risk of injury, team sport organisations are investing in tracking systems that can quantify training and competition characteristics. It is expected that such information can support objective decision-making for the prescription and manipulation of training load. This narrative review aims to summarise, and critically evaluate, different tracking systems and their use within team sports. The selection of systems should be dependent upon the context of the sport and needs careful consideration by practitioners. The selection of metrics requires a critical process to be able to describe, plan, monitor and evaluate training and competition characteristics of each sport. An emerging consideration for tracking systems data is the selection of suitable time analysis, such as temporal durations, peak demands or time series segmentation, whose best use depends on the temporal characteristics of the sport. Finally, examples of characteristics and the application of tracking data across seven popular team sports are presented. Practitioners working in specific team sports are advised to follow a critical thinking process, with a healthy dose of scepticism and awareness of appropriate theoretical frameworks, where possible, when creating new or selecting an existing metric to profile team sport athletes.

More than a Metric: How Training Load is Used in Elite Sport for Athlete Management.

Training load monitoring is a core aspect of modern-day sport science practice. Collecting, cleaning, analysing, interpreting, and disseminating load data is usually undertaken with a view to improve player performance and/or manage injury risk. To target these outcomes, practitioners attempt to optimise load at different stages throughout the training process, like adjusting individual sessions, planning day-to-day, periodising the season, and managing athletes with a long-term view. With greater investment in training load monitoring comes greater expectations, as stakeholders count on practitioners to transform data into informed, meaningful decisions. In this editorial we highlight how training load monitoring has many potential applications and cannot be simply reduced to one metric and/or calculation. With experience across a variety of sporting backgrounds, this editorial details the challenges and contextual factors that must be considered when interpreting such data. It further demonstrates the need for those working with athletes to develop strong communication channels with all stakeholders in the decision-making process. Importantly, this editorial highlights the complexity associated with using training load for managing injury risk and explores the potential for framing training load with a performance and training progression mindset.

A Standardized Small Sided Game Can Be Used to Monitor Neuromuscular Fatigue in Professional A-League Football Players.

Introduction: Training and competition load can cause neuromuscular fatigue (NMF) and modified movement strategy such as an increase in the contribution of the medio-lateral [PlayerLoadTMML(%)] and decrease in the % vertical [PlayerLoadTMV(%)] vectors, to total PlayerLoadTM (accelerometer derived measurement in vertical, medio-lateral, and anterior-posterior planes) in matches. NMF assessment involves expensive equipment, however, given the modification of match movement strategy with NMF, this may be present in a standardized drill. The aim of this study was to determine the utility of a small sided game (SSG) for the measurement of NMF. Materials and Methods: Data was collected throughout a competitive football season. External load was quantified using global positioning system (GPS) and accelerometry, and internal load by session rating of perceived exertion (sRPE). A 5 vs. 5 SSG and countermovement jump (CMJ), for determination of flight time:contraction time (FT:CT), were performed the day prior to each match. Weekly volume from GPS, PlayerLoadTM and sRPE were calculated across the season. Weekly SSG activity profile and FT:CT was compared between "high" and "low" load weeks determined relative to season average. SSG activity profile was assessed between weeks where FT:CT was above or below pre-season baseline. Impact on match activity profile was examined between weeks where FT:CT and SSG activity profile were higher or lower than baseline. The difference (high vs. low load and < or > pre-season baseline) was calculated using the effect size (ES) ± 90% CI and practically important if there was a >75% likelihood of exceeding an ES of 0.2. Results: All weekly load metrics increased SSG PlayerLoadTM⋅m⋅min-1 when above season average, however, the impact on FT:CT was trivial. Reduced weekly FT:CT compared to baseline resulted in lower SSG PlayerLoadTM⋅min-1 and PlayerLoadTMSlow⋅min-1. FT:CT below baseline increased match PlayerLoadTMML(%) and decreased PlayerLoadTMV(%) during subsequent match play. Similarly, a reduction in SSG PlayerLoadTM⋅m⋅min-1 was followed by increased match PlayerLoadTMML(%). Conclusion: Changes in select match activity profile variables following a reduction in SSG PlayerLoadTM m.min-1, mirror those seen when FT:CT is reduced. Increased PlayerLoadTMML(%) during matches likely represents fatigue driven modification to movement strategy. Small-sided games may be a useful tool to detect NMF.