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Three lessons for genetic toxicology from baseball analytics

机译：来自棒球分析的遗传毒理学的三节课

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In many respects the evolution of baseball statistics mirrors advances made in the field of genetic toxicology. From its inception, baseball and statistics have been inextricably linked. Generations of players and fans have used a number of relatively simple measurements to describe team and individual player's current performance, as well as for historical record-keeping purposes. Over the years, baseball analytics has progressed in several important ways. Early advances were based on deriving more meaningful metrics from simpler forerunners. Now, technological innovations are delivering much deeper insights. Videography, radar, and other advances that include automatic player recognition capabilities provide the means to measure more complex and useful factors. Fielders' reaction times, efficiency of the route taken to reach a batted ball, and pitch-framing effectiveness come to mind. With the current availability of complex measurements from multiple data streams, multifactorial analyses occurring via machine learning algorithms have become necessary to make sense of the terabytes of data that are now being captured in every Major League Baseball game. Collectively, these advances have transformed baseball statistics from being largely descriptive in nature to serving data-driven, predictive roles. Whereas genetic toxicology has charted a somewhat parallel course, a case can be made that greater utilization of baseball's mindset and strategies would serve our scientific field well. This paper describes three useful lessons for genetic toxicology, courtesy of the field of baseball analytics: seek objective knowledge; incorporate multiple data streams; and embrace machine learning. Environ. Mol. Mutagen. 58:390-397, 2017. (c) 2017 Wiley Periodicals, Inc.
机译：在许多方面，棒球统计学镜子的演变在遗传毒理学领域的进步。从成立开始，棒球和统计数据已经过分联系起来。几代球员和粉丝使用了一些相对简单的测量来描述团队和个人球员的当前性能，以及历史记录保存目的。多年来，棒球分析已经以几种重要方式进行了进展。早期进步是基于从更简单的先行者获得更有意义的指标。现在，技术创新正在提供更深入的洞察力。摄像，雷达和包括自动播放器识别功能的其他进步提供了测量更复杂和有用因素的方法。现场商的反应时间，达到击球球的途径的效率，并想到了横向框架的效果。随着来自多个数据流的复杂测量的当前可用性，通过机器学习算法发生的多因素分析是必要的，了解现在在每个主要联盟棒球比赛中捕获的数据的特拉。统称，这些进步使棒球统计数据在很大程度上与提供数据驱动的预测角色的基本性质。虽然遗传毒理学已经绘制了一个有点平行的课程，但可以使棒球心态和策略的利用更好地利用我们的科学领域。本文描述了遗传毒理学的三种有用的课程，由棒球分析领域提供：寻求客观知识;包含多个数据流;并拥抱机器学习。环境。摩尔。诱惑。 58：390-397,20172。（c）2017 Wiley期刊，Inc。

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