builtbykev
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READ-ONLY. Nothing built or fixed; the four challengers untouched. 41% OF THE REPORTED GAP WAS A MEASUREMENT ARTIFACT. p_win is P(graded side); proj_p_over_line is P(over); 31.4% of settled rows are UNDER-graded, so comparing them raw measures the ladder backwards on a third of the sample. Matched + direction-aligned (n=437): 0.252 vs champion 0.352, not 0.108 vs 0.331. The PRODUCT is not making this mistake -- I checked; projectionChallenger normalises both to the over basis deliberately. The error was in the measurement. THE LOSS IS CONCENTRATED. hits (n=245, res 0.060) and total_bases (n=49, res 0.009) are 67% of rows and carry essentially no signal. Everything else is fine or better: walks 0.519 vs champion 0.544, runs mean 0.345 vs 0.392, and on DOUBLES the ladder's mean BEATS the champion's (0.207 vs -0.062). IT IS THE MEAN, NOT THE SHAPE. On the two failing families the mean itself carries no signal (0.052, -0.019) against the champion's 0.158 and 0.085. Where the mean is good the probability is good -- shape follows mean. A HYPOTHESIS I TESTED AND DISPROVED: prediction compression. I expected P(>=1 hit) to sit in a narrow band and fail to rank. It does not -- spread ratio 0.94 overall, 0.80 for hits, 0.94 for total_bases. The ladder has comparable spread; it is spread in a direction uncorrelated with outcomes. Recorded because it was a plausible story the data refused. PRIORS AND PLUMBING CLEAN. proj_factors carries form_rate, combined_multiplier and breakdown on every row; proj_point 100% populated with sane centres (hits 0.830 vs line 0.578). Not the environment-style silent-null failure. NAMED CAUSE (structural, flagged as hypothesis not finding): the count model mismatches those two stats. total_bases is a WEIGHTED SUM (1B..HR = 1..4), so an NB treats one home run as four events and mis-states variance -- and TB has the worst result in the table. hits is BOUNDED BY AT-BATS and mostly traded at 0.5, so almost everything rides on P(0), the region where the wrong family hurts most. walks/runs/doubles ARE genuine low-rate counts and are exactly the ones that work. FIX BRANCH: targeted per-stat fix for hits and total_bases. THIS REMOVES THE MLB SIMILARITY BUILD FROM THE CRITICAL PATH -- that branch assumed a GLOBAL mean weakness, and the mean is fine or better on three of six stat families. Similarity may be worth building later, on evidence, not on this. Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01QJs13VsyiSKYQP6rj3NNmc
160 lines
6.2 KiB
Markdown
160 lines
6.2 KiB
Markdown
# WHY proj-v1.1 LOSES — DIAGNOSIS
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**Date:** 2026-08-02 · **READ-ONLY** — nothing built, fixed or wired. The four
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accruing challengers were not touched.
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---
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## HEADLINE: THE GAP IS REAL BUT WAS OVERSTATED, AND IT IS NOT A SIMILARITY PROBLEM
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> **CAUSE: a bad MEAN on TWO stat families — `hits` and `total_bases` — which are
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> 67% of the sample. Everything else works.**
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>
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> **FIX BRANCH: targeted per-stat model fix. NOT an MLB similarity build.**
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The diagnosis removes the biggest remaining build, which is what it was for.
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---
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## STEP 1 — MATCHED ROWS: 41% OF THE "GAP" WAS A MEASUREMENT ARTIFACT
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`p_win` is P(**graded side**). `proj_p_over_line` is P(**over**). **31.4% of settled
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rows are UNDER-graded**, so comparing raw P(over) against a graded-under outcome
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measures the ladder backwards on a third of the sample.
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Matched rows, n=437:
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| | resolution |
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|---|---:|
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| champion `p_win` | **0.3523** |
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| proj-v1.1, **unaligned** (as previously reported) | 0.1494 |
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| proj-v1.1, **direction-aligned** | **0.2521** |
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**Aligning direction recovers 41% of the apparent gap.** The previously reported
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*0.108 vs 0.331* substantially overstated the loss.
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**The product itself is NOT making this mistake** — I checked. `projectionChallenger`
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deliberately normalises both quantities to the over basis (`proj_book_implied` =
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`fairOver`, converting the graded-side fair when direction is under). **The error
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was in the measurement, not the model.** The real gap is **0.252 vs 0.352**.
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---
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## STEP 2 — PER-STAT: THE LOSS IS CONCENTRATED, NOT SYSTEMIC
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| stat | n | base | **res champ** | **res proj** | champ MEAN | proj MEAN |
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|---|---:|---:|---:|---:|---:|---:|
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| **hits** | **245** | .588 | 0.204 | **0.060** | 0.158 | **0.052** |
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| **total_bases** | **49** | .612 | 0.273 | **0.009** | 0.085 | **−0.019** |
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| rbi | 42 | .333 | 0.412 | 0.218 | 0.349 | 0.159 |
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| runs | 38 | .579 | 0.467 | 0.270 | 0.392 | **0.345** |
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| **walks** | 29 | .517 | 0.544 | **0.519** | 0.297 | **0.519** |
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| **doubles** | 28 | .179 | 0.315 | 0.213 | **−0.062** | **0.207** |
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| **ALL** | 437 | .533 | 0.352 | 0.252 | 0.199 | 0.166 |
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**`hits` + `total_bases` = 294 of 437 rows (67%), and on both the ladder has
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essentially NO signal.** They drag the aggregate on their own.
