#44's third ask was an opt-in mode so a caller with partially-known
teams need not pre-filter. The requested shape was `Skip` — drop unknown
members. Measured, that is the wrong mode to build.
A team's performance is the *sum* of its members, so dropping one drops
its variance too. On a two-member team with one unknown:
SKIP (drop the member) : performance sigma 2.37
PRIOR (member at prior) : performance sigma 6.53 (2.76x wider)
Skipping makes the model *more* certain because it knows *less*, which
is backwards. `Prior` is also the answer the model already gives for a
competitor it knows about but has no evidence for — measured, such a
competitor sits at sigma 4.99 against the prior's 6.0 — so it
corresponds to a state the model can actually be in. Skipping does not.
So the enum is `Reject` (default, unchanged) and `Prior`, and it is
`#[non_exhaustive]` in case a real use for skipping turns up later.
Placed on `HistoryBuilder` rather than per-call. Neither consumer wants
it to vary between queries: one scores thousands of candidate matchups
in a loop, the other's headline feature is predicting a competitor
nobody has faced. That makes it a property of how the model is being
used, and keeps five prediction signatures unchanged.
This also gives #48 the semantics it asked for — "I have never seen this
competitor, here is the prior-informed answer" — which it needs for
predicting a course nobody has played.
`an_unknown_member_widens_its_team_rather_than_narrowing_it` pins the
property that ruled `Skip` out, so a future convenience cannot quietly
reintroduce it.
Closes#44. Refs #48
Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_011hcFjNDmHXZF8URGLku5zZ
logaritmiskandClaude Opus 5
71554fd944