Methodology
How MyMatchRadar reads the game
This guide explains how MyMatchRadar compares the available odds with the model's estimate, and what context helps you read each analysis. The information is statistical, subject to updates and does not predict the match result.
01Comparing prices
Available odds
The odds the operator shows for a specific outcome at the time you check. They change as the market updates.
Model estimate
The odds calculated from the probability the model estimates for the same outcome. It does not predict the match result.
Price difference
Compares the available odds with the model estimate for the same outcome. Shows whether the odds are above, below or close to the estimate.Above the estimateBelow the estimateClose to the estimate
02Market dynamics
Market reading
MyMatchRadar tracks odds updates and compares them with the model's estimate to identify when the two references are closer together or further apart.
Stable: odds close to the model's estimate.
Updating: the odds are changing frequently.
Large difference: odds further from the model's estimate.
Match context
Beyond comparing odds and estimates, MyMatchRadar gathers public information that can help interpret the match.
- Injuries, absences and squad changes
- Probable line-ups published publicly
- Match conditions, such as the weather
- Competition context
03Sizing and context
Kelly criterion
A mathematical model relating the size of a position to the estimated probability and the expected variability. It illustrates why disproportionate positions increase the variability of results over time.
High-variability signal: when the model points to a proportion above ~5%, the system flags high variability. Smaller fractions (1/4, 1/2) reduce the dispersion of results.
Dutching (covering multiple outcomes)
A concept where the return is equalised regardless of which of the covered outcomes occurs. It illustrates how spreading across prices affects exposure — presented as a statistical context tool.
Combining events
Combining multiple events multiplies uncertainty: each extra event lowers the joint probability and widens the variability. Presented for statistical context only.