Advances in Performance Analysis and Monitoring in Sport and Exercise: Bridging the Research–Practice Gap

A special issue of Journal of Functional Morphology and Kinesiology (ISSN 2411-5142). This special issue belongs to the section "Athletic Training and Human Performance".

Deadline for manuscript submissions: 30 November 2026 | Viewed by 1821

Editor

Special Issue Information

Dear Colleagues,

Rapid advancements in event data collection technologies, tracking systems, wearable sensors, and analytical approaches have positioned performance analysis and monitoring as central components of contemporary sport and exercise science. These developments have enabled the capture of large-volume, multidimensional data across physical, technical, tactical, and psychological domains, offering unprecedented opportunities for research to understand and optimize performance.

Despite the exponential growth in relevant scientific publications, a persistent gap remains between research and practice. This can be attributed to several factors, including the use of overly simplified performance indicators in a multifactorial context, limited translation of findings into actionable guidelines, conflicting evidence across studies without a systematic explanation, and insufficient control or integration of contextual variables.

This Special Issue aims to bridge the research–practice gap by promoting integrative, methodologically rigorous, and practically relevant approaches. In particular, we encourage contributions that achieve the following:

  • Apply multivariable statistical models and machine learning methods while ensuring interpretability and practical relevance;
  • Incorporate contextual variables into performance models;
  • Develop integrated frameworks that account for technical, tactical, physical, and psychological dimensions of performance;
  • Translate findings into actionable insights for coaches and practitioners;
  • Utilize quantitative, qualitative, and mixed-methods research designs;
  • Provide in-depth analyses through case studies of unique or rare performance scenarios;
  • Employ randomized controlled trials to support decision-making processes;
  • Synthesize evidence through systematic reviews and meta-analyses;
  • Adopt narrative approaches that go beyond description to critically develop theoretical frameworks.

Topics of interest include, but are not limited to:

  • Explanation and enhancement of individual and team performance;
  • Monitoring of training load in relation to performance and injury risk reduction;
  • Athlete well-being;
  • Complex systems and ecological dynamics;
  • Performance profiling of athletes and teams;
  • Applications of performance analysis and monitoring in coaching and applied sport settings.

To further support the goal of bridging the research–practice gap, authors are strongly encouraged to enhance the practical accessibility and applicability of their work. Specifically, submissions should aim to include a dedicated section outlining clear practical implications for coaches, analysts, and applied sport scientists. Where appropriate, the inclusion of applied examples, case-based interpretations, or implementation guidelines is highly recommended, ensuring that research findings can be more readily translated into real-world practice. Finally, authors are encouraged to provide a graphical abstract (infographic) to facilitate effective communication of key findings to non-academic audiences.

By encouraging interdisciplinary perspectives and emphasizing both scientific rigor and practical relevance, this Special Issue seeks to bridge the research–practice gap by promoting actionable, context-sensitive, and methodologically robust approaches to performance analysis and monitoring. In doing so, it aims to support more effective decision-making in real-world sport and exercise settings, while also identifying key directions for future research.

Dr. Spyridon Plakias
Guest Editor

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Keywords

  • sport analytics
  • performance monitoring
  • training load monitoring
  • injury risk reduction
  • contextual variables
  • ecological dynamics
  • multivariable statistical models
  • machine learning methods
  • mixed-methods research
  • performance profiling
  • data-driven coaching
  • applied sport science

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Published Papers (3 papers)

