Planning function design for file edits and simulations

I’m looking at a file in /mnt/data and thinking using Python to edit it would be easiest. I need to focus on the functions like getTotalsPick(), gapP calc, and blending. I’m also thinking I should add an option in the simulation for disabling certain features, especially tail control when computing the fair line. I'll implement that as a flag or separate function to keep it simple.

Adjusting simulation for price discovery and Markov calculations

I need to modify the existing runMatchSimulation_Exact() by passing an option to trigger price discovery. Inside the simulation, I’ll disable certain conditions like tapering and blowout tanking. Additionally, I’ll update the _markovEffectiveHolds_ function to ensure it uses baseline inputs without mismatch penalties, so I'll create a new version for this.

Adjusting Markov for independence and cluster calculations

I need to modify _markovEffectiveHolds_ to return baseline holds without the mismatch penalty, and change comments to reflect this. I'll still compute mismatch for diagnostics, but it shouldn't affect the holds. Inside getTotalsPick(), I’ll calculate pOverMC and pOverMarkov separately, then compute gapP. This will help ensure Markov stays independent, and I'll need to trace the uses of related functions.

Fixing Step 1 seeding and price discovery logic

For Step 1, I need to seed the tentative side from MC, not combined totals engines. So, I'll choose the tentative side as "OVER" or "UNDER" based on pOverMC. I'll let the Markov cross-check adjust confidence but not flip the result. Also, tail suppression logic is leaking into the price discovery layer, so I’ll ensure fairLine comes only from raw totals distribution, using priceDiscovery outputs. I'll compute mcDistPrice based on MC without taper or tank.

Implementing Python edits and ensuring script logic

Since the file is huge, I’ll use Python to search for function definitions, implement changes, and check for syntax or undefined variables. I'll focus on key functions like getTotalsPick, components of the price discovery lane, and updating Markov to be independent. I plan to compute different values for price distribution, conviction distribution, and Markov cross-check while ensuring my changes don't break other existing logic, especially around flags and governors.

Refining fairness and cluster comparison

For fairness, I’ll use the baseline hold inputs that feed the simulation before the governors and apply them for pOverMarkov at the market line. The cluster should compare the raw MC conviction to the raw Markov, but not the blended values. For price discovery, MC should likely exclude tail controls, while for conviction, it can include them.

Clarifying engine output and fair line computation

The user wants the cluster to judge raw engines (MC and Markov) separately. To fix tail suppression impacts on the fair line, we should compute it using an ungoverned distribution. Cluster can still use governed MC, while Markov should remain independent. We'll compute gapP between governed MC and Markov states for better separation.

Addressing anchor logic and fair line

We'll update the code to use MC alone in Step 1 for direction, ensuring Markov only cross-checks. As for market anchor, we should not let it interfere with the fair line directly. It can apply as a last resort if the fair line is too far from the market, adjusting by 30-60% dynamically.

Adjusting ORP and implementing safety rail

We'll apply the anchor as a safety rail, ensuring a strong pull when there's a significant mismatch or if the line goes out of plausible bounds. We’ll also adjust ORP to prevent stacking, limit compression, and demote weights to reduce the impact, avoiding any model-breaking changes. This update focuses solely on totals.

Adjusting ORP functions and compressing toward 0.5

We’ll adjust the functions to ensure that compression only moves toward 0.5 based on the trust gap. This change aligns with the credibility modifier. We'll modify constants, adjust weights, and shrink the trust-gap scaling. We'll implement these adjustments in the relevant functions and keep the double-adjustment guard intact.

Adjusting price discovery and handling simulation tweaks

We'll disable blowout tanking and conditional taper for the price discovery lane in the totals simulation. This involves overriding related flags and functions such as _tankMultiplier, and we’ll ensure not to introduce new issues. We’ll adjust these settings, focusing only on the fair line, then proceed with analysis in Python.

import re, pathlib, textwrap, json, os, sys, math path="/mnt/data/Tennislocks_V17.4_Cluster_Governor_v17.4.5_FIXED.txt" text=pathlib.Path(path).read_text(encoding="utf-8", errors="replace") len(text), text[:200]

find getTotalsPick function start

m = re.search(r'function\s+getTotalsPick\s*(', text) m.start() if m else None

start=m.start() snippet=text[start:start+3000] snippet[:1500]

snippet[1500:3000]

show more of getTotalsPick

snippet2=text[start:start+12000] print(snippet2[3000:6000])

Checking gapP alignment in getTotalsPick

It looks like the getTotalsPick approach is partly aligned with the user's suggestion; however, there’s still reliance on the combined pO. We’ll need to make sure gapP is calculated correctly and not influenced by it. I'll search for where ctx.gapP is set and ensure the logic for its computation is correct.

search for "ctx.gapP" assignments

assigns = [m.start() for m in re.finditer(r'ctx.gapP\s*=', text)] len(assigns), assigns[:5]

Searching for gapP assignment

I haven't found a direct assignment for ctx.gapP. It's possible the variable is stored under ctx.gapP, but I haven't located it yet. To make sure the computation is correct, I'll search for all occurrences of gapP = to track where this value is being set or modified.

occ = [m.start() for m in re.finditer(r'\bgapP\b\s*=', text)] len(occ), occ[:10]

Inspecting getTotalsPick and related sections

Positions are likely defined within getTotalsPick and related functions. To understand more, I need to check the code around line 603381. I'll open the code closer to line 603300 to get a better view of the context, especially the assignments and logic leading up to the gapP computation.

for pos in occ: print(pos, text[pos-50:pos+120].replace("\n"," ")[:160])

Identifying bug in context assignment

I found the assignment at position 819267: _totCtx.gapP = abs(pOverEval - pOverMarkov). It looks like the bug persists in another function that handles the context (ctx). I’ll focus on fixing it at the source. I'll open the code around line 818900 to inspect further and plan the

pos=819267 print(text[pos-500:pos+500])

Reviewing and fixing the logic in getTotalsPick

I need to patch _totCtx.gapP to use pOverMCNoPush instead of pOverEval. Also, pOverEval contributes to _totCtx.pOverBlend, but I’ll have to ensure that only raw outputs are passed to the cluster. I'll review the roles of pO, pMC, and their interactions with getTotalsPick as well as the final output logic.

Extract whole getTotalsPick function by matching braces roughly

def extract_function(name): # naive: find 'function name(' and then count braces start = text.find(f'function {name}(') if start==-1: return None i=start # find first '{' i=text.find('{', start) depth=0 for j in range(i, len(text)): if text[j]=='{': depth+=1 elif text[j]=='}': depth-=1 if depth==0: return text[start:j+1] return None

gtp = extract_function("getTotalsPick") len(gtp) if gtp else None

show end portion return statements

gtp[-1200:]

Reviewing side calculation in getTotalsPick

I need to review earlier parts of getTotalsPick where it sets the initial side or uses pO. I'll focus on sections around where it sets the "side" variable or pOver. Searching within the function for "var side" might help pinpoint where things are being set up.

find where it seeds side

for pat in ["tentative", "Step 1", "side", "pO >= 0.5", "pMC >= 0.5"]: idx = gtp.find(pat) if idx!=-1: print(pat, idx, gtp[idx-80:idx+200].replace("\n"," ")[:250])

Updating getTotalsPick approach with pMC

Step 1 requires changing the seed from pO to pMC inside getTotalsPick. The _side0 in the bimodal guard should likely use the tentative side and pMC instead of pO, since direction comes from MC. For the fragile variable, I might need to use pMC as well. I'll consider updating pick classification with raw MC for better accuracy, especially for side determination and probability evaluation.

Refining probability handling in getTotalsPick

The architecture suggests using pMC for the world-building process and Markov cross-checks, so the probability for over/under at the market line should be based on pMC. We’ll adjust getTotalsPick to compute pEval = pMC by default, and use pO only if pMC isn't available. I’ll update the pipeline accordingly and change where getTotalsPick is called.

calls = [m.start() for m in re.finditer(r'getTotalsPick\s*(', text)] len(calls), calls[:10]

for pos in calls[-3:]: print(pos, text[pos-120:pos+200].replace("\n"," ")[:280])

Adjusting probabilities and fair line handling

We need to change totPick = getTotalsPick(pOverEval, _totCtx); to use pOverMCNoPush (raw MC). For display, we can keep pOverBlend in ctx, but for downstream processing, pOverGames should represent pOverMCNoPush. We'll set fairLineMC from price-discovery MC and implement separate variables like fairLinePriceMC and fairLineGovernedMC.

search in gtp for fairLine usage

for m in re.finditer(r'fairLine', gtp): pass gtp.count('fairLine')

show surrounding each occurrence

for m in re.finditer(r'fairLine\w*', gtp): idx=m.start() print(gtp[idx-60:idx+80].replace("\n"," ")[:160])

Clarifying fair line and MC adjustments

We’ll set ctx.fairLineMC to reflect the price-discovery fair line. For the cluster logic, the fair line may need to come from the governed model, but the Markov line should remain independent. The pick at the market line should rely on the real-world distribution, considering tail suppression and conviction adjustments, but pricing should stay tied to the price-discovery distribution.

Clarifying distribution use in probability and confidence

For computing pick confidence at the market line, we'll use the ungoverned distribution to avoid overconfidence, as the user wants "modest" confidence. The cluster will still use governed features to assess shape and decision-making. The price discovery lane will rely on ungoverned distribution, while the conviction lane will use governed data for final decisions.

Clarifying simulations for pricing and cluster features

For the two simulations: the first one uses ungoverned MC for fair line calculations, probability, and p3 without tapering. The second uses governed MC to adjust features like cluster metrics and p3 to reflect real behavior and blowout risks. Totals pick should use pMC from the price lane and cluster metrics from the governed simulation.

