A Narrative Review for a Machine Learning Application in Sports: An Example Based on Injury Forecasting in Soccer

Artificial Intelligence for Objective Assessment of Acrobatic Movements: Applying Machine Learning for Identifying Tumbling Elements in Cheer Sports

Over the past four decades, cheerleading evolved from a sideline activity at major sporting events into a professional, competitive sport with growing global popularity. Evaluating tumbling elements in cheerleading relies on both objective measures and subjective judgments, such as difficulty and execution quality. However, the complexity of tumbling-encompassing team synchronicity, ground interactions, choreography, and artistic expression-makes objective assessment challenging. Artificial intelligence (AI) revolutionised various scientific fields and industries through precise data-driven analyses, yet their application in acrobatic sports remains limited despite significant potential for enhancing performance evaluation and coaching. This study investigates the feasibility of using an AI-based approach with data from a single inertial measurement unit to accurately identify and objectively assess tumbling elements in standard cheerleading routines. A sample of 16 participants (13 females, 3 males) from a Division I collegiate cheerleading team wore a single inertial measurement unit at the dorsal pelvis. Over a 4-week seasonal preparation period, 1102 tumbling elements were recorded during regular practice sessions. Using triaxial accelerations and rotational speeds, various ML algorithms were employed to classify and evaluate the execution of tumbling manoeuvres. Our results indicate that certain machine learning models can effectively identify different tumbling elements with high accuracy despite inter-individual variability and data noise. These findings demonstrate the significant potential for integrating AI-driven assessments into cheerleading and other acrobatic sports in order to provide objective metrics that complement traditional judging methods.

Subjective recovery in professional soccer players: A machine learning and mediation approach

Coaches often ask players to judge their recovery status (subjective recovery). We aimed to explore potential determinants of subjective recovery in 101 male professional soccer players of 4 Italian Serie C teams and to further investigate whether the relationship between training load and subjective recovery is mediated by fatigue, sleep quality, muscle soreness, stress and mood. A complete season for each of the four teams was recorded for a total of 16,989 training sessions and matches. Every morning, players rated their perceived fatigue, sleep quality, muscle soreness, stress and mood, and judged their recovery using the Total Quality Recovery (TQR) questionnaire. Training load was obtained after each training session or match. A framework of data analytics of time series was employed to detect the factors associated with subjective recovery. Machine learning and mediation analyses suggest that TQR is primarily associated with ratings of fatigue and muscle soreness at the judgements time, and that these factors mediate most of the relationship between training load of the previous day and subjective recovery. These findings suggest that, to maximize subjective recovery, strategies minimizing fatigue and muscle soreness should be implemented. Reducing the training load of the previous day seems the most effective strategy.

Identification of Athleticism and Sports Profiles Throughout Machine Learning Applied to Heart Rate Variability

Heart rate variability (HRV) is a non-invasive health and fitness indicator, and machine learning (ML) has emerged as a powerful tool for analysing large HRV datasets. This study aims to identify athletic characteristics using the HRV test and ML algorithms. Two models were developed: Model 1 (M1) classified athletes and non-athletes using 856 observations from high-performance athletes and 494 from non-athletes. Model 2 (M2) identified an individual soccer player within a team based on 105 observations from the player and 514 from other team members. Three ML algorithms were applied -Random Forest (RF), Extreme Gradient Boosting (XGBoost), and Support Vector Machine (SVM)- and SHAP values were used to interpret the results. In M1, the SVM algorithm achieved the highest performance (accuracy = 0.84, ROC AUC = 0.91), while in M2 Random Forest performed best (accuracy = 0.92, ROC AUC = 0.94). Based on these results, we propose an athleticism index and a soccer identification index derived from HRV data. The findings suggest that ML algorithms, such as SVM and RF, can effectively generate indices based on HRV for identifying individuals with athletic characteristics or distinguishing athletes with specific sports profiles. These insights underscore the importance of integrating HRV assessments systematically into training regimens for enhanced athletic evaluation.

