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6.1 Developing Normative Benchmarks: Z-Scores, T-Scores, and Percentiles
NSCA Certified Performance and Sport Scientist CPSS
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1 Introduction: CPSS Exam Overview, Eligibility, and Case-Study Strategy
1.1 CPSS Exam Format, Eligibility Pathways, and the 2026 Blueprint 1.2 Mastering Athlete Case Studies and Research Review Scenarios
2 Chapter 1: Scientific Process, Adaptation Theory, and Periodization (Domain 1)
2.1 Training Principles, Adaptation Models, and Injury-Risk Mitigation 2.2 Evaluating Performance Programs Using the Scientific Process 2.3 Periodization Paradigms, Fitness-Fatigue Models, and Tapering
3 Chapter 2: Scientific Disciplines in Athlete Preparation (Domain 1)
3.1 Exercise Physiology, Bioenergetics, and Systemic Adaptations 3.2 Applied Biomechanics, Force-Time Kinetics, and Force-Velocity Profiling 3.3 Motor Learning, Skill Acquisition, and Constraints-Led Practice Design 3.4 Performance Nutrition, Recovery Modalities, and Sport Psychology
4 Chapter 3: Technology Review, Research Appraisal, and Knowledge Translation (Domain 1)
4.1 Critically Reviewing Sport Performance Products, Technologies, and Trends 4.2 Evaluating Research Studies: Design, Validity, and Practical Relevance 4.3 Communicating Research Findings to Support Evidence-Based Practice
5 Chapter 4: Sport Demands Analysis, KPIs, and Resource Strategy (Domain 2)
5.1 Researching Biomechanical, Tactical, and Technical Sport Demands 5.2 Physiological Demands, Injury Epidemiology, and Environmental Constraints 5.3 Qualitative and Mixed-Methods Research for Sport Demands Analysis 5.4 Collaborating with Stakeholders to Establish Key Performance Indicators 5.5 Identifying Resources and Strategies for Short- and Long-Term Goals
6 Chapter 5: Profiling Benchmarks and Needs Analysis Communication (Domain 2)
6.1 Developing Normative Benchmarks: Z-Scores, T-Scores, and Percentiles 6.2 Multidimensional Athlete Profiling and Return-to-Performance Milestones 6.3 Communicating Needs Analysis Results to Multidisciplinary Stakeholders
7 Chapter 6: Assessment Ethics, Safety Standards, and Tool Psychometrics (Domain 3)
7.1 Ethical, Legal, and Privacy Considerations in Athlete Data Collection 7.2 Evaluating Assessment Environments, Equipment, and Safety Standards 7.3 Validity, Reliability (ICC, CV%, TE), and Smallest Worthwhile Change
8 Chapter 7: Applied Assessment and Load-Monitoring Systems (Domain 3)
8.1 Movement Tracking Systems: GNSS/GPS, LPS, IMUs, and Optical Tracking 8.2 Neuromuscular Testing: Force Platforms, Isometric Pulls/Pushes, and VBT 8.3 Internal Load, Autonomic Readiness (HRV), Biomarkers, and PROMs
9 Chapter 8: Quality Assurance, Protocols, and Assessment Efficacy (Domain 3)
9.1 Applying Quality Assurance Techniques in Sport Science Data Collection 9.2 Developing, Documenting, and Standardizing Data Collection Protocols 9.3 Systematically Reevaluating Assessments, Protocols, and Interventions
10 Chapter 9: Data Governance, Storage Architecture, and Cleaning (Domain 4)
10.1 Data Privacy, Security Protocols, and Protecting Organizational Information 10.2 Designing Plans for Data Collection, Storage, Analysis, and AMS Workflows 10.3 Evaluating Data Quality, Signal Integrity, Outliers, and Cleaning Procedures
11 Chapter 10: Statistical Modeling, Feedback Delivery, and Analytical Limitations (Domain 4)
11.1 Inferential Statistics, Correlation, Regression, ANOVA, and Effect Sizes 11.2 Longitudinal Load Modeling: ACWR, EWMA, Monotony, Strain, and Change Detection 11.3 Data Visualization, Dashboard Design, and Timely Stakeholder Feedback 11.4 Communicating Analytical Limitations, Bias, and Uncertainty
6.1 Developing Normative Benchmarks: Z-Scores, T-Scores, and Percentiles
Key Takeaways Explain This Topic AI
Normative reference cohorts must match the specific decision question: position-specific internal squad norms guide tactical role assignments, competitive-tier norms guide academy-to-first-team promotion, and intra-individual rolling baselines govern fatigue and return-to-play tracking
