接下来我们会忘记什么?洛佩兹的含义(2020)

IF 1.1 Q3 SOCIAL SCIENCES, MATHEMATICAL METHODS
Samuel L. Ventura
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引用次数: 0

摘要

Lopez(2020)清楚地表明,缺乏精确、高质量的数据会导致不精确的结果或分析。特别是,这篇论文表明,一旦你知道了到第一个底线的精确距离(“要走的码数”),而不仅仅是NFL详细比赛数据中提供的整数距离,教练做出的决定就更接近于我们对理性的、数据驱动的决策者在他们的情况下的期望。然而,从anNFL团队的角度来看,目前尚不清楚在这种特殊情况下,球员跟踪数据是否有必要帮助个别教练。NFL球队和教练是否可以通过一个只接受比赛数据训练的模型做出大致相同的决定,但可以通过更精确的码数输入进行实时评估?第四次进攻的决定通常是用预期点数模型和/或获胜概率模型来分析的(Romer 2006)。在做出第四次进攻决策时,分析师认为NFL球队应该将他们当前的比赛情况输入到其中一个模型中(包括进攻、距离、码线、分差、剩余时间等信息),并分析输出。如果模型在给定情况下计算的获胜概率通过“全力以赴”而最大化,那么教练应该让进攻留在场上;如果通过撑船获胜的可能性最大,教练应该选择撑船;如果通过尝试射门得分来最大化,教练应该把他的射门装置放在场上。Yurko, Horowitz和ventura(2019)详细解释了如何构建期望值和获胜概率模型,但简单地说,期望值模型是线性模型(具体来说是多项式逻辑回归模型),而获胜概率模型是广义加性模型。重要的是,虽然只提供了整数值的距离(“要走的码数”)
本文章由计算机程序翻译,如有差异,请以英文原文为准。
What will we unlearn next? The implications of Lopez (2020)
Lopez (2020) demonstrates clearly how the lack of precise, high-quality data can lead to imprecise results or analyses. In particular, this paper shows that once you know the precise distance to the first down line (“yards to go”) rather than just the integer-valued distances provided in the NFL’s play-by-play data, the decisions made by coaches are more closely in line with what we would expect from rational, data-driven decision-makers in their situation. However, from anNFL team’s perspective, it is unclear if player-tracking data was necessary to help individual coaches in this particular case. Could NFL teams and coaches make approximately the same decisions from a model trained on only play-by-play data, but evaluated in real-time with more precise inputs for yards to go? Fourth-down decisions are typically analyzed with expected points models and/or win probability models (Romer 2006). When making fourth-down decisions, analysts contend that NFL teams should input their current game situation into one of these models (including information such as the down, distance, yard line, score differential, time remaining, etc), and analyze the output. If the model’s computed win probability for a given situation is maximized by “going for it,” the coach should leave the offense on the field; if win probability is maximized by punting, the coach should elect to punt; and if it is maximized by attempting a field goal, the coach should put his field goal unit on the field. Yurko, Horowitz andVentura (2019) provide a detailed explanation of how to build expected points and win probability models, but briefly, the expected points model is a linear model (specifically, a multinomial logistic regression model), and the win probability model is a generalized additive model. Importantly, although only integer-valueddistances (“yards to go”) areprovided in the
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来源期刊
Journal of Quantitative Analysis in Sports
Journal of Quantitative Analysis in Sports SOCIAL SCIENCES, MATHEMATICAL METHODS-
CiteScore
2.00
自引率
12.50%
发文量
15
期刊介绍: The Journal of Quantitative Analysis in Sports (JQAS), an official journal of the American Statistical Association, publishes timely, high-quality peer-reviewed research on the quantitative aspects of professional and amateur sports, including collegiate and Olympic competition. The scope of application reflects the increasing demand for novel methods to analyze and understand data in the growing field of sports analytics. Articles come from a wide variety of sports and diverse perspectives, and address topics such as game outcome models, measurement and evaluation of player performance, tournament structure, analysis of rules and adjudication, within-game strategy, analysis of sporting technologies, and player and team ranking methods. JQAS seeks to publish manuscripts that demonstrate original ways of approaching problems, develop cutting edge methods, and apply innovative thinking to solve difficult challenges in sports contexts. JQAS brings together researchers from various disciplines, including statistics, operations research, machine learning, scientific computing, econometrics, and sports management.
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