How the correction works

The function f(d) = a + b × e^(-d/c) decreases as distance grows. For any race longer than ~10 km, the exponential term approaches zero and f(d) ≈ a.

When predicting from a short distance (5K), f(D1) is meaningfully larger than f(D2), so the ratio exceeds 1 — predicting a more conservative (slower) marathon time. This reflects the reality that 5K performance relies more on VO2max and speed than on the aerobic endurance needed for a marathon.

When predicting from a moderate distance (10K), the correction is small — 10K is already a good aerobic predictor — so Cameron gives a slightly more optimistic marathon estimate.

Example

You ran a 10K in 42:00. What's your predicted marathon?

f(10) = 0.000495 + 0.000985 × e^(-10/1.4485) ≈ 0.000496

f(42.2) ≈ 0.000495 (exponential ≈ 0)

T2 = 2520 × (42.195/10) × (0.000496/0.000495)

T2 ≈ 10,666 s ≈ 2:57:46

Riegel gives ~3:13:00 for the same input — a ~15 min difference.

Cameron vs Riegel — when to use each

Known → Target	Cameron	Riegel	Difference
5K (20:00) → 10K	42:24	41:41	+43s (Cameron more conservative)
5K (20:00) → Marathon	2:59:23	3:11:49	−12min (Cameron more optimistic)
10K (42:00) → Marathon	2:57:46	3:13:00	−15min (Cameron more optimistic)

Neither formula accounts for terrain, training history, or race-day conditions. Use them as reference points, not guarantees.

Cameron Formula: When and How to Use It

The Cameron formula, developed by Dave Cameron in 1997, predicts race times using an exponential correction factor that varies with the known distance. Unlike Riegel's fixed-exponent approach, Cameron applies a larger correction when the starting distance is short — where anaerobic capacity plays a bigger role — and a smaller correction as distance grows toward 10K and beyond.

In practice, Cameron tends to be more conservative than Riegel when predicting long races from short performances (e.g. 5K to marathon), and slightly more optimistic when predicting from moderate distances (e.g. half marathon to marathon). For predictions between adjacent distances, both formulas agree closely. The predictor on Calcpace runs both in parallel so you can compare.

Neither formula is universally more accurate — both are empirical models with known limitations. The most useful approach is to treat the range between the two outputs as a confidence interval for your goal time. If Riegel says 3:10 and Cameron says 3:18, a realistic target sits somewhere in that corridor, assuming your training matches the goal distance.

How does this work?

When should I use Cameron instead of Riegel?

Use Cameron when predicting a long race from a short-distance performance, particularly a 5K to a marathon. Cameron is more conservative in that scenario because it accounts for the fact that 5K relies more on speed than the aerobic endurance needed for a marathon. For adjacent-distance predictions (10K to half marathon), the two formulas give very similar results.

Which formula is more accurate?

Neither is universally superior. Both were derived from large race databases and capture different aspects of the distance-speed relationship. Real-world accuracy depends on effort quality, course profile, weather, and training specificity — none of which either formula models. Run both and use the range.

Can I use these predictions for ultra-marathons?

No — both formulas were calibrated on road race data up to the marathon. At ultra distances (50K, 100K, 100 miles), performance is dominated by fueling, sleep, terrain, and mental resilience that no pace-based formula captures. Course-specific databases and coach guidance are far more reliable for ultras.

The Cameron Formula