Parametric descriptors (Mean and Standard Deviation) apply only to normally distributed metrics, whereas skewed distributions require non-parametric descriptors (Median and Interquartile Range, IQR = Q3 - Q1) or empirical percentiles
For time-based performance tests where lower values represent faster outputs (such as 10 m sprints or 5-0-5 agility), the Z-score numerator must be inverted to (Mean - X) / SD so positive values consistently reflect superior athleticism
Transforming Z-scores into T-scores (T = 50 + 10Z) or STEN scores (STEN = 5.5 + 2Z) removes negative numbers and decimals that athletes and coaches frequently misinterpret as failing marks
Allometric scaling (Metric / Body Mass^b, where b = 0.67 for muscular force and b = 0.75 for aerobic power) corrects the geometric similarity bias of simple ratio scaling (Metric / Body Mass^1.0) that penalizes heavier athletes
Last updated: October 2026
Developing Normative Benchmarks: Z-Scores, T-Scores, and Percentiles
Core Principle: Raw physical test outputs ( 3,400 N 3{,}400\text{ N} 3 , 400 N peak force, 1.68 s 1.68\text{ s} 1.68 s sprint time, or 46 cm 46\text{ cm} 46 cm jump height) carry no inherent diagnostic meaning in isolation. Statistical benchmarking transforms raw measurements in disparate units into a unified, dimensionless scale that quantifies an athlete's relative standing against a relevant reference population or their own historical baseline.
In high-performance sport science ( Domain 2, Task 2C ), benchmarking and profiling models convert raw needs analysis data into actionable performance standards. To avoid flawed training prescriptions, the sport scientist must select an appropriate normative reference cohort, verify the underlying statistical distribution, standardize metrics onto a common directional scale, and account for body mass scaling biases.
Constructing Normative Reference Databases
The validity of any standardized score depends entirely on the reference cohort ( μ \mu μ and σ \sigma σ ) against which the athlete is compared. Sport scientists construct four distinct classes of normative databases depending on the operational question:
Normative Reference Cohort Primary Comparison Group Best Operational Use Case Methodological Limitations
Position-Specific Internal Squad Norms Athletes playing the same tactical role within the same club or program Day-to-day training group allocation and identifying role-specific weaknesses Small sample sizes ( n < 15 n < 15 n < 15 ) allow single extreme outliers to distort μ \mu μ and σ \sigma σ
League / Competitive-Tier Norms Multi-club datasets stratified by tier (e.g., NCAA Division I vs. Pro First-Team vs. Academy U18 \text{U18} U18 ) Talent identification, draft evaluation, and academy-to-senior promotion readiness Requires multi-season data pooling or league-wide standardized hardware protocols
Published Peer-Reviewed Norms Academic literature cohorts from comparable sports and competition levels Establishing initial benchmarks for a newly formed program or small positional group Hardware sampling rates, warm-up protocols, and seasonal testing phases often differ
Intra-Individual Historical Baselines The individual athlete's own rolling 30 -day 30\text{-day} 30 -day healthy mean or pre-injury baseline Daily neuromuscular fatigue monitoring and Return-to-Performance ( RTP \text{RTP} RTP ) clearance Does not indicate whether the athlete's healthy baseline is competitive at the league level
Parametric vs. Non-Parametric Distributions
Before computing standardized scores, the sport scientist must inspect the shape of the cohort's data distribution using histograms, quantile-quantile (Q-Q) plots, skewness/kurtosis ratios, or the Shapiro-Wilk test :
Parametric Distributions (Gaussian / Normal): When a metric is symmetrically distributed around its central tendency without extreme tail distortion, sport scientists summarize the cohort using the Sample Mean ( x ˉ \bar{x} x ˉ or μ \mu μ ) and Standard Deviation ( s s s or σ \sigma σ ) . Parametric standardization ( Z -scores Z\text{-scores} Z -scores and T -scores T\text{-scores} T -scores ) requires normality so that standard deviation intervals map accurately to known probability percentiles.
