Journal of Quantitative Analysis in Sports最新文献

Improving the aggregation and evaluation of NBA mock drafts 改进 NBA 模拟选秀的汇总和评估

IF 0.8

Journal of Quantitative Analysis in Sports Pub Date : 2024-08-22 DOI: 10.1515/jqas-2023-0100

Jared D. Fisher, Colin Montague

{"title":"Improving the aggregation and evaluation of NBA mock drafts","authors":"Jared D. Fisher, Colin Montague","doi":"10.1515/jqas-2023-0100","DOIUrl":"https://doi.org/10.1515/jqas-2023-0100","url":null,"abstract":"If professional teams can accurately predict the order of their league’s draft, they would have a competitive advantage when using or trading their draft picks. Many experts and enthusiasts publish forecasts of the order players are drafted into professional sports leagues, known as mock drafts. Using a novel dataset of mock drafts for the National Basketball Association (NBA), we explore mock drafts’ ability to forecast the actual draft. We analyze authors’ mock draft accuracy over time and ask how we can reasonably aggregate information from multiple authors. For both tasks, mock drafts are usually analyzed as ranked lists, and in this paper, we propose ways to improve on these methods. We propose that rank-biased distance is the appropriate error metric for measuring accuracy of mock drafts as ranked lists. To best combine information from multiple mock drafts into a single consensus mock draft, we also propose a combination method based on the ideas of ranked-choice voting. We show that this method provides improved forecasts over the standard Borda count combination method used for most similar analyses in sports, and that either combination method provides a more accurate forecast across seasons than any single author.","PeriodicalId":16925,"journal":{"name":"Journal of Quantitative Analysis in Sports","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2024-08-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142185558","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

A basketball paradox: exploring NBA team defensive efficiency in a positionless game 篮球悖论：探索无位置比赛中 NBA 球队的防守效率

IF 0.8

Journal of Quantitative Analysis in Sports Pub Date : 2024-08-16 DOI: 10.1515/jqas-2024-0010

Charles South

{"title":"A basketball paradox: exploring NBA team defensive efficiency in a positionless game","authors":"Charles South","doi":"10.1515/jqas-2024-0010","DOIUrl":"https://doi.org/10.1515/jqas-2024-0010","url":null,"abstract":"In the last decade, the offensive and defensive philosophies employed by teams in the National Basketball Association (NBA) have changed substantially. As a result, most players can no longer be classified into only one of the five traditional positions (PG, SG, SF, PF, C) and instead spend a percentage of their playing time at multiple positions, making positional data compositional. Further, given the desirability for versatile players, an argument can be made that traditional positions themselves are archaic. Using data from the 2016–17, 2017–18, and 2018–19 seasons, I explore how Bayesian hierarchical models can be used to estimate team defensive strength in three ways. First, only considering players classified by their majority traditional position. Second, by using compositional traditional positional data. Third, using compositional data from modern positions (archetypes) defined by fuzzy k-means clustering. I find that the fuzzy k-means approach leads to a modest improvement in both the root mean squared error and median 95 % posterior predictive interval width for the test data, and, more importantly, identifies 11 modern archetypes that, when combined, are correlated with team win total and adjusted team defensive rating. The modern archetype compositions can be used by stakeholders to better understand team defensive strength.","PeriodicalId":16925,"journal":{"name":"Journal of Quantitative Analysis in Sports","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142185559","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

Bayesian bivariate Conway–Maxwell–Poisson regression model for correlated count data in sports 体育运动中相关计数数据的贝叶斯双变量康威-麦克斯韦-泊松回归模型

IF 1.1

Journal of Quantitative Analysis in Sports Pub Date : 2024-08-12 DOI: 10.1515/jqas-2024-0072