Predicting noncontact injuries of professional football players using machine learning

Noncontact injuries are prevalent among professional football players. Yet, most research on this topic is retrospective, focusing solely on statistical correlations between Global Positioning System (GPS) metrics and injury occurrence, overlooking the multifactorial nature of injuries. This study introduces an automated injury identification and prediction approach using machine learning, leveraging GPS data and player-specific parameters. A sample of 34 male professional players from a Portuguese first-division team was analyzed, combining GPS data from Catapult receivers with descriptive variables for machine learning models-Support Vector Machines (SVMs), Feedforward Neural Networks (FNNs), and Adaptive Boosting (AdaBoost)-to predict injuries. These models, particularly the SVMs with cost-sensitive learning, showed high accuracy in detecting injury events, achieving a sensitivity of 71.43%, specificity of 74.19%, and overall accuracy of 74.22%. Key predictive factors included the player's position, session type, player load, velocity and acceleration. The developed models are notable for their balanced sensitivity and specificity, efficiency without extensive manual data collection, and capability to predict injuries for short time frames. These advancements will aid coaching staff in identifying high-risk players, optimizing team performance, and reducing rehabilitation costs.

Assessment of non-fatal injuries among university students in Hainan: a machine learning approach to exploring key factors

Background

Injuries constitute a significant global public health concern, particularly among individuals aged 0-34. These injuries are affected by various social, psychological, and physiological factors and are no longer viewed merely as accidental occurrences. Existing research has identified multiple risk factors for injuries; however, they often focus on the cases of children or the older adult, neglecting the university students. Machine learning (ML) can provide advanced analytics and is better suited to complex, nonlinear data compared to traditional methods. That said, ML has been underutilized in injury research despite its great potential. To fill this gap, this study applies ML to analyze injury data among university students in Hainan Province. The purpose is to provide insights into developing effective prevention strategies. To explore the relationship between scores on the self-rating anxiety scale and self-rating depression scale and the risk of non-fatal injuries within 1 year, we categorized these scores into two groups using restricted cubic splines.

Methods

Chi-square tests and LASSO regression analysis were employed to filter factors potentially associated with non-fatal injuries. The Synthetic Minority Over-Sampling Technique (SMOTE) was applied to balance the dataset. Subsequent analyses were conducted using random forest, logistic regression, decision tree, and XGBoost models. Each model underwent 10-fold cross-validation to mitigate overfitting, with hyperparameters being optimized to improve performance. SHAP was utilized to identify the primary factors influencing non-fatal injuries.

Results

The Random Forest model has proved effective in this study. It identified three primary risk factors for predicting non-fatal injuries: being male, favorable household financial situation, and stable relationship. Protective factors include reduced internet time and being an only child in the family.

Conclusion

The study highlighted five key factors influencing non-fatal injuries: sex, household financial situation, relationship stability, internet time, and sibling status. In identifying these factors, the Random Forest, Logistic Regression, Decision Tree, and XGBoost models demonstrated varying effectiveness, with the Random Forest model exhibiting superior performance.

Non-contact lower limb injuries in Rugby Union: A two-year pattern recognition analysis of injury risk factors

The cause of sport injuries are multifactorial and necessitate sophisticated statistical approaches for accurate identification of risk factors predisposing athletes to injury. Pattern recognition analyses have been adopted across sporting disciplines due to their ability to account for repeated measures and non-linear interactions of datasets, however there are limited examples of their use in injury risk prediction. This study incorporated two-years of rigorous monitoring of athletes with 1740 individual weekly data points across domains of training load, performance testing, musculoskeletal screening, and injury history parameters, to be one of the first to employ a pattern recognition approach to predict the risk factors of specific non-contact lower limb injuries in Rugby Union. Predictive models (injured vs. non-injured) were generated for non-contact lower limb, non-contact ankle, and severe non-contact injuries using Bayesian pattern recognition from a pool of 36 Senior Academy Rugby Union athletes. Predictors for non-contact lower limb injuries included dorsiflexion angle, adductor strength, and previous injury history (area under the receiver operating characteristic (ROC) = 0.70) Dorsiflexion angle parameters were also predictive of non-contact ankle injuries, along with slower sprint times, greater body mass, previous concussion, and previous ankle injury (ROC = 0.76). Predictors of severe non-contact lower limb injuries included greater differences in mean training load, slower sprint times, reduced hamstring and adductor strength, reduced dorsiflexion angle, greater perceived muscle soreness, and playing as a forward (ROC = 0.72). The identification of specific injury risk factors and useable thresholds for non-contact injury risk detection in sport holds great potential for coaches and medical staff to modify training prescriptions and inform injury prevention strategies, ultimately increasing player availability, a key indicator of team success.

