New York Lotto: 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.

🤖 AI lab (neural networks) ›

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

For each of the 105 draws from 25.10.2025 to 24.10.2026, 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
0540547535.0
1394397401.2
210197102.4
315910.9
4000.5
5000.0
6000.0

Wins by tier

TierMatchesQuick PickHotExpected by chance
16 matches000.00
25 matches + Bonus number000.00
35 matches000.01
44 matches000.48
53 matches15910.92

Result: the difference between the two groups is not statistically significant (chi² = 1.64, p = 0.651). 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 13.12.2023 to 24.10.2026.

Matches per prediction

ObservedExpected
0 Matches162 / 152.9
1 Matches97 / 114.6
2 Matches33 / 29.2
3 Matches8 / 3.1
4 Matches0 / 0.1
5 Matches0 / 0.0
6 Matches0 / 0.0

Average: 0.623 matches per prediction – chance gives 0.610.

Deviation from the expectation of chance: chi² = 5.48, p = 0.064. The model hits exactly as often as numbers picked at random.

Model line for the next draw

71113142254

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

Latest predictions

DatePredictionMatches
24.10.2026 81415213056 0
21.10.2026 242941425055 1
14.10.2026 51317304459 0
07.10.2026 41122293043 1
03.10.2026 41115172238 0
30.09.2026 1418304253 0
26.09.2026 62729303749 1
23.09.2026 11032355057 1
19.09.2026 82022415154 0
16.09.2026 141522303537 1
12.09.2026 2622304453 0
09.09.2026 81527333649 1
05.09.2026 112032344246 0
02.09.2026 193035373955 0
29.08.2026 1516334950 1
26.08.2026 111527293551 1
22.08.2026 151727313648 1
19.08.2026 21420213138 0
15.08.2026 33135374855 1
12.08.2026 202328365256 0
08.08.2026 111529313234 0
05.08.2026 202122355054 0
01.08.2026 31027395157 1
29.07.2026 101318202654 0
25.07.2026 81015273649 1
22.07.2026 21415232456 0
18.07.2026 182034415457 1
15.07.2026 192831373954 1
11.07.2026 1312161934 0
08.07.2026 91318313545 0
04.07.2026 3635374852 0
01.07.2026 111621313655 0
24.06.2026 152329404244 0
20.06.2026 41417335455 0
17.06.2026 11420213856 0
13.06.2026 124132433 0
10.06.2026 113134373958 0

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 59, 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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