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Journal of Quantitative Analysis in Sports

An official journal of the American Statistical Association

Editor-in-Chief: Gil Fellingham

Language: English

First published: January 1, 2005

Publication Frequency: 4 issues per year

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About this journal

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.

Interdisciplinary

• Statistics
• Operations Research
• Economics
• Sports Management
• Business

Your Benefits

The Journal of Quantitative Analysis in Sports is proud to announce a new feature, the Editor’s Choice free access article. To download the featured article free of charge, please click the link below.

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Vol. 20, Issue 4 Introducing Grid WAR: rethinking WAR for starting pitchers 　by Ryan S. Brill and Abraham J. Wyner

Vol. 20, Issue 3 Estimating positional plus-minus in the NBA by Hua Gong and Su Chen

Vol. 20, Issue 2 Equity, diversity, and inclusion in sports analytics by Craig Fernandes, Jason D. Vescovi, Richard Norman, Cheri L. Bradish, Nathan Taback, and Timothy C.Y. Chan

Vol. 20, Issue 1 Editor’s Note: on fairness in sports analytics by Benjamin S. Baumer / 　Evaluating plate discipline in Major League Baseball with Bayesian Additive Regression Trees by Ryan Yee and Sameer K. Deshpande

Vol. 19, Issue 4 A Bayesian analysis of the time through the order penalty in baseball by Ryan S. Brill, Sameer K. Deshpande and Abraham J. Wyner

Vol. 19, Issue 3 Feeling fast? Beliefs and performance among high school sprinters by Travis J. Lybbert and Geyi Zheng

Vol. 19, Issue 2 Testing styles of play using triad census distribution: an application to men’s football 　by Lucio Palazzo, Riccardo Ievoli and Giancarlo Ragozini

Vol. 19, Issue 1 Kelly criterion and fractional Kelly strategy for non-mutually exclusive bets by Benjamin P. Jacot and Paul V. Mochkovitch

Vol. 18, Issue 4 Bayesian modelling of elite sporting performance with large databases by Jim E. Griffin, Laurentiu C. Hinoveanu, and James G. Hopker

Vol. 18, Issue 2 Quantifying the impact of imbalanced groups in FIFA Women’s World Cup tournaments 1991-2019 by Michael A. Lapre and Elizabeth M. Palazzolo

Vol. 18, Issue 2 A reinforcement learning based approach to play calling in football by Preston Biro and Stephen G. Walker

Vol. 18, Issue 1 Optical tracking in team sports: a survey on player and ball tracking methods in soccer and other team by Pegah Rahimian and Laszlo Toka

Vol. 17, Issue 4 Opening up the court: analyzing player performance across tennis Grand Slams by Shannon K. Gallagher, Kayla Frisoli, and Amanda Luby

Vol. 17, Issue 3 Winning and losing streaks in the National Hockey League: are teams experiencing momentum or are games a sequence of random events? By Gregory M. Steeger, Johnathan L. Dulin, and Gerardo O. Gonzalez

Vol. 17, Issue 2 A Bayesian marked spatial point processes model for basketball shot chart by Jiejing Jiao, Guanyu Hu and Jun Yan

Vol. 17, Issue 1 Smart kills and worthless deaths: eSports analytics for League of Legends by Philip Z. Maymin

Vol. 16, Issue 4 Evaluating the effectiveness of different network flow mofits in association football by Else Marie Håland, Astrid Salte Wiig, Lars Magnus Hvattum, Magnus Stålhane

Vol. 16, Issue 3 Profiting from overreaction in soccer betting odds by Edward Wheatcroft

Vol. 16, Issue 2 Going deep: models for continuous-time within-play valuation of game outcomes in American football with tracking data by Ronald Yurko, Francesca Matano, Lee F. Richardson, Nicholas Garnered, Taylor Pospisil, Konstantinos Pelechrinis, Samuel L. Ventura