Adjusting cluster logic and simulation pipeline

In the totals evaluation section, we need to modify the pipeline to create _totSimPrice and _totSimGov. The existing code calculates _totSim from runMatchSimulation_Exact with taper conditions, so we will need to adjust it. I’ll locate the function call around line 820928 and extract more context.

context = text[818000:823200] print(context[:4000])

Incorporating pOverGames and mcOU computation

It seems that pOverGames is initially computed from mcOU and later becomes the final pOverGames. I need to locate where mcOU and pOverGames are calculated earlier in the code, probably before line 815000. I’ll scroll up to find more context on how these are set.

earlier = text[812000:818000] print(earlier[:4000])

Adjusting pOverEval and pOverMC for final pick

We need to make sure that pOverEval is used for diagnostics and possibly displayed in the preview, but not for the pick or gapP. Instead, pOverMC (price discovery lane) should be used for the final pick and direction. I’ll need to find where pOverGames is computed, likely in reference to mcOU, and adjust accordingly.

find first assignment to pOverGames in this totals section

idx = text.rfind("pOverGames", 812000, 818000) idx

print(text[813900:814300])

for m in re.finditer(r'\bpOverGames\s*=', text[800000:812000]): print(800000+m.start(), text[800000+m.start()-40:800000+m.start()+60].replace("\n"," "))

print(text[807700:812200])

Adjusting Markov independence and mismatch shrinkage

We’ll modify the Markov effective holds to return holds as a baseline rather than compressing them. To maintain independence, we need to remove the penalty shrinkage from decider calculation. We could let mismatch come from _effMk for its output, but need to ensure no shrinkage, especially for totals Markov. For conviction lane's UNDER-trigger policy, maybe MC decider is more appropriate than Markov.

Restoring MC independence and Markov decider adjustments

It looks like the Markov decider for under-score penalties currently overrides the MC decider, which could be causing the "tail suppression leak." We should switch to using the simulation decider for under-triggers rather than Markov. I'll adjust the code to keep Markov diagnostic-only and prevent it from overriding MC, while defining an additional simulation for price discovery based on the MC list.

locate where gamesListAdj set

for m in re.finditer(r'gamesListAdj\s*=', text): if 800000 < m.start() < 825000: print(m.start(), text[m.start()-100:m.start()+200].replace("\n"," ")[:300])

Searching for earlier definitions

It seems like there’s no assignment in this particular region of the code, so it’s possible that the variable or element is defined earlier. I'll search globally to see if I can locate its earlier definition and get a better sense of what’s going on.

matches=[] for m in re.finditer(r'var\s+gamesListAdj\b', text): if 780000 < m.start() < 830000: matches.append(m.start()) matches[:10], len(matches)

Searching for gamesListAdj

It looks like gamesListAdj might be derived from _totSim.gamesList after some adjustment. I’ll search for "gamesListAdj" in the code to see where it appears and get a better understanding of how it's being used and adjusted.

occGL = [m.start() for m in re.finditer(r'gamesListAdj', text)] len(occGL), occGL[:20]

Inspecting code near 799800

I’ll take a look at the code around 799800 to understand how things are functioning. The occurrences around 800285 and the surrounding area could provide context on the behavior or adjustments that are happening, so it’ll help if I inspect that section closely.

print(text[799800:801200])

Locating gamesListAdj and relevant code

This section is in the MATCH_PREVIEW output build, not in compute totals, but it’s still important. They define _pOverEval and later set it to pOverGames, but we’ll adjust that. I’ll check around 762000 to understand where gamesListAdj is created in the simulation, as it seems relevant for the computations.

print(text[763800:766800])

Locating _totSim definition and its usage

This part shows an adjusted totals list built from _totSim.gamesList and corrInt. _totSim seems to be the output of a Monte Carlo simulation for totals, likely with governors and set tapers. We'll need two versions of _totSim: one governed and one for price. Let's search for "runMatchSimulation_Exact" or "_totSim =" to find where it's created.

search for '_totSim =' near 760k region

for m in re.finditer(r'\b_totSim\s*=', text): if 740000 < m.start() < 770000: print(m.start(), text[m.start()-80:m.start()+120].replace("\n"," ")[:250])

Looking for _totSim declaration

It seems like _totSim might have been declared earlier. I'll search for the term "var _totSim" to locate where it's initialized. That could help clarify its role and how it's being used in the overall process.

for m in re.finditer(r'var\s+_totSim\b', text): if 700000 < m.start() < 830000: print(m.start(), text[m.start()-60:m.start()+100].replace("\n"," ")[:200])

Inspecting totals simulation range

It looks like the totals simulation is happening around 737521. I should focus on inspecting the lines between 736800 to 739200 to understand its context better. This way, I can figure out the specifics of how the simulation is affecting the rest of the code.

print(text[736600:739700])

Considering price discovery simulation alternatives

Okay, so "runMatchSimulation_Exact" is used for totals simulations with options like totalsOnly: true, which includes tapering and blowout tanking. To create a new simulation for price discovery, I could adjust this with the priceDiscovery: true flag, though running another might exceed the time budget. Alternatively, I could tweak the original simulation by disabling taper/tanking and reducing iterations. Still, I need to balance these adjustments with the time constraints.

Adjusting simulation for price discovery

For the price discovery fair line, I'll use Markov distribution alone since it's a simpler constant hold. However, the user prefers the fair line around 21.5-22.5 and wants it from the MC engine, not Markov. I'll reuse the same totals simulation runs, but with smaller iterations (around 4,000). If time runs low, I'll optimize and fall back to the existing fair line. Time budget permitting, this should be feasible.

Implementing opts flags for simulation

To minimize overhead, I'll use Markov for the fair line if the price simulation isn't run, although I shouldn't rely on that. Now, I need to find the runMatchSimulation_Exact function, so I can adjust the global flags like USE_CONDITIONAL_SET_TAPER based on the opts passed and set local flags accordingly. Guess I'll start by searching for the function definition.

m = re.search(r'function\s+runMatchSimulation_Exact\s*(', text) m.start() if m else None

rms = extract_function("runMatchSimulation_Exact") len(rms)

check top part for flags usage

print(rms[:2000])

Considering tanking and tapering usage

I continue to see the use of tanking and tapering in the simulation. These components might play a significant role in how the model behaves. It’s important to stay mindful of their impacts, especially as we refine the price discovery simulation approach and adjust various parameters.

print(rms[2000:4500])

Considering tanking penalty and adjustments

The tanking penalty, _densityTankPenalty, has been computed. Now, I’ll need to add a condition to disable tanking when certain flags are set, like opts.priceDiscovery or opts.disableTanking. I should also check if _tankMultiplier needs any adjustments. I’ll search within the match simulation code for more uses of "tank."

search within rms

for kw in ["tank", "TAPER", "USE_CONDITIONAL_SET_TAPER", "APPLY_TAPER_TO_SET3"]: if kw in rms: print(kw, rms.find(kw))

print(rms[6800:7600])

Disabling conditional set dependence

The conditional set dependence precomputes, utilizing the USE_CONDITIONAL_SET_TAPER later. Now, I’ll disable it. Next, I’ll focus on locating where it’s applied in the code to make sure everything is properly adjusted. Let’s track down its implementation and make the necessary changes.

print(rms[17600:18850])

print(rms[18850:21050])

Updating conditionals for tapering and tanking

I’ll need to adjust the conditional logic for tapering, especially using USE_CONDITIONAL_SET_TAPER. I'll define a local flag to manage whether to apply it based on the opts settings. I’ll replace references in the code to use these new flags. Also, I'll search for _densityTankPenalty to see where it's influencing mismatches.

idx = rms.find("_densityTankPenalty") idx

show usage beyond definition

for m in re.finditer(r'_densityTankPenalty', rms): print(m.start(), rms[m.start()-60:m.start()+80].replace("\n"," ")[:150])

Checking usage of _densityTankPenalty

It looks like _densityTankPenalty is defined but not used here, and I wonder if it's being replaced by a base penalty computed elsewhere. To confirm, I'll search for occurrences of "basePenalty" and check where that’s used, potentially in other parts of the code outside this function.

for kw in ["basePenalty", "blowout", "tankPenalty", "_tankMultiplier", "_densityTankPenalty"]: print(kw, rms.count(kw))

Looking into basePenalty usage

I’ll investigate where and how basePenalty is applied in the code. Since it seems potentially related to _densityTankPenalty, I'll look for instances of basePenalty and check how it’s being used. This might give me clarity on its role or any relation to the earlier variable.

for m in re.finditer(r'basePenalty', rms): print(m.start(), rms[m.start()-80:m.start()+150].replace("\n"," ")[:230])

Disabling blowout tanking in priceDiscovery

The blowout tanking is implemented around line 12094 inside runMatchSimulation_Exact. To disable it during priceDiscovery, I’ll either set basePenalty to 0 or skip the block entirely. I need to check the surrounding context a bit more to determine the best approach.

print(rms[11850:13150])

Disabling tanking and modifying Markov independence

To disable tanking during priceDiscovery or specific scenarios, I’ll set basePenalty = 0 or skip the block if opts.disableTanking is true. I’ll define these flags in runMatchSimulation_Exact. For Markov independence, I'll modify _markovEffectiveHolds_ to return unchanged baseline holds, and handle mismatch for diagnostics. I'll check if this change affects other areas beyond totals.

Revising shrink handling and inspecting matchOutcomeProbs

I plan to remove the shrink adjustment for Markov, so I’ll compute pMarkov3Set from pDecRaw without any shrink or penalty and keep mismatch for information. I'll check if matchOutcomeProbs uses winProb and investigate its logic. Depending on the findings, I may adjust the options used.

m = re.search(r'function\s+matchOutcomeProbs\s*(', text) m.start() if m else None

mo = extract_function("matchOutcomeProbs") len(mo)

inspect beginning to see opts usage

print(mo[:1200])

Investigating matchOutcomeProbs and tankMultiplier

It looks like matchOutcomeProbs reads tourCode and surfaceAlias from the sheet, and includes a tank multiplier, but it might not apply to holds—I'll need to confirm that. It may call simulateSet_Markov. I’ll search for “basePenalty” or “tank” within the function to see how it might be used.

for kw in ["basePenalty", "tank", "TAPER", "markovEffectiveHolds"]: print(kw, mo.find(kw))

Checking for 'TAPER' usage in matchOutcomeProbs

I’ve noticed a mention of 'tank', possibly in comments, but nothing definitive. It might have something to do with a conditional set taper, potentially affecting match total distribution. If that’s the case, it could impact Markov independence. I’ll check where "TAPER" is referenced to be sure.

print(mo[10650:11250])

Disabling taper and tanking in Markov cross-check

I’ve confirmed that matchOutcomeProbs includes conditional taper logic, which messes with Markov independence. To fix that, we'll disable taper logic within matchOutcomeProbs when used for Markov cross-checks. We’ll add a new flag to skip taper and tanking logic, ensuring independence. I’ll also check for tanking usage in simulateGame.