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**Where the ladder works, it works well:**
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- **`walks`: 0.519 vs the champion's 0.544** — competitive.
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- **`doubles`: the ladder's MEAN (0.207) BEATS the champion's (−0.062).**
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- `runs`: mean 0.345 vs 0.392 — close.
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**The machinery is not broken. Two stat families are.**
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---
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## STEP 3 — MEAN vs SHAPE: IT IS THE MEAN
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For the two failing families the **mean itself carries no signal** — `hits` 0.052,
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`total_bases` **−0.019** — while the champion's mean on the same rows carries 0.158
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and 0.085. The distribution cannot rescue a mean that does not rank.
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Conversely, where the mean is good (`walks` 0.519, `runs` 0.345, `doubles` 0.207)
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the ladder's probability is good. **Shape follows mean, cleanly.**
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### A hypothesis I tested and DISPROVED
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I expected **prediction compression** — that P(≥1 hit) would land in a narrow band
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across players and so could not rank. **Wrong:**
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| stat | sd champion | sd proj | ratio |
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|---|---:|---:|---:|
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| ALL | 0.2048 | 0.1932 | **0.94** |
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| hits | 0.1703 | 0.1369 | 0.80 |
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| total_bases | 0.1876 | 0.1758 | **0.94** |
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**The ladder has comparable spread. It is not compressed — it is spread in a
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direction uncorrelated with outcomes.** Recording this because it was a plausible
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story that the data refused.
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---
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## STEP 4 — PRIOR / PLUMBING INTEGRITY: CLEAN
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No silent default found. On a real snapshot, `proj_factors` carries `form_rate`,
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`combined_multiplier`, `breakdown`, `book_implied_basis` on every row, and
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`proj_point` is populated 100%. The central values are sane:
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| stat | mean `proj_point` | mean line |
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|---|---:|---:|
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| hits | 0.830 | 0.578 |
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| total_bases | 1.639 | 1.500 |
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| strikeouts | 4.972 | 5.204 |
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**The priors are reaching the posterior. This is not the environment-style
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silent-null failure.**
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---
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## THE CAUSE, AND WHY THOSE TWO STATS
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**Named cause: the count model does not match the generative structure of `hits`
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and `total_bases`.**
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Stated as a **hypothesis** — it follows from the structure, and this diagnosis did
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not test it directly:
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- **`total_bases` is not a count of events — it is a WEIGHTED SUM** (1B=1, 2B=2,
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3B=3, HR=4). A negative binomial fitted to TB treats one home run as "four
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events", which mis-states the variance badly. This is a structural mismatch, not
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a tuning error — and TB has the worst result in the table (**−0.019**).
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- **`hits` is bounded by at-bats** (~4/game). It is closer to
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binomial(AB, avg) than to an unbounded Poisson/NB, and most lines are 0.5
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(mean line 0.578), so almost everything rides on P(0) — the exact region where
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the wrong family hurts most.
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- **`walks`, `runs`, `doubles` ARE genuine low-rate event counts** — and they are
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precisely the ones that work.
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---
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## FIX BRANCH — and what it rules OUT
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**RECOMMENDED NEXT ORDER: a targeted per-stat model fix for `hits` and
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`total_bases`.** Small, contained, and aimed at 67% of the sample.
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**This diagnosis REMOVES the MLB similarity build from the critical path.** The
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order's "bad MEAN → build similarity (large)" branch assumed a *global* mean
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weakness. It is not global: the mean is fine or better than the champion's on
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`walks`, `runs` and `doubles`. **A similarity layer would not fix a
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family-mismatched count model, and building one now would be a large project aimed
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at the wrong defect.**
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Similarity may still be worth building later — but **on evidence, not on this.**
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## TAGS
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**VERIFIED:** matched-row aligned gap 0.252 vs 0.352 (n=437) · 31.4% under-graded ·
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`projectionChallenger` normalises direction correctly, so the misalignment was
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measurement-only · per-stat table above · mean-vs-shape isolation · priors and
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plumbing clean.
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**DISPROVED:** prediction compression (spread ratio 0.80–0.94).
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**HYPOTHESIS, NOT VERIFIED:** that the NB family mismatch is *why* hits and TB fail.
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Structurally motivated; the fix order should test it before committing to a family
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change.
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**CORRECTED:** "proj-v1.1 loses 0.108 vs 0.331" — that comparison was unaligned on
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direction and across different row sets.
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