Research on Performance Analysis and Auxiliary Decision Making System of Sports Competitions Driven by Deep Learning
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Abstract
In today’s increasingly scientific, refined, and intelligent development of global competitive sports, precise analysis and scientific decision-making of sports performance have become key factors determining training quality, competitive level, and competition outcomes. Traditional sports performance analysis mainly relies on coaches’ experience judgment, manual statistics, post event video review and other methods, which have many limitations such as strong subjectivity, single indicator dimensions, incomplete information extraction, poor real-time performance, difficult quantification, and inability to support dynamic decision-making. Deep learning frameworks offer distinct computational advantages over traditional heuristics, particularly in high-dimensional feature extraction and non-linear spatiotemporal modeling. These capabilities facilitate robust applications in athlete biomechanics, tactical trajectory assessment, and predictive injury diagnostics, moving beyond descriptive statistics toward predictive precision. The multi-source sensing structure can be extended to wearable and radio-frequency motion monitoring in sports biomechanics.
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