A competing risk survival analysis of the impacts of team formation on goals in professional football.

Introduction: This study investigated the influence of team formation on goal-scoring efficiency through analysing the time required for a goal to be scored in elite football matches. Method: The analysis was conducted using a comprehensive open access dataset encompassing eight major football competitions, including prestigious events such as the World Cup and the UEFA Champions League. It notably focused on the competing risks framework and employed the Fine and Gray model to account for the interplay between two competing events: team A scoring and team B scoring. Results: Through analysis of Team A's goal occurrences, we assessed the offensive capabilities of its formation and the defensive effectiveness of Team B's composition in relation to the time it took for Team A to score a goal. Findings revealed that teams employing the 4-3-3 and 4-2-3-1 formations outperformed other formations (3-4-3, 3-5-2, 4-4-2, 4-5-1, 5-3-2, 5-4-1) regarding goal-scoring efficiency. Discussion: By shedding light on the impact of team formation on goal scoring, this research contributes to a deeper understanding of some of the successful strategic aspects of elite football.

A New Training Load Quantification Method at Supramaximal Intensity and Its Application in Injuries Among Members of an International Volleyball Team.

ABSTRACT: Bouzigues, T, Maurelli, O, Imbach, F, Prioux, J, and Candau, R. A new training load quantification method at supramaximal intensity and its application in injuries among members of an international volleyball team. J Strength Cond Res XX(X): 000-000, 2024-The quantification of training loads (TLs) is essential for optimizing jump performance and reducing the occurrence of injuries. This study aimed to (a) propose a new method for quantifying TLs in explosive exercises, (b) determine the nature of the relationship between TLs dynamics and injury occurrence, and (c) assess a TL critical for training schedule purposes, above which the risk of injury occurrence becomes unacceptable. This study was conducted with 11 male volleyball players on a national team during a 5-month international competitive period. The proposed new method for quantifying TLs is based on a weighting factor applied to relative jumping intensities, determined by the number of sustainable jumps and their intensities measured by G-Vert accelerometer. The relationship between TLs dynamics and injury occurrence was assessed using a variable dose-response model. A high coefficient of determination was found between the maximum number of jumps possible and their intensities ( r2 = 0.94 ± 0.14, p < 0.001), indicating a strong physiological relationship between jumping intensities and the constraints imposed. The occurrence of injury was dependent on TLs dynamics for 2 players ( r2 = 0.26 ± 0.01; p < 0.001). The TL critical corresponded to 11 jumps over 80% of maximum jump height during games and approximately 130 jumps at <80% of maximal jump height. The present study proposes a new approach for quantifying supramaximal exercises and provides tools for training schedules and the prevention of volleyball injuries.

Using global navigation satellite systems for modeling athletic performances in elite football players.

This study aims to predict individual Acceleration-Velocity profiles (A-V) from Global Navigation Satellite System (GNSS) measurements in real-world situations. Data were collected from professional players in the Superleague division during a 1.5 season period (2019-2021). A baseline modeling performance was provided by time-series forecasting methods and compared with two multivariate modeling approaches using ridge regularisation and long short term memory neural networks. The multivariate models considered commercial features and new features extracted from GNSS raw data as predictor variables. A control condition in which profiles were predicted from predictors of the same session outlined the predictability of A-V profiles. Multivariate models were fitted either per player or over the group of players. Predictor variables were pooled according to the mean or an exponential weighting function. As expected, the control condition provided lower error rates than other models on average (p = 0.001). Reference and multivariate models did not show significant differences in error rates (p = 0.124), regardless of the nature of predictors (commercial features or extracted from signal processing methods) or the pooling method used. In addition, models built over a larger population did not provide significantly more accurate predictions. In conclusion, GNSS features seemed to be of limited relevance for predicting individual A-V profiles. However, new signal processing features open up new perspectives in athletic performance or injury occurrence modeling, mainly if higher sampling rate tracking systems are considered.

The Use of Fitness-Fatigue Models for Sport Performance Modelling: Conceptual Issues and Contributions from Machine-Learning.

The emergence of the first Fitness-Fatigue impulse responses models (FFMs) have allowed the sport science community to investigate relationships between the effects of training and performance. In the models, athletic performance is described by first order transfer functions which represent Fitness and Fatigue antagonistic responses to training. On this basis, the mathematical structure allows for a precise determination of optimal sequence of training doses that would enhance the greatest athletic performance, at a given time point. Despite several improvement of FFMs and still being widely used nowadays, their efficiency for describing as well as for predicting a sport performance remains mitigated. The main causes may be attributed to a simplification of physiological processes involved by exercise which the model relies on, as well as a univariate consideration of factors responsible for an athletic performance. In this context, machine-learning perspectives appear to be valuable for sport performance modelling purposes. Weaknesses of FFMs may be surpassed by embedding physiological representation of training effects into non-linear and multivariate learning algorithms. Thus, ensemble learning methods may benefit from a combination of individual responses based on physiological knowledge within supervised machine-learning algorithms for a better prediction of athletic performance.In conclusion, the machine-learning approach is not an alternative to FFMs, but rather a way to take advantage of models based on physiological assumptions within powerful machine-learning models.

Training load responses modelling and model generalisation in elite sports.

This study aims to provide a transferable methodology in the context of sport performance modelling, with a special focus to the generalisation of models. Data were collected from seven elite Short track speed skaters over a three months training period. In order to account for training load accumulation over sessions, cumulative responses to training were modelled by impulse, serial and bi-exponential responses functions. The variable dose-response (DR) model was compared to elastic net (ENET), principal component regression (PCR) and random forest (RF) models, while using cross-validation within a time-series framework. ENET, PCR and RF models were fitted either individually ([Formula: see text]) or on the whole group of athletes ([Formula: see text]). Root mean square error criterion was used to assess performances of models. ENET and PCR models provided a significant greater generalisation ability than the DR model ([Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text] for [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text], respectively). Only [Formula: see text] and [Formula: see text] were significantly more accurate in prediction than DR ([Formula: see text] and [Formula: see text]). In conclusion, ENET achieved greater generalisation and predictive accuracy performances. Thus, building and evaluating models within a generalisation enhancing procedure is a prerequisite for any predictive modelling.

Validity of the Stryd Power Meter in Measuring Running Parameters at Submaximal Speeds.

This study assessed the Stryd running power meter validity at sub-maximal speeds (8 to 19 km/h). Six recreational runners performed an incremental indoor running test. Power output (PO), ground contact time (GCT) and leg spring stiffness (LSS) were compared to reference measures recorded by portable metabolic analyser, force platforms and motion capture system. A Bayesian framework was conducted for systems validity and comparisons. We observed strong and positive linear relationships between Stryd PO and oxygen consumption ( R 2 = 0.82 , B F 10 > 100 ), and between Stryd PO and external mechanical power ( R 2 = 0.88 , B F 10 > 100 ). Stryd power meter underestimated PO ( B F 10 > 100 ) whereas GCT and LSS values did not show any significant differences with the reference measures ( B F 10 = 0.008 , B F 10 = 0.007 , respectively). We conclude that the Stryd power meter provides valid measures of GCT and LSS but underestimates the absolute values of PO.