Finland: Lotto
Lotto Markov Chains — Transition Probabilities
Lotto: which numbers most often follow certain ones? The transition matrix will show
Markov chains analyze which "Lotto" lottery numbers most often follow others. For each number, a transition probability vector is built: "if number X was drawn in draw N, what is the probability that number Y will be drawn in draw N+1?"
Analysis based on 20 draws from to
Select a Number for Analysis
Click a number to see its "favorites" — numbers that most often appear in the next draw
Select a number above
Click a number to see its favorites
Transition Probability Heatmap
NxN matrix: rows — current numbers, columns — next numbers
Hide values below: 0%
| → | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 | 17 | 18 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 30 | 31 | 32 | 33 | 34 | 35 | 36 | 37 | 38 | 39 | 40 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 0 | 0 | 7 | 7 | 4 | 0 | 4 | 4 | 4 | 0 | 11 | 0 | 0 | 0 | 7 | 0 | 4 | 7 | 0 | 4 | 7 | 4 | 7 | 0 | 4 | 0 | 0 | 0 | 4 | 0 | 0 | 4 | 0 | 0 | 0 | 4 | 4 | 4 | 0 | 0 |
| 2 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | 0 | 0 | 0 | 7 | 0 | 7 | 0 | 0 | 0 | 0 | 7 | 0 | 0 | 0 | 0 | 7 | 0 | 7 | 7 | 0 | 0 | 7 | 7 | 7 | 0 | 7 | 0 | 7 | 0 | 7 | 0 | 7 | 0 |
| 3 | 0 | 4 | 4 | 11 | 0 | 0 | 4 | 0 | 0 | 4 | 4 | 0 | 0 | 0 | 4 | 0 | 4 | 7 | 4 | 0 | 4 | 7 | 0 | 0 | 7 | 4 | 4 | 0 | 0 | 0 | 4 | 7 | 7 | 0 | 0 | 4 | 4 | 0 | 4 | 0 |
| 4 | 2 | 0 | 2 | 4 | 2 | 2 | 2 | 2 | 2 | 2 | 4 | 6 | 0 | 0 | 4 | 0 | 4 | 6 | 2 | 2 | 2 | 2 | 4 | 0 | 8 | 0 | 2 | 0 | 2 | 4 | 4 | 2 | 4 | 0 | 0 | 4 | 4 | 4 | 4 | 0 |
| 5 | 0 | 5 | 5 | 5 | 5 | 0 | 0 | 0 | 5 | 0 | 5 | 0 | 0 | 0 | 5 | 0 | 5 | 10 | 0 | 5 | 5 | 10 | 5 | 0 | 0 | 5 | 5 | 0 | 5 | 0 | 0 | 5 | 5 | 0 | 0 | 0 | 0 | 5 | 0 | 0 |
| 6 | 4 | 0 | 4 | 7 | 4 | 4 | 4 | 7 | 4 | 0 | 11 | 7 | 4 | 0 | 4 | 0 | 0 | 4 | 0 | 4 | 7 | 4 | 7 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 4 | 0 | 0 | 4 | 0 | 0 | 4 | 0 | 0 |
| 7 | 4 | 4 | 7 | 7 | 0 | 4 | 0 | 0 | 0 | 4 | 0 | 4 | 0 | 4 | 4 | 0 | 0 | 11 | 0 | 0 | 0 | 4 | 0 | 4 | 4 | 7 | 0 | 0 | 0 | 4 | 7 | 4 | 0 | 11 | 0 | 0 | 0 | 0 | 7 | 0 |
| 8 | 0 | 0 | 5 | 10 | 0 | 0 | 0 | 0 | 0 | 5 | 10 | 5 | 5 | 0 | 5 | 0 | 0 | 10 | 0 | 0 | 10 | 5 | 5 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 5 | 5 | 0 | 0 | 5 | 0 | 0 | 5 | 5 | 0 |
| 9 | 3 | 3 | 6 | 9 | 0 | 0 | 6 | 3 | 3 | 0 | 6 | 0 | 3 | 0 | 9 | 0 | 3 | 3 | 0 | 0 | 6 | 6 | 6 | 3 | 0 | 3 | 3 | 0 | 0 | 3 | 0 | 6 | 6 | 3 | 0 | 0 | 0 | 3 | 0 | 0 |
| 10 | 0 | 0 | 0 | 0 | 0 | 0 | 14 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 14 | 14 | 14 | 0 | 0 | 14 | 0 | 0 | 14 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 14 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 11 | 3 | 0 | 3 | 6 | 3 | 3 | 0 | 3 | 3 | 3 | 3 | 9 | 0 | 3 | 3 | 0 | 3 | 3 | 0 | 3 | 0 | 0 | 3 | 0 | 3 | 0 | 3 | 0 | 0 | 3 | 6 | 6 | 3 | 0 | 0 | 6 | 3 | 6 | 6 | 3 |
