What I do

I design athlete-specific human performance systems that translate internal signals into strategic clarity. By combining Human Performance Engineering with Human Data Science, I create adaptive models that track readiness, recovery, and fatigue adaptation with precision. I aggregate, analyze, and translate biometric signals into an actionable performance intelligence layer that provides athletes and coaching teams with clear, time-accurate guidance.

These systems directly support both on- and off-court periodization strategies, aligning training loads, taper sequences, and recovery cycles with the athlete’s biology. Every output is designed to synchronize physiology with competitive demands so that players sustain resilience and deliver stability across the professional tour.

All services are built to function as a travel-based performance lab, ensuring full integration into coaching teams and availability to travel the tour. This allows decision systems to be delivered in real time, wherever competition or training takes place, preserving clarity and precision under the pressure of elite competition.

Core Capabilities

Signal Modeling
Longitudinal integration of HRV, load reactivity, hormonal rhythms, recovery slope dynamics, sleep architecture, hydration balance, metabolic efficiency, respiratory dynamics, thermal strain, RPE, and environmental stress.

  • Women (W10NIS): emphasis on menstrual phase-specific autonomic shifts and endocrine volatility.

  • Men (M10NIS): focus on testosterone-driven load tolerance, workload adaptation, and sympathetic stability.

Readiness Forecasting
Predictive mapping of performance windows, taper alignment, and suppression risk across the competitive calendar.

  • Women (W10NIS): integration of follicular peaks, luteal suppression windows, and contraceptive-influenced physiology.

  • Men (M10NIS): anticipation of readiness dips linked to travel strain, cumulative load, and power–endurance imbalances.

On-Court Periodization
Structuring training intensity, taper phases, and surface transitions based on physiological signals.

  • Women (W10NIS): endocrine-aware alignment of practice volume with phase-specific resilience and volatility.

  • Men (M10NIS): recovery timing and adaptation aligned with surface-specific strain.

Off-Court Periodization
Calibration of strength, conditioning, and recovery blocks to sustain adaptive balance across a season.

  • Women (W10NIS): cycle-aware S&C integration and hormonal rhythm protection.

  • Men (M10NIS): emphasis on workload balance across strength, power, and endurance demands.

Fatigue and Recovery Diagnostics
Identification of autonomic instability, latent fatigue, and taper-load mismatches.

  • Women (W10NIS): detection of luteal-phase suppression, late-season vagal drift, and hormonal misalignment.

  • Men (M10NIS): mapping of cumulative strain, micro-overreaching, and adaptation plateaus.

Environmental and Travel Adaptation
Simulation of timezone shifts, altitude exposure, climate stress, and surface load.

  • Women (W10NIS): mitigation of suppression risks linked to hormonal phase × travel interactions.

  • Men (M10NIS): maintenance of sympathetic balance and optimization of circadian recovery under compressed schedules.

Hydration, Thermal, Metabolic, and Respiratory Analytics
Integration of hydration balance, thermal strain, respiratory efficiency, and metabolic dynamics into longitudinal models. These signals refine recovery diagnostics, inform taper calibration, and strengthen readiness forecasting.

  • Women (W10NIS): aligning hydration, metabolic, and respiratory dynamics with cycle phases and endocrine sensitivity.

  • Men (M10NIS): calibrating thermal and respiratory load tolerance, hydration recovery, and metabolic efficiency under cumulative stress.

My approach

Internal signals guide external strategy. Each athlete has a distinct physiological rhythm, and my role is to interpret that rhythm with precision.

I integrate HRV dynamics, endocrine timing, recovery slope behavior, hydration balance, metabolic efficiency, respiratory dynamics, RPE, thermal strain, and environmental overlays into adaptive data modeling architectures. These systems detect multivariate patterns such as circadian strain, taper misalignment, cumulative fatigue, and travel-linked volatility.

From these insights, I build periodization frameworks both on and off court, shaping intensity blocks, taper phases, recovery windows, and competition entry points around the athlete’s biology.

  • Women (W10NIS): models emphasize cycle-aware planning, hormonal volatility management, and endocrine-sensitive tapering.

  • Men (M10NIS): models emphasize strength-power-endurance balance, cumulative load adaptation, and travel recovery optimization.

The outcome is a structured framework of Human Performance and Data Science that evolves continuously with the player, supports coaching teams with clarity, and sustains competitive performance under the pressure of the professional tennis tour.