Brazil: Timemania

Timemania Autocorrelation Analysis

Timemania: 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 "Timemania" lottery.

Analysis based on 20 draws from 04/07/2026, 11:00 PM to 05/26/2026, 11:00 PM

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	-0.0026	Normal
2	-0.0553	Normal
3	-0.1167	Normal
4	-0.0833	Normal
5	0.2069	Normal
6	-0.0553	Normal
7	0.0000	Normal
8	-0.1167	Normal
9	-0.0553	Normal
10	-0.1853	Normal
11	0.0000	Normal
12	-0.0553	Normal
13	-0.0553	Normal
14	-0.0833	Normal
15	-0.1167	Normal
16	-0.1167	Normal
17	0.0500	Normal
18	0.0000	Normal
19	0.0000	Normal
20	0.4389	Significant
21	0.4250	Normal
22	-0.1853	Normal
23	-0.1853	Normal
24	0.0000	Normal
25	-0.0553	Normal
26	0.0500	Normal
27	0.1125	Normal
28	-0.0553	Normal
29	-0.1167	Normal
30	-0.0611	Normal
31	-0.0553	Normal
32	-0.1167	Normal
33	0.0000	Normal
34	-0.0553	Normal
35	-0.1265	Normal
36	0.1833	Normal
37	-0.0611	Normal
38	0.0000	Normal
39	-0.0553	Normal
40	-0.0026	Normal
41	0.0000	Normal
42	-0.0553	Normal
43	-0.0553	Normal
44	-0.0553	Normal
45	0.0000	Normal
46	-0.1853	Normal
47	-0.0833	Normal
48	-0.0553	Normal
49	0.0000	Normal
50	-0.0553	Normal
51	0.0000	Normal
52	0.4250	Normal
53	0.0000	Normal
54	-0.2000	Normal
55	0.0000	Normal
56	0.2069	Normal
57	0.0000	Normal
58	-0.1265	Normal
59	-0.1167	Normal
60	-0.1167	Normal
61	-0.0026	Normal
62	-0.0026	Normal
63	-0.0553	Normal
64	-0.1853	Normal
65	0.2069	Normal
66	-0.0553	Normal
67	-0.0611	Normal
68	0.2657	Normal
69	0.0000	Normal
70	-0.1167	Normal
71	-0.1167	Normal
72	0.0000	Normal
73	0.4389	Significant
74	-0.0553	Normal
75	-0.1167	Normal
76	-0.0553	Normal
77	0.0000	Normal
78	-0.1853	Normal
79	0.0500	Normal
80	-0.0553	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.