Jennings Jacob, Wundersitz Daniel, Sullivan Courtney, Cousins Stephen, Kingsley Michael
Holsworth Research Initiative, La Trobe Rural Health School, La Trobe University, Bendigo, VIC 3552, Australia.
La Trobe University Bendigo Pioneers, Bendigo, Bendigo, VIC 3550, Australia.
Sports (Basel). 2023 Mar 9;11(3):63. doi: 10.3390/sports11030063.
Determining characteristics that define talent is critical for recruitment and player development. When developing predictive models, sensitivity is important, as it describes the ability of models to identify players with draft potential (true positives). In the current literature, modelling is limited to a small number of selected variables, and model sensitivity is often poor or unreported. The aim of this study was to determine how a technical factor combined with physical and in-game movement factors affects position-specific model sensitivity when evaluating draft outcome in an elite-junior National Australia Bank (NAB) League population. Physical, in-game movement, and technical involvement data were collated from draft-eligible (18th year) participants in the under 18 boys NAB League competition (n = 465). Factors identified through parallel analysis were used in binomial regression analyses. Models using factor combinations were developed to predict draft success for all-position, nomadic, fixed-position, and fixed&ruck players. Models that best characterised draft success were all-position (physical and technical: specificity = 97.2%, sensitivity = 36.6%, and accuracy = 86.3%), nomadic (physical and technical: specificity = 95.5%, sensitivity = 40.7%, and accuracy = 85.5%), fixed (physical: specificity = 96.4%, sensitivity = 41.7%, and accuracy = 86.6%), and fixed&ruck (physical and in-game movement: specificity = 96.3%, sensitivity = 41.2%, and accuracy = 86.7%). Including a technical factor improved sensitivity in the all-position and nomadic models. Physical factors and physical and in-game movement yielded the best models for fixed-position and fixed&ruck players, respectively. Models with improved sensitivity should be sought to assist practitioners to more confidently identify the players with draft potential.
确定定义天赋的特征对于招募球员和球员发展至关重要。在开发预测模型时，敏感性很重要，因为它描述了模型识别具有选秀潜力球员（真阳性）的能力。在当前文献中，建模仅限于少数选定变量，并且模型敏感性往往较差或未报告。本研究的目的是确定在评估澳大利亚国家银行（NAB）精英青年联赛人群的选秀结果时，技术因素与身体和比赛中的运动因素相结合如何影响特定位置模型的敏感性。从18岁以下男子NAB联赛符合选秀资格（第18年）的参与者（n = 465）中整理出身体、比赛中的运动和技术参与数据。通过平行分析确定的因素用于二项式回归分析。使用因素组合开发模型，以预测所有位置、游动型、固定位置和固定&中锋球员的选秀成功情况。最能表征选秀成功的模型是所有位置（身体和技术：特异性 = 97.2%，敏感性 = 36.6%，准确性 = 86.3%）、游动型（身体和技术：特异性 = 95.5%，敏感性 = 40.7%，准确性 = 85.5%）、固定位置（身体：特异性 = 96.4%，敏感性 = 41.7%，准确性 = 86.6%）和固定&中锋（身体和比赛中的运动：特异性 = 96.3%，敏感性 = 41.2%，准确性 = 86.7%）。纳入技术因素提高了所有位置和游动型模型的敏感性。身体因素以及身体和比赛中的运动分别为固定位置和固定&中锋球员产生了最佳模型。应寻求具有更高敏感性的模型，以帮助从业者更自信地识别具有选秀潜力的球员。