Enhancing Sports Injury Risk Assessment in Soccer Through Machine Learning and Training Load Analysis

Sports injuries pose significant challenges in athlete welfare and team dynamics, particularly in high-intensity sports like soccer. This study used machine learning algorithms to assess non-contact injury risk in professional male soccer players from physiological and mechanical load variables. Twenty-five professional male soccer players with a first-time, non-contact muscle injury were included in this study. Recordings of external load (speed, distance, and acceleration/deceleration data) and internal load (heart rate) were obtained during all training sessions and official matches over a 4-year period. Machine learning model training and evaluation features were calculated for each of nine different metrics for a 28-day period prior to the injury and an equal-length baseline epoch. The acute surge in the values of each workload metric was quantified by the deviation of maximum values from the average, while the variations of cumulative workload over the last four weeks preceding injury were also calculated. Seven features were selected by the model as prominent estimators of injury incidence. Three of the features concerned acute load deviations (number of sprints, training load score-incorporating heart rate and muscle load- and time of heart rate at the 90-100% of maximum). The four cumulative load features were (total distance, high speed and sprint running distance and training load score). The accuracy of the muscle injury risk assessment model was 0.78, with a sensitivity of 0.73 and specificity of 0.85. Our model achieved high performance in injury risk detection using a limited number of training load variables. The inclusion, for the first time, of heart rate related variables in an injury risk assessment model highlights the importance of physiological overload as a contributor to muscle injuries in soccer. By identifying the important parameters, coaches may prevent muscle injuries by controlling surges of training load during training and competition.

Performance and healthcare analysis in elite sports teams using artificial intelligence: a scoping review

Introduction

In competitive sports, teams are increasingly relying on advanced systems for improved performance and results. This study reviews the literature on the role of artificial intelligence (AI) in managing these complexities and encouraging a system thinking shift. It found various AI applications, including performance enhancement, healthcare, technical and tactical support, talent identification, game prediction, business growth, and AI testing innovations. The main goal of the study was to assess research supporting performance and healthcare.

Methods

Systematic searches were conducted on databases such as Pubmed, Web of Sciences, and Scopus to find articles using AI to understand or improve sports team performance. Thirty-two studies were selected for review.

Results

The analysis shows that, of the thirty-two articles reviewed, fifteen focused on performance and seventeen on healthcare. Football (Soccer) was the most researched sport, making up 67% of studies. The revised studies comprised 2,823 professional athletes, with a gender split of 65.36% male and 34.64% female. Identified AI and non-AI methods mainly included Tree-based techniques (36%), Ada/XGBoost (19%), Neural Networks (9%), K-Nearest Neighbours (9%), Classical Regression Techniques (9%), and Support Vector Machines (6%).

Conclusions

This study highlights the increasing use of AI in managing sports-related healthcare and performance complexities. These findings aim to assist researchers, practitioners, and policymakers in developing practical applications and exploring future complex systems dynamics.

Association between match-related physical activity profiles and playing positions in different tasks: A data driven approach

Assessing the intensity characteristics of specific soccer drills (matches, small-side game, and match-based exercises) could help practitioners to plan training sessions by providing the optimal stimulus for every player. In this paper, we propose a data analytics framework to assess the neuromuscular or metabolic characteristics of a soccer-specific exercise in relation with the expected match intensity. GPS data describing the physical tasks' external intensity during an entire season of twenty-eight semi-professional soccer players competing at the fourth Italian division were used in this study. A supervised machine-learning approach was tested in order to detect difference in playing positions in different sport-specific drills. Moreover, a non-supervised machine-learning model was used to profile the match neuromuscular and metabolic characteristics. Players' playing positions during matches and match-based exercises are characterised by specific metabolic and neuromuscular characteristics related to tactical demands, while in the small-side game these differences are not detected. Additionally, our framework permits to evaluate if the match performance request is mirrored during training drills. Practitioners could evaluate the type of stimulus performed by a player in a specific training drill in order to assess if they reflect the matches characteristics of their specific playing position.

