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OpenAlex Citation Counts

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OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

A Narrative Review for a Machine Learning Application in Sports: An Example Based on Injury Forecasting in Soccer
Alessio Rossi, Luca Pappalardo, Paolo Cintia
Sports (2021) Vol. 10, Iss. 1, pp. 5-5
Open Access | Times Cited: 51

Showing 1-25 of 51 citing articles:

Predicting noncontact injuries of professional football players using machine learning
Diogo Freitas, Sheikh Shanawaz Mostafa, Romualdo Caldeira, et al.
PLoS ONE (2025) Vol. 20, Iss. 1, pp. e0315481-e0315481
Open Access | Times Cited: 1
Subjective recovery in professional soccer players: A machine learning and mediation approach
Carlo Simonelli, Damiano Formenti, Alessio Rossi
Journal of Sports Sciences (2025), pp. 1-8
Closed Access | Times Cited: 1
Machine Learning for Understanding and Predicting Injuries in Football
Aritra Majumdar, Rashid Bakirov, Dan Hodges, et al.
Sports Medicine - Open (2022) Vol. 8, Iss. 1
Open Access | Times Cited: 37
Applications of Machine Learning to Optimize Tennis Performance: A Systematic Review
Tatiana Sampaio, João P. Oliveira, Daniel A. Marinho, et al.
Applied Sciences (2024) Vol. 14, Iss. 13, pp. 5517-5517
Open Access | Times Cited: 8
MHfit: Mobile Health Data for Predicting Athletics Fitness Using Machine Learning Models
Jonayet Miah, Muntasir Mamun, Md Minhazur Rahman, et al.
(2022), pp. 584-589
Closed Access | Times Cited: 25
Predicting Injuries in Football Based on Data Collected from GPS-Based Wearable Sensors
Tomasz Piłka, Bartłomiej Grzelak, Aleksandra Sadurska, et al.
Sensors (2023) Vol. 23, Iss. 3, pp. 1227-1227
Open Access | Times Cited: 15
VO2max prediction based on submaximal cardiorespiratory relationships and body composition in male runners and cyclists: a population study
Szczepan Wiecha, Przemysław Seweryn Kasiak, Piotr Szwed, et al.
eLife (2023) Vol. 12
Open Access | Times Cited: 15
An Efficient Approach to Sports Rehabilitation and Outcome Prediction Using RNN-LSTM
Yanli Cui
Mobile Networks and Applications (2024)
Closed Access | Times Cited: 6
Identifying Key Factors for Securing a Champions League Position in French Ligue 1 Using Explainable Machine Learning Techniques
Spyridon Plakias, Christos Kokkotis, Michalis Mitrotasios, et al.
Applied Sciences (2024) Vol. 14, Iss. 18, pp. 8375-8375
Open Access | Times Cited: 6
Performance and healthcare analysis in elite sports teams using artificial intelligence: a scoping review
Adolfo Antonio Munoz-Macho, Manuel Domínguez-Morales, José Luis Sevillano
Frontiers in Sports and Active Living (2024) Vol. 6
Open Access | Times Cited: 5
A comprehensive analysis of the machine learning pose estimation models used in human movement and posture analyses: A narrative review
Federico Roggio, Bruno Trovato, Martina Sortino, et al.
Heliyon (2024) Vol. 10, Iss. 21, pp. e39977-e39977
Closed Access | Times Cited: 5
Relationship between a daily injury risk estimation feedback (I-REF) based on machine learning techniques and actual injury risk in athletics (track and field): protocol for a prospective cohort study over an athletics season
Pierre‐Eddy Dandrieux, Laurent Navarro, David Blanco, et al.
BMJ Open (2023) Vol. 13, Iss. 5, pp. e069423-e069423
Open Access | Times Cited: 13
Identification of Athleticism and Sports Profiles Throughout Machine Learning Applied to Heart Rate Variability
Tony Estrella, Lluís Capdevila
Sports (2025) Vol. 13, Iss. 2, pp. 30-30
Open Access
Future of Machine Learning in Sports Engineering
V V Prasanth, G. Nallavan
Lecture notes in mechanical engineering (2025), pp. 1-20
Closed Access
Artificial Intelligence for Objective Assessment of Acrobatic Movements: Applying Machine Learning for Identifying Tumbling Elements in Cheer Sports
Sophia Wesely, E. Hofer, Robin Curth, et al.
Sensors (2025) Vol. 25, Iss. 7, pp. 2260-2260
Open Access
Association between internal load responses and recovery ability in U19 professional soccer players: A machine learning approach
Guglielmo Pillitteri, Alessio Rossi, Carlo Simonelli, et al.
Heliyon (2023) Vol. 9, Iss. 4, pp. e15454-e15454
Open Access | Times Cited: 8
A multi-season machine learning approach to examine the training load and injury relationship in professional soccer
Aritra Majumdar, Rashid Bakirov, Dan Hodges, et al.
Journal of Sports Analytics (2024) Vol. 10, Iss. 1, pp. 47-65
Open Access | Times Cited: 3
Optimization and comparison of machine learning algorithms for the prediction of the performance of football players
Gianluca Morciano, Andrea Zingoni, G. Calabró
Neural Computing and Applications (2024) Vol. 36, Iss. 31, pp. 19653-19666
Closed Access | Times Cited: 3
Knowledge in Motion: A Comprehensive Review of Evidence-Based Human Kinetics
André Ramalho, João Petrica
International Journal of Environmental Research and Public Health (2023) Vol. 20, Iss. 11, pp. 6020-6020
Open Access | Times Cited: 7
Effectiveness evaluation of sprint sports techniques and tactics based on deep learning
Jiankui Yan
Service Oriented Computing and Applications (2024)
Closed Access | Times Cited: 2
Analyzing ECG signals in professional football players using machine learning techniques
Adolfo Antonio Munoz-Macho, Manuel Domínguez-Morales, José Luis Sevillano
Heliyon (2024) Vol. 10, Iss. 5, pp. e26789-e26789
Open Access | Times Cited: 2
Enhancing Sports Injury Risk Assessment in Soccer Through Machine Learning and Training Load Analysis
Theodoros Tsilimigkras, Iοannis Kakkos, George K. Matsopoulos, et al.
Journal of Sports Science and Medicine (2024), pp. 537-547
Open Access | Times Cited: 2
Current Trend of Analysis in High-Performance Sport and the Recent Updates in Data Mining and Machine Learning Application in Sports
Rabiu Muazu Musa, Anwar P. P. Abdul Majeed, Mohamad Razali Abdullah, et al.
SpringerBriefs in applied sciences and technology (2022), pp. 1-11
Closed Access | Times Cited: 10
Blood sample profile helps to injury forecasting in elite soccer players
Alessio Rossi, Luca Pappalardo, Cristoforo Filetti, et al.
Sport Sciences for Health (2022) Vol. 19, Iss. 1, pp. 285-296
Open Access | Times Cited: 9
Wellness Forecasting by External and Internal Workloads in Elite Soccer Players: A Machine Learning Approach
Alessio Rossi, Enrico Perri, Luca Pappalardo, et al.
Frontiers in Physiology (2022) Vol. 13
Open Access | Times Cited: 7
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