Step 0 input check: stop before wiring opportunity, and why
READ-ONLY. Live grade path byte-identical -- no layer wired, no threshold
moved, no challenger added, no holdout run.
INPUTS ARE 100% POPULATED (n=80 real MLB props, through the grader's own
path): ab_per_game, rest_days, l5_avg, l20_avg, l10_stddev and
game_count_in_7d all 100%; opp_rank_stat 65% overall and 0% on
stolen_bases. So there is no honest-degradation problem to solve.
FOUR FINDINGS THAT STOP THE WIRING, three of which would have made the
work unmeasurable or wrong:
1. THE PREMISE IS WRONG. There is no built opportunity layer to connect.
ab_per_game is consumed in exactly one place -- analyzeViaEngine1:379,
which renders "4.3 AB/G" on the grade card. engine1 has NO opportunity
or usage factor at all. A projected opportunity was never built;
building one is construction, not connection.
2. THE INPUT IS THE WRONG SHAPE. ab_per_game = season atBats/games. It is
a per-player CONSTANT (measured: varies for 3 of 20 players, and those
cannot be legitimate since the value can't depend on stat_type), so it
can only move all of a player's props together, never separate them.
And it is collinear with the projection: l20_avg = seasonTotal/games,
the SAME denominator, so l20_avg already embeds opportunity
multiplicatively. Adding it additively double-counts.
3. THE REAL INPUT DOES NOT EXIST. depthChartService returns battingOrder:
null for MLB ("the one lineup slot the free schedule feed exposes") and
PropLine /context carries lineup_confirmed as a BOOLEAN, not the order.
4. ARCHITECTURE: wiring it into engine1 would be unmeasurable BY THIS
ORDER'S OWN TEST. Step 2 proves reliability and resolution, both
measured on p_win. engine1 factors move the grade LETTER and never
touch p_win. The layer belongs in probabilityEstimator, which already
adjusts on opp_rank_stat, home_away and a consistency pull.
SEQUENCING IS ALSO STALE: challengerProjection (arch-v1) is already live
with archetype, matchup (platoon) and environment (park) axes, writing
p_win_challenger to the ledger. Step 2 of the order's sequence is partly
done -- and the harness this order needed already exists.
RECOMMENDED INSTEAD, as its own order: an `opportunity` axis on that
harness driven by DRIFT, not level -- recent AB/G (last 5) over season
AB/G. A deviation is not collinear the way the level is. Per-game atBats
is present in the statsapi log rows but MLB_LOG_FIELD never maps it, so it
is a small contained BUILD, which is why it gets its own order. Honest
caveat carried forward: it is still a proxy, not tonight's opportunity.
PROBE BUG RECORDED: the first run reported 0% for every feature including
l5_avg, on a pipeline that had just graded 365 props -- impossible, so the
probe was wrong. getFeatures takes camelCase and returns { features: {} };
I passed snake_case and read the top level. Fixed to call
computeFeaturesForProp. Same class as the earlier silent-false harness: a
measurement that makes working code look broken invites you to "fix"
something that was never broken.
Gates: 4,059 tests / 325 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
This commit is contained in:
@@ -78,6 +78,7 @@ multi-book data.
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| **soccer** | **settles** — ~15 competitions, 30d | "grades into a void" is a **$19/mo Pro-tier** problem, not a data problem |
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| **CLV + results feeds** | `/odds/closing` + `/movement` **redacted**; `/results` **403 `required_tier: hobby`**; `/exports/resolved-props` **403 `required_tier: pro`** | **verified on our keys** — plain tier exclusion, not a key or plan fault. **$9/mo** buys CLV + steam + results; **$19/mo** adds the 90-day settlement export |
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| **books SERVED** | 5 → **13**; props rendered **546 → 2,780** (5.1×); mean **4.22** books/prop | **AGGREGATOR widening is LIVE.** Of 2,234 newly-visible props, **31.2% carry a real non-DFS price**; **68.8% are DFS-only** — shown, tagged, never a market |
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| **opportunity layer** | **NOT BUILT** — `ab_per_game` is display-only; engine1 has no usage factor; MLB batting order unavailable in every wired source | Step 0 stopped before wiring. Recommended instead: an `opportunity_drift` axis on the EXISTING `challengerProjection` (arch-v1). See `specs/connect-opportunity-step0.md` |
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| **MLB board size** | **7 → 365 graded props** (52×) in 114s | the 25-cap discarded 95.7% of the slate. Raised to 500 on measured cost. Refusals were **43.8% deliberate policy suppression**, not a data gap |
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| **model input** | **byte-identical** — 546 gradeable props, `v1_first_book` | `MODEL_BOOKS` gate in `dedupeProps` + `indexOddsProps`. Lifts only on the re-run |
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| **accrual clock** | **sequential, post-completion** | see §11. Pre-completion data does not count and is never pooled |
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@@ -0,0 +1,173 @@
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# CONNECT PROJECTION LAYERS — STEP 0: OPPORTUNITY/USAGE INPUT CHECK
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**Date:** 2026-08-01 · **READ-ONLY** · live grade path byte-identical ·
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probe committed (`src/services/featureCoverage.js`,
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`GET /api/internal/feature-coverage`).