Analyzing ECG signals in professional football players using machine learning techniques

Background

Football player's health is important, and preventing sudden cardiac arrest may be a critical issue. Professional football players have different ECG signals than the average population, yet there are considerable gaps in study whereas the general population has been extensively studied.

Objectives

(a) Generate a reference and innovative resting 12-lead ECG database from 54 UEFA PRO level male football players from La Liga. This is a novel approach to cope the ECG and possible arrythmias in athletes. (b) Manage each XML athlete ECG data and develop a free-use program to visualize, denoise and filter the signal with the capacity to automate the labelling of the waves and save the reports. (c) Study the ECG wave shape and generate models through ML to analyse its utility to automate basic diagnosis.

Methods

The dataset collection is based on a prospective observational cohort and includes 10 s, 12-lead ECGs and rhythm and condition labels for each athlete. Physiological sport arrhythmias, T-Wave shape and other findings were studied and labelled. ECG Visualizer was developed and used for 3 machine learning (ML) methods to automate sinus bradycardia arrhythmia diagnosis.

Results

A dataset with 163 ECGs in XML format was collected comprising the Pro Football 12-lead Resting Electrocardiogram Database (PF12RED). "ECG Visualizer" software was developed, and ML was shown to be useful in detecting sinus bradycardia.

Conclusions

The study demonstrates that AI and machine learning can detect simple arrhythmias with accuracy, also it provides a valuable dataset and a free software application.

A novel approach for sports injury risk prediction: based on time-series image encoding and deep learning

The rapid development of big data technology and artificial intelligence has provided a new perspective on sports injury prevention. Although data-driven algorithms have achieved some valuable results in the field of sports injury risk assessment, the lack of sufficient generalization of models and the inability to automate feature extraction have made it challenging to deploy research results in the real world. Therefore, this study attempts to build an injury risk prediction model using a combination of time-series image encoding and deep learning algorithms to address this issue better. This study used the time-series image encoding approach for feature construction to represent relationships between values at different moments, including Gramian Angular Summation Field (GASF), Gramian Angular Difference Field (GADF), Markov Transition Field (MTF), and Recurrence Plot (RP). Deep Convolutional Auto-Encoder (DCAE) learned the image-encoded data for representation to obtain features with good discrimination, and the classifier was performed using Deep Neural Network (DNN). The results from five repeated experiments show that the GASF-DCAE-DNN model is overall better in the training (AUC: 0.985 ± 0.001, Gmean: 0.930 ± 0.007, Sensitivity: 0.997 ± 0.003, Specificity: 0.868 ± 0.013) and test sets (AUC: 0.891 ± 0.026, Gmean: 0.830 ± 0.027, Sensitivity: 0.816 ± 0.039, Specificity: 0.845 ± 0.022), with good discriminative power, robustness, and generalization ability. Compared with the best model reported in the literature, the AUC, Gmean, Sensitivity, and Specificity of the GASF-DCAE-DNN model were higher by 23.9%, 27.5%, 39.7%, and 16.2%, respectively, which confirmed the validity and practicability of the model in injury risk prediction. In addition, differences in injury risk patterns between the training and test sets were identified through shapley additivity interpretation. It was also found that the training volume was an essential factor that affected injury risk prediction. The model proposed in this study provides a powerful injury risk prediction tool for future sports injury prevention practice.

