Evidence-based training
The Science Behind
Eventyr Performance
Every metric in Eventyr's training load model is grounded in peer-reviewed sports science. This article explains the research, the formulas, and the reasoning - so coaches and athletes understand exactly what they are looking at.
Section 1
Supercompensation - the foundation of all training
Every training adaptation begins with the same biological principle: apply a stimulus, recover, and emerge stronger. This cycle - called supercompensation - was formally described by Yakovlev (1977) and remains the cornerstone of modern periodization theory.
The four phases are:
- Training stress: A session disrupts homeostasis - muscles fatigue, glycogen depletes, microtrauma accumulates.
- Recovery: The body repairs and rebuilds, targeting the specific systems stressed.
- Supercompensation: Fitness rises above the pre-training baseline - this is the adaptation window.
- Detraining: If no new stimulus arrives, fitness returns to baseline within 10–14 days.
Key insight for coaches
Critically, adaptation only occurs during recovery - not during the session itself. An athlete who never recovers does not improve; they degrade. The art of coaching is managing the stress-recovery balance across days, weeks, and seasons.
Section 2
Training load - why we need a number
Two sessions of equal duration can have vastly different physiological impacts. A 60-minute low-intensity paddle and a 60-minute race-intensity interval session are not the same stress on the body. Any useful training model must account for both duration andintensity.
Heart rate is the most practical and validated proxy for internal training intensity. It responds to exercise intensity, environmental conditions, and fatigue - capturing factors that external load metrics (speed, watts) cannot. For sports like windsurfing where power meters are impractical, heart rate is the gold standard.
The challenge is combining duration and heart rate into a single, meaningful number. That is precisely what Eric Banister solved in 1975.
Section 3
Banister TRIMP - quantifying each session
TRIMP (Training Impulse) was introduced by Banister et al. (1975) and refined by Morton et al. (1990). It weights each minute of exercise by the relative heart rate intensity - giving high-intensity minutes far more load value than easy ones, which reflects the non-linear physiological cost of hard effort.
Banister TRIMP formula (Morton 1990)
TRIMP = duration(min) × ΔHR × 0.64 × e^(1.92 × ΔHR) where ΔHR = (HRavg − HRrest) / (HRmax − HRrest) ΔHR is clamped to [0, 1]
The exponential term e^(1.92 × ΔHR) is the key innovation. It captures the well-established physiological fact that blood lactate - and therefore metabolic stress - rises exponentially above the lactate threshold. A session at 90% of max HR is not three times harder than one at 30% - it is far more than that.
Normalisation to 0–100 scale
When heart rate data is unavailable (GPS-only sessions), Eventyr falls back to a conservative estimate of 50 TRIMP per hour, equivalent to moderate aerobic effort. This keeps the model populated even for incomplete data sessions.
Section 4
CTL, ATL, and TSB - the Performance Management Model
The Performance Management Chart (PMC) was formalised by Banister (1991) and popularised for endurance sport by Dr Andrew Coggan in the early 2000s. It models fitness and fatigue as two competing exponential moving averages of daily training load, and derives form as the difference between them.
CTL - Chronic Training Load (fitness)
CTL(today) = CTL(yesterday) + [Load(today) − CTL(yesterday)] × (1 − e^(−1/42)) 42-day exponential moving average of daily TRIMPRepresents the athlete's aerobic base built over months
ATL - Acute Training Load (fatigue)
ATL(today) = ATL(yesterday) + [Load(today) − ATL(yesterday)] × (1 − e^(−1/7)) 7-day exponential moving average of daily TRIMPRepresents recent training stress - recovers in days
TSB - Training Stress Balance (form)
TSB = CTL − ATL Positive TSB → more recovered than fatiguedNegative TSB → more fatigued than recovered
The 42-day and 7-day time constants are not arbitrary. They approximate the biological half-lives of fitness gains and fatigue dissipation, validated across endurance sports in research by Banister, Coggan, and others. A longer window (42 days) captures the slow accumulation of aerobic adaptations; a shorter window (7 days) captures how quickly fatigue accumulates and resolves.
Why CTL goes up slowly but ATL spikes fast
Section 5
TSB zones - what each state means for the athlete
TSB is not simply a measure of how tired the athlete is. It describes the balance between fitness and fatigue - and crucially, the ideal TSB for training is different from the ideal TSB for competition (Bosquet et al., 2007; Mujika & Padilla, 2003).
Eventyr uses a six-zone model that reflects this nuance:
Productive (negative TSB) is intentionally green
Section 6
ACWR - predicting overtraining and illness risk
The Acute:Chronic Workload Ratio (ACWR) was described by Gabbett (2016) in The British Journal of Sports Medicine and has since become one of the most widely adopted injury and illness prediction metrics in professional sport.