Vol. 16, Issue 1 Models for generating NCAA men’s basketball tournament bracket pools by Ian G. Ludden, Arash Khatibi, Douglas M. King, Sheldon H. Jacobson

Vol. 15, Issue 4 Bayesian statistics meets sports: a comprehensive review by Edgar Santos-Fernandez, Paul Wu, and Kerrie L. Mengersen

Vol. 15, Issue 3 nflWAR: a reproducible method for offensive player evaluation in football by Ronald Yurko, Samuel Ventura, and Maksim Horowitz

Vol. 15, Issue 2 Rao-Blackwellizing field goal percentage by Daniel Daly-Grafstein and Luke Bornn

Vol. 15, Issue 1 The Advantage of Lefties in One-on-One Sports by Francois Fagan, Martin Haugh, and Hal Cooper

Vol. 14, Iss. 4 New Metrics for Evaluating Home Plate Umpire Consistency and Accuracy by David J. Hunter

Vol. 14, Iss 3 A Network Diffusion Ranking Family that includes the Methods of Markov, Massey and Colley by Stephen Devlin and Thomas Treloar

Vol. 14, Iss. 2 Estimating the effect of plate discipline using a causal inference framework: an application oft he G-computation algorithm David Michael Vock and Laura Frances Boehm Vock

Vol. 14, Iss. 1 Modifying Kelly criteria by Dani Chu, Yifan Wu and Tim B. Swartz

Vol. 13, Iss. 4 Decomposing Pythagoras by Edward H. Kaplan and Candler Rich

Vol. 13, Iss 3 A hierarchical Bayesian model of pitch framing Sameer K. Deshpande and Abraham Wyner

Vol. 13, Iss. 2 Identifying NCAA tournament upsets using Balance Optimization Subset Selection by Shouvik Dutta, Sheldon H. Jackson, and Jason J. Sauppe

Vol. 13, Iss. 1 An examination of statistical disclosure issues related to publication of aggregate statistics in the presence of a known subset of the dataset using Baseball Hall of Fame ballots by Gregory J. Matthews, Petala Gardenia da Silva Estrela Tuy, and Robert K. Arthur

Vol. 12, Iss. 4 Meta-analytics: tools for understanding the statistical properties of sports metrics by Alexander M. Franks, Alexander D’Amour, Daniel Cervone, and Luke Bornn

Vol. 12, Iss. 3: Searching for the GOAT of tennis win prediction by Stephanie Ann Kovalchik

Vol. 12, Iss. 2: Estimating an NBA’s player’s impact on his team’s chances of winning by Sameer K. Deshpande and Shane T. Jensen

Vol. 12, Iss. 1: An analytical approach for fantasy football draft and lineup management by Adrian Becker and Xu Andy Sun

Vol. 11, Iss. 4: A finite mixture latent trajectory model for modeling ultrarunners’ behavior in a 24-hour race by Francesco Bartolucci and Thomas B. Murphy

Vol. 11, Iss. 3: The implied volatility of a sports game by Nicholas G. Polson and Hal S. Stern

Vol. 11, Iss. 2: openWAR: An open source system for evaluating overall player performance in major league baseball by Benjamin S. Baumer, Shane T. Jensen, Gregory J. Matthews

Vol. 11, Iss. 1: A generative model for predicting outcomes in college basketball by Francisco J. R. Ruiz and Fernando Perez-Cruz

Open Access Policy

De Gruyter offers the authors of JQAS the opportunity to publish their contributions Open Access for a price of $2,450 per article. If interested, please email rights@degruyter.com to inquire. For further information on De Gruyter’s Open Access policies, click here.

