Medal Prediction and Analysis For 2028 Olympic Games Based on MTLSTM Modeling

This study aims to accurately predict the number of medals in the 2028 Olympic Games, analyze the award-winning situation of each country and related influencing factors. The current Olympic medal prediction model has problems such as incomplete consideration of factors and insufficient algorithmic ability to handle complex data. To solve the above problems, in this paper, we construct the MTLSTM strength assessment model by using the Long Short-Term Memory Network (LSTM) and Monte Carlo algorithm. The data are first preprocessed to determine the weights of multifactor indicators such as participating countries and locations, and then the MTLSTM algorithm is used to predict the number of medals and analyze the relationship between the events and the number of medals. The results show that the model has a small prediction error and successfully predicts the medal rankings in 2028, clarifying the advantageous programs of each country. The study shows that the MTLSTM model is highly reliable and provides strong support for Olympic medal prediction and sports strategy development, and it is expected to be applied to other sports event prediction fields.