Análise de dados da concorrência: Usando técnicas de análise de séries temporais para prever os resultados da concorrência

Resumo

Sports data analysis has evolved with the use of advanced time series techniques for predicting outcomes in competitions. The objective was to evaluate the effectiveness of ARIMA, recurrent neural networks and deep learning models in predicting outcomes of sports competitions, comparing their accuracy and ability to adapt to different contextual variables. The methodology was based on the analysis of historical data from sports competitions, applying time series models and machine learning techniques. Sports databases with information on team and player performance were used, evaluating the predictive capacity of the implemented models. The results indicated that LSTM neural networks performed better in predicting outcomes compared to traditional models. The inclusion of contextual variables, such as the physical condition of the players and the game environment, improved the accuracy of the predictions. In the discussion, the findings coincided with research highlighting the effectiveness of deep learning in sports data analysis. However, the need to further optimize the integration of multiple data sources to improve the accuracy of predictions was identified. It was concluded that the use of time series techniques represents a valuable tool in the prediction of sports outcomes, with applications in strategic decision-making and competitive performance analysis. It is recommended to continue exploring hybrid approaches and the use of real-time data to strengthen predictive accuracy in future studies.