| 12 | 5 | 0 | 2 | 2 | 7 | 2 | 2 | 2 | 7 | 0 | 5 | 7 | 5 | 0 | 5 | 0 | 7 | 5 | 0 | 7 | 7 | 2 | 2 | 0 | 0 | 2 | 0 | 0 | 2 | 0 | 0 | 0 | 2 | 0 | 2 | 2 | 0 | 5 | 0 | 0 |
| 13 | 7 | 0 | 7 | 7 | 4 | 4 | 4 | 4 | 0 | 0 | 4 | 7 | 0 | 0 | 4 | 0 | 4 | 4 | 0 | 4 | 0 | 4 | 4 | 4 | 0 | 4 | 0 | 0 | 0 | 4 | 0 | 4 | 0 | 7 | 0 | 4 | 0 | 4 | 4 | 0 |
| 14 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | 0 | 7 | 0 | 0 | 7 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | 0 | 0 | 7 | 0 | 0 | 0 | 7 | 0 | 0 | 14 | 0 | 7 | 0 | 14 | 7 | 7 | 0 |
| 15 | 3 | 0 | 3 | 11 | 0 | 3 | 0 | 3 | 0 | 0 | 9 | 6 | 3 | 3 | 9 | 0 | 0 | 0 | 0 | 0 | 6 | 0 | 0 | 3 | 6 | 3 | 3 | 0 | 0 | 3 | 3 | 3 | 0 | 3 | 3 | 6 | 3 | 0 | 3 | 3 |
| 16 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 17 | 4 | 7 | 7 | 7 | 4 | 0 | 0 | 0 | 4 | 0 | 0 | 0 | 0 | 4 | 4 | 0 | 4 | 7 | 0 | 4 | 0 | 4 | 0 | 4 | 4 | 7 | 4 | 0 | 4 | 4 | 4 | 4 | 4 | 7 | 0 | 0 | 0 | 0 | 0 | 0 |
| 18 | 2 | 5 | 0 | 5 | 2 | 2 | 5 | 2 | 2 | 0 | 2 | 2 | 2 | 5 | 0 | 0 | 2 | 5 | 2 | 2 | 0 | 5 | 0 | 0 | 5 | 7 | 2 | 0 | 0 | 0 | 5 | 5 | 7 | 2 | 2 | 2 | 2 | 0 | 2 | 2 |
| 19 | 0 | 7 | 7 | 7 | 0 | 0 | 7 | 7 | 0 | 0 | 7 | 0 | 0 | 7 | 0 | 0 | 0 | 7 | 0 | 0 | 0 | 0 | 7 | 0 | 7 | 7 | 0 | 0 | 0 | 0 | 7 | 7 | 0 | 7 | 0 | 0 | 0 | 0 | 0 | 0 |
| 20 | 0 | 5 | 5 | 5 | 5 | 0 | 0 | 0 | 5 | 0 | 5 | 0 | 0 | 0 | 5 | 0 | 5 | 10 | 0 | 5 | 5 | 10 | 5 | 0 | 0 | 5 | 5 | 0 | 5 | 0 | 0 | 5 | 5 | 0 | 0 | 0 | 0 | 5 | 0 | 0 |
| 21 | 7 | 0 | 4 | 7 | 4 | 4 | 0 | 4 | 0 | 0 | 4 | 7 | 0 | 4 | 7 | 0 | 4 | 0 | 0 | 4 | 0 | 0 | 0 | 4 | 4 | 0 | 4 | 0 | 0 | 4 | 4 | 4 | 0 | 4 | 0 | 7 | 0 | 4 | 4 | 4 |
| 22 | 4 | 4 | 0 | 4 | 4 | 4 | 0 | 4 | 4 | 0 | 7 | 4 | 0 | 7 | 0 | 0 | 0 | 7 | 0 | 4 | 0 | 0 | 4 | 0 | 7 | 4 | 0 | 0 | 4 | 4 | 11 | 4 | 0 | 4 | 0 | 4 | 0 | 0 | 0 | 4 |
| 23 | 0 | 0 | 5 | 10 | 0 | 0 | 5 | 0 | 5 | 5 | 5 | 0 | 5 | 5 | 5 | 0 | 5 | 5 | 0 | 0 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 10 | 10 | 5 | 0 | 0 | 5 | 0 | 0 | 5 | 5 |
| 24 | 0 | 0 | 0 | 14 | 0 | 0 | 0 | 0 | 0 | 0 | 14 | 0 | 0 | 0 | 14 | 0 | 0 | 0 | 0 | 0 | 14 | 0 | 0 | 0 | 14 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 14 | 14 | 0 | 0 | 0 |
| 25 | 0 | 4 | 0 | 4 | 0 | 4 | 4 | 4 | 0 | 0 | 4 | 7 | 7 | 4 | 4 | 0 | 0 | 4 | 0 | 0 | 4 | 0 | 0 | 0 | 7 | 11 | 4 | 0 | 0 | 0 | 4 | 0 | 4 | 4 | 7 | 0 | 4 | 0 | 7 | 0 |
| 26 | 5 | 0 | 2 | 2 | 5 | 5 | 5 | 5 | 2 | 0 | 5 | 7 | 2 | 0 | 0 | 0 | 2 | 5 | 0 | 5 | 0 | 2 | 5 | 0 | 2 | 5 | 0 | 0 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 5 | 0 |
| 27 | 0 | 0 | 0 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 14 | 7 | 7 | 0 | 0 | 0 | 0 | 7 | 0 | 0 | 7 | 0 | 7 | 0 | 7 | 7 | 0 | 0 | 7 | 7 | 7 | 0 | 0 | 0 | 7 | 0 | 0 | 0 | 0 | 0 |