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---
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## VERDICT: STOPPED AT STEP 0 — AND THE REASON IS THE DELIVERABLE
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**Inputs are 100% populated. The layer still should not be wired as specified**,
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for four reasons the input check surfaced. Three of them would have made the
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work either unmeasurable or wrong.
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---
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## 1. INPUT COVERAGE (MLB, n=80 real props, through the grader's own path)
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| feature | populated | rate |
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|---|---:|---:|
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| `ab_per_game` | 80/80 | **100%** |
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| `rest_days` | 80/80 | 100% |
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| `l5_avg` | 80/80 | 100% |
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| `l20_avg` | 80/80 | 100% |
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| `l10_stddev` | 80/80 | 100% |
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| `game_count_in_7d` | 80/80 | 100% |
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| `opp_rank_stat` | 52/80 | 65% |
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| `minutes_per_game` / `usage_rate` | 0/80 | 0% *(NBA-only — correct for MLB)* |
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**No honest-degradation problem exists** for the opportunity input: there is
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nothing sparse to fall back from. The one real hole is `opp_rank_stat`, which is
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**0% for `stolen_bases`** (no opponent SB-defence rank) and ~84% elsewhere.
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---
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## 2. 🔴 THE PREMISE: there is no built opportunity layer to connect
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The order says the expensive layers are *"BUILT and DISCONNECTED"* and that this
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is *"CONNECTION, not construction."* **For opportunity, that is not the case.**
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`ab_per_game` is computed in `featureCache` and consumed in exactly one place —
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`analyzeViaEngine1:379`, which renders **"4.3 AB/G"** on the grade card. It is a
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**display field**. `engine1` has **no opportunity or usage factor at all**.
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**A projected opportunity — expected plate appearances with its own uncertainty —
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was never built.** §10.3 of MASTER-PLAN said exactly this and called it *"the
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single biggest modelling upgrade available."* Building it is construction.
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## 3. 🔴 The available input is the wrong shape for the job
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`ab_per_game = season atBats ÷ games`. Two consequences:
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- **It is a per-player constant.** Measured: it varies across a player's own
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props for **3 of 20 players** (and those three are almost certainly a cache /
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refresh artifact, since the value cannot legitimately depend on `stat_type`).
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**A constant can only move all of a player's props together — it cannot
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separate them**, which is what a per-prop opportunity signal has to do.
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- **It is collinear with the projection that already exists.** `l20_avg` is
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`seasonTotal ÷ games` — the *same denominator*. For a batter,
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`hits/game ≈ (hits/AB) × (AB/game)`, so **`l20_avg` already embeds
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opportunity multiplicatively.** Adding `ab_per_game` as an independent additive
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factor double-counts it rather than adding information.
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**What is actually missing is tonight's deviation from the season baseline** —
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batting-order slot, a platoon sit, a role change.
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## 4. 🔴 That input does not exist in any wired source
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- `depthChartService.getLineup` returns, for MLB, **only the probable pitcher**,
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with `battingOrder: null`. Its own comment: *"the one lineup slot the free
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schedule feed exposes."*
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- PropLine `/context` (free, verified this session) carries `lineup_confirmed` —
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**a boolean**, not the order.
