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Applied Sciences
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Data-Driven Insights: Intelligent Sensors and Technology in Sports Science
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Data-Driven Insights: Intelligent Sensors and Technology in Sports Science

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: 20 September 2025 | Viewed by 2694

Special Issue Editors

Prof. Dr. Nuno Mateus

E-Mail Website
Guest Editor
1. Research Center in Sports Sciences, Health Sciences and Human Development, CIDESD, Elite Research Community, 5000-801 Vila Real, Portugal
2. Department of Sports Science, Exercise and Health, School of Life Sciences and Environment, University of Trás-os-Montes and Alto Douro, 5000-801 Vila Real, Portugal
3. Department of Sports Sciences and Physical Education, University of Maia, 4475-690 Maia, Portugal
Interests: team sports performance analysis; comprehensive player monitoring during training and off-training periods; sports technology; data analysis and visualization
Prof. Dr. Jose Luis Felipe Hernández

E-Mail Website
Guest Editor
1. Performance Analysis Department, UD Las Palmas, Las Palmas de Gran Canaria, Spain
2. IGOID Research Group, Department of Physical Activity and Sport Sciences, University of Castilla-La Mancha, Toledo, Spain
Interests: physical activity; sport sciences; sport technology; sport surfaces
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The increasing sophistication of intelligent sensors and data science is transforming sports science, offering new opportunities to enhance performance analysis, training optimization, and athlete well-being. Advances in wearable technology, tracking systems, and physiological monitoring enable sports professionals to collect detailed data on workload distribution, performance efficiency, and physiological responses. However, despite these advancements, a critical challenge remains: refining data interpretation methods to ensure that raw sensor outputs translate into meaningful and actionable insights for training strategies, competition dynamics, and injury prevention.

Addressing these challenges requires a multidisciplinary approach, integrating technology with applied sports science. This Special Issue explores the cutting-edge applications of intelligent sensors and data-driven methodologies in sports performance. We encourage submissions that examine AI and machine learning in real-time decision-making, training load adjustments, and recovery optimization. Research on multimodal sensor data and predictive analytics for player tracking, talent identification, sleep monitoring, and competition performance is also welcomed.

By bridging technology and applied sports science, this Special Issue highlights how intelligent sensors and data science will shape the future of performance assessment and athlete development. We welcome original research, systematic reviews, and case studies that foster interdisciplinary collaboration to drive innovation in athlete monitoring and performance assessment.

Prof. Dr. Nuno Mateus
Prof. Dr. Jose Luis Felipe Hernández
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 100 words) can be sent to the Editorial Office for announcement on this website.

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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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

  • data science
  • intelligent sensors
  • multimodal sensor data in sports
  • technology in sports science
  • training and performance modeling

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Published Papers (2 papers)

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Research

13 pages, 1370 KiB  
Article
Quantifying Football Shooting Precision: The Expected Shot Impact Timing (xSIT) Approach
by Blanca De-la-Cruz-Torres, Miguel Navarro-Castro and Anselmo Ruiz-de-Alarcón-Quintero
Appl. Sci. 2025, 15(12), 6735; https://doi.org/10.3390/app15126735 - 16 Jun 2025
Viewed by 421
Abstract
Background: Current advanced metrics do not sufficiently isolate and quantify the quality of the shooter’s technical execution under match conditions. Objective: This study aimed to develop an Expected Shot Impact Timing (xSIT) model to evaluate the shooting action by considering the spatial configuration [...] Read more.
Background: Current advanced metrics do not sufficiently isolate and quantify the quality of the shooter’s technical execution under match conditions. Objective: This study aimed to develop an Expected Shot Impact Timing (xSIT) model to evaluate the shooting action by considering the spatial configuration of the shooter, the goalkeeper (GK), and all outfield players, as well as incorporating dynamic variables such as ball velocity and player reaction time. Additionally, this study sought to compare the performance and discriminative capacity of two existing post-shot expected goal metrics (xSIT and xGOT, expected goals on target) in evaluating the probability of scoring for shots on target after the moment of execution. Methods: Formal definitions were established for the following: (i) the ball shot location, (ii) the ball velocity, (iii) the GK location, and (iv) the outfield player’s location. An xSIT model incorporating geometric parameters was designed to optimize performance based on ball position and players’ position. The model was tested using all shots from the 2023 Women’s World Cup and the 2022 Men’s World Cup. A 5-fold cross-validation procedure was applied to evaluate the x SIT model’s performance, and an independent Student’s t-test was performed to statistically compare the performance of the xSIT and xGOT models. Results: The k-fold cross-validation yielded an AUC-ROC score of 0.92 and 84% accuracy, confirming the model’s ability to differentiate successful shooter performance. Statistically and clinically significant differences were observed between the xSIT and xGOT metrics across all analyzed variables, including total shots on target, goal shots, and saved shots (p < 0.001 in all cases). Conclusions: The xSIT metric offers a more nuanced and context-sensitive assessment of shot execution by the shooter, representing a significant advancement over existing post-shot evaluation models. Significant differences were observed between men’s and women’s tournaments. Full article
(This article belongs to the Special Issue Data-Driven Insights: Intelligent Sensors and Technology in Sports Science)
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17 pages, 3502 KiB  
Article
Real-Time Accurate Determination of Table Tennis Ball and Evaluation of Player Stroke Effectiveness with Computer Vision-Based Deep Learning
by Zilin He, Zeyi Yang, Jiarui Xu, Hongyu Chen, Xuanfeng Li, Anzhe Wang, Jiayi Yang, Gary Chi-Ching Chow and Xihan Chen
Appl. Sci. 2025, 15(10), 5370; https://doi.org/10.3390/app15105370 - 12 May 2025
Viewed by 1972
Abstract
The adoption of artificial intelligence (AI) in sports training has the potential to revolutionize skill development, yet cost-effective solutions remain scarce, particularly in table tennis. To bridge this gap, we present an intelligent training system leveraging computer vision and machine learning for real-time [...] Read more.
The adoption of artificial intelligence (AI) in sports training has the potential to revolutionize skill development, yet cost-effective solutions remain scarce, particularly in table tennis. To bridge this gap, we present an intelligent training system leveraging computer vision and machine learning for real-time performance analysis. The system integrates YOLOv5 for high-precision ball detection (98% accuracy) and MediaPipe for athlete posture evaluation. A dynamic time-wrapping algorithm further assesses stroke effectiveness, demonstrating statistically significant discrimination between beginner and intermediate players (p = 0.004 and Cohen’s d = 0.86) in a cohort of 50 participants. By automating feedback and reducing reliance on expert observation, this system offers a scalable tool for coaching, self-training, and sports analysis. Its modular design also allows adaptation to other racket sports, highlighting broader utility in athletic training and entertainment applications. Full article
(This article belongs to the Special Issue Data-Driven Insights: Intelligent Sensors and Technology in Sports Science)
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