Overtraining Risk Calculator

Overtraining risk can be monitored using two complementary metrics: the acute-to-chronic workload ratio (ACWR), which detects sudden load spikes relative to habitual training, and the training strain score, which captures high-volume monotonous training. Enter your last 5 weeks of total training loads (AU) to assess your current risk level.

Enter weekly total training loads (AU = RPE x total minutes). Week 5 is the most recent (current) week.

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ACWR and overtraining formulas

Acute load = current week total training load (AU)
Chronic load = average of preceding 4 weeks of training load (AU)
ACWR = acute load / chronic load
Risk: ACWR 0.8-1.3 = low; 1.3-1.5 = moderate; above 1.5 = high

The ACWR model was developed in rugby league by Gabbett (2016, BJSM) and has been applied across many sports. Training load units are arbitrary (AU) from the Foster session-RPE method.

Interpreting your overtraining risk

  • ACWR below 0.8: undertraining or deload week; fitness may be declining.
  • ACWR 0.8-1.3: optimal training zone (low injury and overtraining risk).
  • ACWR 1.3-1.5: moderate risk; monitor for fatigue signs and reduce if symptoms appear.
  • ACWR above 1.5: high risk of injury or overreaching; reduce load immediately.
  • Foster training strain above 6,000 AU (total load x monotony) also indicates elevated overtraining risk even at acceptable ACWR levels.

Overtraining risk: frequently asked questions

What is the acute-to-chronic workload ratio (ACWR)?

The ACWR is the ratio of the current week's training load (acute load) to the average load over the preceding 4 weeks (chronic load). Research by Gabbett (2016) in the British Journal of Sports Medicine found an ACWR of 0.8-1.3 is associated with the lowest injury risk. An ACWR above 1.5 is associated with sharply elevated injury risk.

What is a safe ACWR range?

The protective zone is generally considered 0.8 to 1.3. A ratio below 0.8 may indicate undertraining or inadequate preparation. A ratio of 1.3 to 1.5 is a cautionary zone. Above 1.5 is a high-risk zone associated with significantly elevated injury rates in team sport research. These thresholds were established in rugby league research by Gabbett et al.

What is training strain and how does it relate to overtraining?

Training strain = total weekly load x training monotony. High strain (above 6,000 AU) is associated with overtraining syndrome and increased illness rates in the Foster et al. (2001, 1998) research. Strain captures both the volume of training and its repetitiveness, making it a more sensitive overtraining indicator than load alone.

What are the signs of overtraining syndrome?

Overtraining syndrome (OTS) presents as persistent performance decrement despite adequate rest, combined with mood disturbances, fatigue, frequent illness, sleep disturbance, and loss of motivation. It requires weeks to months of reduced training to resolve. The European College of Sport Science and ACSM published a joint consensus statement on OTS diagnosis in 2013.

How do I track my training load for ACWR?

Use the session RPE method: multiply each session's CR-10 RPE by its duration in minutes to get a load in AU. Sum all sessions for the week to get acute (current week) load. Calculate chronic load as the rolling 4-week average. Track these weekly to keep your ACWR in the 0.8-1.3 range.

Official sources

  • Gabbett TJ (2016). The training-injury prevention paradox: should athletes be training smarter and harder? British Journal of Sports Medicine, 50(5), 273-280. PubMed 26758673.
  • Foster C et al. (2001). A new approach to monitoring exercise training. Journal of Strength and Conditioning Research, 15(1), 109-115. PubMed 11708692.
  • Meeusen R 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. Medicine and Science in Sports and Exercise, 45(1), 186-205. PubMed 23247672.

Reviewed by the CalculatorHub team, edited by James Graham, 14 June 2026. See our methodology.