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**Tonight's batting order, the actual driver of MLB plate appearances, is not
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available from anything we have wired.**
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## 5. 🔴 ARCHITECTURE: wiring it into `engine1` would be unmeasurable by this order's own test
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Step 2 requires proving **reliability and resolution** improve. Both are measured
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on **`p_win`**.
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**`engine1` factors move the grade LETTER. They do not touch `p_win`.**
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`p_win` comes from `probabilityEstimator.estimateProbability`, which builds from
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`frequencyOver(gameLogs, line)` plus feature adjustments.
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So an opportunity factor added to `engine1` would produce a change that **Step 2
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literally cannot measure**. The layer belongs in `probabilityEstimator` (or the
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challenger below), which *does* consume features — it already adjusts on
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`opp_rank_stat`, `home_away`, and a consistency pull off `l10_stddev/l20_avg`.
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---
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## 6. THE SEQUENCING ASSUMPTION IS STALE — matchup is already connected
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The order sequences *opportunity → matchup granularity (archetype × opponent,
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park/weather/platoon) → distribution ladder.*
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**`challengerProjection` (`arch-v1`) is already live in production** and already
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carries three axes: **archetype**, **matchup (platoon)** and **environment
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(park)**. It writes `p_win_challenger`, `challenger_delta`,
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`challenger_adjustments` and `challenger_version` to the ledger on every graded
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prop.
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So step 2 of the sequence is **partly done**, and — more usefully — **the harness
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this order needed already exists.** Any new axis should be added there, not
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invented.
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---
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## WHAT I RECOMMEND INSTEAD (its own order, per "one layer at a time")
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**An `opportunity` axis on `challengerProjection`, driven by opportunity DRIFT
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rather than by the season level:**
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```
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opportunity_drift = recent AB/G (last 5) ÷ season AB/G
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```
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- **>1** — batting higher / playing more than his baseline → lean over
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- **<1** — reduced role, platoon, lower slot → lean under
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- **null** — no at-bat data → **no adjustment**, challenger ≡ champion on that row
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This is a **deviation**, so it is not collinear with `l20_avg` the way the raw
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level is, and it *does* vary per player over time.
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**The input exists but is not extracted.** Per-game `atBats` is present in the
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statsapi game-log rows (the Session-56 box-score audit lists `atBats` among the
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batting fields), but `MLB_LOG_FIELD` has no entry for it and nothing computes a
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recent AB/G. That is a small, contained build — **and it is a build**, which is
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why it belongs in its own order rather than being smuggled into a connection
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order.
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**Honest caveat to carry into it:** this is still a *proxy* for tonight's
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opportunity, not tonight's opportunity. The real input is the confirmed batting
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order, and that needs a lineup source we do not have.
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---
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## WHAT DID NOT HAPPEN, DELIBERATELY
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No layer was wired. **The live grade path is byte-identical.** No threshold moved,
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no challenger was added, no holdout was run — running one would have measured a
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change that could not have occurred.
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## A PROBE BUG WORTH RECORDING
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The first coverage run reported **0% for every feature, including `l5_avg`** — on
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a pipeline that had just graded 365 props, which `projectionFor` cannot do
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without a positive `l5_avg` or `l20_avg`. **Impossible, therefore the probe was
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wrong.**
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Two bugs, both mine: `featureCache.getFeatures` takes **camelCase**
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(`playerName`/`statType`) and I passed the prop's snake_case shape; and it
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returns **`{ features: {...} }`** while I read the top level. Either alone yields
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all zeros. Fixed by calling `computeFeaturesForProp` — the grader's own entry
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point.
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Same class as the earlier harness that returned a silent `false`: **a measurement
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that makes working code look broken is more dangerous than no measurement**, because
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it invites you to "fix" something that was never broken.
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## TAGS
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**VERIFIED:** `ab_per_game` 100% populated · per-player constant (varies for
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3/20) · `opp_rank_stat` 0% on stolen_bases · engine1 has no opportunity factor ·
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`ab_per_game` is display-only · MLB batting order unavailable in depthChart and
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`/context` · `probabilityEstimator` consumes features, `engine1` does not affect
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`p_win` · `challengerProjection` arch-v1 already carries archetype/matchup/
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environment.
