Alexis SancilioRunning Coach
← Back to home

Runner tools

Three free calculators I also use with my athletes, built on the scientific literature. They give you a solid starting point. On race day, how you feel still matters most.

Built by Alexis Sancilio: mechanical engineer (MSc, licensed in Italy) and certified Lydiard® Level II running coach.

Road race pace

Enter a recent race: get your potential time for the other distances. Under Advanced you get the realistic estimate, based on how much you train.

Advanced: weekly km, course and weather

Weather on race day

Sky

DistanceTimePace
5K
10K
Half marathon
Marathon

The gap between your potential and your realistic time is made by preparation.

Want a plan to reach your potential?

Race fueling

How much carbohydrate, fluid and sodium per hour, based on duration, speed, conditions and your body.

Advanced: conditions and personal details
Gut trained to take carbs while running?
Salty sweat? (white marks on your shirt)
Carbohydrates
Fluids
Sodium

Practise your race fueling in long runs, never for the first time on race day.

These numbers are a starting point. In coaching we test them in your long runs, train your gut week by week and choose your products and aid-station strategy together.

Want a fueling plan tested on you?

Trail finish time

Your finish time from distance and elevation gain, starting from a road race, your UTMB index or a previous trail race.

Type of course
Forest or mountain trails with steep climbs you walk (like WTF Spartacus, OCC, CCC, UTMB).
Estimate from
Longest trail race you have finished
Plan: aid stations and cut-offs
Advanced: calibrate on last year’s results
Estimated finish time
Effort km
Average pace

Effort km = km + D+/100 (ITRA). From a road race, on runnable courses (under ~60 m D+ per km, races up to ~80 effort km) climbs count less, because descents give time back (assumes a loop or start and finish at similar altitude): measured on 6 athletes. With the same average slope, steep climbs you walk count more (WTF Buda vs WTF Spartacus). Calibrated on 10 UTMB races; the experience factor and the bad-day scenario are coaching estimates. On 100-mile races feet, stomach, night and sleep matter more than pace. Heat and altitude are not included.

Your finish time depends on how you pace the climbs, the descents and your fueling. That is what we prepare together.

Want a plan built around your race?
What these calculators are based on

From the scientific literature

  1. Riegel PS (1981). Athletic records and human endurance. American Scientist 69:285–290.
  2. Vickers AJ, Vertosick EA (2016). An empirical study of race times in recreational endurance runners. BMC Sports Sci Med Rehabil 8:26. doi.org/10.1186/s13102-016-0052-y
  3. UTMB (2026). Race score – how it works (score = race coefficient × speed). Calibration: results of Nice Côte d’Azur by UTMB 2026 (20K, 50K, 100K, 100M), Puglia by UTMB 2026 (35K, 100M), OCC, CCC, UTMB and Tarawera T102 2025. utmb.world/race-score-utmb-index
  4. UTMB Group (2026). How the UTMB Index scored the CCC 2024: a step-by-step case study (one coefficient per edition; ~4% difference between editions from weather and course). medium.com/@utmbgroup/case-study-scoring-the-ccc-2024-bf77ab91b72c
  5. Strava Engineering (2017). An improved GAP model (grade adjusted pace from heart-rate data): basis for the lighter weight of climbs on runnable courses. medium.com/strava-engineering/an-improved-gap-model-8b07ae8886c3
  6. Knechtle B et al. (2011). Predictor variables for a 100-km race time in male ultra-marathoners. Perceptual and Motor Skills 112:1–14. pubmed.ncbi.nlm.nih.gov/21319608/
  7. Blythe DAJ, Király FJ (2016). Prediction and quantification of individual athletic performance of runners. PLoS ONE 11:e0157257. doi.org/10.1371/journal.pone.0157257
  8. Emig T, Peltonen J (2020). Human running performance from real-world big data. Nature Communications 11:4936. doi.org/10.1038/s41467-020-18737-6
  9. Mantzios K et al. (2022). Effects of weather parameters on endurance running performance. Med Sci Sports Exerc 54:153–161. doi.org/10.1249/MSS.0000000000002769
  10. Ely MR et al. (2007). Impact of weather on marathon-running performance. Med Sci Sports Exerc 39:487–493. doi.org/10.1249/mss.0b013e31802d3aba
  11. El Helou N et al. (2012). Impact of environmental parameters on marathon running performance. PLoS ONE 7:e37407. doi.org/10.1371/journal.pone.0037407
  12. Stull R (2011). Wet-bulb temperature from relative humidity and air temperature. J Appl Meteorol Climatol 50:2267–2269. doi.org/10.1175/JAMC-D-11-0143.1
  13. Thomas DT, Erdman KA, Burke LM (2016). Nutrition and athletic performance (ACSM, AND, DC joint position). Med Sci Sports Exerc 48:543–568. doi.org/10.1249/MSS.0000000000000852
  14. Jeukendrup A (2014). A step towards personalized sports nutrition: carbohydrate intake during exercise. Sports Med 44 Suppl 1:S25–S33. doi.org/10.1007/s40279-014-0148-z
  15. Costa RJS et al. (2017). Gut-training: the impact of two weeks repetitive gut-challenge during exercise. Appl Physiol Nutr Metab 42:547–557. doi.org/10.1139/apnm-2016-0453
  16. Tiller NB et al. (2019). ISSN position stand: nutritional considerations for single-stage ultra-marathon training and racing. J Int Soc Sports Nutr 16:50. doi.org/10.1186/s12970-019-0312-9
  17. Sawka MN et al. (2007). ACSM position stand: exercise and fluid replacement. Med Sci Sports Exerc 39:377–390. doi.org/10.1249/mss.0b013e31802ca597
  18. Hew-Butler T et al. (2015). Statement of the 3rd International Exercise-Associated Hyponatremia Consensus Conference. Clin J Sport Med 25:303–320. doi.org/10.1097/JSM.0000000000000221
  19. Barnes KA et al. (2019). Normative data for sweating rate, sweat sodium concentration, and sweat sodium loss in athletes. J Sports Sci 37:2356–2366. doi.org/10.1080/02640414.2019.1633159
  20. Minetti AE et al. (2002). Energy cost of walking and running at extreme uphill and downhill slopes. J Appl Physiol 93:1039–1046. doi.org/10.1152/japplphysiol.01177.2001
  21. ITRA – effort points (km + D+/100). itra.run/FAQ/Runner

My estimates (to be checked with your own data)

  • Heat: slowdown per °C of WBGT by runner level and race duration; +3 °C WBGT in full sun
  • Simplified sweat-rate equation from heat production (speed × body mass), temperature and humidity
  • Trail: fatigue exponents beyond the marathon (1.15 up to 100 effort km, 1.20 beyond), terrain and experience factors, ranges