Handling independence and taper logic in Markov simulations

We’ll add local flags in matchOutcomeProbs to manage tapering logic, based on conditions like independence or price discovery. Markov should remain independent, so I'll ensure _useSetTaper is always false when used for Markov cross-checks. I'll consider other uses for matchOutcomeProbs, but prioritize Markov independence.

Adding independence flag and refining matchOutcomeProbs

We need to ensure that the necessary flag is passed when calling matchOutcomeProbs in the totals section, with the flag set to { tourLabel: _mkTour, independent: true }. I'll also remove winProbA/B seeds since they’re not necessary. I'll verify the use of matchOutcomeProbs in matchTotalDistribution too.

m = re.search(r'function\s+matchTotalDistribution\s*(', text) m.start() if m else None

mtd = extract_function("matchTotalDistribution") len(mtd)

print(mtd[:900])

Markov independence and fair line adjustments

Disabling taper within matchOutcomeProbs ensures Markov independence by adjusting the distribution. I'll compute pOverMarkov from the Markov distribution to enforce this. For the fair line, it seems fairLineMC is derived from the earlier computed totFairLine. I'll search for where totFairLine is computed, likely in findFairLine.

pos = text.find("totFairLine") pos

print(text[pos-200:pos+200])

Fixing the fair line based on price discovery

The fairLine derived from fairLineFromSortedTotals is influenced by the governed distribution, but we need it to come from the price discovery distribution, which is ungoverned. We'll build a price discovery games list, simulate without taper or tanking, and apply the same correction factors—but without relying on tail suppression from the governed simulation.

Adjusting fair line calculation based on price discovery

For the fair line, we need to compute it from a Markov distribution with rank/surface adjustments, but using the price discovery distribution is important for accuracy. So, we’ll create a new adjusted price discovery list using the corrected integer adjustment (corrInt) based on the price simulation. However, tail suppression could affect the projection, so this will need careful handling to ensure accuracy.

Adjusting the correction logic and base mean calculations

We’ll recompute the correction adjustment relative to the price simulation mean for better accuracy. After adjusting for rank-based projections, we'll calculate the corrected total games. We'll need both the governed and ungovened simulations for different parts of the process, like the total simulation and cluster medians.

Adjusting governing and price simulations for fair line computation

For the totals, I’ll continue using the governed simulation for cluster shapes and adjustments, but compute the price mean and median separately with the price simulation. I'll apply the same correction to both simulations to maintain consistency, ensuring the market and fair line definitions remain aligned.

Recomputing fair line with price discovery while maintaining adjustments

The correction logic may be redundant but is still part of the process. To handle this, I'll recompute the fair line by using the price discovery simulation but applying the same correction. This ensures outputs use consistent adjustments while keeping cluster diagnostics from the governed simulation. I'll adapt the code for both price discovery and conviction (cluster) lanes.

Updating outputs to reflect fair line corrections and price lane

To ensure the user avoids corruption, we'll display the corrected fair line and probability from the price lane at the market line. We will carefully modify the code, keeping the mcOU for shape while introducing mcOUPrice. Changes will focus on gamesListPriceAdj and related calculations, updating totBaseMean based on the new price simulation.

Updating gamesList adjustments and ensuring price lane for fair line

We'll use gamesListPriceAdj for price-discovery, built from _totSimPrice.gamesList and corrInt. Existing gamesListAdj will stay for shape metrics, but will now be updated to reflect the price lane where needed. Changes will ensure fair line, probability, and diagnostics include this adjustment, while keeping some code unchanged for diagnostic display.

Setting up distributions for price lane and governed shape metrics

In the totals pick calculation, I’ll make sure pOverGames comes from the price distribution, using gamesListPriceAdj for price lane computations. For shape metrics, I’ll stick with the governed distribution (gamesListAdj). This keeps the tail metrics separate but ensures fair line and probability are based on the price distribution.

Ensuring correct fair line setup and handling cluster medians

I need to make sure the fair line is set correctly by adjusting ctx.fairLineMC to use the price distribution from fairLineMC. To do this, I'll ensure that gamesListPriceAdj is available early on at 804528. For cluster medians, I’ll check the correction block to confirm how _totCtx.median2set is derived.

find where median2set is set in totals context

for m in re.finditer(r'.median2set', text): if 730000 < m.start() < 830000: print(m.start(), text[m.start()-80:m.start()+120].replace("\n"," ")[:250])

Inspecting object creation for totCtx

I'll check between lines 780800 and 782200 to inspect how the totCtx object is created and ensure everything is properly in place for the upcoming calculations. This will allow me to confirm the details needed for the next steps in the logic.

print(text[780700:782200])

Checking cluster metrics and median2set in totals ctx

I see that totals ctx includes cluster metrics from _totSim, which is governed. Now, I need to investigate how ctx.median2set is set up, especially for the getTotalsPick function. I'll look around line 806471, where it seems they store median2set, and check if there's earlier computation.

for m in re.finditer(r'_totCtx.median2set', text): print(m.start(), text[m.start()-60:m.start()+80].replace("\n"," ")[:200])

Investigating _totCtx and median2set initialization

It seems like in the totals section, they might set _totCtx.clusterMedian2set and later use ctx.clusterMedian2set. In the getTotalsPick function, I see a reference to ctx.clusterMedian2set. But in the bimodal guard, they use ctx.median2set instead, so I'll search for how _totCtx and median2set are set, especially around 806471.

search for "var _totCtx" creation in MATCH_PREVIEW

idx = text.find("var _totCtx", 730000, 780000) idx

Locating definition of _totCtx

It looks like _totCtx is used in the snippet around 807700, likely in the match preview function near the totals pick. So, it must be defined earlier, probably around 739700. I'll search for _totCtx = in that region to find where it's initialized.

for m in re.finditer(r'_totCtx\s*=\s*{', text): if 730000 < m.start() < 830000: print(m.start(), text[m.start()-40:m.start()+120].replace("\n"," ")[:220])

Inspecting _totCtx creation

The _totCtx object is created at 806338. I’ll take a look at the area around 805900 to 806700 to better understand its context and how it’s being used. This should give me more clarity on its role and the relevant functions.

print(text[805900:807150])

Clarifying _totCtx adjustments for preview and price discovery

The _totCtx object handles mean/median from adjusted game stats like totAdjMean/Median, which uses correlation-based adjustments. I'll later incorporate a fair price line (fairLineMC) for price distribution from the price lane. Currently setting fairLineMC to NaN, but later, I’ll define these stats for final adjustments based on the price distribution's mean/median from the price lane.

Finding totAdjMean computation for price adjustments

I’m planning to compute totAdjMeanPrice and totAdjMedianPrice and integrate them into ctx. But I realized that the code already uses totAdjMean earlier. I should adjust it accordingly. Let's locate where totAdjMean is first computed by searching near the 804xxx region.

idx = text.find("totAdjMean", 802000, 806500) idx

print(text[801900:802300])

Adjusting for price simulation in totRawMedian and totRawMean

I noticed that totRawMedian is derived from _totSim.medianGames or projections. To make sure we're using the price simulation, we should adjust both totRawMedian and totRawMean. Let me check the lines above for more context on how they're currently defined.

print(text[801400:801950])

Modifying for price simulation and market calibration

We'll modify totRawMedian and totRawMean to use _totSimPrice, adjusting as needed. The projectedMedian uses a corrected value, so we'll leave that. For tails and other factors based on gamesListAdj, we'll keep those in place. Also, there's a "MARKET CALIBRATION HOOK" fix at totals, which may adjust weights — let's check the code around 802300 for it.

print(text[802300:804200])

Adjusting market calibration and sanity rail logic

We’ll update the market calibration hook for dynamic calibration weights, based on lineDiff and totAdjMean. If lineDiff exceeds certain thresholds, we’ll increase the weight to 0.30-0.55. We'll also ensure the price simulation doesn’t cause wild totAdjMean values. Moreover, the fair line won’t be influenced by this blending anymore, and market anchor adjustments will apply to the mean/median for outputs.

Adjusting fair line calibration and architectural fixes

We’ll calculate the fair line from the price distribution CDF scan but might use an anchor for extreme deviations. If the deviation exceeds 5.5, we’ll blend the fair line with a weight similar to the calibration one. Additionally, we'll adjust key architectural elements, like using pOverMCNoPush for computing pOverEval, and updating _totCtx.gapP accordingly.

Adjusting empirical anchor and display probabilities

We'll keep pOverMCNoPush for pOverGames without overwriting it. The previous empirical anchor adjusted pOverEval, but we might apply it only to pOverEvalDisplayed for display purposes, not the core calculations. This way, we avoid affecting the fair line but still allow for anchor calibration in the display.

Switching to raw values and keeping display adjustments

We’ll use pOverMCNoPush for the raw calculation in totPick while keeping pOverEvalDisplay for display purposes, maintaining the separation of price and conviction lanes. I'll also retain _pOverEval for debug purposes and ensure display variables don't influence core outcomes.

