Prediction of adult performance from early age talent identification in sport remains difficult. Talent identification research has generally been performed using univariate analysis, which ignores multivariate relationships. To address this issue, this study used a novel higher-dimensional model to orthogonalize multivariate anthropometric and fitness data from junior rugby league players, with the aim of differentiating future career attainment. Anthropometric and fitness data from 257 Under-15 rugby league players was collected. Players were grouped retrospectively according to their future career attainment (i.e., amateur, academy, professional). Players were blindly and randomly divided into an exploratory (n = 165) and validation dataset (n = 92). The exploratory dataset was used to develop and optimize a novel higher-dimensional model, which combined singular value decomposition (SVD) with receiver operating characteristic analysis. Once optimized, the model was tested using the validation dataset. SVD analysis revealed 60 m sprint and agility 505 performance were the most influential characteristics in distinguishing future professional players from amateur and academy players. The exploratory dataset model was able to distinguish between future amateur and professional players with a high degree of accuracy (sensitivity = 85.7%, specificity = 71.1%; p<0.001), although it could not distinguish between future professional and academy players. The validation dataset model was able to distinguish future professionals from the rest with reasonable accuracy (sensitivity = 83.3%, specificity = 63.8%; p = 0.003). Through the use of SVD analysis it was possible to objectively identify criteria to distinguish future career attainment with a sensitivity over 80% using anthropometric and fitness data alone. As such, this suggests that SVD analysis may be a useful analysis tool for research and practice within talent identification.
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机译：从运动中的早期才艺识别来预测成人表现仍然很困难。人才识别研究通常使用单变量分析进行，而单变量分析忽略了多变量关系。为了解决这个问题，本研究使用了一种新颖的高维模型来对来自初级橄榄球联盟球员的多元人体测量学和健身数据进行正交处理，以期区分未来的职业成就。收集了257位15岁以下橄榄球联盟球员的人体测量和健身数据。根据球员的未来职业成就（即业余，学院，职业）对球员进行回顾性分组。玩家被盲目地随机分为探索性（n = 165）和验证数据集（n = 92）。探索性数据集用于开发和优化新型高维模型，该模型将奇异值分解（SVD）与接收器工作特性分析相结合。优化后，将使用验证数据集对模型进行测试。 SVD分析显示，60m的短跑速度和505的敏捷性是区分未来职业球员与业余球员和学院球员的最有影响力的特征。探索性数据集模型能够以较高的准确度区分未来的业余和职业球员（敏感性= 85.7％，特异性= 71.1％； p <0.001），尽管它不能区分未来的职业和学术球员。验证数据集模型能够以合理的准确性将未来的专业人员与其他专业人员区分开（敏感性= 83.3％，特异性= 63.8％； p = 0.003）。通过使用SVD分析，可以仅凭人体测量和适应性数据客观地确定标准，以区分80％以上的敏感性的未来职业成就。因此，这表明SVD分析对于人才鉴定中的研究和实践而言可能是有用的分析工具。
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