Authors:
Giovanni Capobianco
1
;
Umberto Di Giacomo
1
;
Francesco Mercaldo
2
;
Vittoria Nardone
3
and
Antonella Santone
1
Affiliations:
1
Department of Bioscience and Territory, University of Molise, Pesche (IS) and Italy
;
2
Department of Bioscience and Territory, University of Molise, Pesche (IS), Italy, Institute for Informatics and Telematics, National Research Council of Italy (CNR), Pisa and Italy
;
3
Department of Engineering, University of Sannio, Benevento and Italy
Keyword(s):
Result Prediction, Game Analysis, Sport Analytics, Soccer Match Prediction, Machine Learning.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Computational Intelligence
;
Data Mining
;
Databases and Information Systems Integration
;
Enterprise Information Systems
;
Evolutionary Computing
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Machine Learning
;
Sensor Networks
;
Signal Processing
;
Soft Computing
;
Symbolic Systems
Abstract:
Sport result prediction proposes an interesting challenge considering as popular and widespread are sport games, for instance tennis and soccer. The outcome prediction is a difficult task because there are a lot of factors that can afflict the final results and most of them are related to the player human behaviour. In this paper we propose a new feature set (related to the match and to players) aimed to model a soccer match. The set is related to characteristics obtainable not only at the end of the match, but also when the match is in progress. We consider machine learning techniques to predict the results of the match and the number of goals, evaluating a dataset of real-world data obtained from the Italian Serie A league in the 2017-2018 season. Using the RandomForest algorithm we obtain a precision of 0.857 and a recall of 0.750 in won match prediction, while for the goal prediction we obtain a precision of 0.879 in the number of goal prediction less than two, and a precision of
0.8 in the number of goal prediction equal or greater to two.
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