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We introduce the PerfoRisk-KDB model to precisely estimate athlete performance and injury risk by combining K-means and DBSCAN clustering techniques. By combining these two clustering techniques, the idea of this work surpasses the constraints of a single technique and increases accuracy and robustness for complex and high-dimensional data. This work tests the performance assessment and injury risk prediction of a real athlete dataset against conventional models. 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