Updating to MC and improving Markov independence

We’ll adjust Step 1 by switching from combined to Monte Carlo (MC). Specifically, pO will be set to pMC to ensure consistent direction from the MC engine. I’ll also modify the logic for Markov independence, ensuring the appropriate configurations and conditions for outcome probability calculations.

for m in re.finditer(r'_tankMultiplier', mo): print(m.start(), mo[m.start()-60:m.start()+120].replace("\n"," ")[:180])

Implementing minimal ORP demotion and constants adjustment

I’ll proceed with the minimal ORP demotion, as requested, by adding constants like ORP_MAX_FORM_WEIGHT and ORP_TRUSTGAP_SCALE_MAX. These will affect several functions. I'll also locate the definition of oppAdjFormLayer to ensure everything aligns correctly. I’ll keep it simple for now.

m = re.search(r'function\s+oppAdjFormLayer\s*(', text) m.start() if m else None

oppLayer = extract_function("oppAdjFormLayer") len(oppLayer), oppLayer[:400]

print(oppLayer)

Modifying base values and trust gap adjustments

We'll modify the base values as follows: 0.10 for ATP, 0.11 for WTA, and 0.12 for ITF/CH. Trust gap boost will be smaller, with a cap on the increase. We'll look into possible changes for cov/trust multipliers and apply a cap of 0.30 overall. Now, I need to inspect the oppAdjFormDeltaP function.

m = re.search(r'function\s+oppAdjFormDeltaP\s*(', text) m.start() if m else None

deltaF = extract_function("oppAdjFormDeltaP") print(deltaF[:1000])

print(deltaF[1000:2000])

Demoting ORP and adjusting parameters

We'll demote ORP by removing the trustGap scaling of k and the cap. For k, I'll set values to: 0.018 ATP, 0.019 WTA, and 0.020 ITF/CH. No scaling with trustGap, but a mild adjustment based on it. We'll also reduce the cap under certain conditions and shrink it more when trustMin is low. Now, I'll check the applyOppTrustToProb function.

m = re.search(r'function\s+applyOppTrustToProb\s*(', text) m.start() if m else None

applyTrust = extract_function("applyOppTrustToProb") print(applyTrust[:1200])

print(applyTrust[1200:2400])

Adjusting ORP and implementing new flags

So, the ORP bias is likely coming from other layers. To reduce the confidence without affecting direction, we'll leave it as is but reduce the form layer weight and delta amplitude. To demote ORP overall, this seems like the best way forward. Next, we’ll modify runMatchSimulation_Exact by adding flags to control tail governors and tanking. This change will be implemented by replacing text within the function and updating the relevant code.

Ensuring proper modifications and consistency

To avoid accidental replacements, we’ll carefully modify specific functions like matchOutcomeProbs for taper blocks and make sure independentMarkov config is allowed. We’ll also adjust the _markovEffectiveHolds_ function to return baseline holds. Then, modifications will be made to the totals calculation block, adjusting various parts including price discovery and game adjustments. Additionally, we'll ensure the proper consistency in using pOverMC instead of the blend and update comments, ensuring all elements compute correctly based on these changes.

Stepwise adjustments for ORP functions and regex patching

I'll begin by making minimal adjustments to pass pMC raw while modifying the necessary ORP functions. After refactoring, we’ll run a syntax check and ensure all newly introduced variables are declared. I'll patch sections of the large file programmatically using regex, starting with updating the _markovEffectiveHolds_ function using the new logic. This includes computing mismatch index and updating holds while ensuring a straightforward rewrite. I'll implement this step by step.

Patching matchOutcomeProbs function with conditionals

I'll add a local boolean to the matchOutcomeProbs function to handle independent conditions and the effect of taper settings. Specifically, I'll modify the conditional checks for _moIndependent, _moUseSetTaper, and _moApplySet3. This will require me to replace occurrences of certain global variables with their new local counterparts within the function to ensure everything is aligned.

Patching runMatchSimulation_Exact and tweaks

I’ll start by defining flags for conditions like enabling tail governors, tanking, or set taper simulation based on options provided. Next, I’ll update the tanking block by adding a conditional wrapper. Afterward, I’ll adjust the use of variables like USE_CONDITIONAL_SET_TAPER to their new flag equivalents. Finally, I’ll implement a modification in the MATCH_PREVIEW section to integrate price-discovery simulation.

Adding price-discovery lane and updating blocks

We’ll add the price-discovery lane after the fallback check. This includes defining simTotalsPrice, parameterizing the simulation, and ensuring the route for price-discovery if it fails. Then, we’ll update the correction block to use _totSimPrice instead of _totSim. Additionally, I’ll define gamesListPriceAdj just after gamesListAdj, follow the same pattern, and insert it accordingly.

Updating list management and price handling

We'll add a gamesListPriceAdj based on simTotalsPrice, and ensure it's updated accordingly after the correction block. Then, we'll replace gamesListAdj references in computations like totRawMedian, totFair, and probOU to use gamesListPriceAdj where necessary. This ensures consistency across the price lane adjustments, including possible recomputations of totOverPct based on the updated list.

Updating price lane and Markov computations

At 807700, I'll switch to using gamesListPriceAdj for variables like totOverPct, totUnderPct, and totPush. I’ll also update related Markov and distribution calculations using these price-adjusted values. Later, during the evaluation phase, we'll set pOverEval correctly and ensure the computations reflect the price lane adjustments.

Updating getTotalsPick and ORP adjustments

I need to update the comment in getTotalsPick, specifically the part about "pOverNoPush" to clarify it's the Monte Carlo (price-discovery) no-push value. I’ll also revise pO and pMC handling. For ORP adjustments, I’ll patch oppAdjFormLayer and oppAdjFormDeltaP, and make sure formWeight is adjusted properly in related functions.

Adjusting formWeight and _winnerEngineLayers caps

I’ll reduce the base formWeight to 0.12/0.13/0.14 and set the max cap to 0.18 for a smaller trustGap boost. I'll also adjust the cap in winnerEngineLayers to 0.25. After making these changes, I’ll check for any broken CIT and ensure everything works by running appropriate tests and patches in Python.

import pathlib, re, textwrap, os, subprocess, json, math, sys, typing

orig_text = text

def replace_once(haystack, old, new, desc=""): if old not in haystack: raise ValueError(f"Pattern not found for {desc or old[:30]}") return haystack.replace(old, new, 1)

def replace_in_function(full_text, func_name, transform): func = extract_function(func_name) if func is None: raise ValueError(f"Function {func_name} not found") new_func = transform(func) if func == new_func: print(f"No change for {func_name}") return replace_once(full_text, func, new_func, desc=f"function {func_name}")

new_text = orig_text

Patching markovEffectiveHolds

I need to update markovEffectiveHolds by creating a new function string. This will likely adjust its behavior to better align with the modifications I’m making, ensuring everything flows together correctly. It’s a small but key update to ensure consistency across the logic.

def transform_markov_effective(func_str): # We'll replace the entire body with a simpler independent implementation, preserving signature. # Find signature line up to first "{" header_end = func_str.find("{") header = func_str[:header_end+1] # Build new body body = """ // INDEPENDENT MARKOV HOLDS (v17.4.6 FIX — 2026-03-01) // Markov is an analytic cross-check and MUST remain independent of Monte Carlo tail governors. // Therefore, Markov uses baseline (pre-taper / pre-tanking) hold probabilities directly. // We still compute a mismatch index for diagnostics, but we DO NOT compress holds. hA = clamp01(hA); hB = clamp01(hB);

// Win prob seed fallback: hold-gap logistic (diagnostic only) var p = Number(pWinA); if (!Number.isFinite(p)) { try { var _tsFallAvg = ((hA || 0.75) + (hB || 0.75)) / 2; var _tsFallTour = 'ATP'; try { _tsFallTour = taperTourKey(tourLabel); } catch(e2) { _tsFallTour = 'ATP'; } var _tsFallScale = kernelScale(_tsFallAvg, _tsFallTour); var _tsFallK = 1.0 / Math.max(0.01, _tsFallScale); p = 1 / (1 + Math.exp(-_tsFallK * ((hA || 0.75) - (hB || 0.75)))); } catch (e) { p = 0.5; } } p = clamp01(p);

var favIsA = (p >= 0.5); var favWp = favIsA ? p : (1 - p);

var _mkTourK = taperTourKey(tourLabel); var _mkIsWta = (_mkTourK === 'WTA'); var _mkIsCh = (_mkTourK === 'CH'); var _mkFloor = _mkIsWta ? 0.50 : _mkIsCh ? 0.51 : 0.52; var _mkRange = _mkIsWta ? 0.28 : _mkIsCh ? 0.29 : 0.30; var mismatch = Math.max(0, (favWp - _mkFloor) / _mkRange); mismatch = Math.min(1, mismatch);

return { hA_eff: hA, hB_eff: hB, mismatch: mismatch, favWp: favWp, holdDrop: 0 }; """ # close brace return header + body + "\n}"

new_text = replace_in_function(new_text, "markovEffectiveHolds", transform_markov_effective)

Patching matchOutcomeProbs

I’ll need to transform the matchOutcomeProbs function. This change will likely be related to how calculations or logic are being handled within the function, ensuring it aligns with the latest updates I’m making elsewhere. It'll be a focused update, but important for consistency.

def transform_matchOutcomeProbs(func_str): # insert independent flags after tour is set; find where _simIsWtaStyle set maybe insert_after = " try {\n var _moTierInfo = getTournamentTier();" if insert_after not in func_str: # fallback insert after _simIsWtaStyle assignment line insert_after = "_simIsWtaStyle = (_moTourUpper === "WTA" || (_moTourUpper.indexOf("WTA") >= 0));" # Build insertion insertion = """ // Markov/DP totals must remain independent of Monte Carlo tail governors. // When independentMarkov/independent is true, disable conditional set dependence + tail shaving. var _moIndependent = (propConfig && (propConfig.independentMarkov === true || propConfig.independent === true || propConfig.priceDiscovery === true || propConfig.disableTailGovernors === true)); var _moUseSetTaper = (USE_CONDITIONAL_SET_TAPER && !_moIndependent); var _moApplySet3 = (APPLY_TAPER_TO_SET3 && !_moIndependent); """ if insertion.strip() not in func_str: func_str = func_str.replace(insert_after, insert_after + insertion, 1) # replace taper guards func_str = func_str.replace("if (USE_CONDITIONAL_SET_TAPER) {", "if (_moUseSetTaper) {") func_str = func_str.replace("if (APPLY_TAPER_TO_SET3", "if (_moApplySet3") return func_str

new_text = replace_in_function(new_text, "matchOutcomeProbs", transform_matchOutcomeProbs)

Patching runMatchSimulation_Exact

I’m planning to patch the runMatchSimulation_Exact function. This will involve transforming it using local flags and applying those changes to adjust the function’s behavior. It’ll help ensure that everything runs smoothly in line with the other updates I’ve been making.

