5.4 Collaborating with Stakeholders to Establish Key Performance Indicators

Collaborating with Stakeholders to Establish Key Performance Indicators

Core Principle: A metric is not a Key Performance Indicator ($\text{KPI}$) simply because a sensor can measure it. True $\text{KPIs}$ are reverse-engineered from competitive success through hierarchical deterministic modeling, validated against performance or injury outcomes, pruned for statistical parsimony, and co-owned by an interdisciplinary stakeholder team.

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Interdisciplinary vs. Multidisciplinary Team (IDT vs. MDT) Collaboration

High-performance sport organizations bring together diverse domain specialists: technical and tactical sport coaches, strength and conditioning ($\text{S\&C}$) practitioners, sport scientists, athletic trainers/physiotherapists, team physicians, sports dietitians, and sport psychologists. How these specialists interact determines whether data translates into competitive advantage or organizational friction:

• Multidisciplinary Team ($\text{MDT}$) Model (Departmental Silos): Specialists operate independently in parallel vertical silos. The biomechanist runs force-plate tests, the dietitian conducts body-composition scans, the physiotherapist screens joint range of motion, and each hands a separate technical report to the head coach or athlete. When recommendations conflict—such as the $\text{S\&C}$ coach pushing heavy eccentric loading to build hypertrophy during the exact microcycle the tactical coach increases high-speed pressing volume and the medical staff tries to unload an irritated tendon—the athlete bears the cost of fragmented planning.
• Interdisciplinary High-Performance Team ($\text{IDT}$) Model (Integrated Systems): Stakeholders collaborate horizontally around a shared performance problem. Before collecting data, the sport scientist facilitates stakeholder meetings to clarify decision rights, establish common terminology, and integrate perspectives into a unified athlete plan. Medical personnel hold clinical authority over tissue healing and return-to-play clearance; sport coaches define the tactical game model and technical KPIs; and sport scientists and $\text{S\&C}$ practitioners translate those game-model demands into physical preparation targets and readiness monitoring.

Stakeholder Communication and Buy-In

Technical coaches care about winning matches, player availability, and tactical execution—not raw Newton-seconds of impulse or principal component eigenvalues. To earn stakeholder buy-in, the sport scientist must act as a bilingual translator:

1. Start with the Coach's Question: Frame every needs analysis around concrete coaching problems (e.g., "Why are our fullbacks losing 1-v-1 defensive transitions in the final 15 minutes of the second half?").
2. Co-Design the KPI Framework: When coaches participate in selecting the metrics that define their tactical model, they view subsequent testing and monitoring as verification of their game plan rather than external auditing.
3. Establish Psychological Safety with Athletes: Clearly communicate why a metric is collected, how it protects their career longevity or improves on-field output, and guarantee that monitoring data will be used to optimize individualized training support rather than punish effort or dictate contract cuts.

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Reverse-Engineering KPIs via Hierarchical Deterministic Modeling

To avoid arbitrary test selection (e.g., running a $2\text{-mile}$ timed run for offensive linemen simply because it is tradition), sport scientists utilize Hierarchical Deterministic Modeling, pioneered in sport biomechanics by James Hay and J. Gavin Reid (and expanded by Chow & Knudson). A deterministic model is a top-down causal pyramid where the variable at the apex is completely or substantially determined by the mechanical, physiological, or tactical factors directly beneath it:

• Tier 1 — Ultimate Competition Outcome: The definitive measure of competitive success (e.g., $100\text{ m}$ race time, $2{,}000\text{ m}$ rowing velocity, baseball pitching strikeout-to-walk ratio, or invasion-sport win probability / net expected goals).
• Tier 2 — Biomechanical, Tactical, & Technical Outcome KPIs: The macro-level competitive behaviors that directly dictate Tier 1. In a $100\text{ m}$ sprint, race time ($t = d / \bar{v}$) is governed by reaction/block clearance time, acceleration phase velocity ($0\text{–}30\text{ m}$), maximal sprinting velocity ($30\text{–}60\text{ m}$), and velocity maintenance ($60\text{–}100\text{ m}$). In an invasion sport using a high-pressing tactical model, Tier 2 KPIs include high-regain pressing transitions, sprint recovery into defensive shape, and contested aerial duel win percentage.
• Tier 3 — Physical & Physiological Performance Determinants (PPDs): The underlying trainable neuromuscular, metabolic, and morphological capacities that govern Tier 2 execution. For example, $0\text{–}30\text{ m}$ acceleration is governed by relative horizontal power ($P_{\max}$) and Ratio of Forces ($\text{RF}$); maximal velocity is governed by vertical stiffness ($k_{\text{vert}}$) and fast-$\text{SSC}$ RFD within $< 100\text{ ms}$; and repeated pressing transitions are governed by maximal aerobic speed ($\text{MAS}$), $\text{PCr}$ resynthesis kinetics, and eccentric braking capacity.
• Tier 4 — Diagnostic Assessment Battery: The specific, standardized laboratory or field tests selected to quantify each Tier 3 determinant (e.g., $30\text{ m}$ radar sprint profiling, dual force-plate countermovement jump $[\text{CMJ}]$ and $30\text{ cm}$ drop jump $[\text{RSI}]$, Nordic eccentric hamstring dynamometry, and the 30-15 Intermittent Fitness Test $[V_{\text{IFT}}]$).

