论文标题
预测新秀美国职业棒球大联盟投手中的尺骨副韧带受伤
Predicting Ulnar Collateral Ligament Injury in Rookie Major League Baseball Pitchers
论文作者
论文摘要
在不断增长的机器学习和数据分析世界中，学者正在寻找解决现实世界问题的新方法。一种解决方案是通过医疗保健，体育统计和数据科学之间的交集来实现的。在美国职棒大联盟（MLB）的领域内，投手被视为最重要的阵容。他们通常是薪水最高的球员之一，并且对于特许经营的成功至关重要，但是他们更容易受到伤害，使他们在整个赛季中占据一席之地。尺骨副韧带（UCL）是肘部中的小韧带，可控制投手投掷手臂的强度和稳定性。由于重复性的压力，投手在职业生涯中部分或完全撕裂它并不少见。修复这种伤害需要UCL重建手术，以非正式地称为汤米·约翰手术。在这个讲台摘要中，我们想调查是否可以通过分析在线投手数据来使用机器学习技术来预测UCL伤害。
In the growing world of machine learning and data analytics, scholars are finding new and innovative ways to solve real-world problems. One solution comes by way of an intersection between healthcare, sports statistics, and data sciences. Within the realm of Major League Baseball (MLB), pitchers are regarded as the most important roster position. They often are among the highest paid players and are crucial to a franchise's success, but they are more at risk to suffer an injury that sidelines them for over a complete season. The ulnar collateral ligament (UCL) is a small ligament in the elbow that controls the strength and stability of a pitcher's throwing arm. Due to repetitive strain, it is not uncommon for pitchers to tear it partially or completely during their careers. Repairing this injury requires UCL reconstruction surgery, as known informally as Tommy John surgery. In this podium abstract, we want to investigate whether we can use machine learning techniques to predict UCL injury by analyzing online pitcher data.
