Computer vision technology has revolutionized sports analytics by enabling automated analysis of athlete performance and game dynamics through sophisticated video processing algorithms. In basketball, these systems facilitate real-time player tracking and performance evaluation, transforming how coaches approach game analysis. However, player re-identification in basketball presents significant challenges due to similar player appearances, dynamic movements, and variable imaging conditions. This thesis investigates Siamese Neural Networks for basketball player re-identification, focusing on systematic testing and performance enhancement through architectural optimization and training procedure improvements. The research employs a Siamese triplet network architecture based on EfficientNet-B0, integrated with the TwinPlay.ai basketball analytics platform. A comprehensive experimental framework evaluates triplet loss margins ranging from 1 to 5000, analyzing their impact on embedding space geometry and re-identification performance.