Predict your finish times for 5K, 10K, half marathon, marathon, and custom distances using the Riegel and Cameron formulas from a known race result.
Can a single race result predict your potential at any distance? Yes — with remarkable accuracy. This calculator uses established prediction formulas to estimate your finish time for any distance based on a recent race result. It applies the Riegel formula (the gold standard since 1977) and the Cameron model for comparison.
The underlying principle is simple: as race distance increases, your average pace slows in a predictable way. A person who runs a 25-minute 5K will run a 10K in approximately 52 minutes, not 50, because fatigue compounds over longer distances. These formulas model that relationship mathematically.
Enter any recent race time and distance, and instantly see equivalent performances across all standard race distances. Whether you are a beginner or experienced professional, this free online tool provides instant, reliable results without manual computation. By automating the calculation, you save time and reduce the risk of costly errors in your planning and decision-making process.
Race prediction helps you set realistic goals, choose training paces, and evaluate whether you're ready to step up to a longer distance. If your predicted marathon time from a 10K is 4:15, you know what fitness level you need to target before marathon day. Having a precise figure at your fingertips empowers better planning and more confident decisions.
Riegel Formula (1977): T2 = T1 × (D2 / D1)^1.06 Where: • T1 = known race time • D1 = known race distance • D2 = target race distance • 1.06 = fatigue exponent Cameron Formula: T2 = T1 × (D2 / D1) × adjusted decay factor The fatigue exponent varies by fitness level (1.06 for most runners, 1.01–1.04 for elites).
Result: 5K: 24:09 | Half Marathon: 1:50:15 | Marathon: 3:50:56
From a 50:00 10K: Riegel predicts the 5K at 24:09 (T = 50 × (5/10)^1.06 = 24.15 min). The half marathon prediction is 1:50:15, and the full marathon is 3:50:56. These assume fitness is specific to the known distance — if you've only trained for 10K, the marathon prediction may be optimistic.
Peter Riegel published his formula in 1977, demonstrating that race performance follows a power-law relationship with distance. The exponent of 1.06 was derived from analysis of world records across distances from 100 meters to 100 miles. Remarkably, this simple formula explains over 99% of the variance in world record times.
The biggest prediction failures occur when: (1) the runner hasn't trained specifically for the target distance, (2) race conditions differ dramatically (flat 5K vs hilly marathon), (3) the runner has a significant speed/endurance imbalance. A sprinter-type runner will outperform predictions at 5K but underperform at the marathon.
Race predictions aren't just for setting goals — they help calibrate training paces. If your predicted marathon is 4:00:00, you know your goal marathon pace is 9:09/mile. From this, you can derive your easy pace (10:30–11:30), tempo pace (8:20–8:40), and interval pace (7:15–7:45) using established coaching tables.
The Riegel formula is accurate to within 1–3% for well-trained runners predicting between similar distances (e.g., 10K to half marathon). Accuracy decreases when predicting much longer distances from short races (e.g., 5K to marathon), as it doesn't account for fueling, hydration, and long-distance specific training.
Predictions from 5K to marathon are the least reliable because they span a 8.4× distance multiplier. Your 5K fitness reflects neuromuscular speed and VO₂max, while marathon performance depends heavily on fat oxidation, glycogen storage, and long-run durability. Adjust the prediction by adding 5–10% for a more realistic marathon estimate.
The exponent (1.06 in Riegel's formula) represents how much pace slows as distance increases. A value of 1.0 would mean no slowdown (same pace at all distances). Higher values mean more fatigue effect. Elite runners have lower exponents (1.01–1.04) because their endurance training minimizes pace decay.
Both are well-validated. Riegel's formula is simpler and widely used since 1977. The Cameron model uses a slightly different mathematical approach that some studies find marginally more accurate for marathon predictions specifically. Using both gives you a range, which is more useful than a single point estimate.
They work best for runners who are well-trained at their known distance. Beginners often have disproportionate speed at short distances (e.g., fast 5K) but lack the endurance base for equivalent long-distance performance. If you're new to running, add 5–15% to the marathon prediction for a more conservative goal.
Use your most recent race time (within 4–6 weeks) that reflects your current fitness, not necessarily your all-time PR. A PR from two years ago may not represent your current capabilities. The prediction is only as good as the input data.