Thunderball: 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 209 draws from 18.10.2025 to 17.10.2026, each group plays 10 lines: random Quick Pick versus lines built from the 15 most drawn numbers over the previous 100 draws. The Thunderball are random in both groups so that only the main numbers are compared. 2,090 lines per group in total.

MatchesQuick PickHotExpected by chance
09911,0401,010.1
1845815841.7
2233204217.2
3213020.4
4010.6
5000.0

Wins by tier

TierMatchesQuick PickHotExpected by chance
15 matches + Thunderball000.00
25 matches000.00
34 matches + Thunderball000.04
44 matches010.57
53 matches + Thunderball411.45
63 matches172918.91
72 matches + Thunderball17715.52
81 match + Thunderball696560.12
90 match + Thunderball576772.15

Result: the difference between the two groups is not statistically significant (chi² = 5.57, p = 0.134). 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 13.05.2025 to 17.10.2026.

Matches per prediction

ObservedExpected
0 Matches136 / 145.0
1 Matches133 / 120.8
2 Matches29 / 31.2
3 Matches2 / 2.9
4 Matches0 / 0.1
5 Matches0 / 0.0

Average: 0.657 matches per prediction – chance gives 0.641.

Deviation from the expectation of chance: chi² = 2.08, p = 0.353. The model hits exactly as often as numbers picked at random.

Model line for the next draw

78172435

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

Latest predictions

DatePredictionMatches
17.10.2026 118202137 1
16.10.2026 418212632 1
14.10.2026 110151937 0
13.10.2026 2629313234 0
10.10.2026 3681632 1
09.10.2026 39121823 1
07.10.2026 530323738 1
06.10.2026 34173237 1
02.10.2026 313252737 0
30.09.2026 78212630 0
29.09.2026 316202137 0
26.09.2026 118202334 0
25.09.2026 1223313236 1
22.09.2026 316232528 0
19.09.2026 316212534 1
18.09.2026 815162528 1
16.09.2026 1516192434 1
15.09.2026 15131628 1
12.09.2026 513152529 1
09.09.2026 2324273037 1
08.09.2026 18162330 1
05.09.2026 37101523 1
04.09.2026 911242539 0
02.09.2026 1518283335 1
01.09.2026 1116181923 1
29.08.2026 625282936 0
28.08.2026 2025303137 0
26.08.2026 89183036 0
25.08.2026 916232439 1
22.08.2026 717212437 0
21.08.2026 1516242832 1
19.08.2026 18152934 0
18.08.2026 228313438 0
15.08.2026 215213235 1
14.08.2026 1012212830 0
12.08.2026 49202124 0
11.08.2026 38152426 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 5 from 39, 0.6 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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