Module Performance
How each prop module is actually performing on settled outcomes. The model
can only learn from settlements — picks that have graded as W/L. As more
games complete, this report sharpens.
By Market Family
Market
N
W
L
Hit %
Net Units
Hit Rate
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By Sport
Sport
N
W
L
Hit %
Net Units
Hit Rate
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By Source Module
Source
N
W
L
Hit %
Net Units
Hit Rate
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How learning works: Each settled pick (win/loss) feeds back
into self_learn_weights.py, which adjusts the per-source
shrinkage weighting. Modules with high settled hit rates get more weight in
confluence ranking; bad sources get downweighted. With most picks still
pending, weights are calibrating slowly. As outcomes accumulate, the brain
gets sharper.
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