Mauro Florez, Michele Guindani, Marina Vannucci

{"title":"Bayesian bivariate Conway–Maxwell–Poisson regression model for correlated count data in sports","authors":"Mauro Florez, Michele Guindani, Marina Vannucci","doi":"10.1515/jqas-2024-0072","DOIUrl":"https://doi.org/10.1515/jqas-2024-0072","url":null,"abstract":"\u0000 Count data play a crucial role in sports analytics, providing valuable insights into various aspects of the game. Models that accurately capture the characteristics of count data are essential for making reliable inferences. In this paper, we propose the use of the Conway–Maxwell–Poisson (CMP) model for analyzing count data in sports. The CMP model offers flexibility in modeling data with different levels of dispersion. Here we consider a bivariate CMP model that models the potential correlation between home and away scores by incorporating a random effect specification. We illustrate the advantages of the CMP model through simulations. We then analyze data from baseball and soccer games before, during, and after the COVID-19 pandemic. The performance of our proposed CMP model matches or outperforms standard Poisson and Negative Binomial models, providing a good fit and an accurate estimation of the observed effects in count data with any level of dispersion. The results highlight the robustness and flexibility of the CMP model in analyzing count data in sports, making it a suitable default choice for modeling a diverse range of count data types in sports, where the data dispersion may vary.","PeriodicalId":16925,"journal":{"name":"Journal of Quantitative Analysis in Sports","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141919111","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

Bayesian bivariate Conway–Maxwell–Poisson regression model for correlated count data in sports 体育运动中相关计数数据的贝叶斯双变量康威-麦克斯韦-泊松回归模型

IF 1.1

Journal of Quantitative Analysis in Sports Pub Date : 2024-08-12 DOI: 10.1515/jqas-2024-0072

Mauro Florez, Michele Guindani, Marina Vannucci

{"title":"Bayesian bivariate Conway–Maxwell–Poisson regression model for correlated count data in sports","authors":"Mauro Florez, Michele Guindani, Marina Vannucci","doi":"10.1515/jqas-2024-0072","DOIUrl":"https://doi.org/10.1515/jqas-2024-0072","url":null,"abstract":"\u0000 Count data play a crucial role in sports analytics, providing valuable insights into various aspects of the game. Models that accurately capture the characteristics of count data are essential for making reliable inferences. In this paper, we propose the use of the Conway–Maxwell–Poisson (CMP) model for analyzing count data in sports. The CMP model offers flexibility in modeling data with different levels of dispersion. Here we consider a bivariate CMP model that models the potential correlation between home and away scores by incorporating a random effect specification. We illustrate the advantages of the CMP model through simulations. We then analyze data from baseball and soccer games before, during, and after the COVID-19 pandemic. The performance of our proposed CMP model matches or outperforms standard Poisson and Negative Binomial models, providing a good fit and an accurate estimation of the observed effects in count data with any level of dispersion. The results highlight the robustness and flexibility of the CMP model in analyzing count data in sports, making it a suitable default choice for modeling a diverse range of count data types in sports, where the data dispersion may vary.","PeriodicalId":16925,"journal":{"name":"Journal of Quantitative Analysis in Sports","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141919753","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

Success factors in national team football: an analysis of the UEFA EURO 2020 国家队足球的成功因素：对 2020 年欧洲杯的分析

IF 0.8

Journal of Quantitative Analysis in Sports Pub Date : 2024-07-20 DOI: 10.1515/jqas-2023-0026

Vincent Renner, Konstantin Görgen, Alexander Woll, Hagen Wäsche, Melanie Schienle

