Kazakhstan: KENO2

KENO2 Autocorrelation Analysis

KENO2: do draws have "memory"? Are results correlated between draws?

Autocorrelation shows whether the results of draw N are related to draw N-1, N-2, and beyond. If significant autocorrelation is detected, it's a valuable signal for forecasting. If not, it confirms the randomness of the "KENO2" lottery.

Analysis based on 20 draws from 05/08/2026, 09:30 AM to 05/27/2026, 09:30 AM

Max lag:

Draw sums autocorrelation

Correlogram with 95% confidence intervals

Observations

Significant lags

±0.4383

95% confidence interval

No autocorrelation detected

All ACF values are within the 95% confidence interval. The sequence is statistically random.

ACF(1) for All Numbers

Autocorrelation at lag 1 — quick overview of each number's "memory"

Ball	ACF(1)	Status
1	-0.0553	Normal
2	0.1833	Normal
3	-0.0489	Normal
4	-0.2000	Normal
5	0.1709	Normal
6	0.4500	Significant
7	0.2500	Normal
8	0.2657	Normal
9	-0.1405	Normal
10	0.3625	Normal
11	0.3357	Normal
12	-0.0833	Normal
13	0.1833	Normal
14	0.1833	Normal
15	-0.0833	Normal
16	-0.0833	Normal
17	0.0262	Normal
18	-0.1265	Normal
19	-0.1853	Normal
20	-0.1405	Normal
21	-0.1375	Normal
22	-0.1853	Normal
23	0.4389	Significant
24	0.1333	Normal
25	-0.2000	Normal
26	-0.0833	Normal
27	0.1833	Normal
28	-0.0167	Normal
29	-0.1167	Normal
30	-0.1167	Normal
31	0.2069	Normal
32	-0.1405	Normal
33	0.0262	Normal
34	-0.3500	Normal
35	-0.2833	Normal
36	-0.2625	Normal
37	-0.1167	Normal
38	-0.2000	Normal
39	-0.0489	Normal
40	0.0500	Normal
41	-0.1853	Normal
42	-0.0833	Normal
43	0.0262	Normal
44	0.0940	Normal
45	-0.1853	Normal
46	0.1833	Normal
47	0.4389	Significant
48	-0.3071	Normal
49	-0.0750	Normal
50	-0.1853	Normal
51	0.3625	Normal
52	-0.2000	Normal
53	-0.1621	Normal
54	0.2643	Normal
55	0.4389	Significant
56	-0.0167	Normal
57	-0.0167	Normal
58	-0.0833	Normal
59	-0.0489	Normal
60	-0.2833	Normal
61	0.1690	Normal
62	-0.2625	Normal
63	0.0940	Normal
64	-0.0553	Normal
65	0.1333	Normal
66	-0.3786	Normal
67	0.0940	Normal
68	-0.2833	Normal
69	0.3167	Normal
70	-0.3786	Normal
71	0.5990	Significant
72	-0.2119	Normal
73	-0.0167	Normal
74	0.2643	Normal
75	-0.4500	Significant
76	-0.0167	Normal
77	-0.2833	Normal
78	-0.2000	Normal
79	-0.3250	Normal
80	-0.0833	Normal

About Autocorrelation

Mathematical foundations

The autocorrelation function (ACF) measures the linear dependence between values of a time series separated by k steps (lag). In the context of a lottery: is the result of draw N related to the result of draw N-k?

ACF Formula

ACF(k) = Σ(xₜ - x̄)(xₜ₊ₖ - x̄) / [n · Var(x)]

ACF values range from -1 to +1. If |ACF| exceeds the confidence interval ±1.96/√n, the correlation is statistically significant.