Decision Support System Based on MLP: Formula One (F1) Grand Prix Study Case

Neural networks are widely used due to the adaptability of models to many problems and high efficiency. These solutions are also gaining popularity in the design of Decision Support Systems. It leads to increased use of such techniques to support the decision-maker in practical problems. In this paper, we propose an Artificial Neural Network Decision Support System (ANN-DSS) based on Multilayer Perceptron. The model structure was determined by searching the optimal hyperparameters with Tree-structured Parzen Estimator. Based on the qualification results, the proposed system was directed to evaluate the Formula 1 divers’ best lap time during the race. Obtained rankings were compared with reference rankings using the WS rank similarity. Model performance proves to be highly consistent in rankings predictions, which makes it a reliable tool for the given problem.