The real corruption: you accidentally destroyed in...

The real corruption: you accidentally destroyed independence between MC, Markov, and the Governor

Your totals governance comment says the intended architecture is:

“MC creates the full world… Markov gives the independent math cross-check… Cluster reads the match shape…” (Tennislocks_V17.4_Cluster_Governor_v17.4.5_FIXED.txt)

But your current wiring contradicts that in two ways:

A) “Markov” is being used to pre-crush holds before simulation

In the totals-only MC pass you literally do:

seed mismatch
adjust simHA/simHB by schedule-trust
then call markovEffectiveHolds(simHA, simHB, seed, tour)
and overwrite simHA/simHB with the result (Tennislocks_V17.4_Cluster_Governor_v17.4.5_FIXED.txt)
That means the Monte Carlo engine is not simulating from “raw skill holds.” It’s simulating from already-tapered holds.

B) markovEffectiveHolds is not “Markov math”—it’s a mismatch/taper penalty injector

Look at what markovEffectiveHolds actually does:

derives mismatch from win-prob seed
pulls taper params (lambda, minMult, maxPen, holdFloor)
computes a dog penalty penDog
applies hA_eff = clamp(hA - penFav) and hB_eff = clamp(hB - penDog) (or vice versa) (Tennislocks_V17.4_Cluster_Governor_v17.4.5_FIXED.txt)
So the function named “markovEffectiveHolds” is a blowout/taper compressor. When you run MC on those crushed holds and later run Markov/Dist using the same family of assumptions, your Governor can’t “see” disagreement anymore—both engines are being pushed into the same collapsed basin.

That’s exactly the “cluster governor misalignment / incestuous confirmation loop” you described.

3. How to restore sovereignty (without me patching your code)

Step 1 — Rename the concept in your head: you have 3 different things

Baseline skill holds (what the player would do vs average opposition on this surface)
Behavioral blowout/tank/taper (what happens conditional on match path)
Cross-check math (Markov/Dist should live here)
Right now, you’re mixing (2) into (1) before MC runs (via markovEffectiveHolds in the totals-only sim). (Tennislocks_V17.4_Cluster_Governor_v17.4.5_FIXED.txt)

Step 2 — Make Markov’s inputs “pure”

Your Markov/Dist engine should take:

baseline hold estimates (hA_base, hB_base)
start server
best-of
…and compute match totals distribution from that. Full stop.

Concretely: Markov must not import:

conditional-set taper knobs (lambda/minMult/maxPen)
hold floors meant for blowout containment
tanking logic
ORP “punish the dog” mechanics
Because the moment Markov shares those, your disagreement metrics go dead.

Step 3 — Keep blowout suppression inside MC, and only after state exists

Your MC sim already has explicit tanking / clamp floors during set simulation. (Tennislocks_V17.4_Cluster_Governor_v17.4.5_FIXED.txt)
That’s the correct place for it conceptually (because it depends on game differential / path).

So: instead of pre-shaving holds with markovEffectiveHolds, you want MC to start closer to baseline and let the in-sim tank/taper apply only when the match state warrants it.

Step 4 — Governor compares raw outputs, not blended ones

Your totals block describes Markov/Dist as a cross-check that can downshift confidence / force PASS, but shouldn’t flip sides. (Tennislocks_V17.4_Cluster_Governor_v17.4.5_FIXED.txt)
That’s fine—as long as the Governor sees two genuinely independent signals.

To keep it clean:

Compute:
MC_fairLine, MC_pOver(line), MC_p3
MK_fairLine, MK_pOver(line), MK_p3
Then compute disagreement:
gapP = |MC_pOver - MK_pOver|
gapLine = |MC_fairLine - MK_fairLine|
Only then apply “matrix rules” (downgrade, PASS, cap to LEAN, etc.)
If you blend MC+Markov first, your “gap” shrinks by construction and the Governor loses its job.

