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Technical analysis in Tsuri-goshi through three complementary observational analysis.

I Prieto-Lage1, M Rodríguez-Souto1, M A Prieto2

  • 1Faculty of Education and Sport, University of Vigo, Pontevedra, Spain.

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Summary
This summary is machine-generated.

This study identifies key technical errors in judo learning, revealing common chains of mistakes like poor balance and foot placement. It proposes a new analytical method to improve error correction and enhance teacher-student feedback for better learning outcomes.

Keywords:
JudoT-patternTeachingVideo analysis

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Area of Science:

  • Sports Science
  • Motor Learning
  • Martial Arts Pedagogy

Background:

  • Learning judo involves complex motor skills.
  • Novice judo practitioners frequently exhibit technical errors.
  • Effective feedback is crucial for skill acquisition in martial arts.

Purpose of the Study:

  • To identify and analyze the relationships between technical errors in novice judo learners.
  • To propose a novel analytical method for understanding and correcting judo technique errors.
  • To enhance the effectiveness of teacher-student communication and feedback in judo instruction.

Main Methods:

  • Observational study utilizing a self-generated instrument (OI-JUDO-TG) and recording software (Lince).
  • Descriptive statistics and sequential analysis (polar coordinates, T-Patterns) using Hoisan and Theme software.
  • Analysis of technical errors in a sample of 78 novice judo students.

Main Results:

  • Identified frequent error chains: lack of initial imbalance, incorrect foot/head placement, absence of body load during tsukuri, and poor balance post-projection.
  • Demonstrated strong relationships between specific errors, indicating causal links.
  • Highlighted the limitations of traditional experience-based feedback in novice judo training.

Conclusions:

  • The proposed analytical method optimizes understanding of performance errors and feedback delivery.
  • Addressing identified error chains can significantly improve judo technique acquisition.
  • Enhanced, data-driven feedback can activate new neural pathways, accelerating judo learning.