Journal of Quantitative Analysis in Sports最新文献_第8页

A Skellam regression model for quantifying positional value in soccer 足球场上位置价值量化的Skellam回归模型

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Journal of Quantitative Analysis in Sports Pub Date : 2020-12-03 DOI: 10.1515/JQAS-2019-0122

K. Pelechrinis, Wayne L. Winston

{"title":"A Skellam regression model for quantifying positional value in soccer","authors":"K. Pelechrinis, Wayne L. Winston","doi":"10.1515/JQAS-2019-0122","DOIUrl":"https://doi.org/10.1515/JQAS-2019-0122","url":null,"abstract":"Abstract Soccer is undeniably the most popular sport world-wide and everyone from general managers and coaching staff to fans and media are interested in evaluating players’ performance. Metrics applied successfully in other sports, such as the (adjusted) +/− that allows for division of credit among a basketball team’s players, exhibit several challenges when applied to soccer due to severe co-linearities. Recently, a number of player evaluation metrics have been developed utilizing optical tracking data, but they are based on proprietary data. In this work, our objective is to develop an open framework that can estimate the expected contribution of a soccer player to his team’s winning chances using publicly available data. In particular, using data from (i) approximately 20,000 games from 11 European leagues over eight seasons, and, (ii) player ratings from the FIFA video game, we estimate through a Skellam regression model the importance of every line (attackers, midfielders, defenders and goalkeeping) in winning a soccer game. We consequently translate the model to expected league points added above a replacement player (eLPAR). This model can further be used as a guide for allocating a team’s salary budget to players based on their expected contributions on the pitch. We showcase similar applications using annual salary data from the English Premier League and identify evidence that in our dataset the market appears to under-value defensive line players relative to goalkeepers.","PeriodicalId":16925,"journal":{"name":"Journal of Quantitative Analysis in Sports","volume":"25 1","pages":"187 - 201"},"PeriodicalIF":0.8,"publicationDate":"2020-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82584379","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

Frontmatter

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Journal of Quantitative Analysis in Sports Pub Date : 2020-11-18 DOI: 10.1515/jqas-2020-frontmatter4

引用次数: 0

Evaluating the effectiveness of different network flow motifs in association football 评价不同网络流动机在足协足球运动中的有效性

IF 0.8

Journal of Quantitative Analysis in Sports Pub Date : 2020-11-03 DOI: 10.1515/jqas-2019-0097

Else Marie Håland, Astrid Salte Wiig, L. M. Hvattum, M. Stålhane

引用次数: 3

Does confirmation bias exist in judged events at the Olympic Games? 确认偏见是否存在于奥运会的裁判项目中?

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Journal of Quantitative Analysis in Sports Pub Date : 2020-11-02 DOI: 10.1515/jqas-2019-0043

Christiana E. Hilmer, Michael J. Hilmer

引用次数: 1

G-Elo: generalization of the Elo algorithm by modeling the discretized margin of victory G-Elo:对Elo算法的泛化，通过建模离散化的胜利余量

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Journal of Quantitative Analysis in Sports Pub Date : 2020-10-20 DOI: 10.1515/jqas-2020-0115

L. Szczecinski

{"title":"G-Elo: generalization of the Elo algorithm by modeling the discretized margin of victory","authors":"L. Szczecinski","doi":"10.1515/jqas-2020-0115","DOIUrl":"https://doi.org/10.1515/jqas-2020-0115","url":null,"abstract":"Abstract In this work we develop a new algorithm for rating of teams (or players) in one-on-one games by exploiting the observed difference of the game-points (such as goals), also known as a margin of victory (MOV). Our objective is to obtain the Elo-style algorithm whose operation is simple to implement and to understand intuitively. This is done in three steps: first, we define the probabilistic model between the teams’ skills and the discretized MOV variable: this generalizes the model underpinning the Elo algorithm, where the MOV variable is discretized into three categories (win/loss/draw). Second, with the formal probabilistic model at hand, the optimization required by the maximum likelihood rule is implemented via stochastic gradient; this yields simple online equations for the rating updates which are identical in their general form to those characteristic of the Elo algorithm: the main difference lies in the way the scores and the expected scores are defined. Third, we propose a simple method to estimate the coefficients of the model, and thus define the operation of the algorithm; it is done in a closed form using the historical data so the algorithm is tailored to the sport of interest and the coefficients defining its operation are determined in entirely transparent manner. The alternative, optimization-based strategy to find the coefficients is also presented. We show numerical examples based on the results of the association football of the English Premier League and the American football of the National Football League.","PeriodicalId":16925,"journal":{"name":"Journal of Quantitative Analysis in Sports","volume":"124 1","pages":"1 - 14"},"PeriodicalIF":0.8,"publicationDate":"2020-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89446626","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: 6

Algorithmically deconstructing shot locations as a method for shot quality in hockey 基于算法解构的冰球击球位置分析方法

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Journal of Quantitative Analysis in Sports Pub Date : 2020-10-10 DOI: 10.1515/jqas-2020-0012