Back Issues

Open Access
Exploring the difficulty of estimating win probability: a simulation study

September 25, 2025

Ryan S. Brill, Ronald Yurko, Abraham J. Wyner

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Estimating win probability is one of the classic modeling tasks of sports analytics. Many widely used win probability estimators use machine learning to fit the relationship between a binary win/loss outcome variable and certain game-state variables. To illustrate just how difficult it is to accurately fit such a model from noisy and highly correlated observational data, in this paper we conduct a simulation study. We create a simplified random walk version of football in which true win probability at each game-state is known, and we see how well a model recovers it. We find that the dependence structure of observational play-by-play data substantially inflates the bias and variance of estimators and lowers the effective sample size. Further, to achieve approximately valid marginal coverage, win probability confidence intervals need to be substantially wide. Concisely, these are high variance estimators subject to substantial uncertainty. Our findings are not unique to the particular application of estimating win probability; they are broadly applicable across sports analytics, as myriad other sports datasets are clustered into groups of observations that share the same outcome.
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Generation of threat: crediting football players for creating dangerous actions in an unbiased way

September 24, 2025

Ali Baouan, Sébastien Coustou, Mathieu Lacome, Sergio Pulido, Mathieu Rosenbaum

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We introduce an innovative methodology to identify football players at the origin of threatening actions in a team. In our framework, a threat is defined as entering the opposing team’s danger area . We investigate the timing of threat events and ball touches of players, and capture their correlation using Hawkes processes. Our model-based approach allows us to evaluate a player’s ability to create danger both directly and through interactions with teammates. We define a new index, called Generation of Threat (GoT), that measures in an unbiased way the contribution of a player to threat generation. For illustration, we present a detailed analysis of Chelsea’s 2016–2017 season, with a standout performance from Eden Hazard. We are able to credit each player for his involvement in danger creation and determine the main circuits leading to threat. In the same spirit, we investigate the danger generation process of Stade Rennais in the 2021–2022 season. Furthermore, we establish a comprehensive ranking of Ligue 1 players based on their generated threat in the 2021–2022 season. Our analysis reveals surprising results, with players such as Jason Berthomier, Moses Simon and Frederic Guilbert among the top performers in the GoT rankings.
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Investigating experiential effects in online chess using a hierarchical Bayesian analysis

September 19, 2025

Adam Gee, Sydney O. Seese, James P. Curley, Owen G. Ward

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The presence or absence of winner-loser effects is a widely discussed phenomenon across both sports and psychology research. Investigation of such effects is often hampered by the limited availability of data. Online chess has exploded in popularity in recent years and provides vast amounts of data which can be used to explore this question. With a hierarchical Bayesian regression model, we carefully investigate the presence of such experiential effects in online chess. Using a large quantity of online chess data, we see little evidence for experiential effects that are consistent across all players, with some individual players showing some evidence for such effects. Given the challenging temporal nature of this data, we discuss several methods for assessing the suitability of our model and carefully check its validity.
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Assessing end-of-season performance as a function of average minutes played for NBA players

September 18, 2025

Matthew Jewell, Garritt L. Page, C. Shane Reese

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Since the National Basketball Association (NBA) fined the San Antonio Spurs in 2012 for their decision to rest their best players during a nationally televised game, there has been much discussion surrounding player “load” management. In fact, more teams in the NBA have begun to adopt the Spurs approach of strategically resting players in an effort to avoid injury and ensure players are at peak performance towards the end of the season. Although there is general agreement that load management is important for some players to avoid injury, it isn’t clear that managing minutes during the beginning of a season results in peak performance towards the end. In this paper we attempt to address this. We do this by formulating a Bayesian hierarchical model that borrows strength among players to estimate a league-wide effect that average minutes played has on end-of-season performance based on 14 modern performance metrics. Generally speaking, it appears that end-of-season offensive and defensive performance tends be above average as average minutes played is increased.
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Leveraging minute-by-minute soccer match event data to adjust Team’s offensive production for game context

