An objective and consistent calculation method
Convert your performance into a race score
This method ensures scores are objective, comparable across races, and independent of external conditions on the day. Whether the race was run in ideal or challenging weather, a strong performance earns the score it deserves — because all runners are measured against the same field on the same day.The result is a fair, consistent, and meaningful race for every finisher.
4 steps to calculating Race Scores.
Each race result undergoes the follwing four steps to calculate the Race Score.
For each race, the system follows these exact steps:
Finding similar races
Calculating the expected score for each runner
Grouping data – selecting runners
Regression on selected runners
A detailed explainination of each step is listed below.
Step 1 - Finding similar races
Which past races in the database can give us useful insights into this specific race?
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Step 2 - Calculating the expected score for each runner
We predict how each runner should perform in this race based on their relevant past results.
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Step 3 - Selecting runners
We want the most reliable data possible to represent the whole field.
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Step 4 -Regression (creating the race-specific model)
The final data set for the regression.
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From regression to score
Converting the regression to a score
The result is a simple mathematical formula unique to this race:
Final Score = Coefficient × Race Speed (km/h)
This coefficient is different for every race because every race has different terrain and conditions.
Why this method is fair?
Because every runner in the race ran on the same day under the same conditions, their speeds are directly comparable. If the weather was unusually fast, everyone ran faster — and the coefficient automatically adjusts. If it was slow, everyone ran slower — the coefficient adjusts again. The final score stays consistent and meaningful no matter the conditions.
Example conversion table (from a real race):
Example conversion table (from a real race):
| Speed km/hr | Score |
|---|---|
| 12.2 | 950 |
| 11.6 | 900 |
| 10.3 | 800 |
| 9.7 | 750 |
| 9.0 | 700 |
| 7.7 | 600 |
| 6.4 | 500 |
You can visualise this on the graph:
Draw a horizontal line at any expected score (e.g. 900), see where it crosses the regression line, and that speed becomes the speed that earns exactly 900 points in this race.
Want to find out more?
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