Sports Analytics is not a new idea, but the way it is implemented nowadays have brought a
revolution in the way teams, players, coaches, general managers but also reporters, betting
agents and simple fans look at statistics and at sports.
Machine Learning is also dominating business and even society with its technological
innovation during the past years. Various applications with machine learning algorithms on core
have offered implementations that make the world go round.
Inevitably, Machine Learning is also used in Sports Analytics. Most common applications
of machine learning in sports analytics refer to injuries prediction and prevention, player
evaluation regarding their potential skills or their market value and team or player performance
prediction. The last one is the issue that the present dissertation tries to resolve.
This dissertation is the final part of the MSc in Data Science, offered by International
Hellenic University.
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