Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
9.8
4.4
Journals
BDCC
Special Issues
AI and Data Science in Sports Analytics
share announcement

AI and Data Science in Sports Analytics

A special issue of Big Data and Cognitive Computing (ISSN 2504-2289). This special issue belongs to the section "Data Mining and Machine Learning".

Deadline for manuscript submissions: 30 June 2026 | Viewed by 7547

Special Issue Editors

Dr. Vangelis Sarlis

E-Mail Website
Guest Editor
School of Science & Technology, International Hellenic University, 57400 Thessaloniki, Greece
Interests: sports analytics; data mining; data science; machine learning; artificial intelligence
Prof. Dr. Christos Tjortjis

E-Mail Website
Guest Editor
School of Science & Technology, International Hellenic University, 57400 Thessaloniki, Greece
Interests: smart cities; big data and cognitive computing; AI; information systems; electrical engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

(1) Introduction

Sports analytics has transformed the way athletes train, compete, and recover. The integration of data science, machine learning, and artificial intelligence (AI) in sports provides valuable insights into performance optimization, injury prevention, and tactical decision-making. With the increasing availability of advanced performance metrics, sensor data, wearable technology, and video analysis, the application of big data analytics in sports has gained significant traction. These advancements contribute to more precise performance assessment, personalized training programs, and enhanced forecasting models for game outcomes and player evaluation.

The importance of this research area is underscored by the growing demand for data-driven methodologies in professional and amateur sports. Teams, coaches, and medical professionals now rely on predictive analytics to optimize training loads, reduce injury risks, and refine in-game strategies. Despite these developments, challenges remain in data collection, interpretation, and ethical considerations. This Special Issue seeks to address these challenges by exploring cutting-edge methodologies and applications in sports analytics.

(2) Aim of the Special Issue

This Special Issue aims to gather innovative research that leverages data science techniques to advance sports analytics. By focusing on big data, AI, and machine learning applications, this Special Issue aligns with the broader scope of Big Data and Cognitive Computing (BDCC), which emphasizes computational intelligence, cognitive computing, and data-driven decision-making.

Contributions to this Special Issue will not only enhance scientific knowledge in sports analytics, but also bridge the gap between academia and industry, offering real-world applications for performance enhancement, forecasting, and injury prevention. We welcome original research and review articles that explore novel data-driven approaches, predictive modeling techniques, and the ethical implications of AI in sports.

(3) Suggested Themes

We invite contributions covering a wide range of topics, including but not limited to the following:

  • Performance Analytics: Data-driven performance evaluation, real-time monitoring, and optimization strategies.
  • Injury Prevention and Rehabilitation: Predictive modeling for injury risk assessment, recovery tracking, and return-to-play protocols.
  • Game Strategy and Tactical Analysis: AI-powered decision-making, opponent scouting, and play pattern recognition.
  • Wearable Technology and Sensor Data: The role of IoT devices and biometric data in athlete monitoring.
  • Big Data and Machine Learning in Sports: Applications of deep learning, reinforcement learning, and advanced analytics.
  • Sports Forecasting and Betting Analytics: Predictive modeling for game outcomes, player performance, and market trends.
  • Ethical and Privacy Considerations in Sports Analytics: Responsible AI usage, data privacy, and athlete consent.

We look forward to receiving your contributions.

Dr. Vangelis Sarlis
Prof. Dr. Christos Tjortjis
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Big Data and Cognitive Computing is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • sports analytics
  • big data in sports
  • machine learning
  • performance optimization
  • injury prevention
  • predictive modeling
  • wearable technology
  • game strategy analysis
  • AI in sports
  • sports forecasting

