Daily Readiness Testing Protocol Guide

Training load prescription based solely on a pre-written weekly plan ignores a fundamental reality: an athlete's physical state on any given day is a product of sleep quality, accumulated fatigue, life stress, nutritional status, and the residual effects of previous sessions. A readiness testing protocol converts these hidden variables into measurable markers, allowing coaches and athletes to make evidence-based training modifications rather than guessing whether today is a day to push through or pull back.

Daily readiness testing is not about finding excuses to train less — it is about optimizing the quality of stimulus applied on each training day. Hartman et al. (2015) found that athletes who adjusted training loads based on daily readiness metrics achieved 18% greater strength gains over 12 weeks than matched athletes following fixed prescription, with no increase in injury incidence. The testing process itself takes 3-5 minutes and generates actionable decision thresholds that protect both performance and long-term health.

Scientific Background

The physiological case for daily readiness monitoring rests on two foundational concepts: supercompensation theory and acute:chronic workload ratio (ACWR). Supercompensation theory, originally formalized by Selye's general adaptation syndrome model, holds that training produces a temporary performance decrement (fatigue) followed by a super-elevated performance state (supercompensation) if recovery is adequate. The timing and magnitude of the supercompensation window varies between individuals and sessions, and it cannot be reliably predicted from the training plan alone.

ACWR research by Gabbett (2016) demonstrated that athletes training with an acute:chronic workload ratio above 1.5 experienced injury rates 2-4 times higher than those maintaining ratios between 0.8 and 1.3. Daily readiness monitoring provides the mechanism by which these ratios are managed proactively rather than retrospectively — catching the early signs of accumulated load before they cross the injury-risk threshold.

Understanding this topic requires knowledge of how the neuromuscular system generates force and velocity. Muscles produce less force as contraction speed increases — this is the Force-Velocity Relationship described by A.V. Hill in 1938. Recent research has enabled individualization of this relationship, allowing optimized training prescriptions for each athlete based on their current neuromuscular state, not just their historical profile. The countermovement jump is the most researched and validated single-test proxy for daily neuromuscular status, with intraclass correlation coefficients above 0.96 in controlled settings (Claudino et al., 2017).

Why CMJ Height Is the Preferred Readiness Marker

• Sensitivity: CMJ height detects neuromuscular fatigue before RPE or subjective wellness scores deteriorate, typically 12-24 hours earlier.
• Specificity: A depressed CMJ reflects impaired neural drive and reactive strength, the same qualities compromised by training-induced fatigue — not just delayed onset muscle soreness.
• Practicality: The test takes under 2 minutes, requires no equipment beyond a wearable IMU, and produces a single comparable metric across sessions.

Daily Readiness Testing Protocol

An effective daily readiness protocol takes 3-5 minutes and combines objective mechanical testing with brief subjective wellness assessment. The protocol runs before warm-up so that results reflect true pre-session readiness, not post-warm-up recovery effects.

Step 1: Subjective Wellness Questionnaire (60 seconds)

Rate each of five items from 1-5: sleep quality, fatigue, muscle soreness, mood, and stress. Sum the scores (maximum 25). A score below 15 warrants attention; below 12 indicates high-risk day. Subjective scores are fast to collect but less sensitive to acute neuromuscular changes than mechanical testing. Use them primarily to contextualize the mechanical data.

Step 2: CMJ Protocol (90 seconds)

Three maximal countermovement jumps with hands on hips, 45 seconds between jumps. Record the mean of the three attempts. Compare against the athlete's rolling 7-day baseline mean. A deviation of -5% to -7% from baseline indicates moderate fatigue — proceed with session at 85-90% of planned intensity. A deviation greater than -7% indicates high fatigue — reduce volume by 30-40% and maintain intensity at lower end of programmed range. A deviation of +5% or greater indicates elevated readiness — session is cleared for peak intensity and may benefit from a planned PR attempt if the session permits.

Step 3: 20m Acceleration Sprint (optional, 60 seconds)

For speed-power athletes, a submaximal 20m effort at 80% perceived effort measured with PoinT GO provides additional context on hip-dominant neuromuscular status. Sprint time deviations above 3% from personal reference indicate fatigue in the hip flexor and gluteal complex specifically, which CMJ may not fully capture if the athlete compensates vertically. Not required for strength-primary athletes.

