Πρόβλεψη αποτελεσμάτων αγώνων με τεχνικές αναλυτικής δεδομένων αθλητών
Match outcome forecasting with sport analytic techniques
In the era of rapid information transfer and the continuous development of sports data collection and analytics methods, the global soccer industry could not be uninvolved. Exorbitant amounts of money are invested in the proper team formation and game planning analysis. Considerable amounts of money are invested in sports data mining, with the intention of profit and ratings increase. Football players evaluation is an analysis field that helps in strategic planning and constructing powerful teams. In this study we implement an innovative framework of rating and ranking football players which is based on machine learning algorithms and accepts a specific data type as input, the so-called soccer-logs. The results showed that this framework performs better on big data of this type.