Characterization of in-season elite football trainings by GPS features : the identity card of a short-term football training cycle

Football training periodization is widely acknowledged as crucial to obtain the best performance throughout matches and to reduce the risk of injuries. Thus, the aim of this study is to detect the in-season short-term training cycles in an Italian elite football team. 80 trainings of 26 elite football players were monitored during 23 in-season weeks by a global position system (GPS). Machine learning process and autocorrelation analyses were performed in order to detect pattern inside in-season football trainings. Extra tree random forest classifier (ETRFC) was used to create a supervised machine learning process able to describe the football trainings cycle. This analytical model allows us to produce reliable decisions and results learning from historical relationships and trends in the data. In addition, the autocorrelation analysis allows us to detect similarity between observations between the data. Based on these analysis, it was found that the in-season football trainings are characterized by a series of short-term cycles. This kind of periodization follows a sinusoidal model because the short-term cycle detected in the in-season trainings is composed of two parts with different training loads. In particular, in the days long before the match football players perform higher training loads than in the close ones. To enhance performance and reduce risk of injuries, it would be essential to provide correct stimuli in each short-term cycle per day. Thus, developing a valid method able to define the correct training loads in each training day may be central for coaches and athletic trainers to periodize correctly the football trainings.