DECISION MODELING
Relying solely on heuristics is a thing of the past!
Whether you’re optimizing for endurance, agility, or operational flow—decision modeling brings clarity to complexity, helping you perform sustainably in ever-changing conditions.
(Near) daily, you’re navigating decisions, uncertainty, and preferences – all can be modeled quantitatively.
Modeling Decisions:
- Main focus – optimizing choice(s) such as: performance, injury, recovery
- Used when – you have specific options &/or alternatives to choose from
- Common techniques – linear programming, integer programming, & dynamic programming such as ‘shortest path’, ‘knapsack’, ‘inventory control’, ‘Fibonacci sequence’, & ‘game theory’
- Example use-cases:
- VO2Max Performance Optimization: Use data-driven modeling to optimize the balance between intensity, duration, and recovery to maximize VO2Max. This involves simulating different training scenarios to find the most efficient path to improving aerobic capacity over time
- Endurance Training Resource Allocation: Apply optimization techniques to allocate training time across different energy systems (aerobic, anaerobic) to maximize overall cardiovascular fitness while avoiding burnout or plateaus
- Predictive Training Load and Fatigue Management: Use predictive analytics to model the relationship between training load, fatigue, and recovery, providing actionable insights into how to adjust future training intensity to prevent overtraining while still driving VO2Max improvements
Modeling Uncertainty:
- Main focus – account for & quantify the inherent unpredictability, variability, & risk associated with different aspects of a decision-making process
- Used when – you want to quantify risk, optimize decision strategies, enhance decision quality, plan for contingencies, improve resiliency, &/or enhance transparency & clarity about potential outcomes & risks
- Common sources – incomplete / insufficient information, measurement errors, random variability, &/or bias / influence
- Common techniques – probability theory, Monte Carlo simulations, & stochastic programming
- Example use-cases:
- Impact of Environmental Conditions: Incorporate environmental uncertainty (e.g., extreme weather, air quality, or altitude) into training plans for athletes and SOF teams, forecasting the impact of these external factors on VO2Max and adjusting strategies accordingly for optimal performance under varying conditions.
- Equipment and Gear Selection: Model uncertainty in the effectiveness of different performance-enhancing equipment (e.g., shoes, wearables) in improving VO2Max and performance. Account for variability in individual responses to various gear under differing environmental and physical conditions.
- Long-Term Performance Forecasting: Account for long-term uncertainties (e.g., aging, injury history) and model how these affect sustained VO2Max and performance over the years. Create predictive models that simulate the impact of various variables on athletes’ potential for sustained performance in challenging conditions
Modeling Preferences:
- Main focus – when multiple / conflicting objectives &/or preferences need to be optimized
- Used when incorporating preferences &/or judgements are beneficial in optimizing alignment between features & expectations
- Common techniques: multi-objective optimization, aka “multicriteria decision analysis (MCDA),” utility theory, benefit-cost analysis (BCA), Markov decision processes (MDPs), game theory, recommendation systems, & fuzzy logic/sets
- Example use-cases:
- Short-Term Performance vs. Long-Term Career Sustainability: Trade-off between optimizing VO₂Max and physical performance for immediate needs (e.g., competition, deployment) while ensuring long-term health and sustainability (career)
- Speed vs Endurance Training: Balance the developments of optimizing both explosive power (sprints) and sustained effort (long-duration endurance) while improving VO₂Max – without compromizing one for the other
- Mental Resilience vs Physical Exhaustion: Balance maximizing mental toughness while optimizing physical performance – how much emphasis to put on each capability
For in-depth explorations into decision modeling and operational analyses, explore expanded and enhanced content on the ‘Resources‘ pages:
{Note: To ease your experience, pages will be linked here as content is posted.}
- Blogs
- Documentation (FAQs, How To Guides, Case Studies, & Technical References)
- Tutorials
- Tech & Gear
