Tennis is a popular and complex sport influenced by various factors. Early training increases the risk of career dropout before peak performance. This study analyzed game statistics of World Junior Tennis Final participants (2012-2016), their career paths and it examined how game statistics impact rankings of top 300 female players, aiming to develop an accurate model using percentage-based variables. Descriptive and inferential statistics, including neural networks, were employed. Four machine learning models with categorical predictors and one response were created. Seven models with up to 18 variables and one ordinal (WTA rank) were also developed. Tournament rankings could be predicted using categorical data, but not subsequent professional rankings. Although effects on rankings among top 300 female players were identified, a reliable predictive model using only percentage-based data was not achieved. AI models provided insights into rankings and performance indicators, revealing a lower dropout rate than reported. Participation in elite junior tournaments is crucial for career development and designing training plans in tennis. Further research should explore game statistics, dropout rates, additional variables, and fine-tuning of AI models to improve predictions and understanding of the sport.

中文翻译：

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分析女性精英青少年网球运动员的比赛统计数据和职业轨迹：一种机器学习方法。

网球是一项流行且复杂的运动，受多种因素影响。早期培训会增加在达到最佳表现之前退出职业生涯的风险。本研究分析了世界青少年网球决赛参与者（2012-2016）的比赛统计数据、他们的职业道路，并研究了比赛统计数据如何影响前 300 名女选手的排名，旨在使用基于百分比的变量开发一个准确的模型。采用了描述性统计和推论统计，包括神经网络。创建了四种具有分类预测变量和一种响应的机器学习模型。还开发了七个模型，最多包含 18 个变量和一个序数（WTA 排名）。可以使用分类数据来预测锦标赛排名，但不能使用随后的职业排名。尽管确定了对前 300 名女选手排名的影响，但仅使用基于百分比的数据的可靠预测模型尚未实现。人工智能模型提供了对排名和绩效指标的洞察，显示的辍学率低于报告的水平。参加青少年精英锦标赛对于职业发展和设计网球训练计划至关重要。进一步的研究应该探索比赛统计数据、退出率、附加变量以及人工智能模型的微调，以提高对这项运动的预测和理解。