Overview of Total Score of Athleticism (TSA)

• The TSA is a metric used in strength and conditioning to quantify athletic performance through consolidated data from various tests.

• Provides a holistic view of athleticism, enabling comparison across athletes with a single score.

• Assists coaches by simplifying complex datasets into digestible formats for analysis and evaluation.

Importance of TSA

• Facilitates straightforward comparisons, such as athlete A vs. athlete B.

• Reduces the complexity of presenting multiple data points, making dialogues more effective during coaching meetings.

• Provides context to individual scores, allowing for better assessment of strengths and weaknesses relative to team averages.

Calculation of TSA

Raw Data Consolidation

• Direct raw data averaging is ineffective due to differing units (e.g., kilograms vs. seconds).

• Standardized scores are crucial for ensuring comparability across various tests.

Standardized Scores

Definition of Z Scores

• A z score represents the number of standard deviations a score is from the mean.

  • Formula: $z = \frac{x - \mu}{\sigma}$

  • Where:

    • $x$ = individual score

    • $\mu$ = mean of the group

    • $\sigma$ = standard deviation of the group

• E.g., a z score of zero indicates average performance; positive z scores indicate above average; negative z scores indicate below average.

Characteristics of Z Scores

• Mean of z scores is always zero; standard deviation is one.

• Enables comparisons across different measures regardless of unit differences.

• Normal distribution visualizes performance: most scores fall within +/- 1 standard deviation of the mean.

Adjustments for Certain Tests

• Some tests (like sprint times) require a score inversion for interpretation:

  • Example of inversion: $z_{ ext{adjusted}} = z imes -1$ for swift conditioning metrics, ensuring positive z scores indicate strengths.

Usage of T Scores

• T scores are an adaptation of z scores, making them user-friendly.

• Average T score is 50 with a standard deviation of 10:

  • Conversion: A z score of +1 corresponds to a T score of 60, and a z score of -1 corresponds to a T score of 40.

• Enables direct raw score to T score conversion through a specific formula.

Overall TSA Calculation

• TSA is computed as the average of either z scores or t scores from multiple performance tests:

  • Formula: $TSA = \frac{\text{Sum of Z scores}}{n}$

  • Where n is the total number of tests included.

• Justification for using a simple average:

  • Facilitates handling of missing data due to injury without skewing the score.

  • Promotes well-roundedness in athletic profiles, balancing high performance in one area against deficits in others.

  • Reflects on scientific validity linking athletic scores with performance outcomes in competitions and longevity of careers.

Test Selection Considerations

• Conduct a thorough needs analysis focusing on the sport's specific physical demands:

  • Different tests for various sports and player positions (e.g., lineman vs. midfielder in football).

• Avoid redundancy in testing to maintain overall score balance, preventing any singular quality from disproportionately impacting TSA results.

Implementing TSA in Excel

Z Score Calculation

• Formula in Excel:

  • $= \text{(Athlete's Score - Team Average) / Team Standard Deviation}$

• Example Calculation:

  • Athlete score in A2, team average in A18, standard deviation in A19:

  • Formula: $= (A2 - A18) / A19$

• Importance of using absolute references (with $ signs) to maintain cell reference integrity when copying formulas.

Built-in Z Score Function

• Using Excel's standard function:

  • Formula: $= \text{STANDARDIZE(A2, A18, A19)}$

Handling Inverted Scores

• For tests with better outcomes indicated by lower scores, a new column for adjusted z scores is created by multiplying the original z score by -1.

Calculating TSA in Excel

• Utilize average function for TSA:

  • $= \text{AVERAGE(range)}$ where range contains all z scores for the athlete.

Data Visualization Strategies

• Generate histograms or bar charts for z scores:

  • The x-axis represents z scores, with a line for team average (zero).

  • Heights above the line indicate strengths, while heights below signify weaknesses.

• Consistency in axis scales across multiple charts is essential for accurate visual comparison.

Ranking Athletes

• Sort by TSA in descending order to rank athletes from highest to lowest scores within the dataset:

  • Use Excel's sort function for this purpose.

Conditional Formatting

• Implement a traffic light system based on TSA performance:

  • Green for ≥ 75%, Yellow for 50-75%, Red for < 50%.

Considerations for Smaller Samples

• Adjusted T score formula for small teams:

  • $= \frac{\text{Athlete Score - Team Average}}{\frac{\text{Team Standard Deviation}}{\sqrt{n}}}$

• Interpreting these requires referencing a t distribution table to ascertain nuances in distributions.

• Still allowing TSA computation via the average of modified T scores.

Conclusion and Reflection

• Emphasizes the value of TSA for understanding athletic performance comprehensively.

• Encourages continual learning and resource exploration in this field.