Abstracts

Due to the fact that team sports such as football have a complex multidirectional and intermittent nature, an accurate planning of the training workload is needed in order to maximise the athletes performance during the matches and reduce their risk of injury. Despite the evaluation of external workloads during trainings and matches has become more and more easier thanks to the advent of the tracking system technologies such as Global Position System (GPS), the planning of the best training workloads aimed to obtain the higher performance during the matches and a lower risk of injury during sport stimuli, is still a very difficult challenge for sport scientists, athletic trainers and coaches. The application of machine learning approaches on sport sciences aims to solve this crucial issue. Hence, the combination between data and sport scientists peculiarities could maximize the information that can be obtained from the football training and match analysis. Thus, the aim of this thesis is to provide examples of the application of the machine learning approach on sport science. In particular, two studies are provided with the aim of detecting a pattern during in-season football training weeks and predicting injuries. For these studies, 23 elite football players were monitored in eighty in-season trainings by using a portable non-differential 10 Hz global position system (GPS) integrated with 100 Hz 3-D accelerometer, a 3-D gyroscope, and a 3-D digital, Northern Ireland compass (STATSports Viper). Information about non-traumatic injuries were also recorded by the clubs medical staff. In order to detect a pattern during the in-season training weeks and the injuries, Extra Tree Random Forest (ETRFC) and Decision Tree (DT) Classifier were computed, respectively.In the first study it was found that the in-season football trainings follow a sinusoidal model (i.e. zig-zag shape found in autocorrelation analysis) because their periodization is characterized by repeated short-term cycles which are constituted by two parts: the first one (i.e. trainings long before the match) is constituted by high training loads, and the second one (i.e. trainings close to the match) by low ones. This short-term structure appears to be a strategy useful both to facilitate the decay of accumulated fatigue from high training loads performed at the beginning of the cycle and to promote readiness for the following performance. As a matter of fact, a patter was detected through the in-season football training weeks by ETRFC. This machine learning process can accurately define the training loads to be performed in each training day to maintain higher performance throughout the season. Moreover, it was found that the most important features able to discriminate short-term training days are the distance covered above 20 Wkg-1, the acceleration above 2 ms-2, the total distance and the distance covered above 25.5 WKg-1 and below 19.8Kmh-1. Thus, in accordance with the results found in this study, athletic trainers and coaches may use machineAdvisors/Committee Members: docente tutor: G. Alberti, coordinatore: C. Sforza, ALBERTI, GIAMPIETRO, SFORZA, CHIARELLA.