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Reading the draws and working out what chance predicts.

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Anytime Bias Watch

Evidence you are allowed to keep watching.

Ordinary p-values break when we peek after every draw and stop on an exciting day. This instrument uses evidence values designed for exactly that behaviour—and never treats different scheduled games as one stream.

Evidence capital by ball

SiVinceTutto6/90 era · since Sep 2024104 draws

Global evidence
0.19×
Alarm line
20×
Null inclusion rate
6.67%
80% power scale
±10.50 points
Bonferroni, approximate

Beta-mixture e-processbeta-mixture-e-v1global value is the valid average across balls

20 is the 5% Ville threshold. Crossing it is a prompt to investigate data and machinery, not an allegation.

Every ball on its own

SiVinceTutto6/90 era · since Sep 2024104 draws

Plain 20× lineCorrected threshold (20 × 90)
321.00×
371.00×
441.00×
661.00×
40.78×
590.78×
690.78×
850.78×
470.34×
500.34×
600.34×
80.23×

The reference line is the plain 20×, which is the right threshold for the global reading above and the wrong one for any single ball here.

Beta-mixture e-processbeta-mixture-e-v1per-ball e-processes averaged into one valid global e-process; the per-ball column is descriptive and needs 1800x before it is a claim

Descriptive; not anytime-valid on its own. Each ball’s e-process is a valid test for that ball, but this column shows the largest of 90 of them and picking the largest is a selection — Ville’s guarantee covers one process, not a maximum over many.
Analysis ledger
Draw stream
main
Usable evidence
104 / 104 draws
Trust filter
all canonical draws
Source omitted
none
Stopping rule
anytime-valid; no fixed end date
∑The maths, in plain language

Evidence safe under peeking

For one ball, Xt=1X_t=1Xt​=1 when it appears and p=k/Np=k/Np=k/N. With a Beta⁡(1/2,1/2)\operatorname{Beta}(1/2,1/2)Beta(1/2,1/2) alternative,

Et=B(S+1/2,t−S+1/2)B(1/2,1/2)pS(1−p)t−S.E_t=\frac{B(S+1/2,t-S+1/2)}{B(1/2,1/2)p^S(1-p)^{t-S}}.Et​=B(1/2,1/2)pS(1−p)t−SB(S+1/2,t−S+1/2)​.

Under the fair null its expectation never exceeds one, so Ville's inequality gives P(sup⁡tEt≥1/α)≤αP(\sup_t E_t\geq1/\alpha)\leq\alphaP(supt​Et​≥1/α)≤α.

✦The story behind it

A statistic built for the monitoring room

A monitoring room must be able to look every day. Safe testing prices that freedom into the statistic itself. The result is less theatrical than repeated significance tests—and far harder to fool by choosing when to stop.