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Four Scientific Criteria for Selecting Valid KPIs

Once candidate determinants are mapped on the deterministic hierarchy, each metric must pass four rigorous selection gates before entering the active monitoring or testing battery:

1. Construct, Criterion, and Discriminant Validity

Does the metric statistically and mechanistically account for a meaningful proportion of variance ($R^2$) in competitive performance or injury incidence? In complex team sports where single physical metrics rarely correlate directly with team win-loss records due to tactical confounding, sport scientists evaluate discriminant validity: does the test reliably differentiate between elite first-team starters and sub-elite non-starters/academy athletes, or between injured and uninjured cohorts (typically requiring a standardized effect size of $d \ge 0.60$ or significant receiver operating characteristic area under the curve, $\text{AUC} \ge 0.70$)?

2. Modifiability (Trainability) vs. Talent Identification Traits

Practitioners must distinguish non-modifiable anthropometric or genetic traits (e.g., standing stature, wingspan, Achilles tendon moment arm, tibial length) from modifiable physical or technical KPIs (e.g., relative concentric impulse, eccentric hamstring peak torque, $\text{vOBLA}$, reactive strength index). Non-modifiable traits belong in talent identification and positional slotting models, whereas routine seasonal KPI batteries must focus on modifiable determinants that respond to training interventions within a $4\text{–}12\text{ week}$ mesocycle.

3. Measurability and Signal-to-Noise Sensitivity

A theoretically valid KPI is useless in practice if its measurement noise exceeds the biological signal of training adaptation. Every chosen KPI must exhibit high test-retest reliability (Intraclass Correlation Coefficient $\text{ICC} > 0.85$ and Coefficient of Variation $\text{CV} < 5\%$) such that its Typical Error ($\text{TE} = \text{SD}_{\text{diff}} / \sqrt{2}$) is less than or equal to the Smallest Worthwhile Change ($\text{SWC} = 0.2 \times \text{SD}_{\text{between}}$). If $\text{TE} \gg \text{SWC}$, practitioners cannot detect whether an athlete has meaningfully improved or fatigued.

4. Parsimony and Eliminating Multicollinearity

Modern dual force plates output over $120$ variables per jump, and $10\text{ Hz}$ GNSS units export over $80$ locomotive metrics per session. Presenting $50$ variables to a coaching staff induces decision paralysis, while entering highly correlated variables (e.g., $\text{CMJ}$ jump height, peak concentric velocity, take-off velocity, and net vertical propulsive impulse, which correlate at $r = 0.90\text{–}0.99$) into statistical models creates severe multicollinearity (inflating variance inflation factors, $\text{VIF} > 5\text{–}10$). Sport scientists enforce parsimony by applying:

• Pairwise Correlation Pruning: Removing redundant variables when $|r| > 0.80$, retaining the single most reliable and mechanically interpretable metric.
• Principal Component Analysis ($\text{PCA}$) or Exploratory Factor Analysis: Reducing dozens of collinear variables into $3\text{–}5$ orthogonal (uncorrelated) latent dimensions—such as Maximal Force Capacity, Fast-SSC Reactive Stiffness, Horizontal Acceleration, and Intermittent Aerobic Capacity—and selecting one anchor KPI per principal component.

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Weighting KPIs by Position, Tactical Role, and Developmental Stage

Even within the same squad, a uniform composite score (such as an unweighted average of z-scores across all tests) distorts athlete evaluation unless KPIs are weighted by positional demands:

• American Football Offensive Tackle vs. Wide Receiver: In a Total Score of Athleticism ($\text{TSA}$) or weighted z-score composite, an offensive tackle's profile heavily weights absolute body mass, isometric mid-thigh pull ($\text{IMTP}$) peak force, heavy-load concentric impulse, and $0\text{–}10\text{ m}$ sprint momentum ($p = m \cdot v_{0\text{–}10\text{m}}$), because blocking inertia depends on mass $\times$ velocity. Conversely, a wide receiver's composite heavily weights relative horizontal force ($F_H/\text{kg}$), $30\text{–}40\text{ m}$ maximal velocity ($v_{\max}$), high-speed running capacity, and reactive deceleration $\text{RSI}$.
• Developmental Stage (Academy vs. Senior Elite): For youth athletes ($12\text{–}16\text{ years}$), KPI frameworks prioritize biological maturation tracking (Years from Peak Height Velocity $[\text{PHV}]$ / Maturity Offset and percentage of predicted adult height via the Khamis-Roche method), fundamental movement competence, and tissue tolerance during growth spurts. For senior elite veterans, KPIs shift toward maintaining peak power output, post-match recovery kinetics, and tissue availability across congested competitive calendars.