tb-v1: model total_bases as a compound outcome (challenger)

Current ladder (proj_p_over_line) and champion p_win are BYTE-IDENTICAL.
tb-v1 writes alongside them, on total_bases props only.

STEP 0 -- components confirmed on real data, not assumed. statsapi has no
singles field, but hits - doubles - triples - homeRuns reproduces stored
totalBases EXACTLY on a real 10-game log. So the decomposition is exact,
not an approximation.

THE MODEL. Each component gets its own per-game Poisson rate; TB is their
weighted sum, and the PMF is built by exact convolution rather than
simulated (TB support is small). It inherits the SAME combined multiplier
proj-v1.1 computes, so the two models differ only in STRUCTURE.

Why this is the fix: with identical mean TB of 1.0, a pure-HR hitter and a
pure-singles hitter get P(TB>=4) of 0.221 vs 0.019 -- a 12x difference an NB
on TB alone cannot express, because it treats one home run as four events.
A test asserts that separation, and asserts P(TB>=4) for a pure-HR hitter
equals P(at least one HR) exactly.

INDEPENDENCE IS AN APPROXIMATION AND IS LABELLED AS ONE: a plate appearance
that becomes a double cannot also become a single, so the components are
weakly negatively correlated and independent Poissons slightly overstate
the tail. Closer to the truth than what it replaces; not a solved problem.

HONEST-ABSENT throughout: fewer than 3 usable games, or no derivable
component, returns null and the prop keeps the current ladder value. An
inconsistent row (hits < extra-base hits) is SKIPPED rather than clamped to
zero -- clamping would invent a plausible line out of a broken one.

I HIT THE Number(null)===0 TRAP IN MY OWN CODE and a test caught it: a null
rate passed a naive finite check and was treated as a measured zero, which
is the difference between "this player never triples" and "we do not know
his triple rate". Both tbPmf and tbMean now reject null/''/boolean strictly.

Holdout committed: TB ROWS ONLY (49 of 437 settled -- averaging into other
stats would hide the effect) and DIRECTION-ALIGNED, since the unaligned
comparison is the artifact that accounted for 41% of the ladder's apparent
loss. If tb-v1 does NOT improve, the family-mismatch hypothesis is wrong
and the mean/similarity branch reopens -- recorded in the query header.

Migration applied: proj_tb_p_over + proj_tb_meta, NULL-meaningful.

Gates: 4,104 tests / 329 suites green; next build exit 0.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01QJs13VsyiSKYQP6rj3NNmc

scripts/tb-compound-holdout.sql

@@ -0,0 +1,42 @@
+-- tb-compound-holdout.sql — TOTAL BASES ONLY, direction-aligned.
+--
+-- TWO guards this query exists to enforce:
+--   1. TB ROWS ONLY. Averaging into other stats would hide the effect, since
+--      total_bases is 49 of 437 settled rows.
+--   2. DIRECTION-ALIGNED. p_win is P(GRADED SIDE); proj_p_over_line and
+--      proj_tb_p_over are P(OVER). 31.4% of rows are under-graded, and comparing
+--      raw P(over) against an under-side outcome measures the model BACKWARDS —
+--      that artifact alone accounted for 41% of the ladder's apparent loss.
+--
+-- Promote tb-v1 ONLY if it materially improves TB resolution toward/past the
+-- champion. If it does NOT, the family-mismatch hypothesis is WRONG and the
+-- mean-weakness / similarity branch REOPENS. Record which.
+
+with tb as (
+  select
+    game_date, id, lower(side) side, (outcome='hit')::int won,
+    p_win::numeric champ,
+    case when lower(side)='under' then 1 - proj_p_over_line::numeric
+         else proj_p_over_line::numeric end ladder_al,
+    case when lower(side)='under' then 1 - proj_tb_p_over::numeric
+         else proj_tb_p_over::numeric end tbv1_al
+  from public.ledger_entries
+  where sport='mlb' and user_id is null
+    and stat = 'total_bases'
+    and outcome in ('hit','miss')
+    and p_win is not null
+    and proj_p_over_line is not null
+    and proj_tb_p_over is not null          -- matched rows: all three present
+)
+select
+  count(*)                                            n,
+  count(*) filter (where side='under')                under_rows,
+  round(avg(won::numeric),3)                          base_rate,
+  round(corr(champ,     won::numeric)::numeric,4)     res_champion,
+  round(corr(ladder_al, won::numeric)::numeric,4)     res_ladder_v11,
+  round(corr(tbv1_al,   won::numeric)::numeric,4)     res_tb_v1,
+  round(stddev(ladder_al)::numeric,4)                 sd_ladder,
+  round(stddev(tbv1_al)::numeric,4)                   sd_tb_v1,
+  round(avg(ladder_al)::numeric,4)                    mean_ladder,
+  round(avg(tbv1_al)::numeric,4)                      mean_tb_v1
+from tb;