| 28 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 29 | 0 | 14 | 0 | 14 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 14 | 0 | 0 | 0 | 14 | 14 | 0 | 0 | 0 | 0 | 14 | 14 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| 30 | 0 | 0 | 0 | 7 | 0 | 0 | 7 | 0 | 7 | 0 | 7 | 0 | 7 | 0 | 14 | 0 | 7 | 0 | 0 | 0 | 14 | 0 | 0 | 0 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | 0 | 0 | 7 | 7 | 0 | 0 | 0 |
| 31 | 0 | 0 | 0 | 0 | 0 | 0 | 10 | 0 | 5 | 0 | 0 | 5 | 5 | 0 | 0 | 0 | 5 | 5 | 5 | 0 | 0 | 5 | 5 | 0 | 5 | 5 | 0 | 0 | 0 | 5 | 0 | 0 | 14 | 0 | 5 | 0 | 10 | 5 | 5 | 0 |
| 32 | 0 | 0 | 4 | 4 | 0 | 0 | 4 | 0 | 4 | 4 | 4 | 4 | 0 | 0 | 0 | 0 | 4 | 11 | 4 | 0 | 0 | 4 | 7 | 0 | 7 | 0 | 0 | 0 | 4 | 7 | 7 | 4 | 7 | 0 | 0 | 0 | 4 | 4 | 4 | 0 |
| 33 | 3 | 3 | 3 | 3 | 0 | 3 | 3 | 0 | 3 | 0 | 3 | 3 | 3 | 3 | 6 | 0 | 3 | 9 | 0 | 0 | 3 | 3 | 3 | 3 | 6 | 6 | 0 | 0 | 3 | 6 | 6 | 0 | 3 | 9 | 0 | 0 | 0 | 0 | 3 | 0 |
| 34 | 5 | 0 | 0 | 5 | 5 | 5 | 5 | 5 | 5 | 0 | 5 | 5 | 5 | 0 | 5 | 0 | 0 | 0 | 0 | 5 | 5 | 0 | 0 | 0 | 5 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 5 | 5 | 10 | 0 | 5 | 0 |
| 35 | 7 | 0 | 0 | 0 | 7 | 7 | 0 | 0 | 0 | 0 | 0 | 14 | 0 | 0 | 0 | 0 | 7 | 7 | 0 | 7 | 0 | 7 | 0 | 0 | 0 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 7 | 0 | 7 | 0 | 7 | 7 | 0 |
| 36 | 0 | 0 | 7 | 4 | 4 | 4 | 4 | 7 | 7 | 0 | 4 | 7 | 0 | 0 | 4 | 0 | 4 | 4 | 0 | 4 | 0 | 0 | 7 | 0 | 4 | 0 | 4 | 0 | 4 | 4 | 0 | 4 | 4 | 0 | 0 | 0 | 4 | 4 | 4 | 0 |
| 37 | 0 | 0 | 0 | 0 | 0 | 10 | 5 | 5 | 5 | 0 | 0 | 10 | 5 | 0 | 10 | 0 | 5 | 5 | 0 | 0 | 5 | 5 | 0 | 0 | 5 | 5 | 5 | 0 | 0 | 0 | 0 | 0 | 5 | 5 | 0 | 0 | 0 | 0 | 10 | 0 |
| 38 | 0 | 0 | 5 | 5 | 5 | 0 | 5 | 0 | 10 | 0 | 5 | 0 | 5 | 5 | 5 | 0 | 10 | 5 | 0 | 5 | 5 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | 0 | 5 | 5 | 5 | 0 | 0 | 5 | 0 | 0 | 0 | 5 |
| 39 | 4 | 0 | 0 | 4 | 4 | 7 | 4 | 4 | 4 | 0 | 4 | 11 | 4 | 0 | 0 | 0 | 4 | 7 | 4 | 4 | 4 | 7 | 0 | 0 | 4 | 7 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 4 | 4 | 0 | 0 | 0 | 4 | 0 |
| 40 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 14 | 0 | 0 | 14 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 14 | 0 | 0 | 0 | 0 | 0 | 0 | 14 | 0 | 0 | 14 | 0 | 0 | 0 | 14 | 14 | 0 | 0 |
Summary Table
For each number — its top favorite and probability
Added to generator 0 / 40
Selected 0
| Ball added | Ball | Top favorite | Probability, % |
|---|---|---|---|
Add | 7 | 14.29 | |
Add | 33 | 14.29 | |
Add | 4 | 14.29 | |
Add | 11 | 14.29 | |
Add | 2 | 14.29 | |
Add | 15 | 14.29 | |
Add | 33 | 14.29 | |
Add | 12 | 14.29 | |
Add | 9 | 14.29 | |
Add | 4 | 11.43 | |
Add | 11 | 10.71 | |
Add | 4 | 10.71 | |
Add | 11 | 10.71 | |
Add | 18 | 10.71 | |
Add | 31 | 10.71 | |
Add | 26 | 10.71 | |
Add | 18 | 10.71 | |
Add | 12 | 10.71 | |
Add | 18 | 9.52 | |
Add | 4 | 9.52 | |
Add | 18 | 9.52 | |
Add | 4 | 9.52 | |
Add | 37 | 9.52 | |
Add | 6 | 9.52 | |
Add | 9 | 9.52 | |
Add | 4 | 8.57 | |
Add | 12 | 8.57 | |
Add | 18 | 8.57 | |
Add | 25 | 8.16 | |
Add | 7 | 7.14 | |
Add | 5 | 7.14 | |
Add | 1 | 7.14 | |
Add | 2 | 7.14 | |