Exploring the interplay between metabolic power and equivalent distance in training games and official matches in soccer: a machine learning approach

Introduction: This study aimed to explore the interplay between metabolic power (MP) and equivalent distance (ED) and their respective roles in training games (TGs) and official soccer matches. Furthermore, the secondary objective was to investigate the connection between external training load (ETL), determined by the interplay of metabolic power and equivalent distance, and internal training load (ITL) assessed through HR-based methods, serving as a measure of criterion validity. Methods: Twenty-one elite professional male soccer players participated in the study. Players were monitored during 11 months of full training and overall official matches. The study used a dataset of 4269 training games and 380 official matches split into training and test sets. In terms of machine learning methods, the study applied several techniques, including K-Nearest Neighbors, Decision Tree, Random Forest, and Support-Vector Machine classifiers. The dataset was divided into two subsets: a training set used for model training and a test set used for evaluation. Results: Based on metabolic power and equivalent distance, the study successfully employed four machine learning methods to accurately distinguish between the two types of soccer activities: TGs and official matches. The area under the curve (AUC) values ranged from 0.90 to 0.96, demonstrating high discriminatory power, with accuracy levels ranging from 0.89 to 0.98. Furthermore, the significant correlations observed between Edwards' training load (TL) and TL calculated from metabolic power metrics confirm the validity of these variables in assessing external training load in soccer. The correlation coefficients (r values) ranged from 0.59 to 0.87, all reaching statistical significance at p < 0.001. Discussion: These results underscore the critical importance of investigating the interaction between metabolic power and equivalent distance in soccer. While the overall intensity may appear similar between TGs and official matches, it is evident that underlying factors contributing to this intensity differ significantly. This highlights the necessity for more comprehensive analyses of the specific elements influencing physical effort during these activities. By addressing this fundamental aspect, this study contributes valuable insights to the field of sports science, aiding in the development of tailored training programs and strategies that can optimize player performance and reduce the risk of injuries in elite soccer.

Knowledge in Motion: A Comprehensive Review of Evidence-Based Human Kinetics

This comprehensive review examines critical aspects of evidence-based human kinetics, focusing on bridging the gap between scientific evidence and practical implementation. To bridge this gap, the development of tailored education and training programs is essential, providing practitioners with the expertise and skills to effectively apply evidence-based programs and interventions. The effectiveness of these programs in improving physical fitness across all age groups has been widely demonstrated. In addition, integrating artificial intelligence and the principles of slow science into evidence-based practice promises to identify gaps in knowledge and stimulate further research in human kinetics. The purpose of this review is to provide researchers and practitioners with comprehensive information on the application of scientific principles in human kinetics. By highlighting the importance of evidence-based practice, this review is intended to promote the adoption of effective interventions to optimize physical health and enhance performance.

Relationship between a daily injury risk estimation feedback (I-REF) based on machine learning techniques and actual injury risk in athletics (track and field): protocol for a prospective cohort study over an athletics season

INTRODUCTION

Two-thirds of athletes (65%) have at least one injury complaint leading to participation restriction (ICPR) in athletics (track and field) during one season. The emerging practice of medicine and public health supported by electronic processes and communication in sports medicine represents an opportunity for developing new injury risk reduction strategies. Modelling and predicting the risk of injury in real-time through artificial intelligence using machine learning techniques might represent an innovative injury risk reduction strategy. Thus, the primary aim of this study will be to analyse the relationship between the level of Injury Risk Estimation Feedback (I-REF) use (average score of athletes' self-declared level of I-REF consideration for their athletics activity) and the ICPR burden during an athletics season.

METHOD AND ANALYSIS

We will conduct a prospective cohort study, called Injury Prediction with Artificial Intelligence (IPredict-AI), over one 38-week athletics season (from September 2022 to July 2023) involving competitive athletics athletes licensed with the French Federation of Athletics. All athletes will be asked to complete daily questionnaires on their athletics activity, their psychological state, their sleep, the level of I-REF use and any ICPR. I-REF will present a daily estimation of the ICPR risk ranging from 0% (no risk for injury) to 100% (maximal risk for injury) for the following day. All athletes will be free to see I-REF and to adapt their athletics activity according to I-REF. The primary outcome will be the ICPR burden over the follow-up (over an athletics season), defined as the number of days lost from training and/or competition due to ICPR per 1000 hours of athletics activity. The relationship between ICPR burden and the level of I-REF use will be explored by using linear regression models.

