Lotto Max: 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 14.10.2025 to 13.10.2026, each group plays 10 lines: random Quick Pick versus lines built from the 15 most drawn numbers over the previous 100 draws. The Bonus 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
0347399356.2
1445435447.5
2206180201.4
3493340.9
4233.9
5100.2
6000.0
7000.0

Wins by tier

TierMatchesQuick PickHotExpected by chance
17 Matches000.00
26 Matches + Bonus number000.00
36 Matches000.00
45 Matches + Bonus number000.01
55 Matches100.16
64 Matches + Bonus number000.26
74 Matches233.64
83 Matches + Bonus number733.64
93 Matches423037.29

Result: the two distributions differ over this period (chi² = 8.40, p = 0.038). Across many repeated tests, a gap like this turns up from time to time; it is not proof of a better method, as every combination remains equally likely.

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 01.12.2023 to 13.10.2026.

Matches per prediction

ObservedExpected
0 Matches96 / 101.8
1 Matches126 / 127.9
2 Matches62 / 57.5
3 Matches12 / 11.7
4 Matches4 / 1.1
5 Matches0 / 0.0
6 Matches0 / 0.0
7 Matches0 / 0.0

Average: 1.007 matches per prediction – chance gives 0.942.

Deviation from the expectation of chance: chi² = 1.18, p = 0.555. The model hits exactly as often as numbers picked at random.

Model line for the next draw

14202628394243

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

Latest predictions

DatePredictionMatches
09.10.2026 1121530354150 1
06.10.2026 681530323439 1
02.10.2026 29303239404349 0
29.09.2026 361120313442 1
25.09.2026 6252629343644 0
22.09.2026 1151618414348 1
18.09.2026 2182332384041 1
15.09.2026 151112313349 1
11.09.2026 3111830353637 0
08.09.2026 7111728343743 1
04.09.2026 37824252737 0
01.09.2026 672531323436 1
28.08.2026 6133135384348 1
25.08.2026 561232343743 0
18.08.2026 14252835364041 0
14.08.2026 4121523282937 1
11.08.2026 6182122283243 1
04.08.2026 231220283440 1
24.07.2026 4151624283537 1
21.07.2026 341223242832 0
14.07.2026 672832394345 0
10.07.2026 3111623324449 1
07.07.2026 5151834353645 1
03.07.2026 37910121925 1
30.06.2026 491617253849 1
26.06.2026 7111426364344 1
23.06.2026 67922344344 1
19.06.2026 791230374349 0
12.06.2026 781225263738 1
05.06.2026 3101325344243 1
02.06.2026 681430313848 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 7 from 52, 0.9 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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