ACWR formula
ACWR = ATL ÷ CTL In Eventyr terms: ACWR = 7-day EMA load ÷ 42-day EMA load
The ratio compares how hard the athlete is currently working (ATL) relative to what their body is conditioned for (CTL). A ratio close to 1.0 means the current load matches fitness - sustainable and productive. A ratio significantly above 1.0 means the athlete is working well beyond their conditioned capacity - a physiological red flag.
The 'sweet spot' concept (Gabbett, 2016)
Eventyr displays ACWR as a live metric and applies colour coding that matches the risk zones above. When ACWR exceeds 1.3, the platform generates a coach warning with specific recommended actions.
Section 7
Overtraining Syndrome - when too much becomes harmful
The European College of Sport Science and American College of Sports Medicine published a joint consensus statement (Meeusen et al., 2013) defining the progression from productive overreaching to pathological overtraining:
- Functional overreaching (FOR)Short-term performance decrement with full recovery in days to weeks. This is normal, intentional, and the mechanism behind training camp and loading blocks.
- Non-functional overreaching (NFOR)Sustained performance decline despite rest. Recovery takes weeks to months. Often associated with mood disturbance, elevated resting HR, and frequent illness.
- Overtraining Syndrome (OTS)Chronic, severe performance decline with systemic hormonal disruption (elevated cortisol, suppressed testosterone). Recovery takes months to years. OTS is a medical diagnosis requiring physician involvement.
Immune suppression and illness risk
Exercise immunology research (Gleeson, 2006; Mackinnon, 2000) has established the "open window" hypothesis: in the hours immediately following a hard training session, immune function is transiently suppressed. White blood cell activity decreases, salivary IgA (the first line of defence against respiratory infections) drops, and susceptibility to upper respiratory tract infections rises sharply.
When this window occurs repeatedly without adequate recovery - as happens when ACWR exceeds 1.5 or TSB stays below −30 - the immune system remains chronically suppressed. Athletes in this state are significantly more likely to fall ill, which itself forces training interruption and compounds fitness loss.
Biological markers of overtraining
- Elevated resting heart rate (> 5–7 bpm above baseline)
- Suppressed HRV (Heart Rate Variability)
- Elevated cortisol / decreased testosterone:cortisol ratio
- Low ferritin (< 30 μg/L) - impairs oxygen transport
- Vitamin D deficiency - linked to immune suppression and fatigue
- Persistent mood disturbance, motivation loss, sleep disruption
Section 8
Practical guidance for coaches
The following principles distil the research above into actionable coaching practice:
Section 9
References
- 01.Banister, E.W., Calvert, T.W., Savage, M.V., & Bach, T. (1975). A systems model of training for athletic performance. Australian Journal of Sports Medicine, 7(3), 57–61.
- 02.Banister, E.W. (1991). Modeling elite athletic performance. In H.J. Green, J.D. McDougal & H.A. Wenger (Eds.), Physiological Testing of the High-Performance Athlete (pp. 403–424). Human Kinetics.
- 03.Morton, R.H., Fitz-Clarke, J.R., & Banister, E.W. (1990). Modeling human performance in running. Journal of Applied Physiology, 69(3), 1171–1177.
- 04.Coggan, A.R. (2003). Training and racing using a power meter: an introduction. TrainingPeaks Technical Paper.
- 05.Gabbett, T.J. (2016). The training-injury prevention paradox: should athletes be training smarter and harder? British Journal of Sports Medicine, 50(5), 273–280.
- 06.Bosquet, L., Montpetit, J., Arvisais, D., & Mujika, I. (2007). Effects of tapering on performance: a meta-analysis. Medicine & Science in Sports & Exercise, 39(8), 1358–1365.
- 07.Mujika, I., & Padilla, S. (2003). Scientific bases for precompetition tapering strategies. Medicine & Science in Sports & Exercise, 35(7), 1182–1187.
- 08.Mujika, I., & Padilla, S. (2000). Detraining: loss of training-induced physiological and performance adaptations. Sports Medicine, 30(2), 79–87.
- 09.Meeusen, R., Duclos, M., Foster, C., et al. (2013). Prevention, diagnosis and treatment of the overtraining syndrome: joint consensus statement of the European College of Sport Science and the American College of Sports Medicine. European Journal of Sport Science, 13(1), 1–24.
- 10.Foster, C. (1998). Monitoring training in athletes with reference to overtraining syndrome. Medicine & Science in Sports & Exercise, 30(7), 1164–1168.
- 11.Gleeson, M. (2006). Can nutrition limit exercise-induced immunodepression? Nutrition Reviews, 64(3), 119–131.
- 12.Mackinnon, L.T. (2000). Overtraining effects on immunity and performance in athletes. Immunology and Cell Biology, 78(5), 502–509.
- 13.Yakovlev, N.N. (1977). Sports Biochemistry. Leipzig: Deutsche Hochschule für Körperkultur.
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