def transform_runMatchSimulation_Exact(func_str): # After opts = opts || {}; and var _totalsOnly ... insert flags. marker = " opts = opts || {};\n var _totalsOnly = (opts.totalsOnly === true);" if marker not in func_str: raise ValueError("Marker not found in runMatchSimulation_Exact") insertion = marker + """ // Price-discovery mode disables late-path tail governors from the simulation distribution. // This keeps fair-line (50/50) discovery independent from conviction/tail controls. var _disableTailGovernors = (opts && (opts.priceDiscovery === true || opts.disableTailGovernors === true || opts.disableSetTaper === true)); var _disableTanking = (opts && (opts.priceDiscovery === true || opts.disableTailGovernors === true || opts.disableTanking === true)); var _useSetTaperSim = (USE_CONDITIONAL_SET_TAPER && !_disableTailGovernors); var _applySet3Sim = (APPLY_TAPER_TO_SET3 && !_disableTailGovernors); """ func_str = func_str.replace(marker, insertion, 1)

text
# Disable tanking block by wrapping if (!_disableTanking) { ... }
# Insert opening before comment "// Trigger: normal"
open_pat = "\n        // Trigger: normal = 3, heavy mismatch = 2 (but only after mismatch crosses threshold)\n"
if open_pat in func_str:
    func_str = func_str.replace(open_pat, "\n        if (!_disableTanking) {\n" + open_pat, 1)
    # insert closing brace before the momentum comment line
    close_pat = "\n// v7.2.9: MMC-lite momentum - bounded per-game perturbation to capture runs/break-cascades.\n"
    if close_pat in func_str:
        func_str = func_str.replace(close_pat, "\n        }\n" + close_pat, 1)
    else:
        # alternate: before "        if (MMC_LITE_ON)"
        close_pat2 = "\n        if (MMC_LITE_ON) {"
        func_str = func_str.replace(close_pat2, "\n        }\n" + close_pat2, 1)
else:
    # if pattern not found, leave
    pass

# replace taper flags
func_str = func_str.replace("if (USE_CONDITIONAL_SET_TAPER) {", "if (_useSetTaperSim) {")
func_str = func_str.replace("if (APPLY_TAPER_TO_SET3", "if (_applySet3Sim")
return func_str

new_text = replace_in_function(new_text, "runMatchSimulation_Exact", transform_runMatchSimulation_Exact)

Adding price simulation block

I’m going to add a price simulation block to the MATCH_PREVIEW totals simulation section. First, I'll search for the unique string “var _totSim = simTotals;” at the 737521 snippet. Then, I’ll replace it and make sure it’s unique — also checking how often it appears to avoid conflicts.

new_text.count("var _totSim = simTotals;")

block_old = " var _totSim = simTotals;\n\n // SAFETY GUARD: if simulation failed, return a friendly message instead of throwing." block_new = """ var _totSim = simTotals;

text
// ================= PRICE-DISCOVERY TOTALS LANE =================
// Baseline totals distribution (pre-taper / pre-tanking) used ONLY for fair-line discovery
// and canonical O/U probability at the market line. Conviction/tail logic remains in _totSim.
var simTotalsPrice = null;
try {
  var _priceIters = Math.max(1200, Math.round(_tlPropIters * 0.65));
  simTotalsPrice = runMatchSimulation_Exact(simHA, simHB, bestOf, _priceIters, marketLine, tourLabel, {
    totalsOnly: true,
    priceDiscovery: true,
    mmcLite: false,
    volA: volA,
    volB: volB,
    winProbA: _taperSeedA,
    winProbB: _taperSeedB,
    sgWinRateA: (statsA && statsA.sgWinRate ? statsA.sgWinRate : 0.5),
    sgWinRateB: (statsB && statsB.sgWinRate ? statsB.sgWinRate : 0.5),
    timeBudgetMs: Math.max(2200, _tlRem() - 3500),
    timeCheckEvery: 200,
    minRuns: 1000
  });
} catch (ePD) { simTotalsPrice = null; }
if (!simTotalsPrice || !simTotalsPrice.totalRuns || !simTotalsPrice.gamesList || !simTotalsPrice.gamesList.length) simTotalsPrice = simTotals;
var _totSimPrice = simTotalsPrice;

// SAFETY GUARD: if simulation failed, return a friendly message instead of throwing."""

new_text = replace_once(new_text, block_old, block_new, desc="insert price sim lane")

Ensuring proper usage of _totSimPrice

I need to ensure that _totSimPrice is used later, or else the reference will cause an error. It's already defined in the same scope, so that's good. Next, I’ll patch the initial gamesListAdj definition to also create a gamesListPriceAdj baseline. I’ll find and adjust the corresponding snippet in the new text.

idx = new_text.find("var gamesListAdj = (_totSim && _totSim.gamesList && _totSim.gamesList.length)") idx

print(new_text[idx:idx+400])

Patching to define gamesListPriceAdj

I’ll insert the definition for gamesListPriceAdj right after this part, creating the baseline from _totSimPrice. Then, I'll proceed with the necessary patch to ensure everything is aligned correctly. This will help maintain consistency and avoid any reference errors later.

old = """var gamesListAdj = (_totSim && _totSim.gamesList && _totSim.gamesList.length) ? _totSim.gamesList.slice().sort(function(a,b){return a-b;}) : null;""" new = """var gamesListAdj = (_totSim && _totSim.gamesList && _totSim.gamesList.length) ? _totSim.gamesList.slice().sort(function(a,b){return a-b;}) : null; // Price-discovery totals list (pre-taper / pre-tanking). Falls back to governed list if missing. var gamesListPriceAdj = (_totSimPrice && _totSimPrice.gamesList && _totSimPrice.gamesList.length) ? _totSimPrice.gamesList.slice().sort(function(a,b){return a-b;}) : gamesListAdj;""" new_text = replace_once(new_text, old, new, desc="define gamesListPriceAdj baseline")

Patching correction block with _totSimPrice

I’ll patch the correction block now, using _totSimPrice for the base mean/median. I remember seeing that part around line 763800, so I’ll find those lines in the new text. The search will focus on lines with var _totBaseMean = (_totSim &&.

idx = new_text.find("var _totBaseMean = (_totSim && Number.isFinite(_totSim.avgTotalGames))") idx

print(new_text[idx:idx+250])

Replacing lines for correction

I’ll replace the two lines that need to be updated. This should help ensure the correct values are used for the base mean/median from _totSimPrice. Let's make sure everything lines up properly and apply the changes!

old = """var _totBaseMean = (_totSim && Number.isFinite(_totSim.avgTotalGames)) ? _totSim.avgTotalGames : NaN; var _totBaseMedian = (_totSim && Number.isFinite(_totSim.medianGames)) ? _totSim.medianGames : _totBaseMean;""" new = """var _totBaseMean = (_totSimPrice && Number.isFinite(_totSimPrice.avgTotalGames)) ? _totSimPrice.avgTotalGames : ((_totSim && Number.isFinite(_totSim.avgTotalGames)) ? _totSim.avgTotalGames : NaN); var _totBaseMedian = (_totSimPrice && Number.isFinite(_totSimPrice.medianGames)) ? _totSimPrice.medianGames : ((_totSim && Number.isFinite(_totSim.medianGames)) ? _totSim.medianGames : _totBaseMean);""" new_text = replace_once(new_text, old, new, desc="use price sim for base mean/median")

Building gamesListPriceAdj after correction

I’ll start by building gamesListPriceAdj following the correction. Right now, gamesListAdj is rebuilt in the correction block. I’ll add a similar process for gamesListPriceAdj. First, I’ll search for where gamesListAdj was rebuilt to ensure I place everything correctly.

idx = new_text.find("gamesListAdj = [];", 760000, 770000) idx

print(new_text[idx-200:idx+500])

Building gamesListPriceAdj & ensuring _maxPossibleGames definition

I’ll insert the new block to build gamesListPriceAdj, ensuring the correction is applied. Since it exists already, I'll update the list accordingly.

I need to make sure _maxPossibleGames is defined. It’s within the block, but if _totSim is missing, the variable might not exist. To avoid this, I'll define it separately. Then, I’ll replace the existing block precisely with the updated version.

old = """ if (_totSim && _totSim.gamesList && _totSim.gamesList.length) { gamesListAdj = []; var _maxPossibleGames = (bestOf === 5) ? 65 : 39; for (var _tg = 0; _tg < _totSim.gamesList.length; _tg++) { var _val = _totSim.gamesList[_tg] + corrInt; if (_val < minPossibleGames) _val = minPossibleGames; if (_val > _maxPossibleGames) _val = _maxPossibleGames; gamesListAdj.push(_val); } gamesListAdj.sort(function(a,b){return a-b;}); } else { gamesListAdj = null; }""" new = """ var _maxPossibleGames = (bestOf === 5) ? 65 : 39;

text
// Conviction lane: governed totals list (tail governors active)
if (_totSim && _totSim.gamesList && _totSim.gamesList.length) {
  gamesListAdj = [];
  for (var _tg = 0; _tg < _totSim.gamesList.length; _tg++) {
    var _val = _totSim.gamesList[_tg] + corrInt;
    if (_val < minPossibleGames) _val = minPossibleGames;
    if (_val > _maxPossibleGames) _val = _maxPossibleGames;
    gamesListAdj.push(_val);
  }
  gamesListAdj.sort(function(a,b){return a-b;});
} else {
  gamesListAdj = null;
}

// Price-discovery lane: baseline totals list (tail governors disabled)
if (_totSimPrice && _totSimPrice.gamesList && _totSimPrice.gamesList.length) {
  gamesListPriceAdj = [];
  for (var _tg2 = 0; _tg2 < _totSimPrice.gamesList.length; _tg2++) {
    var _val2 = _totSimPrice.gamesList[_tg2] + corrInt;
    if (_val2 < minPossibleGames) _val2 = minPossibleGames;
    if (_val2 > _maxPossibleGames) _val2 = _maxPossibleGames;
    gamesListPriceAdj.push(_val2);
  }
  gamesListPriceAdj.sort(function(a,b){return a-b;});
} else {
  gamesListPriceAdj = gamesListAdj;
}"""

new_text = replace_once(new_text, old, new, desc="build governed + price totals lists post-corr")

Patching totRawMedian and totRawMean to use _totSimPrice

I’ll update the totRawMedian and totRawMean to use _totSimPrice. There’s existing code around line 801400 that needs to be replaced with this new approach. I'll ensure I’m patching this part correctly to reflect the necessary changes with _totSimPrice.

