Identifying highly talented athletes: Conception and design of an expert system

Table of Contents

1 Introduction

2 Track and Field Sprint

2.1 History, Overview and Classification

2.2 The Skill of Running

2.2.1 Stride Length and Stride Frequency

2.2.2 The Race Sections

2.2.3 The Start and Acceleration Phase

2.2.4 Running at Maximum Velocity

2.3 Performance Structure

2.3.1 Emotional and Mental Attributes

2.3.2 Physical Fitness

2.3.2.1 Strength

2.3.2.2 Endurance

2.3.2.3 Flexibility

2.3.2.4 Speed

2.3.3 Technique and Coordinative Abilities

2.3.4 Other Factors

2.3.4.1 Physique

2.3.4.2 Tactical Abilities

3 The Search for Talent

3.1 The Elements of Talent

3.1.1 Distinctive Performance

3.1.2 Rate of Progression

3.1.3 Utilization

3.1.4 Physical Tolerance

3.1.5 Conclusion and Consequences

3.2 Successful Talent Identification Schemes

3.3 Talent Search with the Help of an Expert System

3.3.1 Goal

3.3.2 Limitations

3.3.3 Testing Procedures

3.3.3.1 Vertical Jump

3.3.3.2 40m Sprint

3.3.3.3 50m Bounding

3.3.4 Evaluation and Selection

4 Introduction to Expert Systems

4.1 Artificial Intelligence and the Emergence of Expert Systems

4.2 What are Expert Systems?

4.3 How do Expert Systems Work?

4.4 Expert Systems in Sports

4.4.1 RunCoach

4.4.2 Expert System for Tactical Player Positioning in Soccer

4.4.3 TESSY

5 An Expert System for Talent Identification

5.1 Phase 1 – Requirements Analysis and Planning

5.1.1 Project Management

5.1.2 Requirements Definition

5.1.3 Requirements Specification

5.2 Phase 2 – System Components Definition

5.2.1 Knowledge Acquisition

5.2.2 Evaluation of the Vertical Jump

5.2.3 Evaluation of the 40m Sprint

5.2.4 Evaluation of the 50m Bounding

5.2.5 Diagnoses

5.3 Phase 3 – Design

5.3.1 Prototyping the Shell

5.3.2 Introduction to d3web

5.3.2.1 The Back-End

5.3.2.2 The Front-End

5.4 Phase 4 – Implementation

5.4.1 Setting Up the Knowledge Base

5.4.1.1 Diagnoses Set

5.4.1.2 Question Hierarchy

5.4.2 The Knowledge Implementation

5.4.2.1 The Rule Editor

5.4.2.2 Heuristic Decision Table

5.4.3 The User Interface

5.5 Phase 5 – Deployment and Maintenance

6 Summary

7 Conclusion and Outlook

Objectives and Research Focus

The primary objective of this thesis is to conceptualize and develop an Expert System that assists coaches and talent scouts in identifying young athletes with the potential to become elite sprinters. By transitioning from traditional, performance-centered selection strategies to a more process-oriented approach, the research aims to address the limitations of existing talent identification methods while leveraging AI technologies to support decision-making in the domain of track and field athletics.

• Designing an expert system architecture using the d3web shell for sport-specific talent assessment.
• Evaluating the performance structure of sprinting through physical metrics such as vertical jump, 40m sprint, and 50m bounding.
• Implementing a heuristic-based grading system to assess athletic aptitude and pinpoint developmental deficits.
• Analyzing the software engineering process required to translate domain-expert knowledge into a functioning diagnostic tool.

Excerpt from the Book

Summary of Chapters

1 Introduction: Provides an overview of the significance of talent identification in modern sports and outlines the thesis's goal of designing an Expert System for this purpose.

2 Track and Field Sprint: Analyzes the technical and physiological requirements of sprinting, focusing on performance structures and key variables like stride length and frequency.

3 The Search for Talent: Defines the concept of talent, critiques current performance-based identification strategies, and introduces the physical test methods implemented in the study.

4 Introduction to Expert Systems: Covers the fundamentals of Artificial Intelligence and Expert Systems, their structure, and potential applications within the field of sports science.

5 An Expert System for Talent Identification: Details the practical implementation, requirements analysis, system architecture, and prototyping of the Expert System using d3web.

6 Summary: Reviews the main findings of the research and the development process of the proposed Expert System concept.

7 Conclusion and Outlook: Reflects on the achievements and limitations of the work, suggesting future directions for incorporating process-oriented talent identification.

Keywords

Expert Systems, Talent Identification, Track and Field, Sprinting, d3web, Knowledge Base, Performance Structure, Software Engineering, Biomechanics, Stride Length, Stride Frequency, Physical Fitness, Diagnostic Systems, Artificial Intelligence, Athletic Assessment.

Frequently Asked Questions

What is the core focus of this research?

This research focuses on designing a conceptual Expert System to assist coaches in the initial talent identification of young athletes for track and field sprinting, moving beyond purely performance-based metrics.

What are the primary thematic areas covered?

The work explores sprint biomechanics, the structural definition of talent, the methodology of AI-based Expert Systems, and the software engineering phases required to build such a diagnostic tool.

What is the primary goal of the developed system?

The primary goal is to provide a standardized, objective tool that evaluates physical tests (vertical jump, 40m sprint, 50m bounding) to categorize a young athlete's aptitude for short-distance running.

Which scientific methods were utilized?

The research relies on literature analysis, expert consultations with domain specialists, and the application of a hybrid waterfall and evolutionary software development methodology to create a functional prototype.

What does the main body of the work address?

The main body examines the performance structure of sprinters, compares global talent identification schemes (Australia, China, USA), and details the knowledge acquisition and rule implementation within the d3web environment.

What keywords best characterize this work?

The most relevant keywords include Expert Systems, Talent Identification, Sprinting, d3web, Knowledge Base, Performance Structure, and Athletic Assessment.

Why is the 50m bounding test significant?

It is considered the most complex test as it assesses both elastic strength and intra-muscular coordination, reflecting the complex, high-frequency nature of sprint movement better than simple tapping tests.

How does the system handle uncertainty?

The system utilizes probability scores for diagnoses, allowing the Expert System to suggest multiple solutions with varying degrees of certainty rather than providing a singular, potentially flawed answer.