The Journal of Big Data publishes open-access original research on data science and data analytics. Deep learning algorithms and all applications of big data are welcomed. Survey papers and case studies are also considered.

The journal examines the challenges facing big data today and going forward including, but not limited to: data capture and storage; search, sharing, and analytics; big data technologies; data visualization; architectures for massively parallel processing; data mining tools and techniques; machine learning algorithms for big data; cloud computing platforms; distributed file systems and databases; and scalable storage systems. Academic researchers and practitioners will find the Journal of Big Data to be a seminal source of innovative material.

Special Issue on Big Data and Data-driven in Sports
截稿日期: 2025-09-30

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