old = """var totRawMedian = (_totSim && Number.isFinite(_totSim.medianGames)) ? _totSim.medianGames : projectedMedian; // FIX (2026-02-15): Keep totals median purely from the simulated totals list (no WCE re-mix). var totRawMean = (_totSim && Number.isFinite(_totSim.avgTotalGames)) ? _totSim.avgTotal"""

Need exact old includes avgTotal?? Let's locate exact segment.

seg_start = new_text.find("var totRawMedian", 800000, 803000) print(seg_start) print(new_text[seg_start:seg_start+220])

Searching for var totRawMedian to account for changes

Since the code might have been altered by earlier changes, I’ll search for var totRawMedian. This will help me locate that part of the code and ensure I’m applying the correct modifications where necessary. I want to make sure nothing gets overlooked because of the earlier adjustments.

seg_start = new_text.find("var totRawMedian") seg_start

print(new_text[seg_start:seg_start+250])

Printing full lines for context

I think I should print the full lines around the code I'm working with. That way, I can get more context and avoid missing anything important. This will help ensure I’m not making changes in the wrong place or overlooking any related code.

print(new_text[seg_start:seg_start+400])

Replacing the lines

I’m planning to replace these two lines in the code. I should carefully check what they do and ensure that the new lines fit correctly into the flow. It’s important to avoid introducing any unintended issues with the change. Let's make sure it works smoothly.

old = """var totRawMedian = (_totSim && Number.isFinite(_totSim.medianGames)) ? _totSim.medianGames : projectedMedian; // FIX (2026-02-15): Keep totals median purely from the simulated totals list (no WCE re-mix). var totRawMean = (_totSim && Number.isFinite(_totSim.avgTotalGames)) ? _totSim.avgTotalGames : projectedGames;""" new = """var totRawMedian = (_totSimPrice && Number.isFinite(_totSimPrice.medianGames)) ? _totSimPrice.medianGames : ((_totSim && Number.isFinite(_totSim.medianGames)) ? _totSim.medianGames : projectedMedian); // FIX (2026-02-15): Keep totals median purely from the simulated totals list (no WCE re-mix). var totRawMean = (_totSimPrice && Number.isFinite(_totSimPrice.avgTotalGames)) ? _totSimPrice.avgTotalGames : ((_totSim && Number.isFinite(_totSim.avgTotalGames)) ? _totSim.avgTotalGames : projectedGames);""" new_text = replace_once(new_text, old, new, desc="totRawMedian/Mean from price sim")

Updating market calibration logic

I need to update the market calibration hook for a stronger sanity rail. I'll patch the calibrationWeight logic by finding the specific snippet and replacing the weight logic. I'll target this segment, which adjusts the weight based on the line difference. Let’s implement the updated dynamic logic.

target = """ var lineDiff = Math.abs(totAdjMean - safeMarketLine); var calibrationWeight = 0.05; // Default low weight to trust sim

if (tierTriggered && lineDiff > 2.5) { calibrationWeight = 0.15; // Only anchor heavily if the sim is being wild }""" idx = new_text.find(target) idx

new_seg = """ var lineDiff = Math.abs(totAdjMean - safeMarketLine);

// Sanity rail (dynamic): the further the sim drifts from market, the stronger the pull. // Default stays light so the sim drives normal matches, but extreme collapses get rescued. var calibrationWeight = 0.05; // baseline

if (tierTriggered && lineDiff > 2.5) calibrationWeight = Math.max(calibrationWeight, 0.18);

if (lineDiff > 3.5) calibrationWeight = Math.max(calibrationWeight, 0.22); if (lineDiff > 4.5) calibrationWeight = Math.max(calibrationWeight, 0.30); if (lineDiff > 5.5) calibrationWeight = Math.max(calibrationWeight, 0.40); if (lineDiff > 7.0) calibrationWeight = Math.max(calibrationWeight, 0.55);

calibrationWeight = Math.max(0.0, Math.min(0.65, calibrationWeight));""" new_text = replace_once(new_text, target, new_seg, desc="market calibration weight dynamic")

Adjusting totFair calculation

I need to update the totFair calculation to use gamesListPriceAdj instead of gamesListAdj. I'll locate the original snippet, which uses gamesListAdj to determine totFair, and replace it with the new list gamesListPriceAdj.

old = "var totFair = (_totSim && gamesListAdj && gamesListAdj.length) ? fairLineFromSortedTotals(gamesListAdj) : null;" idx=new_text.find(old) idx

new = "var totFair = (_totSimPrice && gamesListPriceAdj && gamesListPriceAdj.length) ? fairLineFromSortedTotals(gamesListPriceAdj) : ((_totSim && gamesListAdj && gamesListAdj.length) ? fairLineFromSortedTotals(gamesListAdj) : null);" new_text = replace_once(new_text, old, new, desc="fair line from price list")

Applying sanity rail to totFairLine

I’m considering adding a sanity rail to the totFairLine calculation, potentially in the same block. The idea would be to apply a dynamic anchor when things are significantly off. I'll insert this adjustment right after defining totFairLine. I need to find that exact line first though.

marker = "var totFairLine = (totFair && Number.isFinite(totFair.fairLine)) ? totFair.fairLine : marketLine;" idx=new_text.find(marker) idx

insert = marker + """ // Sanity rail for fair line: prevent pathological prints when the price-discovery sim collapses. try { if (Number.isFinite(safeMarketLine) && safeMarketLine > 15 && safeMarketLine < 60 && Number.isFinite(totFairLine)) { var _flDiff = Math.abs(totFairLine - safeMarketLine); if (_flDiff > 5.5) { var _wFl = Math.min(0.60, Math.max(0.25, 0.10 * _flDiff)); if (tierTriggered) _wFl = Math.min(0.65, Math.max(_wFl, 0.35)); totFairLine = (1 - _wFl) * totFairLine + _wFl * safeMarketLine; } } } catch(eFL) {}""" new_text = replace_once(new_text, marker, insert, desc="fair line sanity rail

Adjusting totals pick computations

I need to update the totals pick computation block, specifically around line 800285. First, I’ll switch to using gamesListPriceAdj and _totSimPrice instead of the current data points. I’ll go back to the snippet at line 799800 and adjust the logic. I’ll need to locate the exact lines first, then patch them.

old = """var _rawOU = (_totSim && gamesListAdj && gamesListAdj.length) ? probOUFromSortedTotals(gamesListAdj, marketLine) : { pOver: _totSim.oversCount / _totSim.totalRuns, pUnder: clamp01(1 - (_totSim.oversCount / _totSim.totalRuns)), pPush: 0 };""" idx = new_text.find(old) idx

new = """var _rawOU = (_totSimPrice && gamesListPriceAdj && gamesListPriceAdj.length) ? probOUFromSortedTotals(gamesListPriceAdj, marketLine) : ((_totSim && gamesListAdj && gamesListAdj.length) ? probOUFromSortedTotals(gamesListAdj, marketLine) : { pOver: _totSim.oversCount / _totSim.totalRuns, pUnder: clamp01(1 - (_totSim.oversCount / _totSim.totalRuns)), pPush: 0 });""" new_text = replace_once(new_text, old, new, desc="rawOU uses price list")

Modifying totals engine for price list

I need to adjust the totals engine block between lines 807700-812200 to use gamesListPriceAdj instead of gamesListAdj. It currently checks if gamesListAdj exists, then uses it in mcDistAdj. I’ll locate that condition and modify it to reference the price list instead.

old = "if (gamesListAdj && gamesListAdj.length > 0 && Number.isFinite(lineTot)) {" idx=new_text.find(old, 807000, 813000) idx

new = "if (gamesListPriceAdj && gamesListPriceAdj.length > 0 && Number.isFinite(lineTot)) {" new_text = replace_once(new_text, old, new, desc="mcDist uses price list condition")

Updating Markov logic with price list

I’m going to replace mcDistAdj = pmfFromTotalsArray(gamesListAdj); with pmfFromTotalsArray(gamesListPriceAdj); in the block. I also need to check if there are other instances where gamesListAdj is used, particularly for Markov logic. I'll make updates where necessary.

old = " mcDistAdj = pmfFromTotalsArray(gamesListAdj);" idx = new_text.find(old, 812000, 814500) idx

new_text = replace_once(new_text, old, " mcDistAdj = pmfFromTotalsArray(gamesListPriceAdj);", desc="pmfFromTotalsArray price")

Updating Markov computation in totals block

Okay, so we are using mcDistAdj derived from price now, which is great. For tail metrics and the blowout bucket, they're still depending on gamesListAdj, which is fine for now. But we need to update the computeTotalsBlend, as well as adjust the Markov independence in the subblock where pMarkov3Set is calculated. I'll locate the right section.

mk_seg_start = new_text.find("var _mkDecShrink", 810000, 820000) mk_seg_start

print(new_text[mk_seg_start-200:mk_seg_start+600])

Adjusting Markov computation for independence

I need to replace the current chunk with an independent version. Specifically, I'll set pMarkov3Set = _pDecRaw(clamp). It's important not to override _pDecForUnder and ctx.pDecider as they are. I'll focus on ensuring mkMismatch and the related calculation stay intact while making this adjustment.

old = """ // Markov decider probability (diagnostic) after penalties: // - tight-match penalty (coinflips) // - mismatch shrink (prevents inflated 3-set mass in blowouts) var _pDecRaw = (_MO_TOT_BLEND && _MO_TOT_BLEND.outcomes) ? Number(_MO_TOT_BLEND.outcomes.pDecider) : NaN; var _mkDecShrink = 0.22 * mismatch; // up to -22% relative in extreme mismatches var _pDecAdj = Number.isFinite(_pDecRaw) ? (_pDecRaw * (1 - _mkDecShrink) - set3Penalty) : NaN; if (Number.isFinite(_pDecAdj)) _pDecAdj = Math.max(0, Math.min(1, _pDecAdj)); pMarkov3Set = _pDecAdj;

text
// Use mismatch-adjusted Markov decider for UNDER-score penalties / confidence.
if (Number.isFinite(pMarkov3Set)) {
  _pDecForUnder = pMarkov3Set;
  if (typeof _totCtx !== 'undefined' && _totCtx) _totCtx.pDecider = pMarkov3Set;
}

""" new = """ // Markov decider probability (diagnostic) — INDEPENDENT (no mismatch penalties). var _pDecRaw = (_MO_TOT_BLEND && _MO_TOT_BLEND.outcomes) ? Number(_MO_TOT_BLEND.outcomes.pDecider) : NaN; if (Number.isFinite(_pDecRaw)) _pDecRaw = Math.max(0, Math.min(1, _pDecRaw)); pMarkov3Set = _pDecRaw;

// IMPORTANT: Markov is a cross-check only. Do NOT overwrite MC decider inputs used by totals policy.

