Journal of Quantitative Analysis in Sports最新文献_第10页

Foul accumulation in the NBA NBA的犯规积累

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Journal of Quantitative Analysis in Sports Pub Date : 2020-09-09 DOI: 10.1515/jqas-2019-0119

Dani Chu

引用次数: 1

Frontmatter

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Journal of Quantitative Analysis in Sports Pub Date : 2020-09-01 DOI: 10.1515/jqas-2020-frontmatter3

引用次数: 0

Corrigendum to: Offensive or defensive play in soccer: a game-theoretical approach 足球中的进攻或防守:一种博弈论的方法

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Journal of Quantitative Analysis in Sports Pub Date : 2020-08-24 DOI: 10.1515/jqas-2020-0080

Daniele Gambarelli, G. Gambarelli, Dries R. Goossens

引用次数: 0

Restoring the real world records in Men’s swimming without high-tech swimsuits 在没有高科技泳衣的情况下恢复男子游泳的真实世界纪录

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Journal of Quantitative Analysis in Sports Pub Date : 2020-08-10 DOI: 10.1515/jqas-2019-0087

Zhenyu Gao, Yixing Li, Zhengxin Wang

{"title":"Restoring the real world records in Men’s swimming without high-tech swimsuits","authors":"Zhenyu Gao, Yixing Li, Zhengxin Wang","doi":"10.1515/jqas-2019-0087","DOIUrl":"https://doi.org/10.1515/jqas-2019-0087","url":null,"abstract":"Abstract The recently concluded 2019 World Swimming Championships was another major swimming competition that witnessed some great progresses achieved by human athletes in many events. However, some world records created 10 years ago back in the era of high-tech swimsuits remained untouched. With the advancements in technical skills and training methods in the past decade, the inability to break those world records is a strong indication that records with the swimsuit bonus cannot reflect the real progressions achieved by human athletes in history. Many swimming professionals and enthusiasts are eager to know a measure of the real world records had the high-tech swimsuits never been allowed. This paper attempts to restore the real world records in Men’s swimming without high-tech swimsuits by integrating various advanced methods in probabilistic modeling and optimization. Through the modeling and separation of swimsuit bias, natural improvement, and athletes’ intrinsic performance, the result of this paper provides the optimal estimates and the 95% confidence intervals for the real world records. The proposed methodology can also be applied to a variety of similar studies with multi-factor considerations.","PeriodicalId":16925,"journal":{"name":"Journal of Quantitative Analysis in Sports","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2020-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91388380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

A Bayesian adjusted plus-minus analysis for the esport Dota 2 电子竞技《dota2》的贝叶斯调整正负分析

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Journal of Quantitative Analysis in Sports Pub Date : 2020-08-03 DOI: 10.1515/jqas-2019-0103

Nicholas J. Clark, Brian Macdonald, Ian Kloo

{"title":"A Bayesian adjusted plus-minus analysis for the esport Dota 2","authors":"Nicholas J. Clark, Brian Macdonald, Ian Kloo","doi":"10.1515/jqas-2019-0103","DOIUrl":"https://doi.org/10.1515/jqas-2019-0103","url":null,"abstract":"Abstract Analytics and professional sports have become linked over the past several years, but little attention has been paid to the growing field of esports within the sports analytics community. We seek to apply an Adjusted Plus Minus (APM) model, an accepted analytic approach used in traditional sports like hockey and basketball, to one particular esports game: Defense of the Ancients 2 (Dota 2). As with traditional sports, we show how APM metrics developed with Bayesian hierarchical regression can be used to quantify individual player contributions to their teams and, ultimately, use this player-level information to predict game outcomes. In particular, we first provide evidence that gold can be used as a continuous proxy for wins to evaluate a team’s performance, and then use a Bayesian APM model to estimate how players contribute to their team’s gold differential. We demonstrate that this APM model outperforms models based on common team-level statistics (often referred to as “box score statistics”). Beyond the specifics of our modeling approach, this paper serves as an example of the potential utility of applying analytical methodologies from traditional sports analytics to esports.","PeriodicalId":16925,"journal":{"name":"Journal of Quantitative Analysis in Sports","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2020-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86655099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

A parametric family of Massey-type methods: inference, prediction, and sensitivity 梅西型方法的参数族:推理、预测和灵敏度