4. Why you’re getting the 18.5-style compression (the mechanical levers)

Even ignoring ORP, you currently have multiple downward compressors stacking:

Pre-sim hold shaving via markovEffectiveHolds (Tennislocks_V17.4_Cluster_Governor_v17.4.5_FIXED.txt) and it’s applied directly to simHA/simHB before totals-only MC. (Tennislocks_V17.4_Cluster_Governor_v17.4.5_FIXED.txt)
In-sim tanking penalty + mismatch-scaled clamp floor inside the set loop (Tennislocks_V17.4_Cluster_Governor_v17.4.5_FIXED.txt)
Extra sweep pressure via set-correlation delta increasing in mismatches (Tennislocks_V17.4_Cluster_Governor_v17.4.5_FIXED.txt)
Those three together will shove the center of mass downward and annihilate the upper tail. If your “true” fair line should be 21.5–22.5, your fix is not “one knob”—it’s removing double counting of blowout suppression (especially the pre-sim shave).

5. ORP: how to “tone it down” without losing the idea

What ORP is currently doing

Your ORP function explicitly:

clamps trust into [0.75, 1.15] (Tennislocks_V17.4_Cluster_Governor_v17.4.5_FIXED.txt)
and is designed to strongly deflate soft schedules (your own doc example shows a huge gap collapsing) (Tennislocks_V17.4_Cluster_Governor_v17.4.5_FIXED.txt)
plus you already guard against double-applying ORP with _oppQualityAdjusted. (Tennislocks_V17.4_Cluster_Governor_v17.4.5_FIXED.txt)
Separately, you apply probability compression based on opponent-quality trust (this is not ORP, but it interacts with it). (Tennislocks_V17.4_Cluster_Governor_v17.4.5_FIXED.txt)

Why it “always centers toward the favorite”

It’s usually one of these failure modes:

Asymmetry: ORP deflates soft schedules much more than it inflates hard schedules (common if “confidence” is shaped wrong). Your ORP comment history literally calls out that this was a problem in earlier versions. (Tennislocks_V17.4_Cluster_Governor_v17.4.5_FIXED.txt)
Double counting schedule softness: you adjust stats via ORP, and you also compress probabilities via trust. Even if applyOppTrustToProb is designed to avoid auto-boosting favorites, stacking it on top of aggressive ORP can still systematically pull edges back toward the pre-existing favorite prior (Elo / baseline). (Tennislocks_V17.4_Cluster_Governor_v17.4.5_FIXED.txt)
Trust range too wide + sensitivities too high: once trust hits the floor (0.75–0.80), your ORP “expectedForSchedule” can move enough that underdogs lose their only differentiator: inflated serve/return stats.
The clean way to tone it down (conceptually)

Instead of ORP acting like a punishment, make it act like an uncertainty-aware shrinkage:

A) Cap the maximum ORP move per stat
Example principle (not code): “ORP may not move hold% more than ±3.0 percentage points and RPW more than ±2.0 pp in one pass.”
This prevents ORP from erasing true underdog skill signals.

B) Make ORP depend heavily on sample size
You already carry sample-ish signals like sgPlayed. (Tennislocks_V17.4_Cluster_Governor_v17.4.5_FIXED.txt)
So: if the underdog’s “soft schedule” is based on 6–10 service games worth of data, ORP should barely move anything; if it’s 60+ SG, it can move more.

C) Only apply strong ORP when there’s a trust gap, not when both are soft
If both players have soft schedules, don’t “punish” either—just compress confidence (your applyOppTrustToProb is already in this spirit). (Tennislocks_V17.4_Cluster_Governor_v17.4.5_FIXED.txt)

D) Separate “direction” from “confidence” Right now ORP can flip who looks better (direction). If your lived experience is “ORP ruins my script,” a practical fix is:

ORP changes confidence / variance a lot,
ORP changes directional inputs only a little (or only when trust gap is extreme and sample size is strong).
That keeps the intuition (“stats padded vs weak opponents”) without turning it into a favorite-locking hammer.

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