""" new_text = replace_once(new_text, old, new, desc="Markov decider independence")

Adjusting mkSet3Penalty assignment

I need to adjust the mkSet3Penalty assignment right below, which currently uses _pDecAdj. I'll locate exactly where that happens and ensure it's properly updated to follow the new structure, keeping everything consistent with the changes we’ve made so far.

find mkSet3Penalty assignment

idx = new_text.find("mkSet3Penalty", 814000, 818000) print(idx) print(new_text[idx-100:idx+180])

Fixing reference to _pDecAdj

The reference to _pDecAdj is not defined, so we need to patch the code. I'll set mkSet3Penalty to 0 or set3Penalty, depending on what makes the most sense in this context. I'll locate that line and make the necessary replacement to avoid errors.

old = " mkSet3Penalty = (Number.isFinite(_pDecRaw) && Number.isFinite(_pDecAdj)) ? Math.max(0, _pDecRaw - _pDecAdj) : set3Penalty;" new = " mkSet3Penalty = 0;" new_text = replace_once(new_text, old, new, desc="mkSet3Penalty independent")

Adjusting matchOutcomeProbs call

I need to ensure that the matchOutcomeProbs call passes the independentMarkov instead. I'll locate the line where it's called:

var _MO_TOT_BLEND = matchOutcomeProbs(hA_eff, hB_eff, _startSrvTB, bestOf, { ... });

Once I find it, I'll modify the object to include the necessary change.

idx = new_text.find("var _MO_TOT_BLEND = matchOutcomeProbs(", 812000, 817000) idx

print(new_text[idx:idx+260])

Simplifying the object for matchOutcomeProbs

I can simplify the object passed to matchOutcomeProbs by explicitly including independentMarkov: true and tourLabel. I’ll patch the snippet accordingly to make these changes. This should make things clearer and more direct.

old = """var _MO_TOT_BLEND = matchOutcomeProbs(hA_eff, hB_eff, _startSrvTB, bestOf, { winProbA: _pFinalForMk, winProbB: 1 - _pFinalForMk, pWinA: _pFinalForMk, tourLabel: _mkTour });""" new = """var _MO_TOT_BLEND = matchOutcomeProbs(hA_eff, hB_eff, _startSrvTB, bestOf, { tourLabel: _mkTour, independentMarkov: true });""" new_text = replace_once(new_text, old, new, desc="matchOutcomeProbs independentMarkov flag")

Adjusting totals block for effective holds

Earlier, they set _totCtx.holdA = hA_eff, but since we changed _markovEffectiveHolds_ to avoid compressing holds, hA_eff/hB_eff should be the baseline now. In the totals block around line 818000, I need to adjust pOverEval and gapP. I’ll find and update the snippet concerning the blended no-push probability.

seg = " // Direction uses a blended no-push probability (MC + Markov) when available.\n // Falls back to MC when Markov is missing/invalid." idx = new_text.find(seg) idx

print(new_text[idx:idx+400])

Updating and preserving necessary adjustments

I’ll replace the block with the new one as described, keeping the pOverBlendForDiag variable intact. To avoid any overlap, I’ll ensure the replacement covers everything up to _totCtx.pOverBlend = pOverEval;. This will make the transition smoother while keeping the necessary structure intact.

old = """ // Direction uses a blended no-push probability (MC + Markov) when available. // Falls back to MC when Markov is missing/invalid. var pOverMCNoPush = pOverGames; var pOverEval = (Number.isFinite(pOverBlendEval) ? pOverBlendEval : pOverMCNoPush); _totCtx.pOverMC = pOverMCNoPush; _totCtx.pOverBlend = pOverEval;""" new = """ // Canonical decision probability comes from the PRICE-DISCOVERY Monte Carlo engine. // Markov + blends are diagnostics only (used to grade conviction, not to move the fair line). var pOverMCNoPush = pOverGames; var pOverEval = pOverMCNoPush; var pOverBlendForDiag = (Number.isFinite(pOverBlendEval) ? pOverBlendEval : pOverMCNoPush); _totCtx.pOverMC = pOverMCNoPush; _totCtx.pOverBlend = pOverBlendForDiag;""" new_text = replace_once(new_text, old, new, desc="pOverEval canonical MC")

Updating gapP calculation

I need to revise the gapP line. Right now, it's set as _totCtx.gapP = abs(pOverEval - pOverMarkov). I'll change it to abs(pOverMCNoPush - pOverMarkov) to reflect the correct calculation. Let's go ahead and patch this adjustment.

old = "_totCtx.gapP = (Number.isFinite(pOverMarkov) ? Math.abs(pOverEval - pOverMarkov) : NaN);" idx = new_text.find(old) idx

new_text = replace_once(new_text, old, "_totCtx.gapP = (Number.isFinite(pOverMarkov) ? Math.abs(pOverMCNoPush - pOverMarkov) : NaN);", desc="gapP raw MC vs Markov")

Adjusting empirical anchor logic

I need to ensure the empirical anchor uses pOverEval properly. It currently refers to MC raw, but that might change. The anchor block uses and modifies pOverEval, then calls getTotalsPick(pOverEval...), which influences the pick. I'll adjust the anchor logic so it modifies pOverBlendForDiag or display, not pOverEval. This will avoid contaminating the price lane and maintain safe calibration.

Updating empirical anchor logic

I’ll adjust the anchor block that checks _empBaseline to reference pOverBlendForDiag instead of pOverEval. It currently modifies pOverEval based on _empDev, but I want to store the result as _totCtx.pOverDisplay to avoid affecting pOverEval. I’ll patch the block accordingly and proceed with the update.

emp_idx = new_text.find("if (Number.isFinite(_empBaseline) && Number.isFinite(pOverEval))") emp_idx

print(new_text[emp_idx:emp_idx+420])

Adjusting anchor logic

I’ll replace the current block with one that uses pOverBlendForDiag. This should ensure we’re working with the right variable for the calculations and avoid unwanted modifications to other values. Time to make the update and see how it affects the overall results.

old = """ if (Number.isFinite(_empBaseline) && Number.isFinite(pOverEval)) { var _empDev = pOverEval - _empBaseline; if (Math.abs(_empDev) > 0.08) { var _empPull = _empDev * 0.12; pOverEval = clamp01(pOverEval - _empPull); _totCtx.empiricalAnchor = { baseline: _empBaseline, deviation: _empDev, pull: _empPull, tour: _empTourK, line: _empLineRound }; } }""" new = """ if (Number.isFinite(_empBaseline) && Number.isFinite(pOverBlendForDiag)) { var _empDev = pOverBlendForDiag - _empBaseline; if (Math.abs(_empDev) > 0.08) { var _empPull = _empDev * 0.12; pOverBlendForDiag = clamp01(pOverBlendForDiag - _empPull); _totCtx.empiricalAnchor = { baseline: _empBaseline, deviation: _empDev, pull: _empPull, tour: _empTourK, line: _empLineRound }; } } // Expose the diagnostic display probability (blended + lightly anchored) without contaminating decision logic. _totCtx.pOverDisplay = pOverBlendForDiag;""" new_text = replace_once(new_text, old, new, desc="empirical anchor diagnostics only")

Adjusting anchor and assignment flow

Since we modified pOverBlendForDiag in the anchor logic, we need to ensure that its assignment to _totCtx.pOverBlend happens after anchoring, but it's not strictly necessary. I'll pass pOverMCNoPush to getTotalsPick instead of pOverEval, for clarity, and make the relevant updates.

old = " totPick = getTotalsPick(pOverEval, _totCtx);" idx=new_text.find(old) idx

new_text = replace_once(new_text, old, " totPick = getTotalsPick(pOverMCNoPush, _totCtx);", desc="getTotalsPick fed raw MC")

Final adjustments and ensuring consistency

After updating the totPick call and checking for consistency in the logic involving pOverGames and pOverEval, I need to ensure old variables like _pDecForUnder are not being overwritten by Markov. We’ve removed the override, so no further issues with pMarkov3Set. I'll verify that mkMismatch is still defined and finalize the changes.

gtp = extract_function("getTotalsPick")

apply transformations

def transform_getTotalsPick(func_str): # update NOTE comment and add pO = pMC after pMC computed func_str = func_str.replace( "// NOTE: pOverNoPush is expected to be the combined totals probability engine output\n // (MC + Markov blend when available). Raw MC no-push pOver can be passed via ctx.pOverMC.", "// NOTE: pOverNoPush is expected to be the Monte Carlo (price-discovery) no-push P(Over) at the market line.\n // Markov is an independent cross-check and may ONLY downshift conviction (never define side / fair line)." ) # after pMC line, insert pO = pMC canonical pat = " var pMC = (ctx && Number.isFinite(ctx.pOverMC)) ? clamp01(Number(ctx.pOverMC)) : pO;\n var pMarkov = (ctx && Number.isFinite(ctx.pOverMarkov)) ? clamp01(Number(ctx.pOverMarkov)) : NaN;\n" if pat in func_str: repl = " var pMC = (ctx && Number.isFinite(ctx.pOverMC)) ? clamp01(Number(ctx.pOverMC)) : pO;\n // Canonical probability for direction/edge is MC (do not let blends leak into pricing).\n pO = pMC;\n var pMarkov = (ctx && Number.isFinite(ctx.pOverMarkov)) ? clamp01(Number(ctx.pOverMarkov)) : NaN;\n" func_str = func_str.replace(pat, repl, 1) # Update Step 1 comment func_str = func_str.replace(" // ===== Step 1: Tentative side from combined totals engines (pO) =====", " // ===== Step 1: Tentative side from Monte Carlo (pO is canonical MC) =====") return func_str

new_text = replace_in_function(new_text, "getTotalsPick", transform_getTotalsPick)

