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Increased data availability has stimulated the interest in studying sportsprediction problems via analytical approaches; in particular, with machinelearning and simulation. We characterize several models that have been proposedin the literature, all of which suffer from the same drawback: they cannotincorporate rational decision-making and strategies from teams/playerseffectively. We tackle this issue by proposing hybrid simulation logic thatincorporates teams as agents, generalizing the models/methodologies that havebeen proposed in the past. We perform a case study on the NBA with two goals:i) study the quality of predictions when using only one predictive variable,and ii) study how much historical data should be kept to maximize predictionaccuracy. Results indicate that there is an optimal range of data quantity andthat studying what data and variables to include is of extreme importance.

Smart Sports Predictions via Hybrid Simulation: NBA Case Study