Add | 26 | 7.14 | |
Add | 2 | 7.14 | |
Add | 1 | 7.14 | |
Add | 12 | 7.14 | |
Add | 3 | 7.14 | |
Add | 0 | 0 | |
Add | 0 | 0 |
Generator by Markov Chains
Mark favorite numbers for combination generation
Selected numbers for generator
0
G on keyboard — generate combination
How to Use Markov Chains for Lotto
Step-by-step guide to analyzing transition probabilities for Lotto lottery
1
Select a number for analysis
Click a number in the ball grid. For multi-drum lotteries, first select the desired field.
2
Study the favorites
You'll see the top numbers that most frequently appear after the selected one. The percentage shows the historical transition probability.
3
Analyze the heatmap
If the drum is small (up to 20 numbers), a heatmap is available — the full transition probability matrix. Bright cells indicate strong connections.
4
Use the summary table and generator
The table shows the top favorite for each number. Mark interesting numbers and generate combinations through the generator.
About Markov Chains
Mathematical foundations
A Markov chain is a stochastic model where the probability of transitioning to the next state depends only on the current state, not on previous ones. In the lottery context: if number X was drawn, what is the probability that number Y will be drawn next?
Transition Matrix
P[i,j] — the probability that number j follows number i. Built from all pairs of consecutive draws in the archive. Each row of the matrix sums to 100%.
Usage Tips
Recommendations for effective application of Markov chains
1.
Use the latest draw results as input: select each drawn number and note its favorites.
2.
The larger the draw archive, the more accurate the transition probabilities. For small samples, results may be unstable.
3.
Markov chains work well in combination with frequency analysis — check favorites against overall draw frequency.
4.
The heatmap is only available for drums with up to 20 numbers — for larger ranges, use the table.
Frequently Asked Questions about Lotto
Answers to common questions about Markov chains in lotteries for Lotto
A Markov chain is a mathematical model where the probability of a future state depends only on the current one. For lotteries, this means: the probability of number Y appearing in the next draw depends on which numbers appeared in the current draw.
For each pair of consecutive draws, it counts which numbers from draw N+1 appeared after each number from draw N. These counts are normalized into probabilities (percentages).
Probabilities are calculated based on historical data. The more draws in the archive, the more stable the estimates. For lotteries with short archives, probabilities may fluctuate significantly.
No, Markov chains show historical transition statistics but do not predict the future. Each lottery draw is an independent random event, and past results do not determine future ones.