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**CORRECTED:** "the opportunity layer is built and disconnected" — it is not
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built. "Connect opportunity, then matchup" — matchup is already connected.
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@@ -0,0 +1,82 @@
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'use strict';
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/**
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* featureCoverage — Step 0 input check (2026-08-01).
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*
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* The property worth locking is the one whose absence produced a false
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* all-zero reading on the first run: a coverage probe must count a feature as
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* populated ONLY when it is a real finite number, and it must read features
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* from the grader's own return shape. A probe that makes working code look
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* broken is more dangerous than no probe.
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*/
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const { coverage, __internals } = require('../../src/services/featureCoverage');
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const { populated } = __internals;
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const isModelBook = (b) => b === 'draftkings';
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const prop = (player, stat) => ({
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player, stat_type: stat, line: 1.5, book: 'draftkings',
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});
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describe('populated — strict, never 0-coercing', () => {
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it('treats null / undefined / empty string as ABSENT, not zero', () => {
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for (const v of [null, undefined, '', NaN, 'abc']) expect(populated(v)).toBe(false);
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});
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it('treats a real zero as PRESENT (0 rest days is a fact, not a gap)', () => {
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expect(populated(0)).toBe(true);
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expect(populated(4.31)).toBe(true);
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});
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});
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describe('coverage — reads the grader’s shape and splits by stat', () => {
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const getOdds = async () => ({
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props: [
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prop('Batter A', 'hits'),
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prop('Batter B', 'rbi'),
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prop('Pitcher C', 'strikeouts'),
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{ ...prop('Ignored', 'hits'), book: 'prizepicks' }, // DFS never reaches the model
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],
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});
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// Mirrors computeFeaturesForProp: features live UNDER `features`.
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const computeFeatures = async (p) => ({
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features: p.stat_type === 'strikeouts'
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? { l5_avg: 6.1, l20_avg: 5.8 } // pitcher: no at-bats
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: { l5_avg: 1.2, l20_avg: 1.1, ab_per_game: 4.3, rest_days: 0 },
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});
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it('reports per-feature coverage from the nested features object', async () => {
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const r = await coverage({ sport: 'mlb', sample: 10, concurrency: 2, getOdds, computeFeatures, isModelBook });
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expect(r.sampled).toBe(3); // the DFS row is excluded upstream
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expect(r.coverage.l5_avg.pct).toBe(100);
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expect(r.coverage.ab_per_game.populated).toBe(2);
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});
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it('SPLITS BY STAT — a pooled number would hide a pitcher-shaped hole', async () => {
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const r = await coverage({ sport: 'mlb', sample: 10, concurrency: 2, getOdds, computeFeatures, isModelBook });
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expect(r.by_stat.hits.ab_per_game).toBe(100);
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expect(r.by_stat.rbi.ab_per_game).toBe(100);
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expect(r.by_stat.strikeouts.ab_per_game).toBe(0); // the hole the pooled 66% would bury
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});
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it('flags a per-player CONSTANT — it cannot separate a player’s own props', async () => {
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const twoProps = async () => ({ props: [prop('Same Guy', 'hits'), prop('Same Guy', 'rbi')] });
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const constant = async () => ({ features: { ab_per_game: 4.3 } });
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const r = await coverage({ sport: 'mlb', sample: 10, concurrency: 2, getOdds: twoProps, computeFeatures: constant, isModelBook });
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expect(r.ab_per_game_shape.players_with_value).toBe(1);
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expect(r.ab_per_game_shape.players_where_it_varies_across_their_props).toBe(0);
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expect(r.ab_per_game_shape.note).toMatch(/CONSTANT per player/);
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});
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it('a thrown feature build counts as ABSENT, never as populated', async () => {
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const boom = async () => { throw new Error('nope'); };
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const r = await coverage({ sport: 'mlb', sample: 10, concurrency: 2, getOdds, computeFeatures: boom, isModelBook });
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expect(r.coverage.l5_avg.populated).toBe(0);
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});
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it('is read-only by contract', async () => {
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const r = await coverage({ sport: 'mlb', sample: 10, concurrency: 2, getOdds, computeFeatures, isModelBook });
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expect(r.read_only).toBe(true);
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});
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});
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Reference in New Issue
Block a user