An Artificial Neural Network-based Prediction Model for Underdog Teams in NBA Matches

In this work, we present an artificial neural network-based prediction model for underdog teams in NBA matches (ANNUT). We describe the steps of our supervised algorithm, starting from data acquisition to prediction selection. We talk about prediction selection because the final stage of our model is represented by a filtration phase. In this phase, the outputs returned from the neural network are evaluated according to how the events are quoted on one of the most famous bookmakers. Experimental results prove that the model is able to select with a certain accuracy winning teams. In particular, it reaches excellent results when we restrict the selection among underdogs (teams which probably will not win). Furthermore, we show that a significant sports prediction model cannot ignore bookmaker’s odds.