ARTICLE TSA

Total Score of Athleticism: Holistic Athlete Profiling to Enhance Decision-Making

Authors and Affiliations

  • Anthony N. Turner, PhD (1)

  • Ben Jones, PhD (2)

  • Perry Stewart, MSc (3)

  • Chris Bishop, MSc (1)

  • Nimai Parmar, PhD (1)

  • Shyam Chavda, MSc (1)

  • Paul Read, PhD (4)

Affiliations:

  1. London Sports Institute, Middlesex University, Allianz Park, London, United Kingdom

  2. Carnegie Applied Rugby Research (CARR) Centre, Institute for Sport, Physical Activity and Leisure, Leeds Beckett University, Leeds, United Kingdom

  3. Arsenal Performance and Research Team, Arsenal Football Club, United Kingdom

  4. Aspetar Orthopaedic and Sports Medicine Hospital, Doha, Qatar

Abstract

In sports clubs, coaching staff, sports scientists, and medical practitioners often seek a single, cohesive representation of an athlete's athleticism. However, there is no established consensus on the definition of athleticism. This paper introduces the Total Score of Athleticism (TSA), which aggregates the average Z-scores (or T-scores for small samples) derived from a sport-specific fitness testing battery. This system allows for a comprehensive evaluation of an athlete's capabilities, reflecting the physical requirements of competition. Additionally, the process for computing the TSA using Microsoft Excel is outlined to facilitate adoption in practice.

Introduction

Strength and conditioning coaches commonly perform fitness assessments to evaluate athletes' physical capabilities, enabling tailored training program designs. Other professionals like psychologists, physiotherapists, and technical coaches also contribute assessments that influence training interventions and team selections.

  • Data Overload: Due to the extensive data collected, discussions in athlete review meetings can become fragmented, as each practitioner presents detailed findings.
      - For instance, while jump height may be important to a strength coach, it might not be as relevant to other team members, complicating discussions.

  • Single Score Approach: To streamline communication, practitioners can provide a single score that encapsulates an athlete's overall physical fitness. This facilitates collaborative planning and maximizes time efficiency. Coaches may prioritize an athlete's ranking relative to teammates over understanding every individual score.

  • Variation Among Tests: It is rare for the athlete scoring highest on one test (e.g., bench press) to also score highest on another (e.g., change of direction speed or Yo-Yo score). This indicates compromises in different fitness components that together constitute athleticism.

  • Holistic Athleticism: Ranking athletes on individual tests is crucial for identifying strengths and weaknesses, but a holistic measure is essential for performance. Moderate scores across various tests may be more advantageous than high scores in some areas and poor in others.

The objective of this article is to detail a methodology offering a holistic fitness score known as the Total Score of Athleticism (TSA).

Using Standardized Scores and Defining Windows of Opportunity

Deriving the Total Score of Athleticism (TSA)

The TSA is computed by averaging standardized scores—Z-scores for larger samples and T-scores for smaller populations—obtained from multiple athlete tests.
 

  • Standardized Scores Application: Standardized scores allow coaches to assess athletes' performance relative to teammates, aiding in setting achievable benchmarks aligned with the club's training goals.

  • Individual Variations: An athlete's fitness might depend on the coach's training philosophy, competition tactics, training time commitment (comparing semiprofessional vs. professional athletes), and age/maturation. Comparative data from outside the club can establish benchmarks that may not suit local conditions due to these differences.

Addressing the Question of Performance

To illustrate the performance comparison using Z-scores, consider an athlete who squats 140 kg and achieves a beep test score of level 15:

  • Data Collection: Highest squat score in the squad is 220 kg; highest beep test is level 17, leading to performance percentages of 64% (squat) and 88% (beep test).

       However, being competent in one test does not necessarily indicate overall athleticism, which requires comparative data analysis via Z-scores to highlight strengths and weaknesses effectively.

Z-Scores Explanation

Z-scores encapsulate a comprehensive evaluation by rescaling values according to how many standard deviations they are from the mean:

  • Mean = 0

  • Standard Deviation (SD) = 1

  • Interpretation of scores (e.g., +2 indicates performance better than 97% of scores, while -2 indicates performance worse than 97%).

  • Data Visualization: Coaches can quickly interpret performance via Z-score graphs, with strengths above 0 and weaknesses below 0. Windows of opportunity exist in areas requiring targeted training.

Calculating a Total Score of Athleticism

The TSA is computed by averaging the Z-scores:

  • Handling Missing Data: In cases where an athlete misses tests due to injury, maintaining blank columns preserves their TSA calculation.

  • Rounding Off: High scores in one area may be mitigated by low scores in another allowing for a balanced view of abilities.

  • Holistic Metrics: Research efforts correlate the TSA with key performance indicators like tackles and shots on field, indicating the multifaceted nature of athletic performance.

Summary of Calculation Steps Using Excel

  1. Z-Score Calculation: Z-scores computed using:
       z=racextathletescore−teammeanextteamSDz = rac{ ext{athlete score - team mean}}{ ext{team SD}}
       - Use Excel functions like STANDARDIZE to aid in calculation.

  2. Rank and Visualize: Once computed, the TSA can be displayed in graphs for easy interpretation, utilizing a traffic light color scheme to denote performance tiers (strong, moderate, or weak).

Considerations for Fitness Testing Battery

The testing battery must reflect the athletic demands of the sport:

  • Balanced Tests: Include a variety of assessments to cover strength, power, speed, and aerobic capacity, ensuring that no particular quality is unduly weighted.

  • Positional Differences: Recognize that certain groups (e.g., soccer goalkeepers) may have different fitness needs, thus requiring differentiated assessment protocols.

Calculating and Presenting Results in Excel

  1. Formula Application: Input the Z-score formula in Excel and drag to compute scores for all athletes across all tests.

  2. Data Presentation: Using graphical elements like histograms can enhance understanding and help in identifying relative strengths and weaknesses.

Converting Z-Scores to T-Scores

The conversion process offers a more relatable metric for athletes and coaches:

  • T-scores can be calculated via:
       t=(zimes10)+50t = (z imes 10) + 50

  • This format centers the mean around 50, simplifying comparisons for a general audience.

Raw Scores Conversion

Raw scores can also be converted to T-scores using:
   t=50+10imesracextathletescore−teammeanextteamSDt = 50 + 10 imes rac{ ext{athlete score - team mean}}{ ext{team SD}}

  • Utilizing this can facilitate easier communication between coaches and players regarding athletic performance.

Fitness Testing for Small Squads

For smaller squads (n < 30), Z-scores may not apply, and T-distributions should be used:

  • Adjusted Formula: t=racextathletescore−teammeanextSD/extSQRT(n)t = rac{ ext{athlete score - team mean}}{ ext{SD}/ ext{SQRT(n)}}

  • Reference tables indicate comparative performance metrics though cumbersome.

Concluding Thoughts

The TSA provides a unified, clear representation of athleticism, enabling efficient evaluations and decision making. Statistically, Z-scores rank athletes, implying some will score below average, thereby highlighting potential development opportunities. Understanding how to interpret and present data through effective visualization maximizes coaching efforts and training interventions.

Conflicts of Interest and Source of Funding

  • No conflicts of interest or sources of funding were reported by the authors:
       - Anthony N. Turner, Associate Professor at Middlesex University
       - Ben Jones, Professor at Leeds Beckett University
       - Perry Stewart, Lead Academy Strength and Conditioning Coach, Arsenal Football Club
       - Others acknowledged accordingly.

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