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Journal of Quantitative Analysis in Sports Pub Date : 2020-07-27 DOI: 10.1515/jqas-2019-0071

E. Bozzo, P. Vidoni, Massimo Franceschet

引用次数: 0

Frontmatter

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Journal of Quantitative Analysis in Sports Pub Date : 2020-06-25 DOI: 10.1515/jqas-2020-frontmatter2

引用次数: 0

What will we unlearn next? The implications of Lopez (2020) 接下来我们会忘记什么?洛佩兹的含义(2020)

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Journal of Quantitative Analysis in Sports Pub Date : 2020-05-29 DOI: 10.1515/jqas-2020-0056

Samuel L. Ventura

{"title":"What will we unlearn next? The implications of Lopez (2020)","authors":"Samuel L. Ventura","doi":"10.1515/jqas-2020-0056","DOIUrl":"https://doi.org/10.1515/jqas-2020-0056","url":null,"abstract":"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","PeriodicalId":16925,"journal":{"name":"Journal of Quantitative Analysis in Sports","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2020-05-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1515/jqas-2020-0056","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72419915","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Distributed lag models to identify the cumulative effects of training and recovery in athletes using multivariate ordinal wellness data 使用多元有序健康数据的分布滞后模型来识别运动员训练和恢复的累积效应

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Journal of Quantitative Analysis in Sports Pub Date : 2020-05-18 DOI: 10.1515/jqas-2020-0051

Erin M. Schliep, Toryn L. J. Schafer, Matt J. Hawkey

{"title":"Distributed lag models to identify the cumulative effects of training and recovery in athletes using multivariate ordinal wellness data","authors":"Erin M. Schliep, Toryn L. J. Schafer, Matt J. Hawkey","doi":"10.1515/jqas-2020-0051","DOIUrl":"https://doi.org/10.1515/jqas-2020-0051","url":null,"abstract":"Abstract Subjective wellness data can provide important information on the well-being of athletes and be used to maximize player performance and detect and prevent against injury. Wellness data, which are often ordinal and multivariate, include metrics relating to the physical, mental, and emotional status of the athlete. Training and recovery can have significant short- and long-term effects on athlete wellness, and these effects can vary across individual. We develop a joint multivariate latent factor model for ordinal response data to investigate the effects of training and recovery on athlete wellness. We use a latent factor distributed lag model to capture the cumulative effects of training and recovery through time. Current efforts using subjective wellness data have averaged over these metrics to create a univariate summary of wellness, however this approach can mask important information in the data. Our multivariate model leverages each ordinal variable and can be used to identify the relative importance of each in monitoring athlete wellness. The model is applied to professional referee daily wellness, training, and recovery data collected across two Major League Soccer seasons.","PeriodicalId":16925,"journal":{"name":"Journal of Quantitative Analysis in Sports","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2020-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89955979","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Profiting from overreaction in soccer betting odds 从足球博彩赔率的过度反应中获利

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Journal of Quantitative Analysis in Sports Pub Date : 2020-05-04 DOI: 10.1515/jqas-2019-0009

E. Wheatcroft

{"title":"Profiting from overreaction in soccer betting odds","authors":"E. Wheatcroft","doi":"10.1515/jqas-2019-0009","DOIUrl":"https://doi.org/10.1515/jqas-2019-0009","url":null,"abstract":"Abstract Betting odds are generally considered to represent accurate reflections of the underlying probabilities for the outcomes of sporting events. There are, however, known to be a number of inherent biases such as the favorite-longshot bias in which outsiders are generally priced with poorer value odds than favorites. Using data from European soccer matches, this paper demonstrates the existence of another bias in which the match odds overreact to favorable and unfavorable runs of results. A statistic is defined, called the Combined Odds Distribution (COD) statistic, which measures the performance of a team relative to expectations given their odds over previous matches. Teams that overperform expectations tend to have a high COD statistic and those that underperform tend to have a low COD statistic. Using data from twenty different leagues over twelve seasons, it is shown that teams with a low COD statistic tend to be assigned more generous odds by bookmakers. This can be exploited and a sustained and robust profit can be made. It is suggested that the bias in the odds can be explained in the context of the “hot hand fallacy”, in which gamblers overestimate variation in the ability of each team over time.","PeriodicalId":16925,"journal":{"name":"Journal of Quantitative Analysis in Sports","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2020-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78433311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2