ETHICS AND DISSEMINATION

This prospective cohort study was reviewed and approved by the Saint-Etienne University Hospital Ethical Committee (Institutional Review Board: IORG0007394, IRBN1062022/CHUSTE). Results of the study will be disseminated in peer-reviewed journals and in international scientific congresses, as well as to the included participants.

Predicting Injuries in Football Based on Data Collected from GPS-Based Wearable Sensors

The growing intensity and frequency of matches in professional football leagues are related to the increasing physical player load. An incorrect training model results in over- or undertraining, which is related to a raised probability of an injury. This research focuses on predicting non-contact lower body injuries coming from over- or undertraining. The purpose of this analysis was to create decision-making models based on data collected during both training and match, which will enable the preparation of a tool to model the load and report the increased risk of injury for a given player in the upcoming microcycle. For this purpose, three decision-making methods were implemented. Rule-based and fuzzy rule-based methods were prepared based on expert understanding. As a machine learning baseline XGBoost algorithm was considered. Taking into account the dataset used containing parameters related to the external load of the player, it is possible to predict the risk of injury with a certain precision, depending on the method used. The most promising results were achieved by the machine learning method XGBoost algorithm (Precision 92.4%, Recall 96.5%, and F1-score 94.4%).

Statistical injury prediction for professional sumo wrestlers: Modeling and perspectives

In sumo wrestling, a traditional sport in Japan, many wrestlers suffer from injuries through bouts. In 2019, an average of 5.2 out of 42 wrestlers in the top division of professional sumo wrestling were absent in each grand sumo tournament due to injury. As the number of injury occurrences increases, professional sumo wrestling becomes less interesting for sumo fans, requiring systems to prevent future occurrences. Statistical injury prediction is a useful way to communicate the risk of injuries for wrestlers and their coaches. However, the existing statistical methods of injury prediction are not always accurate because they do not consider the long-term effects of injuries. Here, we propose a statistical model of injury occurrences for sumo wrestlers. The proposed model provides the estimated probability of the next potential injury occurrence for a wrestler. In addition, it can support making a risk-based injury prevention scenario for wrestlers. While a previous study modeled injury occurrences by using the Poisson process, we model it by using the Hawkes process to consider the long-term effect of injuries. The proposed model can also be applied to injury prediction for athletes of other sports.

A novel lower extremity non-contact injury risk prediction model based on multimodal fusion and interpretable machine learning

The application of machine learning algorithms in studying injury assessment methods based on data analysis has recently provided a new research insight for sports injury prevention. However, the data used in these studies are primarily multi-source and multimodal (i.e., longitudinal repeated-measures data and cross-sectional data), resulting in the models not fully utilising the information in the data to reveal specific injury risk patterns. Therefore, this study proposed an injury risk prediction model based on a multi-modal strategy and machine learning algorithms to handle multi-source data better and predict injury risk. This study retrospectively analysed the routine monitoring data of sixteen young female basketball players. These data included training load, perceived well-being status, physiological response, physical performance and lower extremity non-contact injury registration. This study partitions the original dataset based on the frequency of data collection. Extreme gradient boosting (XGBoost) was used to construct unimodal submodels to obtain decision scores for each category of indicators. Ultimately, the decision scores from each submodel were fused using the random forest (RF) to generate a lower extremity non-contact injury risk prediction model at the decision-level. The 10-fold cross-validation results showed that the fusion model was effective in classifying non-injured (mean Precision: 0.9932, mean Recall: 0.9976, mean F2-score: 0.9967), minimal lower extremity non-contact injuries risk (mean Precision: 0.9317, mean Recall: 0.9167, mean F2-score: 0.9171), and mild lower extremity non-contact injuries risk (mean Precision: 0.9000, mean Recall: 0.9000, mean F2-score: 0.9000). The model performed significantly more optimal than the submodel. Comparing the fusion model proposed with a traditional data integration scheme, the average Precision and Recall improved by 8.2 and 20.3%, respectively. The decision curves analysis showed that the proposed fusion model provided a higher net benefit to athletes with potential lower extremity non-contact injury risk. The validity, feasibility and practicality of the proposed model have been confirmed. In addition, the shapley additive explanation (SHAP) and network visualisation revealed differences in lower extremity non-contact injury risk patterns across severity levels. The model proposed in this study provided a fresh perspective on injury prevention in future research.