Non-Parametric Distributions (Skewed or Outlier-Prone): When metrics exhibit severe positive or negative skewness—common in serum creatine kinase ( CK \text{CK} CK ) concentrations, limb asymmetry percentages, or match high-speed running distances across varying playing minutes—the arithmetic mean is pulled toward the skewed tail and the standard deviation is artificially inflated. For skewed metrics, sport scientists must use non-parametric descriptors: the Median ( 50 th percentile 50\text{th}\text{ percentile} 50 th percentile ) and the Interquartile Range ( IQR \text{IQR} IQR ) , defined as the spread of the middle 50 % 50\% 50% of observations:
IQR = Q 3 − Q 1 = P 75 − P 25 \text{IQR} = Q_3 - Q_1 = P_{75} - P_{25} IQR = Q 3 − Q 1 = P 75 − P 25
Alternatively, skewed raw data can be log-transformed ( ln X \ln X ln X ) prior to computing parametric Z -scores Z\text{-scores} Z -scores , or evaluated directly via empirical Percentiles ( P k P_k P k ) .
- Standardized Scoring Transformations
- The Z -Score Z\text{-Score} Z -Score (Standard Normal Deviate)
A Z -score Z\text{-score} Z -score expresses the exact number of standard deviations an athlete's raw score ( X X X ) lies above or below the reference cohort mean ( μ \mu μ ). For metrics where a higher raw number represents superior athletic performance (e.g., isometric peak force, jump height, VO 2 max \text{VO}_{2\max} VO 2 m a x , Yo-Yo Intermittent Recovery distance), the standard formula is:
- Z = X − μ σ Z = \frac{X - \mu}{\sigma} Z = σ X − μ
- Directional Polarity Inversion for Time-Based Metrics
A critical exam and practical pitfall occurs when profiling time-based performance metrics —such as 10 m 10\text{ m} 10 m or 40 yd 40\text{ yd} 40 yd sprint time, 5 - 0 - 5 5\text{-}0\text{-}5 5 - 0 - 5 agility time, lane agility time, or 2,000 m 2{,}000\text{ m} 2 , 000 m rowing ergometer time—where a lower raw number represents faster, superior performance . If a sport scientist applies the unadjusted formula ( X − μ ) / σ (X - \mu)/\sigma ( X − μ ) / σ to a sprinter who runs 1.62 s 1.62\text{ s} 1.62 s against a squad mean of 1.70 s 1.70\text{ s} 1.70 s ( σ = 0.04 s \sigma = 0.04\text{ s} σ = 0.04 s ), the calculation yields Z = − 2.00 Z = -2.00 Z = − 2.00 . Plotting Z = − 2.00 Z = -2.00 Z = − 2.00 on a multi-metric radar chart or composite index falsely depicts the squad's fastest sprinter as having a severe deficiency.
To ensure that positive standardized scores ( + Z +Z + Z ) universally represent superior athletic performance across every axis of a profile, the numerator polarity must be inverted for all time-based metrics:
Z time = μ − X σ = − ( X − μ σ ) Z_{\text{time}} = \frac{\mu - X}{\sigma} = -\left(\frac{X - \mu}{\sigma}\right) Z time = σ μ − X = − ( σ X − μ )
T -Scores T\text{-Scores} T -Scores and STEN \text{STEN} STEN Scores: Coach and Athlete Communication
Although Z -scores Z\text{-scores} Z -scores are mathematically elegant for statistical modeling, they present two communication barriers when shared with athletes and technical coaches:
Half of a normal squad receives negative numbers ( Z < 0 Z < 0 Z < 0 ): Athletes routinely misinterpret a score of Z = − 0.3 Z = -0.3 Z = − 0.3 as "negative performance" or "failing," even though Z = − 0.3 Z = -0.3 Z = − 0.3 lies squarely within the average squad band.
Decimal granularity: Differentiating Z = + 0.45 Z = +0.45 Z = + 0.45 from Z = + 0.85 Z = +0.85 Z = + 0.85 is less intuitive for non-statisticians than reading a 0 – 100 0\text{–}100 0 – 100 or 1 – 10 1\text{–}10 1 – 10 rating scale.
