Call for papers - Big Data and Data-driven in Sports

Guest Editors

Pierpaolo D’Urso, PhD, Sapienza - Università di Roma, Rome, Italy
Michele Gallo, PhD, Università degli studi di Napoli “L’Orientale”, Naples, Italy
Paola Zuccolotto, PhD, University of Brescia, Brescia, Italy

Submission Status: Open | Submission Deadline: 25 April 2025

Journal of Big Data is calling for submissions to our Collection on Big Data and Data-driven in Sports. Over the past few decades, interest in applying statistical analysis and modeling techniques to sports has been constantly growing. This trend is evident from the increasing body of scientific research and the numerous published works that provide valuable statistical insights across various sports, such as soccer, tennis, American football, baseball, and basketball, among others. This rising interest has been accompanied by a massive expansion in the availability of data, including the rise of big data, which offers a vast and detailed array of information that enhances the depth and accuracy of sports analytics.

We invite researchers to submit articles focused on advancing statistical and machine learning methods encompassing models, algorithms, and multivariate exploratory techniques in the realm of big data sports analytics. We seek contributions that introduce novel methodologies and practical techniques, showcasing significant advancements, extensions, and applications within this context. Submissions should emphasize innovative, complex, or scientifically compelling aspects of statistical analysis across a broad spectrum of sports, including both professional and amateur disciplines.

Topics:

The goal of this special issue is to collect articles that explore a variety of topics, including (but not limited to):

Models and algorithms for forecasting game outcomes
Methods for assessing and measuring teams’, players’ and athletes’ performance
Implementation of optimal game strategies
Methods to deal with players’ tracking data
Analysis of the impact of vital parameters to athletes’ performance
Exploitation of training data collected with sensors, wearables or other technologic equipment
Data analysis problems associated with massive, complex datasets in sports
Novel statistical approaches and data mining methods in sports
Comparing and contrasting techniques for solving research questions in sports
The role of social media and public sentiment analysis in sports analytics
Economic impact analysis of sports events using big data

Submit to Collection

Meet the Guest Editors

Back to top

Pierpaolo D’Urso, PhD, Sapienza - Università di Roma, Rome, Italy

Pierpaolo D'Urso is full professor of Statistics at Sapienza
University of Rome and Elected Dean of the Faculty of Political Science, Sociology, Communication (2024-2027). He received his Ph.D. in Statistics and his bachelor's degree in Statistics both from Sapienza. He is Associate Editor and Member of the Editorial Board of several journals (e.g. Journal of Big Data, Journal of Quantitative Analysis in Sports) . He is World's Top 2% Scientist (Stanford University). His recent research activity is focused on fuzzy clustering, clustering of time series, clustering for complex structures of data, big data and data science, robust clustering, clustering of spatial data, interval-valued data analysis, statistics for democracy, electoral studies, sport, social sciences, and political science.

Michele Gallo, PhD, Università degli studi di Napoli “L’Orientale”, Naples, Italy

Michele Gallo has achieved the degree in Economics and Business with 110/110 cum laude in 1988, at the University of Naples - Federico II. The PhD in Total Quality Management in 2000, at the University of Naples – Federico II. He is full professor of Statistics from October 2020 at the University of Naples – L’Orientale. His primary research interests include Multivariate data analysis Tensor Analysis; Compositional data analysis; Rasch analysis; Applied Statistics. He was Editor in Chief, Statistica & Società from 2010 to 2014 and serving as Associate editor, Computational Statistic (CompStat), Springer, from 2014.

Paola Zuccolotto, PhD, University of Brescia, Brescia, Italy

Paola Zuccolotto is Full Professor of Statistics at the University of Brescia, where she is the Director of the Big&Open Data Innovation Laboratory (BODaI Lab, bodai.unibs.it). She carries out research activity both on methodological and applied issues of Statistical Science, with a special focus on data science for high-dimensional data. She is Elected Member of the International Statistical Institute and member of the Scientific Board of the Special Interest Group on Sport Statistics. She is Associate Editor of Heliyon, and Guest Associate Editor of several special issues on scientific journals. She participates to research groups supported by Italian and European funds for scientific research, on applied themes such as Sustainability, Sport Analytics, Genetics, Economics and Finance. She is the scientific coordinator, with Marica Manisera, of the international network BDsports (Big Data Analytics in Sports, bdsports.unibs.it).

About the Collection

Journal of Big Data is calling for submissions to our Collection on Big Data and Data-driven in Sports. Over the past few decades, interest in applying statistical analysis and modeling techniques to sports has been constantly growing. This trend is evident from the increasing body of scientific research and the numerous published works that provide valuable statistical insights across various sports, such as soccer, tennis, American football, baseball, and basketball, among others. This rising interest has been accompanied by a massive expansion in the availability of data, including the rise of big data, which offers a vast and detailed array of information that enhances the depth and accuracy of sports analytics.

We encourage researchers to submit articles dedicated to advancing statistical and machine learning methods, including models, algorithms and multivariate data exploratory techniques, in big data sports analytics. We seek contributions that introduce novel methodologies and practical techniques, showcasing significant advancements, extensions, and applications in this context. Submissions should highlight innovative, complex, or scientifically compelling aspects of statistical analysis across a broad spectrum of sports, including both professional and amateur disciplines.

We invite researchers to submit articles focused on advancing statistical and machine learning methods encompassing models, algorithms, and multivariate exploratory techniques in the realm of big data sports analytics. We seek contributions that introduce novel methodologies and practical techniques, showcasing significant advancements, extensions, and applications within this context. Submissions should emphasize innovative, complex, or scientifically compelling aspects of statistical analysis across a broad spectrum of sports, including both professional and amateur disciplines.

Topics:

The goal of this special issue is to collect articles that explore a variety of topics, including (but not limited to):

Models and algorithms for forecasting game outcomes
Methods for assessing and measuring teams’, players’ and athletes’ performance
Implementation of optimal game strategies
Methods to deal with players’ tracking data
Analysis of the impact of vital parameters to athletes’ performance
Exploitation of training data collected with sensors, wearables or other technologic equipment
Data analysis problems associated with massive, complex datasets in sports
Novel statistical approaches and data mining methods in sports
Comparing and contrasting techniques for solving research questions in sports
The role of social media and public sentiment analysis in sports analytics
Economic impact analysis of sports events using big data

We particularly encourage contributions that leverage big data to enhance these areas, reflecting the transformative impact of large-scale datasets on sports analytics.

There are currently no articles in this collection.

Submission Guidelines

Back to top

This Collection welcomes submission of original Research Articles. Should you wish to submit a different article type, please read the submission guidelines to confirm that type is accepted by the journal you are submitting to. Articles for this Collection should be submitted via our submission system, Snapp. During the submission process you will be asked whether you are submitting to a Collection, please select "Big Data and Data-driven in Sports" from the dropdown menu.

Articles will undergo the standard peer-review process of the journal they are considered in the Journal of Big Data and are subject to all of the journal’s standard policies. Articles will be added to the Collection as they are published.

The Editors have no competing interests with the submissions which they handle through the peer review process. The peer review of any submissions for which the Editors have competing interests is handled by another Editorial Board Member who has no competing interests.