August 25, 2025

Andrey V. Skripnikov, Ahmet Cemek, David Gillman

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In soccer, game context can result in skewing offensive statistics in ways that might misrepresent how well a team has played. For instance, in England’s 1–2 loss to France in the 2022 FIFA World Cup quarterfinal, England attempted considerably more shots (16 to France’s 8) and more corners (5–2), potentially suggesting they played better despite the loss. However, these statistics were largely accumulated when France was ahead and more willing to concede offensive initiative to England. To explore how game context influences offensive performance, we analyze minute-by-minute event-sequenced match data from 15 seasons across five major European leagues. Using count-response Generalized Additive Modeling, we consider features such as score and red card differential, home/away status, pre-match win probabilities, and game minute. Moreover, we leverage interaction terms to test several intuitive hypotheses about how these features might cooperate in explaining offensive production. The selected model is then applied to project offensive statistics onto a standardized “common denominator” scenario: a tied home game with even men on both sides. The adjusted numbers – in contrast to regular game totals that disregard game context – offer a more contextualized comparison, reducing the likelihood of misrepresenting the relative quality of play.
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An optimal transport based embedding to quantify the distance between playing styles in collective sports

July 28, 2025

Ali Baouan, Mathieu Rosenbaum, Sergio Pulido

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This study presents a quantitative framework to compare teams in collective sports with respect to their style of play. The style of play is characterized by the team’s spatial distribution over a collection of frames. As a first step, we introduce an optimal transport-based embedding to map frames into Euclidean space, enabling efficient computation of distance. Then, building on this frame-level analysis,we use quantization to establish a similarity metric between teams based on a collection of frames from their games. For illustration, we present an analysis of a collection of games from the 2021–2022 Ligue 1 season. We successfully retrieve relevant clusters of game situations and calculate the similarity matrix between teams in terms of style of play. Additionally, we demonstrate the effectiveness of the embedding as a preprocessing tool for prediction tasks. Likewise, we apply our framework to analyze the dynamics in the first half of the NBA season in 2015–2016.
Open Access
The impact of imbalanced groups in UEFA Euro 1980–2024 and comparison with the FIFA World Cup

July 10, 2025

Michael A. Lapré, Julia G. Amato

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Prior research found significant competitive imbalance in FIFA World Cup tournaments because FIFA does not allocate World Cup slots to continental confederations in proportion to the distribution of the best teams in the world. Since the UEFA Euro only consists of teams from Europe, it should be much easier for UEFA to create competitive balance. We empirically investigate competitive imbalance between groups at the UEFA Euro tournaments from 1980 through 2024. We find that competitive imbalance at the Euro is just as bad as it is in the World Cup. We also find that the impact of competitive imbalance on the probability of reaching the quarterfinals is the same across the World Cup and the Euro. UEFA creates competitive imbalance by sometimes protecting multiple low-ranked hosts and, most importantly, using inadequate methods to rank teams. We recommend that UEFA adopt an Elo rating system to rank teams.
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Improving strength of schedule metrics in sports scheduling

July 8, 2025

Adam DeHollander, Mark Karwan

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This paper studies sports Strength of Schedule (SoS) metrics and their utility for decision-makers in creating season schedules. We identify limitations of the widely used Bowl Championship Series (BCS) SoS metric, including infrequent rank changes and reduced variance, which limit its effectiveness in assessing true schedule difficulty. In response, we introduce two novel metrics: Probabilistic Random Walk Length (PRWL) SoS and Customized-Distributional (CD) SoS. The PRWL SoS leverages random walks on a graph to provide a comprehensive measure incorporating the difficulty of a team’s opponents schedules. The CD SoS offers a customizable approach, incorporating team-specific goals and preferences, for a more tailored assessment of schedule strength. Additionally, we propose a SoS variability metric to evaluate the variability of SoS among teams, promoting schedule balance. Through a detailed case study involving a large sports conference, we demonstrate the practical application and impact of our proposed metrics. The case study illustrates the superiority of the proposed scheduling format over the existing one, prompting the conference to adopt a new schedule format and eliminate their previous divisional structure. Our contributions provide more accurate tools for evaluating schedule difficulty, enhancing schedule balance, and optimizing sports schedules, ultimately promoting fairness and competitiveness in sports leagues.
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PEP: a tackle value measuring the prevention of expected points

