Abstract
This article discusses the development of a sports analysis system based on match data processing and the application of artificial intelligence methods. A software solution is proposed that allows statistical data to be uploaded in Excel format and automatically identifies key factors influencing the course of a match, such as winning streaks, draws, and changes in game dynamics. Various artificial intelligence models are examined and a comparative analysis of their applicability to sports analysis tasks is conducted, taking into account the specifics of trigger data. Based on the identified triggers, a structured set of features is generated and sent to the artificial intelligence model for further analysis. A language model is used to interpret the obtained data and generate analytical conclusions and recommendations for players. The results of the study show that the proposed approach automates match analysis, improves the objectivity of performance evaluation, and reduces data processing time. The developed system can be used to optimize training and identify weaknesses in players' performance. The article also presents the mathematical models and formulas used to identify triggers, and justifies the choice of these methods in terms of analytical effectiveness.