Devan G. Becker, D. Woolford, C. Dean

{"title":"Algorithmically deconstructing shot locations as a method for shot quality in hockey","authors":"Devan G. Becker, D. Woolford, C. Dean","doi":"10.1515/jqas-2020-0012","DOIUrl":"https://doi.org/10.1515/jqas-2020-0012","url":null,"abstract":"Abstract Spatial point processes have been successfully used to model the relative efficiency of shot locations for each player in professional basketball games. Those analyses were possible because each player makes enough baskets to reliably fit a point process model. Goals in hockey are rare enough that a point process cannot be fit to each player’s goal locations, so novel techniques are needed to obtain measures of shot efficiency for each player. A Log-Gaussian Cox Process (LGCP) is used to model all shot locations, including goals, of each NHL player who took at least 500 shots during the 2011–2018 seasons. Each player’s LGCP surface is treated as an image and these images are then used in an unsupervised statistical learning algorithm that decomposes the pictures into a linear combination of spatial basis functions. The coefficients of these basis functions are shown to be a very useful tool to compare players. To incorporate goals, the locations of all shots that resulted in a goal are treated as a “perfect player” and used in the same algorithm (goals are further split into perfect forwards, perfect centres and perfect defence). These perfect players are compared to other players as a measure of shot efficiency. This analysis provides a map of common shooting locations, identifies regions with the most goals relative to the number of shots and demonstrates how each player’s shot location differs from scoring locations.","PeriodicalId":16925,"journal":{"name":"Journal of Quantitative Analysis in Sports","volume":"43 1","pages":"107 - 115"},"PeriodicalIF":0.8,"publicationDate":"2020-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82534789","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

An iterative Markov rating method 一种迭代马尔可夫评级方法

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Journal of Quantitative Analysis in Sports Pub Date : 2020-10-05 DOI: 10.1515/jqas-2019-0070

Stephen Devlin, T. Treloar, Molly Creagar, S. Cassels

引用次数: 0

TRAP: a predictive framework for the Assessment of Performance in Trail Running 陷阱:在越野跑性能评估的预测框架

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Journal of Quantitative Analysis in Sports Pub Date : 2020-09-30 DOI: 10.1515/JQAS-2020-0013

Riccardo Fogliato, N. L. Oliveira, Ronald Yurko

{"title":"TRAP: a predictive framework for the Assessment of Performance in Trail Running","authors":"Riccardo Fogliato, N. L. Oliveira, Ronald Yurko","doi":"10.1515/JQAS-2020-0013","DOIUrl":"https://doi.org/10.1515/JQAS-2020-0013","url":null,"abstract":"Abstract Trail running is an endurance sport in which athletes face severe physical challenges. Due to the growing number of participants, the organization of limited staff, equipment, and medical support in these races now plays a key role. Monitoring runner’s performance is a difficult task that requires knowledge of the terrain and of the runner’s ability. In the past, choices were solely based on the organizers’ experience without reliance on data. However, this approach is neither scalable nor transferable. Instead, we propose a firm statistical methodology to perform this task, both before and during the race. Our proposed framework, Trail Running Assessment of Performance (TRAP), studies (1) the assessment of the runner’s ability to reach the next checkpoint, (2) the prediction of the runner’s expected passage time at the next checkpoint, and (3) corresponding prediction intervals for the passage time. We apply our methodology, using the race history of runners from the International Trail Running Association (ITRA) along with checkpoint and terrain-level information, to the “holy grail” of ultra-trail running, the Ultra-Trail du Mont-Blanc (UTMB) race, demonstrating the predictive power of our methodology.","PeriodicalId":16925,"journal":{"name":"Journal of Quantitative Analysis in Sports","volume":"27 1","pages":"129 - 143"},"PeriodicalIF":0.8,"publicationDate":"2020-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73494087","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

Measuring competitive balance in sports 衡量体育竞技平衡

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Journal of Quantitative Analysis in Sports Pub Date : 2020-09-29 DOI: 10.1515/jqas-2020-0006

Matthew Doria, B. Nalebuff

{"title":"Measuring competitive balance in sports","authors":"Matthew Doria, B. Nalebuff","doi":"10.1515/jqas-2020-0006","DOIUrl":"https://doi.org/10.1515/jqas-2020-0006","url":null,"abstract":"Abstract In order to make comparisons of competitive balance across sports leagues, we need to take into account how different season lengths influence observed measures of balance. We develop the first measures of competitive balance that are invariant to season length. The most commonly used measure, the ASD/ISD or Noll-Scully ratio, is biased. It artificially inflates the imbalance for leagues with long seasons (e.g., MLB) compared to those with short seasons (e.g., NFL). We provide a general model of competition that leads to unbiased variance estimates. The result is a new ordering across leagues: the NFL goes from having the most balance to being tied for the least, while MLB becomes the sport with the most balance. Our model also provides insight into competitive balance at the game level. We shift attention from team-level to game-level measures as these are more directly related to the predictability of a representative contest. Finally, we measure competitive balance at the season level. We do so by looking at the predictability of the final rankings as seen from the start of the season. Here the NBA stands out for having the most predictable results and hence the lowest full-season competitive balance.","PeriodicalId":16925,"journal":{"name":"Journal of Quantitative Analysis in Sports","volume":"30 1","pages":"29 - 46"},"PeriodicalIF":0.8,"publicationDate":"2020-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85137780","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: 3

The relative roles of skill and luck within 11 different golfer populations 技术和运气在11个不同高尔夫球手群体中的相对作用

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Journal of Quantitative Analysis in Sports Pub Date : 2020-09-25 DOI: 10.1515/JQAS-2019-0028

Richard J. Rendleman

引用次数: 3