June 30, 2025

Robert Bajons, Jan-Ole Koslik, Rouven Michels, Marius Ötting

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Traditional assessments of tackling in American Football often only consider the number of tackles made, without adequately accounting for their context and importance for the game. Aiming for improvement, we develop a metric that quantifies the value of a tackle in terms of the prevented expected points (PEP). Specifically, we compare the real end-of-play yard line of tackles with the predicted yard line given the hypothetical situation that the tackle had been missed. For this, we use high-resolution tracking data, that capture the position and velocity of players, and a random forest to account for uncertainty and multi-modality in yard-line prediction. Moreover, we acknowledge the difference in the importance of tackles by assigning an expected points value to each individual tree prediction of the random forest. Finally, to relate the value of tackles to a player’s ability to tackle, we fit a suitable mixed-effect model to the PEP values. Our approach contributes to a deeper understanding of defensive performances in American football and offers valuable insights for coaches and analysts.
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Hypergraph adjusted plus-minus

June 6, 2025

Nathaniel Josephs, Elizabeth Upton

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In team sports, traditional ranking statistics do not allow for the simultaneous evaluation of both individuals and combinations of players. Metrics for individual player rankings often fail to include the interaction effects between groups of players, while methods for assessing full lineups cannot be used to identify the value of lower-order combinations of players (pairs, trios, etc.). Given that player and lineup rankings are inherently dependent on each other, these limitations may affect the accuracy of performance evaluations. To address this, we propose a novel adjusted plus-minus (APM) approach that allows for the simultaneous ranking of individual players, lower-order combinations of players, and full lineups within a team. The method adjusts for the complete dependency structure and is motivated by the connection between APM and the hypergraph representation of a team. We discuss the similarities of our approach to other advanced metrics, demonstrate it using NBA data from 2012 to 2022, and suggest potential directions for future work.
Open Access
A Bayesian two-stage framework for lineup-independent assessment of individual rebounding ability in the NBA

December 25, 2024

Nicholas Kiriazis, Christian Genest, Alexandre Leblanc

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

September 23, 2024

Rory Paul Bunker, Vo Nguyen Le Duy, Yasuo Tabei, Ichiro Takeuchi, Keisuke Fujii

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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.
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Improving the aggregation and evaluation of NBA mock drafts

August 22, 2024

Jared D. Fisher, Colin Montague

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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.
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A basketball paradox: exploring NBA team defensive efficiency in a positionless game

August 19, 2024

Charles South

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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.

Special Issue on Player Tracking Data in the National Football League (NFL) / Guest Editor: Michael Lopez

Special Issue: Prediction methodology for the NCAA men‘s basketball tournament

2008 Northern California Symposium on Statistics and Operations Research in Sports

Journal Impact Factor	1.0	2024, Journal Citation Reports (Clarivate, 2025)
5-year Journal Impact Factor	1.3	2024, Journal Citation Reports (Clarivate, 2025)
Journal Citation Indicator	0.41	2024, Journal Citation Reports (Clarivate, 2025)
CiteScore	2.3	2024, Scopus (Elsevier B.V., 2025)
SCImago Journal Rank	0.343	2024, SJR (Scimago Lab, 2025; Data Source: Scopus)
Source Normalized Impact per Paper	1.222	2024, CWTS Journal Indicators (CWTS B.V., 2025; Data Source: Scopus)

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This document provides authors with details on policy, formatting, layout requirements, and copyediting pertaining to final manuscript submission to this journal. All manuscripts must have correct formatting to be considered ready for publication.