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

Jump to: Review

25 pages, 5173 KB  
Article
Spatio-Temporal Analysis of Handball Players’ Actions from Broadcast Videos Using Deep Learning
by Kosmas Katsioulas and Ilias Maglogiannis
Big Data Cogn. Comput. 2026, 10(4), 118; https://doi.org/10.3390/bdcc10040118 - 12 Apr 2026
Viewed by 506
Abstract
Handball performance analysis is still often conducted through the manual review of match videos, while automation on broadcast footage remains challenging due to camera motion, strong perspective effects, and frequent occlusions during dense interactions. This study presents a practical and reproducible monocular pipeline [...] Read more.
Handball performance analysis is still often conducted through the manual review of match videos, while automation on broadcast footage remains challenging due to camera motion, strong perspective effects, and frequent occlusions during dense interactions. This study presents a practical and reproducible monocular pipeline for extracting handball analytics from a single broadcast viewpoint. Players are detected per frame, tracked over time, and projected onto a standardized handball court via homography-based camera calibration. The resulting court-referenced trajectories in metric units enable motion indicators such as distance covered and speed, along with coaching-oriented visual summaries, including trajectory overlays and heatmaps. In addition, clip-level action recognition is performed using interpretable kinematic and scene-derived features and lightweight classifiers, with a comparative evaluation across multiple classical models. The modular design keeps the intermediate steps explicit, supports reproducibility, and facilitates interpretation of both intermediate outputs and final analytics. Experiments on the UNIRI handball dataset demonstrate that meaningful performance analytics and action understanding can be obtained from single-camera broadcast video using transparent intermediate representations. This work highlights the practical potential of interpretable trajectory-based modeling for under-instrumented sports and provides a reproducible baseline for future extensions incorporating richer contextual cues. Full article
(This article belongs to the Special Issue AI and Data Science in Sports Analytics)
►▼ Show Figures

Figure 1

10 pages, 653 KB  
Article
Analysis of Shots Trajectory and Effectiveness in Women’s and Men’s Football European Championship Matches
by Blanca De-la-Cruz-Torres, Miguel Navarro-Castro and Anselmo Ruiz-de-Alarcón-Quintero
Big Data Cogn. Comput. 2025, 9(6), 157; https://doi.org/10.3390/bdcc9060157 - 12 Jun 2025
Cited by 1 | Viewed by 4769
Abstract
Shots on target are a crucial factor in football performance, yet the impact of categorizing shots as low or ground-level and high or parabolic has not been fully explored. The objective of this study was to analyze whether there are differences in the [...] Read more.
Shots on target are a crucial factor in football performance, yet the impact of categorizing shots as low or ground-level and high or parabolic has not been fully explored. The objective of this study was to analyze whether there are differences in the frequency and effectiveness (as measured by xGOT) between parabolic and low shots on target in international men’s and women’s football competitions. The results revealed that the most common shot type was the parabolic shot, occurring in 59.86% of shots on goal in the men’s competition (270 shots) and 67.12% in the women’s competition (196 shots). In the overall set of shots, 62.77% were parabolic (466 shots). No significant differences were observed between the competitions (p > 0.05). Regarding the xGOT values, no significant differences were observed for any of the interaction effects analyzed (gender, shot type and shot outcome). The conclusion was that the parabolic shot was the most frequent type of shot on target in both men’s and women’s football. Full article
(This article belongs to the Special Issue AI and Data Science in Sports Analytics)
►▼ Show Figures

Figure 1

Review

Jump to: Research

16 pages, 284 KB  
Review
Talent Identification and AI-Driven Decision Tools in Sport: A Policy-Oriented Perspective on Algorithmic Bias, Data Privacy, and Digital Determinism in Player Evaluation
by Elia Morgulev and Ofer H. Azar
Big Data Cogn. Comput. 2026, 10(5), 146; https://doi.org/10.3390/bdcc10050146 - 7 May 2026
Viewed by 507
Abstract
Big-data analytics are increasingly used in scouting and talent identification, with machine learning (ML) tools applied to evaluate and predict player performance based on match statistics, video tracking, physical and anthropometric tests, psychological assessments, social media data, and qualitative scouting reports. Advances in [...] Read more.
Big-data analytics are increasingly used in scouting and talent identification, with machine learning (ML) tools applied to evaluate and predict player performance based on match statistics, video tracking, physical and anthropometric tests, psychological assessments, social media data, and qualitative scouting reports. Advances in computer vision, together with the emergence of affordable automated broadcasting and data collection systems, have extended the deployment of ML-driven scouting from professional to youth sport. The use of algorithms in educational, employment, and healthcare settings has been shown to introduce biases and discrimination while wrongly assuming accuracy and objectivity because the decisions are made automatically and quantitatively. In this respect, we briefly describe the development of data-driven performance analysis and how ML-based technologies are currently applied for early screening and comparison of large player populations. Based on a narrative overview of the literature, we draw on evidence from education, employment, and healthcare to identify risks that may also emerge in ML-driven player evaluation, including algorithmic bias, non-representative training data, privacy concerns, and the persistence of model-based labels over time, especially in youth sport. Our main contribution is translating these threats into governance principles and operational safeguards for responsible use of AI in scouting and talent identification. Full article
(This article belongs to the Special Issue AI and Data Science in Sports Analytics)
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop