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.

The Journal of Quantitative Analysis in Sports (JQAS) is the official journal of the American Statistical Association, focusing on the quantitative analysis of professional and amateur sports, including collegiate and Olympic competitions. As the field of sports analytics continues to grow, there is an increasing demand for novel methods to analyze and understand data. JQAS is dedicated to publishing timely, high-quality peer-reviewed research that covers a wide variety of sports and diverse perspectives. Topics addressed include 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 approaches to problems, develop cutting-edge methods, and apply innovative thinking to solve complex challenges in sports contexts. The journal brings together researchers from various disciplines, including statistics, operations research, machine learning, scientific computing, econometrics, and sports management.

《Journal of Quantitative Analysis in Sports》（JQAS）是美国统计协会的官方期刊，专注于专业和业余体育的定量分析，包括大学和奥林匹克比赛。随着体育分析领域的不断发展，对新方法的需求日益增加，JQAS致力于发表及时、高质量的同行评审研究。期刊涵盖多种体育项目和不同视角，探讨的主题包括比赛结果模型、球员表现的测量与评估、比赛结构、规则与裁决分析、比赛策略、体育技术分析以及球员和团队排名方法。JQAS希望发表展示原创性问题解决方法的手稿，开发前沿方法，并运用创新思维解决体育领域中的复杂挑战。JQAS汇聚了来自统计学、运筹学、机器学习、科学计算、计量经济学和体育管理等多个学科的研究者。

Journal of Quantitative Analysis in Sports

Abstract In basketball, traditional methods of assessing individual rebounding ability are problematic because they depend on all players present on the court rather than just on the player of interest. Although there exist modeling approaches to correct for this dependence, they are generally unsuitable for events with binary outcomes. In this paper, a Bayesian two-stage model is proposed to predict both individual and team rebound allocation. This approach makes it possible to identify players who help their team win the fight for rebounds, regardless of their individual rebounding totals. Although similar in flavor to the popular Adjusted Plus-Minus (APM) framework, the proposed strategy is different in that it does not assume that individual contributions are linearly additive on the response scale. Furthermore, the regularization approach is improved through rebounding-specific heuristics. A simulation study is performed to show the effectiveness of the proposed model, and the parameters are estimated using data from the 2020–21 NBA season. Predictions are then made for rebounding in the 2021–22 season. This study confirms that relying exclusively on individual rebounding rates could lead to the mis-evaluation of players’ rebounding abilities.

Nicholas Kiriazis

Christian Genest

Alexandre Leblanc

A Bayesian two-stage framework for lineup-independent assessment of individual rebounding ability in the NBA

基于贝叶斯两阶段框架的NBA球员独立于阵容的篮板能力评估

Abstract This paper proposes novel approaches to measuring team productivity and evaluating trading efficiency in Major League Baseball (MLB) from 1995 to 2021 through an application of portfolio theory. The performance of individual players is measured using a structural approach relating player outcomes to team runs developed by Lindsey (1963. An investigation of strategies in baseball. Oper. Res. 11: 477–501). Using a portfolio theory framework, we treat MLB teams as a portfolio of players (assets), each of which can be defined by an expected contribution of runs per game and the variance of this measure. It is found that both the expected value and variance have a positive impact on team runs scored. Given our definition of teams characterized by their expected values and variances, we evaluate trading efficiency between teams given their pre-trade expected values and variances and the acquired player’s pre-trade expected value and variance. We find that trade efficiency has improved over our timeframe, consistent with the growth in data-driven decision making used in MLB front offices.

Ryan Pinheiro

Stefan Szymanski

On the efficiency of trading intangible fixed assets in Major League Baseball

关于美国职业棒球大联盟中无形固定资产交易效率的研究

Abstract While the use of expected goals (xG) as a metric for assessing soccer performance is increasingly prevalent, the uncertainty associated with their estimates is often overlooked. This work bridges this gap by providing easy-to-implement methods for uncertainty quantification in xG estimates derived from Bayesian models. Based on a convenient posterior approximation, we devise an online prior-to-posterior update scheme, aligning with the typical in-season model training in soccer. Additionally, we present a novel framework to assess and compare the performance dynamics of two teams during a match, while accounting for evolving match scores. Our approach is well-suited for graphical representation and improves interpretability. We validate the accuracy of our methods through simulations, and provide a real-world illustration using data from the Italian Serie A league.

