Effective Injury Forecasting In Soccer With Gps Training Data And Machine Learning

Effective Injury Forecasting In Soccer With Gps Training Data And Machine Learning
Abstract
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.

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Citation: Rossi A, Pappalardo L, Cintia P, Iaia FM, Fernàndez J, Medina D (2018) Effective injury forecasting in soccer with GPS training data and machine learning. PLoS ONE 13(7): e . /10.1371/journal.pone. Editor: Jaime Sampaio, Universidade de Tras-os-Montes e Alto Douro, PORTUGAL

Received: February 8, 2018; Accepted: July 10, 2018; Published: July 25, Copyright: © 2018 Rossi et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

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Data Availability: The owner of the data is an elite soccer club in Italy which wants to remain anonymous and did not give the permission to make the original data publicly available. The club has the right to choose which information, results and data can be made public and has granted the access to these data to the authors only for research aims. In accordance with SoBigData Ethical Committee, we can provide upon request transformed data that are processed in such a way that it is not possible to re-identify the subjects involved in the study. The transformed data reflect the same data distribution of real data to guarantee that the experiments performed on the transformed data produce the same results as the ones shown in the paper. We specify that the researchers from FC Barcelona participated to the study only as collaborators, and that FC Barcelona is not the owner of the data. For data requests please contact us at the following email addresses: or

Funding: This work is partially supported by the European Community's H2020 Program under the funding scheme “INFRAIA : Research Infrastructures” grant agreement , , “SoBigData”. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. There was no additional external funding received for this study.

Competing interests: The authors have declared that no competing interests exist.

Introduction
Injuries of professional athletes have a great impact on the sports industry, due to their influence on the mental state of the individuals and the performance of a team [1, 2]. Furthermore, the cost associated with a player’s recovery and rehabilitation is often considerable, both in terms of medical care and missed earnings deriving from the popularity of the player himself [3]. Recent research demonstrates that injuries in Spain cause about 16% of season absence by professional soccer players, corresponding to a cost of around 188 million euros per season [4]. It is not surprising, hence, that injury forecasting is attracting a growing interest from researchers, managers, and coaches, who are interested in intervening with appropriate actions to reduce the likelihood of injuries of their players.

Historically, academic work on injury forecasting has been deterred by the limited availability of data describing the physical activity of players. Nowadays, the Internet of Things have the potential to change rapidly this scenario thanks to Electronic Performance and Tracking Systems (EPTS), new tracking technologies that provide high-fidelity data streams extracted from every training and game session [5, 6]. These data depict in detail the movements of players on the playing field [5, 6] and have been used for many purposes, from identifying training patterns [7] to automatic tactical analysis [5, 8, 9]. Despite this wealth of data, little effort has been put on investigating injury forecasting in professional soccer so far [10, 11, 12]. State-of-the-art approaches provide just a preliminary understanding of which variables affect the injury risk, while an evaluation of the potential of statistical models to forecast injuries is still poor. A major limit of existing studies is that they are mono-dimensional, i.e., they use just one variable at a time to estimate injury risk, without fully exploiting the complex patterns underlying the available data.

Professional soccer clubs are interested in practical, usable and interpretable models as a decision making support for coaches and athletic trainers [13]. In this perspective the creation of injury forecasting models poses many challenges. On one hand, injury forecasters must be highly accurate, as models which frequently produce “false alarms” are useless. On the other hand, a “black box” approach (e.g., a deep neural network) is not desirable for practical use since it does not provide any insights about the reason behind the injuries. It goes hence without saying that injury forecasting models must achieve a good tradeoff between accuracy and interpretability.

In this paper, we consider injury prediction as the problem of forecasting that a player will get injured in the next training session or official game, given his recent training workload. We observe that existing mono-dimensional approaches are not effective in practice due to their low precision (

Related work
The relationship between training workload and injury risk has been widely studied in the sports science literature [14, 15, 16, 17, 18]. For example Gabbett et al. [14, 15, 17, 19] investigate the case of rugby and find that a player has a high injury risk when his workloads are increased above certain thresholds. To assess injury risk in cricket, Hulin et al. [20] propose the Acute Chronic Workload Ratio (ACWR), i.e., the ratio between a player’s acute workload and his chronic workload. When the acute workload is lower than the chronic workload, cricket players are associated with a low injury risk. In contrast, when the acute/chronic ratio is higher than 2, players have an injury risk from 2 to 4 times higher than the other group of players. Hulin et al. [20] and Ehrmann et al. [11] find that injured players, in both rugby and soccer, show significantly higher physical activity in the week preceding the injury with respect to their seasonal averages.

In skating, Foster et al. [21] measure training workload by the session load, i.e., the product of the perceived exertion and the duration of the training session. When the session load outweighs a skater’s ability to fully recover before the next session, the skater suffers from the so-called “overtraining syndrome”, a condition that can cause injury [21]. In basketball, Anderson et al. [18] find a strong correlation between injury risk and the so-called monotony, i.e., the ratio between the mean and the standard deviation of the session load recorded in the past 7 days. Moreover, Brink et al. [8] observe that injured young soccer players (age

Venturelli et al. [12] perform several periodic physical tests on young soccer players (age 22] create a classifier to predict 19% of the injuries that occurred in NBA. They also show that the most important features for predicting injuries are the average speed, the number of past competitions played, the average distance covered, the number of minutes played to date and the average field goals attempted. An attempt to injury forecasting in soccer has been made by Kampakis [23], although it considers a reduced set of features obtaining an accuracy that is, in the best scenario, not significantly better than random classifiers.

Material and method
Data collection and feature extraction
We set up a study on twenty-six Italian professional male players (age = 26±4 years; height = 179±5 cm; body mass = 78±8 kg) during season 2013/2014. Six central backs, three fullbacks, seven midfi