{"title":"Success factors in national team football: an analysis of the UEFA EURO 2020","authors":"Vincent Renner, Konstantin Görgen, Alexander Woll, Hagen Wäsche, Melanie Schienle","doi":"10.1515/jqas-2023-0026","DOIUrl":"https://doi.org/10.1515/jqas-2023-0026","url":null,"abstract":"Identifying success factors in football is of sporting and economic interest. However, research in this field for national teams and their competitions is rare despite the popularity of teams and events. Therefore, we analyze data for the UEFA EURO 2020 and, for comparison purposes, the previous tournament in 2016. To mitigate the challenges of perceived multicollinearity and a small sample size, and to identify the relevant variables, we apply the ‘LASSO Cross-fitted Stability-Selection’ algorithm. This approach involves iterative splitting of data, with variables chosen via a ‘least absolute shrinkage and selection operator’ (LASSO) model (Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. J. Roy. Stat. Soc. B 58: 267–288) on one half of the observations, while coefficients are estimated on the other half. Subsequently, we inspect the frequency of selection and stability of coefficient estimation for each variable over the repeated samples to identify factors as relevant. By that, we are able to differentiate generally valid success factors such as the market value ratio from on-field variables whose importance is tournament-dependent, e.g. the tackles attempted. As the latter is connected to a team’s tactics, we conclude that their observed relevance is correlated to the results of the linked playing style in the specific tournaments. We also show the changing effect of these playing-styles on success across tournaments.","PeriodicalId":16925,"journal":{"name":"Journal of Quantitative Analysis in Sports","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141737664","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

An empirical Bayes approach for estimating skill models for professional darts players 估计职业飞镖运动员技能模型的经验贝叶斯方法

IF 0.8

Journal of Quantitative Analysis in Sports Pub Date : 2024-07-13 DOI: 10.1515/jqas-2023-0084

Martin B. Haugh, Chun Wang

引用次数: 0

A comprehensive survey of the home advantage in American football 美式橄榄球主场优势综合调查

IF 0.8

Journal of Quantitative Analysis in Sports Pub Date : 2024-07-09 DOI: 10.1515/jqas-2024-0016

Luke Benz, Thompson Bliss, Michael Lopez

引用次数: 0

Improving NHL draft outcome predictions using scouting reports 利用球探报告改进国家冰球联盟选秀结果预测

IF 0.8

Journal of Quantitative Analysis in Sports Pub Date : 2024-06-26 DOI: 10.1515/jqas-2024-0047

Hubert Luo

引用次数: 0

Comparison of individual playing styles in football 足球运动中个人比赛风格的比较

IF 0.8

Journal of Quantitative Analysis in Sports Pub Date : 2024-05-27 DOI: 10.1515/jqas-2024-0041

Tianyu Guan, Sumit Sarkar, Tim B. Swartz

引用次数: 0

A generative approach to frame-level multi-competitor races 框架级多人竞赛的生成方法

IF 0.8

Journal of Quantitative Analysis in Sports Pub Date : 2024-05-24 DOI: 10.1515/jqas-2023-0091

Tyrel Stokes, Gurashish Bagga, Kimberly Kroetch, Brendan Kumagai, Liam Welsh

{"title":"A generative approach to frame-level multi-competitor races","authors":"Tyrel Stokes, Gurashish Bagga, Kimberly Kroetch, Brendan Kumagai, Liam Welsh","doi":"10.1515/jqas-2023-0091","DOIUrl":"https://doi.org/10.1515/jqas-2023-0091","url":null,"abstract":"Multi-competitor races often feature complicated within-race strategies that are difficult to capture when training data on race outcome level data. Models which do not account for race-level strategy may suffer from confounded inferences and predictions. We develop a generative model for multi-competitor races which explicitly models race-level effects like drafting and separates strategy from competitor ability. The model allows one to simulate full races from any real or created starting position opening new avenues for attributing value to within-race actions and performing counter-factual analyses. This methodology is sufficiently general to apply to any track based multi-competitor races where both tracking data is available and competitor movement is well described by simultaneous forward and lateral movements. We apply this methodology to one-mile horse races using frame-level tracking data provided by the New York Racing Association (NYRA) and the New York Thoroughbred Horsemen’s Association (NYTHA) for the Big Data Derby 2022 Kaggle Competition. We demonstrate how this model can yield new inferences, such as the estimation of horse-specific speed profiles and examples of posterior predictive counterfactual simulations to answer questions of interest such as starting lane impacts on race outcomes.","PeriodicalId":16925,"journal":{"name":"Journal of Quantitative Analysis in Sports","volume":null,"pages":null},"PeriodicalIF":0.8,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141145809","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