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

The judgement of skill experience and its levels is ambiguous though it is crucial for decision-making in sport sciences studies. We developed a fuzzy decision support system to classify experience of non-elite distance runners. Two Mamdani subsystems were developed based on expert running coaches' knowledge. In the first subsystem, the linguistic variables of training frequency and volume were combined and the output defined the quality of running practice. The second subsystem yielded the level of running experience from the combination of the first subsystem output with the number of competitions and practice time. The model results were highly consistent with the judgment of three expert running coaches (r>0.88, p0.86, p<0.001). From the expert's knowledge and the fuzzy model, running experience is beyond the so-called "10-year rule" and depends not only on practice time, but on the quality of practice (training volume and frequency) and participation in competitions. The fuzzy rule-based model was very reliable, valid, deals with the marked ambiguities inherent in the judgment of experience and has potential applications in research, sports training, and clinical settings

Rules used in the subsystem 2 –experience level.

<p>Rules used in the subsystem 2 –experience level.</p

Surface graph representation of the quality of practice in Relation to training frequency and training volume.

<p>Surface graph representation of the quality of practice in Relation to training frequency and training volume.</p

Rules used in the subsystem 1 –quality of practice.

<p>Rules used in the subsystem 1 –quality of practice.</p

Kappa coefficients of agreement among the five experts in each category classification and the model’s categories, and the general agreement of all classifications of running experience in the real dataset (n = 100).

<p>Kappa coefficients of agreement among the five experts in each category classification and the model’s categories, and the general agreement of all classifications of running experience in the real dataset (n = 100).</p

Pearson’s correlation (r) between the three experts scores and the output of the fuzzy subsystem 1 –quality of practice in the real dataset (n = 100).

<p>Pearson’s correlation (r) between the three experts scores and the output of the fuzzy subsystem 1 –quality of practice in the real dataset (n = 100).</p

Surface graph representation of the levels of running experience in relation to quality of practice and practice time.

<p>Surface graph representation of the levels of running experience in relation to quality of practice and practice time.</p

Output sets of the final fuzzy model: Levels of running experience.

<p>Output sets of the final fuzzy model: Levels of running experience.</p

Pearson’s correlation (r) between the five new experts scores and the output of the fuzzy subsystem 2 –level of running experience in the real dataset (n = 100).

<p>Pearson’s correlation (r) between the five new experts scores and the output of the fuzzy subsystem 2 –level of running experience in the real dataset (n = 100).</p

Representation of the fuzzy model with two subsystems and their respective sets, and the output set of the model.

<p>Representation of the fuzzy model with two subsystems and their respective sets, and the output set of the model.</p