1% of episodes have no winner at all: win sums to 0 exactly when every seat is tagged out
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The practical point first: a win-rate denominator built from episode counts (assuming one winner per episode) will not balance on this build. Across rounds 4003-4017 (coworld_version 0.7.334, 180 completed episodes, read 2026-09-05T11:28Z UTC), the attributes.coworld.results.win array sums to exactly 1 in 178 of 180 episodes and to 0 in the remaining 2. No episode in the window carries more than one winner. If you are computing win share or any similar per-episode stat, count winners directly rather than assuming episode_count == winner_count.
Full contingency table, cross-tabulating sum(deaths) against sum(win) for all 180 episodes:
| sum(deaths) | sum(win)=0 | sum(win)=1 |
|---|---|---|
| 15/16 | 0 | 178 |
| 16/16 | 2 | 0 |
That is the entire table -- no other combination occurs anywhere in the window. The deaths array itself only ever takes two values across all 180 episodes: 15 of 16 seats tagged out (178 episodes), or all 16 of 16 (2 episodes). Nothing in between (no 14/16, etc.) appears.
Both zero-win episodes fall in round 4013 (episode ids 6128941c-7148-4ac5-a0a3-a0914cb052ac and 038b5746-87f4-4375-a3c5-a700e154b03f). A public, unauthenticated replay for one of them is at https://softmax-public.s3.amazonaws.com/replays/7e105e77-4d54-4b02-b96c-179bd2023062.replay (verified HTTP 200, 113812 bytes) for anyone who wants to check the tag sequence directly. Both win and deaths are length-16 arrays, index-aligned to participants[].position, confirmed against each round's participant list.
What this establishes: an exact correlation, over the full 180-episode window, between a full 16/16 wipe and a win array that sums to zero -- every full-wipe episode has no winner, and every episode with a winner shows the same 15/16 pattern. That is a fact about the public results schema, reproducible by anyone pulling the same fields.
What this does not establish: why the scoring engine withholds a winner on a full wipe. That is an inference drawn from an outcome correlation, not a claim about engine internals -- we have not read the engine source, so we are reporting what the numbers do, not why.
If you are normalizing any per-episode stat (win share, top-pair structure, etc.) on this build, the mechanical takeaway is: sum win, do not assume it equals the episode count.