Wellness Forecasting by External and Internal Workloads in Elite Soccer Players: A Machine Learning Approach

Training for success has increasingly become a balance between maintaining high performance standards and avoiding the negative consequences of accumulated fatigue. The aim of this study is to develop a big data analytics framework to predict players’ wellness according to the external and internal workloads performed in previous days. Such a framework is useful for coaches and staff to simulate the players’ response to scheduled training in order to adapt the training stimulus to the players’ fatigue response. 17 players competing in the Italian championship (Serie A) were recruited for this study. Players’ Global Position System (GPS) data was recorded during each training and match. Moreover, every morning each player has filled in a questionnaire about their perceived wellness (WI) that consists of a 7-point Likert scale for 4 items (fatigue, sleep, stress, and muscle soreness). Finally, the rate of perceived exertion (RPE) was used to assess the effort performed by the players after each training or match. The main findings of this study are that it is possible to accurately estimate players’ WI considering their workload history as input. The machine learning framework proposed in this study is useful for sports scientists, athletic trainers, and coaches to maximise the periodization of the training based on the physiological requests of a specific period of the season.

Machine Learning for Understanding and Predicting Injuries in Football

Attempts to better understand the relationship between training and competition load and injury in football are essential for helping to understand adaptation to training programmes, assessing fatigue and recovery, and minimising the risk of injury and illness. To this end, technological advancements have enabled the collection of multiple points of data for use in analysis and injury prediction. The full breadth of available data has, however, only recently begun to be explored using suitable statistical methods. Advances in automatic and interactive data analysis with the help of machine learning are now being used to better establish the intricacies of the player load and injury relationship. In this article, we examine this recent research, describing the analyses and algorithms used, reporting the key findings, and comparing model fit. To date, the vast array of variables used in analysis as proxy indicators of player load, alongside differences in approach to key aspects of data treatment-such as response to data imbalance, model fitting, and a lack of multi-season data-limit a systematic evaluation of findings and the drawing of a unified conclusion. If, however, the limitations of current studies can be addressed, machine learning has much to offer the field and could in future provide solutions to the training load and injury paradox through enhanced and systematic analysis of athlete data.

Blood sample profile helps to injury forecasting in elite soccer players

Purpose

By analyzing external workloads with machine learning models (ML), it is now possible to predict injuries, but with a moderate accuracy. The increment of the prediction ability is nowadays mandatory to reduce the high number of false positives. The aim of this study was to investigate if players’ blood sample profiles could increase the predictive ability of the models trained only on external training workloads.

Method

Eighteen elite soccer players competing in Italian league (Serie B) during the seasons 2017/2018 and 2018/2019 took part in this study. Players’ blood samples parameters (i.e., Hematocrit, Hemoglobin, number of red blood cells, ferritin, and sideremia) were recorded through the two soccer seasons to group them into two main groups using a non-supervised ML algorithm (k-means). Additionally to external workloads data recorded every training or match day using a GPS device (K-GPS 10 Hz, K-Sport International, Italy), this grouping was used as a predictor for injury risk. The goodness of ML models trained were tested to assess the influence of blood sample profile to injury prediction.

Results

Hematocrit, Hemoglobin, number of red blood cells, testosterone, and ferritin were the most important features that allowed to profile players and to analyze the response to external workloads for each type of player profile. Players’ blood samples’ characteristics permitted to personalize the decision-making rules of the ML models based on external workloads reaching an accuracy of 63%. This approach increased the injury prediction ability of about 15% compared to models that take into consideration only training workloads’ features. The influence of each external workload varied in accordance with the players’ blood sample characteristics and the physiological demands of a specific period of the season.

Conclusion

Field experts should hence not only monitor the external workloads to assess the status of the players, but additional information derived from individuals’ characteristics permits to have a more complete overview of the players well-being. In this way, coaches could better personalize the training program maximizing the training effect and minimizing the injury risk.