Injuries have a great impact on professional soccer, due to their large influence on team performance and the considerable costs of rehabilitation for players. Existing studies in the literature provide just a preliminary understanding of which factors mostly affect injury risk, while an evaluation of the potential of statistical models in forecasting injuries is still missing. In this paper, we propose a multi-dimensional approach to injury forecasting in professional soccer that is based on GPS measurements and machine learning. By using GPS tracking technology, we collect data describing the training workload of players in a professional soccer club during a season. We then construct an injury forecaster and show that it is both accurate and interpretable by providing a set of case studies of interest to soccer practitioners. Our approach opens a novel perspective on injury prevention, providing a set of simple and practical rules for evaluating and interpreting the complex relations between injury risk and training performance in professional soccer.

Soccer analytics is attracting increasing interest in academia and industry, thanks to the availability of sensing technologies that provide high-fidelity data streams for every match. Unfortunately, these detailed data are owned by specialized companies and hence are rarely publicly available for scientific research. To fill this gap, this paper describes the largest open collection of soccer-logs ever released, containing all the spatio-temporal events (passes, shots, fouls, etc.) that occured during each match for an entire season of seven prominent soccer competitions. Each match event contains information about its position, time, outcome, player and characteristics. The nature of team sports like soccer, halfway between the abstraction of a game and the reality of complex social systems, combined with the unique size and composition of this dataset, provide an ideal ground for tackling a wide range of data science problems, including the measurement and evaluation of performance, both at individual and at collective level, and the determinants of success and failure.

The purpose of this study was to examine the effects of speed, agility and quickness (SAQ) training on acceleration (5 and 20 m), change of direction speed (CODS) and reactive agility in preadolescent soccer players. Thirty-five participants (age = 10.57 ± 0.26, body mass = 36.78 ± 5.34 kg, body height = 1.42 ± 0.05 m), randomly assigned to experimental (EG, n = 20) and control groups (CG, n = 15), completed a 12-week training intervention, 2 day/week. A significant interaction was found in 5-m sprint (P < 0.05, part η = 0.117) and reactive agility (P < 0.01, part η = 0.248) between EG and CG. In both groups, 20-m sprint time improved significantly (P < 0.05, effect size = 0.3-0.4) while performance on CODS remained unchanged after 12 weeks. These findings indicated that SAQ training could positively affect cognitive skills and initial sprint acceleration through the middle childhood, offering useful guidance to soccer coaches.

Wearable physiological monitors have become increasingly popular, often worn during people’s daily life, collecting data 24 hours a day, 7 days a week. In the last decade, these devices have attracted the attention of the scientific community as they allow us to automatically extract information about user physiology (e.g., heart rate, sleep quality and physical activity) enabling inference on their health. However, the biggest issue about the data recorded by wearable devices is the missing values due to motion and mechanical artifacts induced by external stimuli during data acquisition. This missing data could negatively affect the assessment of heart rate (HR) response and estimation of heart rate variability (HRV), that could in turn provide misleading insights concerning the health status of the individual. In this study, we focus on healthy subjects with normal heart activity and investigate the effects of missing variation of the timing between beats (RR-intervals) caused by motion artifacts on HRV features estimation by randomly introducing missing values within a five min time windows of RR-intervals obtained from the nsr2db PhysioNet dataset by using Gilbert burst method. We then evaluate several strategies for estimating HRV in the presence of missing values by interpolating periods of missing values, covering the range of techniques often deployed in the literature, via linear, quadratic, cubic, and cubic spline functions. We thereby compare the HRV features obtained by handling missing data in RR-interval time series against HRV features obtained from the same data without missing values. Finally, we assess the difference between the use of interpolation methods on time (i.e., the timestamp when the heartbeats happen) and on duration (i.e., the duration of the heartbeats), in order to identify the best methodology to handle the missing RR-intervals. The main novel finding of this study is that the interpolation of missing data on time produces more reliable HRV estimations when compared to interpolation on duration. Hence, we can conclude that interpolation on duration modifies the power spectrum of the RR signal, negatively affecting the estimation of the HRV features as the amount of missing values increases. We can conclude that interpolation in time is the optimal method among those considered for handling data with large amounts of missing values, such as data from wearable sensors.

The present study aimed to determine the contribution of soccer players' anthropometric features to predict their physical performance. Sixteen players, from a professional youth soccer academy, were recruited. Several anthropometric features such as corrected arm muscle area (AMA corr ), arm muscle circumference (AMC) and right and left suprapatellar girths (RSPG and LSPG) were employed in this study. Players' physical performance was assessed by the change of direction (COD), sprint (10-m and 20-m), and vertical jump (CMJ) tests, and Yo-Yo Intermittent Recovery Test level 1 (Yo-Yo IRT1). Using an extra tree regression (ETR) model, the anthropometric features permitted to accurately predict 10-m sprint, 20-m sprint and Yo-Yo IRTL 1 performance (p < 0.05). ETR showed that upper-body features as AMA corr , and AMC affected 10-m and 20-m sprint performances, while lower-body features as RSPG and LSPG influenced the Yo-Yo IRTL 1 (Overall Gini importance ≥ 0.22). The model predicting COD and CMJ presented a poor level of prediction, suggesting that other factors, rather than anthropometric features, may concur to predict their changes in performance. These findings demonstrated that the upper-and lower-body anthropometric features are strictly related to sprint and aerobic fitness performance in elite youth soccer.