To eliminate negative values and decimals while preserving exact linear spacing, sport scientists convert polarity-corrected Z -scores Z\text{-scores} Z -scores into T -scores T\text{-scores} T -scores (anchored at Mean = 50 \text{Mean} = 50 Mean = 50 , SD = 10 \text{SD} = 10 SD = 10 , spanning 0 – 100 0\text{–}100 0 – 100 ) or STEN \text{STEN} STEN (Standard Ten) scores (anchored at Mean = 5.5 \text{Mean} = 5.5 Mean = 5.5 , SD = 2.0 \text{SD} = 2.0 SD = 2.0 , capped on a 1 – 10 1\text{–}10 1 – 10 scale):
- T = 50 + 10 ( Z ) T = 50 + 10(Z) T = 50 + 10 ( Z )
- STEN = 5.5 + 2 ( Z ) \text{STEN} = 5.5 + 2(Z) STEN = 5.5 + 2 ( Z )
- The Empirical Rule ( 68 – 95 – 99.7 % 68\text{–}95\text{–}99.7\% 68 – 95 – 99.7% Normal Distribution Mapping)
In a Gaussian distribution, fixed standard deviation thresholds correspond to exact cumulative area percentages under the normal curve:
68.27 % 68.27\% 68.27% of athletes fall within ± 1.0 SD \pm 1.0\text{ SD} ± 1.0 SD ( Z = − 1.0 to + 1.0 Z = -1.0\text{ to }+1.0 Z = − 1.0 to + 1.0 ; T = 40 to 60 T = 40\text{ to }60 T = 40 to 60 ).
95.45 % 95.45\% 95.45% of athletes fall within ± 2.0 SD \pm 2.0\text{ SD} ± 2.0 SD ( Z = − 2.0 to + 2.0 Z = -2.0\text{ to }+2.0 Z = − 2.0 to + 2.0 ; T = 30 to 70 T = 30\text{ to }70 T = 30 to 70 ).
99.73 % 99.73\% 99.73% of athletes fall within ± 3.0 SD \pm 3.0\text{ SD} ± 3.0 SD ( Z = − 3.0 to + 3.0 Z = -3.0\text{ to }+3.0 Z = − 3.0 to + 3.0 ; T = 20 to 80 T = 20\text{ to }80 T = 20 to 80 ).
Z -Score Z\text{-Score} Z -Score T -Score T\text{-Score} T -Score STEN \text{STEN} STEN Score Normal Percentile Qualitative Benchmarking Classification
− 3.0 -3.0 − 3.0 20 20 20 1.0 1.0 1.0 ∼ 0.13 th \sim 0.13\text{th} ∼ 0.13 th Extreme Deficit / Clinical Rehabilitation Priority
− 2.0 -2.0 − 2.0 30 30 30 1.5 1.5 1.5 ∼ 2.3 rd \sim 2.3\text{rd} ∼ 2.3 rd Well Below Benchmark (Lower 2.3 % 2.3\% 2.3% Squad Tail)
− 1.0 -1.0 − 1.0 40 40 40 3.5 3.5 3.5 ∼ 15.9 th \sim 15.9\text{th} ∼ 15.9 th Below Average (Lower Boundary of 68 % 68\% 68% Normal Band)
0.0 0.0 0.0 50 50 50 5.5 5.5 5.5 50.0 th 50.0\text{th} 50.0 th Reference Cohort Mean / Positional Benchmark
+ 1.0 +1.0 + 1.0 60 60 60 7.5 7.5 7.5 ∼ 84.1 st \sim 84.1\text{st} ∼ 84.1 st Above Average (Upper Boundary of 68 % 68\% 68% Normal Band)
+ 2.0 +2.0 + 2.0 70 70 70 9.5 9.5 9.5 ∼ 97.7 th \sim 97.7\text{th} ∼ 97.7 th Elite Squad Outlier (Top 2.3 % 2.3\% 2.3% Squad Tail)
+ 3.0 +3.0 + 3.0 80 80 80 10.0 10.0 10.0 ∼ 99.87 th \sim 99.87\text{th} ∼ 99.87 th Exceptional / World-Class Outlier
Total Score of Athleticism ( TSA \text{TSA} TSA )
Developed by Turner et al., the Total Score of Athleticism ( TSA \text{TSA} TSA ) provides a single composite index of overall physical preparedness by averaging an athlete's polarity-corrected Z -scores Z\text{-scores} Z -scores (or T -scores T\text{-scores} T -scores ) across a battery of k k k independent key performance indicators ( KPIs \text{KPIs} KPIs ):
TSA Z = 1 k ∑ i = 1 k Z i or TSA weighted = ∑ i = 1 k w i Z i ( where ∑ i = 1 k w i = 1.0 ) \text{TSA}_{Z} = \frac{1}{k}\sum_{i=1}^{k} Z_i \quad \text{or} \quad \text{TSA}_{\text{weighted}} = \sum_{i=1}^{k} w_i Z_i \quad \left(\text{where } \sum_{i=1}^{k} w_i = 1.0\right) TSA Z = k 1 ∑ i = 1 k Z i or TSA weighted = ∑ i = 1 k w i Z i ( where ∑ i = 1 k w i = 1.0 )
Methodological Rule for TSA \text{TSA} TSA Construction: Each test entered into the TSA \text{TSA} TSA must represent an independent physiological determinant (e.g., one maximal force metric, one reactive strength metric, one linear sprint metric, and one aerobic capacity metric). Including multiple colinear variables from the same jump trial—such as CMJ \text{CMJ} CMJ jump height, CMJ \text{CMJ} CMJ take-off velocity, and CMJ \text{CMJ} CMJ peak power—artificially weights vertical jumping at 60 – 75 % 60\text{–}75\% 60 – 75% of the composite score, distorting squad rankings.
