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在韩国,基于机器学习的优化合同续约预测 棒球组织
1Department of Future Convergence Technology, Soonchunhyang University, Asan, 31538, Republic of Korea.
Heliyon
|December 27, 2023
概括
机器学习模型预测韩国棒球组织 (KBO) 的外国球员合同续约. 球员在KBO中的表现显著提高了合同决策的预测准确性.
科学领域:
- 运动分析 运动分析
- 运动中的机器学习
- 棒球表现评价 棒球表现评价
背景情况:
- 韩国棒球组织 (KBO) 在1998年实施了外国球员系统,并在2014年增加了外国球员限制.
- 每年,KBO球队通常会签下两名外国投手和一名外国击球手.
- 外国球员的低续约率 (34-36%) 凸显了需要改进招聘和留守策略的需要.
研究的目的:
- 开发和比较机器学习模型,用于预测KBO的外国球员合同续约决策.
- 确定提高预测准确性的关键绩效指标和数据源.
- 为KBO团队提供数据驱动的洞察力,以优化外国球员的招聘和合同管理.
主要方法:
- 利用机器学习算法来预测外国球员的合同续约决定.
- 纳入了来自小联盟棒球,大联盟棒球和KBO的业绩数据.
- 包括玩家图像数据作为潜在的预测因素.
- 使用精度,接收器操作特征曲线 (AUC) 下的面积和精度评估模型性能.
主要成果:
- 发现KBO中的性能数据是提高预测准确性的最重要因素.
- 机器学习模型在预测合同续约结果方面表现出有效性.
- 最初在KBO取得成功的外国击球手表现下降,可能与国际锦标赛结果有关.
结论:
- 拟议的机器学习方法为KBO团队提供了一种有价值的工具,帮助他们做出明智的合同续约决定.
- 准确的球员绩效评估可以提高外国球员的招聘,球队的表现,以及KBO联赛的整体竞争力.
- 对外国击球员表现趋势的进一步分析可能会提供对长期球员发展和国际竞争影响的见解.
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