Law of Small Numbers
A tool for analyzing small samples and assessing how much the current team form differs from expectations implied by the betting market.
The system converts betting odds into probabilities, analyzes the sequence of recent team results, and combines these data into a single assessment for comparison.
From the Law of Large Numbers to Small Samples
In statistics, the law of large numbers is well known: as the number of observations increases, their actual distribution, all else being equal, gradually approaches the expected one.
In practice, however, we often work with much smaller samples — several recent matches, a series of results, or a short sequence of observations. In such situations, deviations from the expected distribution can become much more noticeable.
This idea is used here under the name “law of small numbers.” It is not a separate strict mathematical law, but rather a practical hypothesis and a way of analyzing small sample behavior: we examine what signals appear within a short sequence and how much they differ from current market expectations.
From Odds and History to the Final Assessment
The tool processes market data and result history step by step, then combines them into an integrated indicator.
Odds → Probabilities
Entered odds are converted into initial probabilities and used to determine bookmaker margin.
History → Assessment
Recent team results are treated as a sequence where not only individual outcomes but also their combinations are considered.
Combinations → Weight
Repeating combinations receive an assigned weight, which can remain standard or be adjusted for your own model.
Result → Comparison
The final assessment is combined with market indicators and shows the difference between the model and current odds.
Small sample analysis can show noticeable deviations even without a stable change in team strength. Therefore, the final assessment is a statistical reference point, not a guarantee of a future outcome.
The smaller the observation history, the more carefully the signal should be interpreted. The tool helps structure data and compare it with the market, but it does not replace independent evaluation.
Configure Analysis
Enter odds, add team result history, and adjust combination weights if needed. Results are entered from newest to oldest. First enter the latest match, then add previous results in order. In the sequence, newer results are on the right, while older results are on the left.
Standard weights can be used without changes or adjusted manually.
Odds
Enter current odds so the system can calculate initial probabilities.
Model Base Coefficients
Select the number of outcomes and set bookmaker odds.
Team Result History
Add sequences of recent outcomes for both teams.
The history should reflect the team's latest actual results. The order matters: the sequence allows consideration not only of individual wins and losses but also their combinations.
Combination weights can remain standard or be changed manually if you want to use your own evaluation model.
Summary Assessment
This section combines odds data and analysis of recent team results.
Set model coefficients and generate team sequences for integrated calculation.