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Publicação
Sports Analytics
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| 1.24 MB | Adobe PDF |
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Orientador(es)
Resumo(s)
As the world of sports expands to never seen levels, so does the necessity for tools which provided material advantages for organizations and other stakeholders. The main objective of this paper is to build a predictive model capable of predicting what are the odds of a baseball player getting a base hit on a given day, with the intention of both winning the game Beat the Streak and to provide valuable information for the coaching staff. Using baseball statistics, weather forecasts and ballpark characteristics several models were built with the CRISP-DM architecture. The main constraints considered when building the models were balancing, outliers, dimensionality reduction, variable selection and the type of algorithm – Logistic Regression, Multi-layer Perceptron, Random Forest and Stochastic Gradient Descent. The results obtained were positive, in which the best model was a Multi-layer Perceptron with an 85% correct pick ratio.
Descrição
Alceo, P., & Henriques, R. (2019). Sports Analytics: maximizing precision in predicting MLB base hits. In A. Fred, & J. Filipe (Eds.), Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2019) (pp. 190-201). (IC3K 2019 - Proceedings of the 11th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management; Vol. 1). SciTePress. https://doi.org/10.5220/0008362201900201
Palavras-chave
Baseball Classification Model Data Mining Machine Learning MLB Predictive Analysis Software
Contexto Educativo
Citação
Editora
SciTePress - Science and Technology Publications