Bernardo Nipoti

Lorenzo Schiavon

Expected goals under a Bayesian viewpoint: uncertainty quantification and online learning

贝叶斯视角下的预期进球：不确定性量化与在线学习

Abstract We analyse football (soccer) player performance data with mixed type variables from the 2014-15 season of eight European major leagues. We cluster these data based on a tailor-made dissimilarity measure. In order to decide between the many available clustering methods and to choose an appropriate number of clusters, we use the approach by Akhanli and Hennig (2020. “Comparing Clusterings and Numbers of Clusters by Aggregation of Calibrated Clustering Validity Indexes.” Statistics and Computing 30 (5): 1523–44). This is based on several validation criteria that refer to different desirable characteristics of a clustering. These characteristics are chosen based on the aim of clustering, and this allows to define a suitable validation index as weighted average of calibrated individual indexes measuring the desirable features. We derive two different clusterings. The first one is a partition of the data set into major groups of essentially different players, which can be used for the analysis of a team’s composition. The second one divides the data set into many small clusters (with 10 players on average), which can be used for finding players with a very similar profile to a given player. It is discussed in depth what characteristics are desirable for these clusterings. Weighting the criteria for the second clustering is informed by a survey of football experts.

Serhat Emre Akhanli

Christian Hennig

Clustering of football players based on performance data and aggregated clustering validity indexes

基于表现数据和聚合聚类有效性指标的足球运动员聚类

Abstract Recent measurement technologies enable us to analyze baseball at higher levels of complexity. There are, however, still many unclear points around pitching strategy. There are two elements that make it difficult to measure the effect of a pitching strategy. First, most public datasets do not include location data where the catcher demands a ball, which is essential information to obtain the battery’s intent. Second, there are many confounders associated with pitching/batting results when evaluating pitching strategy. We here clarify the effect of pitching attempts to a specific location, e.g., inside or outside. We employ a causal inference framework called stratified analysis using a propensity score to evaluate the effects while removing the effect of confounding factors. We use a pitch-by-pitch dataset of Japanese professional baseball games held in 2014–2019, which includes location data where the catcher demands a ball. The results reveal that an outside pitching attempt is more effective than an inside one to minimize allowed run average. In addition, the stratified analysis shows that the outside pitching attempt is effective regardless of the magnitude of the estimated batter’s ability, and the proportion of pitched inside for pitcher/batter. Our analysis provides practical insights into selecting a pitching strategy to minimize allowed runs.

Hiroshi Nakahara

Kazuya Takeda

Keisuke Fujii

Pitching strategy evaluation via stratified analysis using propensity score

基于倾向评分分层分析的投球策略评估

Abstract Improvements in tracking technology through optical and computer vision systems have enabled a greater understanding of the movement-based behaviour of multiple agents, including in team sports. In this study, a multi-agent statistically discriminative sub-trajectory mining (MA-Stat-DSM) method is proposed that takes a set of binary-labelled agent trajectory matrices as input and incorporates Hausdorff distance to identify sub-matrices that statistically significantly discriminate between the two groups of labelled trajectory matrices. Utilizing 2015/16 SportVU NBA tracking data, agent trajectory matrices representing attacks consisting of the trajectories of five agents (the ball, shooter, last passer, shooter defender, and last passer defender), were truncated to correspond to the time interval following the receipt of the ball by the last passer, and labelled as effective or ineffective based on a definition of attack effectiveness that we devise in the current study. After identifying appropriate parameters for MA-Stat-DSM by iteratively applying it to all matches involving the two top- and two bottom-placed teams from the 2015/16 NBA season, the method was then applied to selected matches and could identify and visualize the portions of plays, e.g., involving passing, on-, and/or off-the-ball movements, which were most relevant in rendering attacks effective or ineffective.

Rory Paul Bunker

Vo Nguyen Le Duy

Yasuo Tabei

Ichiro Takeuchi

Multi-agent statistically discriminative sub-trajectory mining and an application to NBA basketball

多智能体统计判别子轨迹挖掘及其在NBA篮球中的应用

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.

Abbreviation	J. Quant. Anal. Sports.
Journal Impact	1.04
Quartiles(Global)	SOCIAL SCIENCES, MATHEMATICAL METHODS(Q3)

ISSN	1559-0410, 2194-6388
h-index	23

Publication Information	Publisher: Walter de Gruyter GmbH，Publishing cycle: ，Journal Type: journal，Open Access Journals: No
Basic data	Year of publication: 2011，Proportion of original research papers: 94.44%，Self Citation Rate:18.20%， Gold OA Rate: 14.29%

Journal of Quantitative Analysis in Sports

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A Bayesian two-stage framework for lineup-independent assessment of individual rebounding ability in the NBA

On the efficiency of trading intangible fixed assets in Major League Baseball

Expected goals under a Bayesian viewpoint: uncertainty quantification and online learning

Clustering of football players based on performance data and aggregated clustering validity indexes

Pitching strategy evaluation via stratified analysis using propensity score

A Bayesian two-stage framework for lineup-independent assessment of individual rebounding ability in the NBA

On the efficiency of trading intangible fixed assets in Major League Baseball

Expected goals under a Bayesian viewpoint: uncertainty quantification and online learning

Multi-agent statistically discriminative sub-trajectory mining and an application to NBA basketball

Improving the aggregation and evaluation of NBA mock drafts