The Application of Deep Learning in Sports Competition Data Prediction

Abstract

In order to predict sports competition data, the author needs to implement the structure and related processes of the relevant competition victory and defeat prediction system, and specifically introduce and plan the implementation of each functional module. The data collection and storage module adopts Alibaba Cloud servers and combines Python to remotely and automatically collect data on a scheduled basis, according to the actual situation of game wins and losses, data cleaning and filtering are carried out, and multiple encoding forms are used to vectorize the data in order to find the best model. The data is divided according to the standard training and testing sets, and multiple classifiers are used for model training and saved locally for direct use next time; Test the above model using the training set; Compare the advantages and disadvantages of each vectorized encoding and classifier based on the final performance evaluation module. Based on the relevant experimental results, a detailed analysis was conducted to compare the advantages and disadvantages of each model, proving that introducing word vectors (word embeddings) into the competition data analysis system is worthwhile. We have obtained an excellent performance prediction model with a highest accuracy P of 0.825, a recall R of 0.729, and a corresponding F1 value of 0.774. For a prediction model that only knows the initial lineup allocation as a prerequisite, this already has sufficient practical guidance significance.