Complex Soccer Motor Skills: Mechanisms, Measurement, and Field Performance

Soccer is one of the world’s most cognitively and motorically demanding team sports, in which match outcomes often depend on a small number of decisive actions. Among these, scoring-related skills are especially important because they emerge under severe temporal pressure, variable environmental constraints, and strong psychological tension. Yet many complex soccer skills, particularly advanced finishing and airborne scoring techniques, remain underexplored scientifically despite their clear practical importance. As a result, coaches often rely on experiential knowledge rather than evidence-based training principles when teaching them. Recent advances in motion capture, wearable sensing, video analytics, and artificial intelligence now offer new opportunities to study these actions in more representative contexts and to generate knowledge that is directly usable in coaching. This Research Topic aims to strengthen the bridge between movement science and real-world soccer practice.

The aim of this Research Topic is to create a focused forum for research that explains, measures, and improves complex soccer motor skills in ways that are both scientifically rigorous and practically useful for coaches. A major gap currently exists between what movement science can describe in controlled settings and what coaches need in dynamic field environments. This gap is particularly visible in the training of complex scoring actions, where scientific evidence is fragmented, highly laboratory-based, or not easily translated into applied training design. The Topic therefore prioritizes studies that reveal underlying movement mechanisms while also showing how findings can inform practice, instruction, feedback, and representative task design. Rather than covering all aspects of soccer performance broadly, the collection will concentrate on complex, trainable soccer motor skills and their acquisition, adaptation, and transfer to performance. In doing so, it will highlight translational movement science that helps close the lab-to-field gap in soccer coaching.

Areas of interest include, but are not limited to the following:
• Biomechanical and coordination analyses of complex scoring techniques
• Motor control and motor learning processes in soccer skill acquisition
• Perception-action coupling and decision-making during representative skill execution
• The use of motion capture, wearable sensors, computer vision, or AI for skill analysis and feedback
• Representative task design and practice conditions that support transfer from training to match play
• Translational studies that convert movement science findings into usable coaching tools, training protocols, or applied performance diagnostics

This Research Topic welcomes Original Research, Reviews, Mini Reviews, Methods, Perspectives, Technology and Code, and Case Reports that advance the understanding and practical training of complex soccer motor skills. Submissions should maintain a clear mechanistic basis in movement behavior and demonstrate relevance to coaching practice in soccer.

Article types and fees

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Clinical Trial
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• ... View all formats

Articles that are accepted for publication by our external editors following rigorous peer review incur a publishing fee charged to Authors, institutions, or funders.

Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Case Report
• Clinical Trial
• Conceptual Analysis
• Data Report
• Editorial
• FAIR² Data
• General Commentary
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Registered Report
• Review
• Study Protocol
• Systematic Review
• Technology and Code

Keywords: AI, Biomechanics, Coaching Science, Decision-Making, Game Strategy Analysis, Motor Control, Motor Learning, Motion Analysis Technologies, Perception–Action Coupling, Performance Analysis, Talent Identification, Translational Research

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.