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Machine Learning in Sports Industry: Discover promising athletes for the future of Handball
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| 4.43 MB | Adobe PDF |
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Resumo(s)
Nowadays, technology applied in industries has increased day by day. As a result, different
ways of collecting and processing data have been investigated. Machine Learning is one of
those ways where, in addition to its applicability in all sectors, it has been increasingly
explored in different sports. Furthermore, the analysis of data at a visual level helps in the
interpretation and understanding of them.
These types of procedures always seek to support in the decision-making work done by
coaches, managers and scouting. It can be inherent to any sport and handball is obviously
included.
This specific investigation addresses methods to create advantages for Handball, introducing
predictive analytics. The discovery of promising athletes based on collected variables is one
of the biggest challenges in this sport and although the data provided by the Federação de
Andebol de Portugal are limited, this study demonstrates a 'direction' of how it can be done
based on a single variable.
In addition to working in the collection and pre-preparation of sports data, examples of visual
presentations such as vertical/horizontal bar graphs and maps are exposed. Finally, Machine
Learning algorithms with and without default parameters are used to predict if the player is
promising. From this perspective, it can be concluded that, based on formation years, models
score is slightly better for Support Vector Machines algorithms despite the proximity of the
results. It is important to point out that relevant conclusions were also drawn from the graphs.
Descrição
Project Work presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Business Analytics
Palavras-chave
Machine Learning Promising forecasting Predictive Analysis Sports industry Performance metrics Handball