Ratio Scaling vs. Allometric Scaling for Body Mass
When comparing strength, force, or power across athletes of different body sizes (e.g., a 78 kg 78\text{ kg} 78 kg defensive back vs. a 135 kg 135\text{ kg} 135 kg offensive tackle), sport scientists must normalize raw outputs for Body Mass ( BM \text{BM} BM ) .
The Flaw of Simple Ratio Scaling ( X / BM 1.0 X / \text{BM}^{1.0} X / BM 1.0 ): Dividing force or 1 RM 1\text{RM} 1 RM load directly by body mass assumes a linear ( 1 : 1 1:1 1 : 1 ) isometric relationship between muscle strength and body mass. However, under the biological principle of geometric similarity , muscle physiological cross-sectional area ( PCSA \text{PCSA} PCSA )—which dictates maximal contractile force—is a two-dimensional area proportional to linear dimension squared ( L 2 L^2 L 2 ), whereas body volume and mass ( BM \text{BM} BM ) are three-dimensional quantities proportional to linear dimension cubed ( L 3 L^3 L 3 ). Therefore, muscular strength scales proportionally to BM 2 / 3 \text{BM}^{2/3} BM 2/3 ( BM 0.67 \text{BM}^{0.67} BM 0.67 ). Simple ratio scaling ( BM 1.0 \text{BM}^{1.0} BM 1.0 ) over-penalizes heavier athletes and artificially inflates the relative rankings of lighter athletes.
Allometric Scaling ( X / BM b X / \text{BM}^{b} X / BM b ): Allometric scaling divides the raw performance metric ( X X X ) by body mass raised to a theoretically or empirically derived allometric exponent ( b b b ):
Allometric Score = X BM b \text{Allometric Score} = \frac{X}{\text{BM}^{b}} Allometric Score = BM b X
Muscular Force and Maximal Strength ( 1 RM 1\text{RM} 1 RM , IMTP \text{IMTP} IMTP Peak Force): b = 0.67 b = 0.67 b = 0.67 ( 2 / 3 2/3 2/3 power law).
Maximal Aerobic Power and Metabolic Capacity ( VO 2 max \text{VO}_{2\max} VO 2 m a x in L/min \text{L/min} L/min , Peak Power): b = 0.75 b = 0.75 b = 0.75 ( 3 / 4 3/4 3/4 power law, Kleiber's biological scaling law).