The entire manuscript submission and review process is handled through an online system named ScholarOne. All manuscripts should be submitted to http://mc.manuscriptcentral.com/dgjqas

The ScholarOne system has been designed to improve the scholarly publication process for authors. Among the many improvements we offer over traditional journals, the most significant is that we have dramatically shortened the period between the initial submission and the final publication of a peer-reviewed article. Much of this time savings is due to the innovative use of electronic publication. These innovations, however, require certain changes in the way authors need to prepare accepted manuscripts for electronic publication.

EDITORIAL POLICY

Unpublished material: Submission of a manuscript implies that the work described is not copyrighted, published or submitted elsewhere, except in abstract form. The corresponding author should ensure that all authors approve the manuscript before its submission.

Copyright: Manuscripts are accepted on condition of transfer of copyright (for U.S. government employees: to the extent transferable) to Journal of Quantitative Analysis in Sports. Once the manuscript is accepted, it may not be published elsewhere without the consent of the copyright holders.

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Hybrid Open Access

In this journal, authors have the option to publish their article under an open access license. Open Access allows you as an author to retain copyright and share your findings with colleagues and interested parties worldwide without any restraints.
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Editor-in-Chief
Gil Fellingham, Brigham Young University　

Editorial Board
Ben Baumer, Smith College
Emily Casleton, Los Alamos National Laboratory
Timothy Chan, University of Toronto
Laszlo Csato, Corvinus University of Budapest (Hungary)
Sameer Deshpande, University of Wisconsin
Christian Deutscher, University of Bielefeld (Germany)
Pierpaolo D’Urso, University of Rome (Italy)
Leonardo Egidi, University of Trieste
Ryan Elmore, University of Denver
Jared Fisher, Brigham Young University
Alexander Franks, University of California Santa Barbara
Mike Fry, University of Cincinnati
Mark Glickman, Harvard University
Dries Goossens, Ghent University
Mario Guajardo, Norwegian Business School, Bergen
Matt Heiner, Brigham Young University
Nobuyoshi Hirotsu, Juntendo University (Japan)
Gordon Hunter, Kingston University (UK)
Matthew E. Hood, Texas State University
Dimitris Karlis, Athens University of Economics and Business (AUEB)
Stephanie Kovalchik, Zelus Analytics, Austin, Texas
Young Hoon Lee, Sogang University (South Korea)
Michael Lapre, Vanderbilt University
Christophe Ley, University of Luxembourg
Francesco Lisi, Universita degli studi di Padova Scuola di Scienze
Michael Lopez, The National Football League
Brian Macdonald, Yale University
Gregory Matthews, Loyola University of Chicago
Ian McHale, University of Liverpool (UK)
Ioannis Nzoufras, Athens University of Economics and Business
Jeff Ohlmann, University of Iowa
Garritt Page, Brigham Young University
Konstantinos Pelechrinis, University of Pittsburgh
Alex Petersen, Brigham Young University
Joshua Price, Southern Utah University
David Radke, Chicago Blackhawks
Lee Richardson, Google
Steve Rigdon, St. Louis University
Nathan Sandholtz, Brigham Young University
Michael Schuckers, St. Lawrence University
Frits Spieksma, KU Leuven (Belgium)
Ray Stefani, California State University, Long Beach
Tim Swartz, Simon Fraser University
Andrew Swift, Univeristy of Nebraska – Omaha
Paul van Staden, University of Pretoria (South Africa)
Tianyu Guan, York University, Toronto
Jan Vecer, Charles University (Czech Republic)
Ron Yurko, Carnegie Mellon University
Keith Willoughby, University of Saskatchewan

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Additional information

eISSN:

1559-0410

ISSN:

2194-6388

Language:

English

Publisher:

De Gruyter

Additional information

First published:

January 1, 2005

Publication Frequency:

4 issues per year

eISSN: 1559-0410

ISSN: 2194-6388

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Walter de Gruyter GmbH
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10785 Berlin
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Journal of Quantitative Analysis in Sports

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