Assessing experience in the deliberate practice of running using a fuzzy decision-support system

Area of Science:

• Sport Sciences
• Fuzzy Logic Systems
• Performance Analysis

Background:

• Skill experience assessment in sports is often subjective and ambiguous.
• Accurate experience evaluation is critical for effective decision-making in sport sciences.
• Non-elite distance runners' experience levels require a more nuanced classification method.

Purpose of the Study:

• To develop a fuzzy decision support system (FDSS) for classifying the experience of non-elite distance runners.
• To address the inherent ambiguities in judging skill experience.
• To provide a reliable and valid model for experience assessment.

Main Methods:

• Developed a Mamdani fuzzy inference system with two subsystems based on expert coaches' knowledge.
• Subsystem 1: Combined training frequency and volume to determine running practice quality.
• Subsystem 2: Integrated practice quality with competition numbers and duration to classify experience level.

Main Results:

• The fuzzy model demonstrated high consistency with expert running coaches' judgments (r>0.88).
• Results indicate running experience depends on practice quality (volume, frequency) and competitions, not just time.
• The model effectively handles ambiguities in experience judgment.

Conclusions:

• The developed fuzzy rule-based model is reliable and valid for assessing non-elite distance runner experience.
• Running experience transcends the '10-year rule,' emphasizing practice quality and competition.
• The FDSS has potential applications in sports research, training, and clinical settings.