TOTO: AI prediction and backtest – what are "statistical" lines worth?

Two honest experiments on the real draws: we compare random Quick Pick lines with "hot" lines built from the most frequent numbers, and we let a learning algorithm (k nearest neighbours) predict each draw, always using only the data known before the draw. Both methods are compared with the exact expectation of pure chance.

Backtest: Quick Pick lines vs hot lines (12 months)

For each of the 105 draws from 13.10.2025 to 12.10.2026, each group plays 10 lines: random Quick Pick versus lines built from the 15 most drawn numbers over the previous 100 draws. The Additional number are random in both groups so that only the main numbers are compared. 1,050 lines per group in total.

MatchesQuick PickHotExpected by chance
0469452457.8
1437433433.7
2121144139.0
3222118.5
4101.0
5000.0
6000.0

Wins by tier

TierMatchesQuick PickHotExpected by chance
16 Matches000.00
25 Matches + Additional number000.00
35 Matches000.02
44 Matches + Additional number000.05
54 Matches100.97
63 Matches + Additional number401.29
73 Matches182117.24

Result: the difference between the two groups is not statistically significant (chi² = 2.42, p = 0.490). It stays within what pure chance produces for a sample of this size.

AI prediction (k nearest neighbours)

For each draw, the model looks for the 7 past moments whose preceding 3 draws most resembled the current ones and plays the numbers most often drawn next. It is reapplied after every draw and never sees the future. 300 draws evaluated from 30.11.2023 to 12.10.2026.

Matches per prediction

ObservedExpected
0 Matches133 / 130.8
1 Matches125 / 123.9
2 Matches39 / 39.7
3 Matches3 / 5.3
4 Matches0 / 0.3
5 Matches0 / 0.0
6 Matches0 / 0.0

Average: 0.707 matches per prediction – chance gives 0.735.

Deviation from the expectation of chance: chi² = 0.29, p = 0.866. The model hits exactly as often as numbers picked at random.

Model line for the next draw

2913192442

For entertainment only: this line has exactly the same chance as any other.

Latest predictions

DatePredictionMatches
08.10.2026 81926404647 0
05.10.2026 789102739 0
01.10.2026 91620232437 1
28.09.2026 4616303944 0
24.09.2026 4723293646 1
21.09.2026 6913384044 0
14.09.2026 1730333946 1
10.09.2026 46891744 1
07.09.2026 21225294048 0
31.08.2026 6834364046 1
27.08.2026 7822233644 1
24.08.2026 41113414348 1
20.08.2026 212835374446 0
13.08.2026 91420213649 1
10.08.2026 141525333435 1
07.08.2026 2921254344 0
03.08.2026 72628304349 1
30.07.2026 4615294849 1
27.07.2026 4614152143 0
23.07.2026 42226283337 1
20.07.2026 61126303849 1
16.07.2026 31121333946 0
09.07.2026 346323746 0
06.07.2026 6711164849 0
29.06.2026 1315162442 0
25.06.2026 2313303334 0
22.06.2026 131620253643 1
18.06.2026 3410262846 0
15.06.2026 31530323446 1
11.06.2026 82125263049 1
04.06.2026 7812283543 0
01.06.2026 147162649 1

How it works

A nearest-neighbour model looks for points in time whose last draws resembled the current ones and picks the numbers that followed most often. It is reapplied draw by draw without looking into the future; a backtest additionally compares Quick Picks with lines made of frequent numbers.

Example

For 6 from 49, 0.7 matches per line are expected on average; the model lands exactly there.

Limitations

No model can predict random draws. The page shows this transparently with a chi-squared test.

Frequently asked questions about this tool

Can AI predict lottery numbers?
No. With independent, uniformly distributed draws there is no pattern a model could learn.
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