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SiVinceTutto Autocorrelation Analysis

SiVinceTutto: 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 "SiVinceTutto" lottery.

Analysis based on 20 draws from 01/14/2026, 07:00 PM to 05/27/2026, 06:00 PM

Max lag:

Draw sums autocorrelation

Correlogram with 95% confidence intervals

Observations

Significant lags

±0.4383

95% confidence interval

Significant autocorrelation detected

Lags with significant correlation: 2 (ACF=-0.4656)

ACF(1) for All Numbers

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

Ball	ACF(1)	Status
1	-0.1167	Normal
2	0.2069	Normal
3	-0.0553	Normal
4	0.0000	Normal
5	0.0000	Normal
6	-0.0611	Normal
7	0.0000	Normal
8	0.4389	Significant
9	0.0000	Normal
10	-0.1167	Normal
11	-0.0026	Normal
12	-0.1167	Normal
13	-0.0553	Normal
14	-0.1167	Normal
15	-0.1167	Normal
16	-0.0553	Normal
17	-0.0553	Normal
18	-0.0553	Normal
19	-0.0553	Normal
20	-0.1167	Normal
21	-0.0611	Normal
22	0.0500	Normal
23	-0.1167	Normal
24	0.0000	Normal
25	-0.1167	Normal
26	-0.1853	Normal
27	-0.1853	Normal
28	-0.0553	Normal
29	-0.0553	Normal
30	0.0000	Normal
31	-0.1167	Normal
32	-0.1167	Normal
33	-0.0553	Normal
34	0.0000	Normal
35	-0.0553	Normal
36	-0.0553	Normal
37	0.0000	Normal
38	-0.0026	Normal
39	-0.0553	Normal
40	-0.0026	Normal
41	-0.0553	Normal
42	-0.0611	Normal
43	-0.0553	Normal
44	0.0000	Normal
45	-0.0026	Normal
46	-0.0553	Normal
47	0.0000	Normal
48	-0.1167	Normal
49	-0.0553	Normal
50	-0.0553	Normal
51	-0.1167	Normal
52	-0.1265	Normal
53	-0.0553	Normal
54	-0.0026	Normal
55	-0.0611	Normal
56	0.0000	Normal
57	0.0000	Normal
58	0.0000	Normal
59	-0.0553	Normal
60	-0.0553	Normal
61	0.2657	Normal
62	0.0000	Normal
63	-0.1853	Normal
64	-0.1167	Normal
65	-0.1853	Normal
66	-0.0553	Normal
67	0.0000	Normal
68	-0.0553	Normal
69	-0.0553	Normal
70	0.2069	Normal
71	-0.1167	Normal
72	0.0000	Normal
73	0.0000	Normal
74	-0.1853	Normal
75	0.5990	Significant
76	-0.0553	Normal
77	-0.0553	Normal
78	0.2069	Normal
79	-0.0553	Normal
80	0.0000	Normal
81	-0.1167	Normal
82	-0.0611	Normal
83	-0.1167	Normal
84	-0.0553	Normal
85	0.2069	Normal
86	-0.0553	Normal
87	-0.0553	Normal
88	0.0000	Normal
89	-0.0553	Normal
90	-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.