Journal of Quantitative Analysis in Sports

Aims & Scope

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.

View Aims & Scope

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Metrics & Ranking

Impact Factor

Year	Value
2025	1
2024	1.10

SJR (SCImago Journal Rank)

Year	Value
2024	0.343

Journal Rank

Year	Value
2024	15335

Quartile

Year	Value
2024	Q2

Journal Citation Indicator

Year	Value
2024	107

h-index

Year	Value
2024	26

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Abstracting & Indexing

Journal is indexed in leading academic databases, ensuring global visibility and accessibility of our peer-reviewed research.

• Scopus
• Google Scholar
• Web of Science

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Subjects & Keywords

Journal’s research areas, covering key disciplines and specialized sub-topics in Decision Sciences and Social Sciences, designed to support cutting-edge academic discovery.

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Most Cited Articles

The Most Cited Articles section features the journal's most impactful research, based on citation counts. These articles have been referenced frequently by other researchers, indicating their significant contribution to their respective fields.

• Searching for the GOAT of tennis win prediction

  Citation: 60

  Authors: Stephanie Ann

• Estimating an NBA player’s impact on his team’s chances of winning

  Citation: 51

  Authors: Sameer K., Shane T.

• Determining the level of ability of football teams by dynamic ratings based on the relative discrepancies in scores between adversaries

  Citation: 46

  Authors: Anthony Costa, Norman Elliott

• A hybrid random forest to predict soccer matches in international tournaments

  Citation: 46

  Authors: Andreas, Cristophe, Gunther, Hans

• Estimating player contribution in hockey with regularized logistic regression

  Citation: 41

  Authors: Robert B., Shane T., Matt

• nflWAR: a reproducible method for offensive player evaluation in football

  Citation: 39

  Authors: Ronald, Samuel, Maksim

• Bayesian statistics meets sports: a comprehensive review

  Citation: 33

  Authors: Edgar, Paul, Kerrie L.

• Seeding the UEFA Champions League participants: evaluation of the reforms

  Citation: 32

  Authors: Dmitry, Vladimir Yu.

• Smart kills and worthless deaths: eSports analytics for League of Legends

  Citation: 31

  Authors: Philip Z.

• Measuring soccer players’ contributions to chance creation by valuing their passes

  Citation: 30

  Authors: Lotte, Jan, Michel

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