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Maturation & Predicted Adult Height Calculator

Compare various maturation prediction methods and check predicted adult height together.

Player Information

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Predicted Adult Height (Khamis-Roche)

Method Comparison

Method Offset APHV Stage
Mirwald (2002) — — —
Moore (2015) — — —
Fransen (2018) — — —
Leg Length: —

Maturation Timeline

Pre-PHV Mid-PHV Post-PHV PHV −4 −3 −2 −1 +1 +2 +3 +4
Mirwald Moore Fransen

Training Considerations by Maturation Stage

Pre-PHV (Offset ≤ −1 year)

  • Favourable window for neuromuscular development — emphasise agility, speed, and coordination
  • Build fundamental movement skills (FMS) and diverse movement patterns
  • Use small-sided games focusing on skill acquisition and decision-making
  • Bodyweight-based strength training over high-load resistance training

Mid-PHV (−1 year < Offset < +1 year)

  • Growth spurt period — increased injury risk (especially growth plates, tendon-bone junctions)
  • Potential flexibility reduction → mobility training important
  • Temporary coordination decline ("adolescent awkwardness") possible → manage expectations
  • Strengthen load monitoring; adjust training load according to growth rate

Post-PHV (Offset ≥ +1 year)

  • Increased capacity to adapt to strength and power training
  • Gradual introduction of adult-level training loads
  • Sensitive period for aerobic endurance development
  • Position-specific fitness demands — individualised training design

How to Measure Sitting Height

  1. Sit on a box (approximately 40–50 cm high) with back against a wall on a flat surface.
  2. Maintain hips and knees at 90°; feet should not touch the floor.
  3. Tuck chin slightly so the eyes look straight ahead at a horizontal level.
  4. Measure the vertical distance from the top of the head to the box surface.
  5. Take at least 2 measurements at the same time of day (morning recommended) and use the average.

Sitting height accuracy has the greatest impact on maturation offset results.

Interpretation Notes

  • This equation is a population-level prediction tool; individual-level accuracy may include an error of ±0.5–1 year.
  • Maturation offset is a cross-sectional estimate. Longitudinal growth data (repeated measures at 3–6 month intervals) is more accurate.
  • Prediction error is greatest around ±1 year of PHV (Mid-PHV).
  • These equations were primarily developed using European/North American Caucasian populations. Khamis-Roche was validated on "white, middle-class Americans only", and all methods require further validation across diverse ethnic groups (Towlson et al., 2021).
  • This tool should be used for training individualisation and injury prevention — not for player selection.

References

Fransen, J., Bush, S., Woodcock, S., Novak, A., Deprez, D., Baxter-Jones, A. D. G., Vaeyens, R., & Lenoir, M. (2018). Improving the prediction of maturity from anthropometric variables using a maturity ratio. Pediatric Exercise Science, 30, 296–307. https://doi.org/10.1123/pes.2017-0009

Khamis, H. J., & Roche, A. F. (1994). Predicting adult stature without using skeletal age: The Khamis-Roche method. Pediatrics, 94(4), 504–507.

Lloyd, R. S., & Oliver, J. L. (2012). The Youth Physical Development Model. Strength and Conditioning Journal, 34(3), 61–72. https://doi.org/10.1519/SSC.0b013e31825760ea

Mirwald, R. L., Baxter-Jones, A. D. G., Bailey, D. A., & Beunen, G. P. (2002). An assessment of maturity from anthropometric measurements. Medicine & Science in Sports & Exercise, 34(4), 689–694. https://doi.org/10.1097/00005768-200204000-00020

Moore, S. A., McKay, H. A., Macdonald, H., Nettlefold, L., Baxter-Jones, A. D. G., Cameron, N., & Brasher, P. M. A. (2015). Enhancing a somatic maturity prediction model. Medicine and Science in Sports and Exercise, 47(8), 1755–1764. https://doi.org/10.1249/MSS.0000000000000588

Towlson, C., Salter, J., Ade, J. D., Enright, K., Harper, L. D., Page, R. M., & Malone, J. J. (2021). Maturity-associated considerations for training load, injury risk, and physical performance in youth soccer: One size does not fit all. Journal of Sport and Health Science, 10(4), 403–412. https://doi.org/10.1016/j.jshs.2020.09.003