Worked Multi-Metric Calculation Example
Consider a 96.0 kg 96.0\text{ kg} 96.0 kg collegiate rugby flanker evaluated across four position-specific KPIs at the start of pre-season:
KPI Metric Athlete Raw ( X X X ) Position Mean ( μ \mu μ ) Position SD ( σ \sigma σ ) Polarity Rule Z -Score Z\text{-Score} Z -Score Calculation T -Score T\text{-Score} T -Score Approx. Percentile
IMTP Peak Force 3,520 N 3{,}520\text{ N} 3 , 520 N 3,200 N 3{,}200\text{ N} 3 , 200 N 320 N 320\text{ N} 320 N Higher = Better ( 3,520 − 3,200 ) / 320 = + 1.00 (3{,}520 - 3{,}200)/320 = +1.00 ( 3 , 520 − 3 , 200 ) /320 = + 1.00 60.0 60.0 60.0 ∼ 84.1 st \sim 84.1\text{st} ∼ 84.1 st
CMJ Height 48.0 cm 48.0\text{ cm} 48.0 cm 44.0 cm 44.0\text{ cm} 44.0 cm 4.0 cm 4.0\text{ cm} 4.0 cm Higher = Better ( 48.0 − 44.0 ) / 4.0 = + 1.00 (48.0 - 44.0)/4.0 = +1.00 ( 48.0 − 44.0 ) /4.0 = + 1.00 60.0 60.0 60.0 ∼ 84.1 st \sim 84.1\text{st} ∼ 84.1 st
10 m Sprint Time 1.64 s 1.64\text{ s} 1.64 s 1.72 s 1.72\text{ s} 1.72 s 0.04 s 0.04\text{ s} 0.04 s Lower = Better (Invert) ( 1.72 − 1.64 ) / 0.04 = + 2.00 (1.72 - 1.64)/0.04 = +2.00 ( 1.72 − 1.64 ) /0.04 = + 2.00 70.0 70.0 70.0 ∼ 97.7 th \sim 97.7\text{th} ∼ 97.7 th
1,200 m Shuttle Time 288 s 288\text{ s} 288 s 276 s 276\text{ s} 276 s 12 s 12\text{ s} 12 s Lower = Better (Invert) ( 276 − 288 ) / 12 = − 1.00 (276 - 288)/12 = -1.00 ( 276 − 288 ) /12 = − 1.00 40.0 40.0 40.0 ∼ 15.9 th \sim 15.9\text{th} ∼ 15.9 th
Step-by-Step Interpretation
Polarity Verification: Because the flanker ran the 10 m 10\text{ m} 10 m sprint in 1.64 s 1.64\text{ s} 1.64 s ( 0.08 s 0.08\text{ s} 0.08 s faster than the 1.72 s 1.72\text{ s} 1.72 s positional mean), inverting the numerator yields Z time = + 2.00 Z_{\text{time}} = +2.00 Z time = + 2.00 ( T = 70 T = 70 T = 70 , top 2.3 % 2.3\% 2.3% of the squad). Conversely, their 1,200 m 1{,}200\text{ m} 1 , 200 m shuttle time of 288 s 288\text{ s} 288 s was 12 s 12\text{ s} 12 s slower than the 276 s 276\text{ s} 276 s positional mean, yielding Z time = − 1.00 Z_{\text{time}} = -1.00 Z time = − 1.00 ( T = 40 T = 40 T = 40 , ∼ 16 th \sim 16\text{th} ∼ 16 th percentile).
Unweighted Composite TSA \text{TSA} TSA :
TSA Z = + 1.00 + 1.00 + 2.00 + ( − 1.00 ) 4 = + 3.00 4 = + 0.75 ( TSA T = 50 + 10 ( 0.75 ) = 57.5 ) \text{TSA}_{Z} = \frac{+1.00 + 1.00 + 2.00 + (-1.00)}{4} = \frac{+3.00}{4} = +0.75 \quad (\text{TSA}_{T} = 50 + 10(0.75) = 57.5) TSA Z = 4 + 1.00 + 1.00 + 2.00 + ( − 1.00 ) = 4 + 3.00 = + 0.75 ( TSA T = 50 + 10 ( 0.75 ) = 57.5 )
Allometric Force Normalization: While the athlete's simple ratio-scaled IMTP \text{IMTP} IMTP force is 3,520 / 96.0 = 36.67 N/kg 3{,}520 / 96.0 = 36.67\text{ N/kg} 3 , 520/96.0 = 36.67 N/kg , their allometrically scaled force is:
IMTP allometric = 3,520 N 96.0 0.67 = 3,520 N 21.29 = 165.3 N ⋅ kg − 0.67 \text{IMTP}_{\text{allometric}} = \frac{3{,}520\text{ N}}{96.0^{0.67}} = \frac{3{,}520\text{ N}}{21.29} = 165.3\text{ N}\cdot\text{kg}^{-0.67} IMTP allometric = 96. 0 0.67 3 , 520 N = 21.29 3 , 520 N = 165.3 N ⋅ kg − 0.67
Needs Analysis Conclusion: Overall athleticism is above average ( TSA T = 57.5 \text{TSA}_{T} = 57.5 TSA T = 57.5 ), driven by elite short-sprint acceleration ( T = 70 T = 70 T = 70 ) and above-average maximal force and vertical power ( T = 60 T = 60 T = 60 ). However, high-intensity intermittent running capacity ( T = 40 T = 40 T = 40 ) is a clear positional limiter requiring targeted Maximal Aerobic Speed ( MAS \text{MAS} MAS ) conditioning.