Prediction and Quantification of Individual Athletic Performance
원문 초록
We provide scientific foundations for athletic performance prediction on an individual level, exposing the phenomenology of individual athletic running performance in the form of a low-rank model dominated by an individual power law. We present, evaluate, and compare a selection of methods for prediction of individual running performance, including our own, \emph{local matrix completion} (LMC), which we show to perform best. We also show that many documented phenomena in quantitative sports science, such as the form of scoring tables, the success of existing prediction methods including Riegel's formula, the Purdy points scheme, the power law for world records performances and the broken power law for world record speeds may be explained on the basis of our findings in a unified way.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
Conformation-Aware Structure Prediction of Antigen-Recognizing Immune Proteins
Cautious Next Token Prediction
Where, What, Why: Towards Explainable Driver Attention Prediction
Out-of-Distribution Semantic Occupancy Prediction
Demystifying Distributed Training of Graph Neural Networks for Link Prediction
Reconsidering Explicit Longitudinal Mammography Alignment for Enhanced Breast Cancer Risk Prediction
토론
이 논문과 연결된 포럼 글이 아직 없습니다.