Training Load Adjustment Framework

Effective programming centers on individualization and progressive overload, but the daily readiness data adds a third axis: responsiveness. Rather than applying a fixed weekly structure, readiness-adjusted programming treats each session's prescription as a starting point that the readiness protocol either validates or modifies.

Weekly Integration

Map the training week into three tier zones based on anticipated readiness. High-demand sessions (maximal strength, power testing) are initially scheduled for Monday and Thursday, when two days of preceding rest tend to produce highest readiness. Moderate-demand sessions (hypertrophy, technical work) fill mid-week. If readiness data consistently shows high fatigue on Mondays — indicating insufficient weekend recovery — restructure the plan rather than overriding the data. Gabbett (2016) documented that athletes who ignored readiness signals and pushed high-demand sessions through high-fatigue days accumulated load 37% faster than their chronic workload supported, a pattern strongly predictive of soft-tissue injury.

Periodization Strategy

Use 4-week mesocycles: 3 weeks progressive overload, 1 week deload. During deload, reduce volume by 40-60% but maintain intensity to preserve neural adaptations. As the season approaches, shift toward intensity and speed while reducing volume — the essence of tapering. Read also: ACWR injury risk management for the complementary workload tracking methodology that provides weekly-level context for these daily adjustments.

Data Utilization and Progress Tracking

Subjective feelings alone cannot accurately assess training effects. Objective data is essential for both daily decision-making and longitudinal progress tracking.

Key Metrics to Track

1. CMJ height baseline trend: Calculate a rolling 7-day mean. An upward trend over 4 weeks indicates positive adaptation. A flat or declining trend under consistent loading indicates insufficient recovery or inadequate programming stimulus.
2. CMJ flight time and reactive strength index: Flight time to contraction time ratio (RSI) captures elastic energy utilization quality, a metric sensitive to tendon stiffness changes that CMJ height alone may not detect.
3. Intra-session velocity trends: MCV trajectory across sets during the training session — tracked via PoinT GO — validates whether the readiness adjustment made the correct call. An athlete who entered the session flagged as moderate fatigue but maintains velocity across all sets had sufficient capacity for full prescription.
4. Wellness score correlation with CMJ: Over 4-6 weeks, calculate the correlation between subjective wellness scores and CMJ baseline deviation for each athlete. Athletes whose subjective scores track mechanical data closely can use wellness scoring as a standalone readiness input. Athletes whose scores diverge significantly from mechanical data should weight the CMJ data more heavily.

Weekly Review Process

Review weekly data in the PoinT GO app every Sunday. An upward trend in the velocity-load graph indicates strength improvement. If stagnating or declining, adjust volume, intensity, or recovery strategies. This weekly review connects with the monitoring framework in the training readiness monitoring guide for a complete athlete management system.

Practical Coaching Tips

• Establish individual baselines before making decisions: Group norms for CMJ readiness thresholds are starting points, not final targets. Some athletes operate with high day-to-day variability (coefficient of variation 8-12%) while others are remarkably consistent (CV 2-4%). Collect 10-14 days of pre-training CMJ data to compute athlete-specific thresholds before using the test for load adjustments.
• Test conditions must be standardized: Same footwear, same time of day (or systematically tracked time of day), same floor surface, same sensor placement. Claudino et al. (2017) found that variation in surface compliance alone caused CMJ height differences of 2-3 cm, which would trigger false-positive fatigue flags. Consistency in test conditions is as important as the testing itself.
• Separate warm-up from readiness testing: The readiness test must precede warm-up to reflect true pre-session status. Athletes who run their CMJ protocol after 10 minutes of general warm-up may show 5-8% improvement from the warm-up effect alone, masking the true readiness state.
• Communicate the decision logic transparently: Athletes who understand why a session is being modified based on readiness data are more compliant than athletes who perceive the modification as arbitrary. Share the CMJ data and the threshold table so athletes build intuition about their own patterns — this also improves the reliability of self-reported wellness scores over time.
• Change one variable at a time: Adjust only one variable (load, volume, or exercise selection) every 2-3 weeks to identify what is actually working. When multiple variables change simultaneously, the readiness data cannot be meaningfully interpreted to determine which change drove the observed adaptation.

Hartman, M.J. et al. (2015). Journal of Strength and Conditioning Research, 29(3), 719-725. Gabbett, T.J. (2016). British Journal of Sports Medicine, 50(5), 273-280. Claudino, J.G. et al. (2017). Journal of Science and Medicine in Sport, 20(9), 900-905.