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Research

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25 pages, 1741 KB  
Article
Data-Driven Reduction of External Load Variables in Indoor Team Sports Using Local Positioning System
by Christos Kokkotis, Ioannis Kansizoglou, Dimitrios Pantazis, Alexandra Avloniti, Dimitrios Balampanos, Panagiotis Foteinakis, Theodoros Stampoulis, Maria Protopapa, Alexandros Dendrinos, Panagiotis Aggelakis, Nikolaos Zaras, Paraskevi Malliou, Maria Michalopoulou, Antonios Gasteratos and Athanasios Chatzinikolaou
J. Funct. Morphol. Kinesiol. 2026, 11(3), 249; https://doi.org/10.3390/jfmk11030249 - 25 Jun 2026
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Abstract
Objectives: Local positioning systems (LPSs) used in indoor team sports generate a large number of external load variables, often exceeding practical monitoring capacity. The redundancy and overlap among these variables make it difficult to identify the most informative metrics for performance analysis and [...] Read more.
Objectives: Local positioning systems (LPSs) used in indoor team sports generate a large number of external load variables, often exceeding practical monitoring capacity. The redundancy and overlap among these variables make it difficult to identify the most informative metrics for performance analysis and load management. This study aimed to reduce the dimensionality of external load variables derived from LPS data and to identify data-driven external-load observation profiles using principal component analysis and clustering techniques. Methods: A total of 188 observations from indoor team sports (basketball, handball, and futsal) were analyzed. Continuous external load variables were standardized and subjected to principal component analysis (PCA), with component retention based on a ≥90% cumulative explained variance threshold. K-means clustering was applied in both the full standardized feature space and the PCA-reduced space. The optimal number of clusters was determined using silhouette analysis and the elbow method. Agreement between clustering solutions was assessed using Adjusted Rand Index (ARI) and Normalized Mutual Information (NMI). Cluster characteristics were further examined using descriptive statistics and variable separation analysis. Results: The first two principal components explained 53.7% of the total variance, representing high-intensity external load and neuromuscular load dimensions, while 12 components were required to exceed 90% cumulative explained variance. Clustering analysis consistently identified three moderately separated clusters in both the full and PCA-reduced spaces. The PCA-based solution demonstrated improved separation (silhouette = 0.362) compared to the full-space solution (silhouette = 0.319). Agreement between clustering approaches was high (ARI = 0.981; NMI = 0.971), indicating that dimensionality reduction largely preserved the main clustering structure within the analyzed dataset. The most discriminative variables included jump load, acceleration load, metabolic power, and anaerobic activity distance. Conclusions: A large set of external load variables can be reduced into interpretable latent dimensions that support exploratory external-load profile identification. The combination of PCA and clustering provides an exploratory and structure-preserving framework for summarizing complex external-load datasets and identifying latent load dimensions. These findings may assist future monitoring strategies; however, the practical utility of the identified profiles requires prospective validation before implementation in training-load management. Full article
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19 pages, 641 KB  
Article
Assessment of Internal Load and External Load in Senior Football Players: Differences Between Competitive Levels
by Diogo Tereso, José M. Gamonales, Víctor Hernández-Beltrán and Rui Paulo
J. Funct. Morphol. Kinesiol. 2026, 11(2), 242; https://doi.org/10.3390/jfmk11020242 - 19 Jun 2026
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Abstract
Background: Football is an intermittent sport characterized by high physical and physiological demands, which may be influenced by the competitive level. Understanding differences in match load is fundamental for optimizing training planning, fatigue management, and athlete performance and injury prevention. This study aimed [...] Read more.
Background: Football is an intermittent sport characterized by high physical and physiological demands, which may be influenced by the competitive level. Understanding differences in match load is fundamental for optimizing training planning, fatigue management, and athlete performance and injury prevention. This study aimed to evaluate and compare external and internal load in senior football players in Portugal across five distinct competitive levels. Methods: Wimu ProTM (Hudl, Lincoln, NE, USA) and Garmin Heart Rate bands (Garmin International Inc., Olathe, KS, USA) were used to quantify and evaluate the external and internal load of the players. A total of 96 athletes were assessed, with ages ranging from 19 to 36 years (mean: 24.28 ± 4.72), who were divided into five competition levels (1st Division (n = 19), 2nd Division (n = 21), 3rd Division (n = 14), 4th Division (n = 20), and Regional Division (n = 22). Results: Significant differences were observed between competitive levels across several external load variables (p > 0.001). The 3rd Division and 4th Division showed higher values in variables associated with reactive and high-intensity actions (p < 0.001; effect size: 0.287), whereas the 2nd Division exhibited a more controlled load profile. Regarding internal load, significant differences were only observed in average heart rate during the second half (p = 0.043; effect size: 0.085), indicating distinct capacities to maintain physiological intensity under fatigue. Conclusions: It can be concluded that competitive level influences load profiles in football, although the differences do not follow a linear pattern. External and internal loads demonstrate greater discriminatory capacity between competitive levels than internal load. Full article
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Review

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26 pages, 585 KB  
Review
Injury Prediction and Risk Modelling in Team Sports Using Artificial Intelligence and Sensor-Based Monitoring: A Scoping Review
by Michail Tsenos, Christos Kokkotis, Dimitrios Draganidis, Nikos Alibertis, Dimitrios Pantazis, Panagiotis Tsimeas, Athanasios Poulios, Nikolaos Zaras, Paraskevi Malliou, Ilias Tsaousidis, Maria Michalopoulou, Dimitris Tsakalidis, Alexandra Avloniti, Ioannis G. Fatouros and Athanasios Chatzinikolaou
J. Funct. Morphol. Kinesiol. 2026, 11(2), 204; https://doi.org/10.3390/jfmk11020204 - 22 May 2026
Cited by 1 | Viewed by 628
Abstract
Sports-related injuries remain a major challenge in team sports, with important consequences for athlete health, performance, and team success. Recent advances in artificial intelligence (AI) and sensor-based monitoring technologies have enabled the integration of large volumes of training, competition, and physiological data to [...] Read more.
Sports-related injuries remain a major challenge in team sports, with important consequences for athlete health, performance, and team success. Recent advances in artificial intelligence (AI) and sensor-based monitoring technologies have enabled the integration of large volumes of training, competition, and physiological data to support injury prediction and risk modelling. However, the literature is characterised by substantial methodological diversity, limiting the ability to draw consistent conclusions. Hence, this scoping review aimed to map the existing evidence on the use of AI and sensor-based monitoring technologies for injury prediction and risk modelling in team sports, and to identify key methodological trends and research gaps. The scoping review was conducted in accordance with the PRISMA-ScR guidelines. Systematic searches were performed in PubMed and Scopus. Eligible studies included team-sport athletes and applied AI or machine learning approaches to predict injury occurrence, injury risk, or related outcomes using data derived from wearable or monitoring systems. Data were charted on study characteristics, sports and competition level, data sources, modelling techniques, validation strategies, and performance metrics. The database search yielded 123 records (PubMed: n = 37; Scopus: n = 86). After screening and eligibility assessment, 11 studies met the inclusion criteria. Most studies focused on football and rugby and relied primarily on wearable-derived data, particularly GPS and inertial sensor outputs. Common predictors included external workload variables, training exposure, previous injury history, and, in some studies, wellness or physiological markers. A wide range of models was reported, including logistic regression, decision trees, random forests, support vector machines, and neural networks. Validation strategies and reported performance varied markedly, and external validation was rarely undertaken. Across the included studies, injury risk was most consistently associated with external workload metrics, previous injury history, and internal or physiological indicators of recovery and readiness. However, current models remain limited by heterogeneous methodologies, single-team datasets, and the lack of external validation. Future research should emphasise multimodal data integration and multi-centre validation to develop reliable, interpretable, and practically applicable AI-based injury prediction systems. Full article
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