Adjusting getTotalsPick and implementing patches

Next step is adjusting the getTotalsPick function to ensure it uses the canonical pO. We’ve already patched the totals context lines where _totCtx.gapP is computed. Now, I need to ensure that in the call site for getTotalsPick, it's using the raw value instead of pOverEval. I’ll also implement ORP demotion and patch the _oppAdjFormLayer_ function.

def transform_oppAdjFormLayer(func_str): # Adjust base and cap func_str = func_str.replace(" var base = 0.16;\n if (t.indexOf('WTA') >= 0) base = 0.17;\n if (t.indexOf('ITF') >= 0 || t.indexOf('CH') >= 0) base = 0.18;", " // ORP demotion: form layer is a credibility modifier, not a primary driver.\n var base = 0.10;\n if (t.indexOf('WTA') >= 0) base = 0.11;\n if (t.indexOf('ITF') >= 0 || t.indexOf('CH') >= 0) base = 0.12;") func_str = func_str.replace(" // v17.1: Boost base weight when schedule quality gap is large\n var trustGap = Math.abs(tA - tB);\n if (trustGap > 0.10) {\n base += Math.min(0.14, (trustGap - 0.10) / 0.20 * 0.14);\n }", " // ORP demotion: reduce trust-gap amplification (avoid schedule getting multiple votes).\n var trustGap = Math.abs(tA - tB);\n if (trustGap > 0.10) {\n base += Math.min(0.06, (trustGap - 0.10) / 0.20 * 0.06);\n }") func_str = func_str.replace(" // v17.1: Max weight raised from 0.32 to 0.48\n out.w = Math.max(0, Math.min(0.48, out.w));", " // ORP demotion: cap authority (trust modifier, not a driver)\n out.w = Math.max(0, Math.min(0.30, out.w));") return func_str

new_text = replace_in_function(new_text, "oppAdjFormLayer", transform_oppAdjFormLayer)

Patching oppAdjFormDeltaP function

My next step is to patch the _oppAdjFormDeltaP_ function. I’ll check the logic to ensure it aligns with the changes we’ve made so far, adjusting it where necessary. Once done, I’ll move forward with testing to ensure everything is functioning as expected.

def transform_oppAdjFormDeltaP(func_str): # Reduce k and cap; soften trustGap scaling func_str = func_str.replace(" var k = 0.026;\n if (t.indexOf('WTA') >= 0) k = 0.027;\n if (t.indexOf('ITF') >= 0 || t.indexOf('CH') >= 0) k = 0.028;", " // ORP demotion: keep this as a small tiebreaker, not a direction driver.\n var k = 0.018;\n if (t.indexOf('WTA') >= 0) k = 0.019;\n if (t.indexOf('ITF') >= 0 || t.indexOf('CH') >= 0) k = 0.020;") func_str = func_str.replace(" // v17.1: Increase k when trust gap is large (sc\n var trustGap = Math.abs(ta - tb);\n if (trustGap > 0.10) {\n k *= (1.0 + Math.min(1.0, (trustGap - 0.10) / 0.20));\n }", " // ORP demotion: limit trust-gap amplification.\n var trustGap = Math.abs(ta - tb);\n if (trustGap > 0.10) {\n k *= (1.0 + 0.25 * Math.min(1.0, (trustGap - 0.10) / 0.20));\n }") func_str = func_str.replace(" // v17.1: Dynamic cap — scales with trust gap\n var cap = 0.080;\n if (rA >= 5 && rB >= 5) cap = 0.095;\n if (trustGap > 0.10) {\n cap += Math.min(0.10, (trustGap - 0.10) / 0.20 * 0.10);\n }\n\n var tMin = Math.min(ta, tb);\n if (Number.isFinite(tMin) && tMin < 0.92) cap = cap * 0.80;", " // ORP demotion: smaller caps; trust gap only lightly increases authority.\n var cap = 0.055;\n if (rA >= 5 && rB >= 5) cap = 0.065;\n if (trustGap > 0.10) {\n cap += Math.min(0.02, (trustGap - 0.10) / 0.20 * 0.02);\n }\n\n var tMin = Math.min(ta, tb);\n if (Number.isFinite(tMin) && tMin < 0.92) cap = cap * 0.65;") return func_str

new_text = replace_in_function(new_text, "oppAdjFormDeltaP", transform_oppAdjFormDeltaP)

Patching winnerEngineLayers wF cap line

Next, I need to patch the _winnerEngineLayers_ function by updating the cap line. I will locate the line where the cap is set to 0.40 and change it to 0.25. After making this adjustment, I’ll test it to ensure the change is correctly applied and functioning as expected.

find and replace in winner engine layers function

winnerLayers = extract_function("winnerEngineLayers") "Math.min(0.40" in winnerLayers, winnerLayers.count("Math.min(0.40")

def transform_winnerEngineLayers(func_str): func_str = func_str.replace(" wF = Math.max(0, Math.min(0.40, wF));", " wF = Math.max(0, Math.min(0.25, wF));") return func_str new_text = replace_in_function(new_text, "winnerEngineLayers", transform_winnerEngineLayers)

Patching getMatchWinProb_Stats function

Now, I'm going to focus on patching the getMatchWinProb_Stats function, specifically where the formWeight is computed. I'll search for references like _fwBase within the function string and replace the appropriate lines, just as I did earlier. This will be done inside the extracted function for consistency.

gms = extract_function("getMatchWinProb_Stats")

check for _fwBase etc

gms.find("var _fwBase"), gms.find("var _fwMax")

print(gms[6260:6500])

Patching the base and max scaling lines

I’ll proceed by replacing the base and max values, along with the trustGap scaling line. The next step is to locate the exact code segment where these lines are being set. Once I find them, I can proceed with the necessary adjustments for consistency.

segment_old = """ var _fwBase = (_fwTourK === 'WTA') ? 0.25 : (_fwTourK === 'CH') ? 0.27 : 0.30; var _fwMax = (_fwTourK === 'WTA') ? 0.35 : (_fwTourK === 'CH') ? 0.37 : 0.40; var formWeight = _fwBase; var trustGap = Math.abs(tA_s - tB_s); if (trustGap > 0.10) { formWeight = _fwBase + Math.min(_fwMax, (trustGap - 0.10) / 0.20 * _fwMax); }""" segment_old in gms

segment_new = """ // ORP demotion: form is a credibility modifier, not a core probability driver. var _fwBase = (_fwTourK === 'WTA') ? 0.12 : (_fwTourK === 'CH') ? 0.13 : 0.14; var _fwMax = (_fwTourK === 'WTA') ? 0.18 : (_fwTourK === 'CH') ? 0.19 : 0.20; var formWeight = _fwBase; var trustGap = Math.abs(tA_s - tB_s); if (trustGap > 0.10) { formWeight = _fwBase + Math.min(_fwMax, (trustGap - 0.10) / 0.20 * (_fwMax * 0.60)); }""" def transform_getMatchWinProb(func_str): return func_str.replace(segment_old, segment_new) new_text = replace_in_function(new_text, "getMatchWinProb_Stats", transform_getMatchWinProb)

Updating match probability and verifying variable use

We updated the function, and _winnerEngineLayers_ now has its form weight from formLayer.w. Next, we ensured that matchOutcomeProbs is using _moUseSetTaper, which replaced the condition check. We’ll confirm no conflicts with _totCtx.pOverDisplay. Lastly, we’ll track _totSimPrice to ensure it's properly referenced.

occ = [m.start() for m in re.finditer(r'\b_totSimPrice\b', new_text)] len(occ), occ[:10]

Ensuring variable scope and updating calculations

I need to make sure that gamesListPriceAdj is in scope where it's used. It's initially declared and reassigned correctly. Then, I'll adjust the totals section where fairLineMC is set to NaN, and make necessary changes around pOverGames, derived from totOverPct. I'll also check for consistency with Markov adjustments and matchOutcomeProbs changes.

"Math.abs(pOverEval - pOverMarkov)" in new_text, new_text.count("Math.abs(pOverEval - pOverMarkov)")

Adjusting getTotalsPick parameter

I need to make sure that getTotalsPick is provided with pOverMC instead of blend. I'll search for all occurrences of getTotalsPick(pOverEval and make the necessary changes to ensure it's getting the right parameter. This will help keep everything aligned.

re.findall(r'getTotalsPick(([^)]{0,30})', new_text[820000:822000])[:5]

matches = re.findall(r'getTotalsPick(\s*([A-Za-z0-9_.]+)', new_text) matches[:20], len(matches)

Checking call sites for pOverEvalGames

There are other call sites like pOverEvalGames around lines 673386 and 674757. I need to inspect these to make sure there aren’t any issues. I'll go through them to be thorough, but it seems like only one might be particularly important to check. I'll locate each one.

for m in re.finditer(r'getTotalsPick(', new_text): pos=m.start() if pos>1000: print(pos, new_text[pos-80:pos+120].replace("\n"," ")[:250])

Inspecting other call sites for potential adjustments

There are call sites in other functions around 640867 and 674733, which still call getTotalsPick(pOverEval...). These may need adjustments, especially if they impact totals price discovery. They could be part of helper functions like TOTAL_GAMES_PICK. I'll inspect the area around 640700 and ensure alignment.

print(new_text[639900:641200])

Aligning MATCH_PICK pipeline with raw MC

It looks like this could be part of the MATCH_PICK pipeline totals, where pOverEvalGames might be blended. We should align everything by using raw MC instead. I'll inspect the function earlier to check how pOverEvalGames is handled. It's likely pOverNoPush from MC, so I'll search for that and check its definition in this section.

sec = new_text[635000:640200]

find pOverEvalGames assignment

for m in re.finditer(r'pOverEvalGames\s*=', sec): print(m.start(), sec[m.start()-60:m.start()+120].replace("\n"," ")[:200])

Verifying that gapP uses MC, not blended

It looks like pOverEvalGames is derived from MC no-push, which is based on pOverGames and pUnderGames. They're then applying an empirical anchor on pOverEvalGames and calling getTotalsPick. This seems fine, as it isn’t a Markov blend. But we should ensure that gapP uses raw MC, not blended. I'll search for gapP in this section to check if that's the case.

for m in re.finditer(r'gapP', sec): print(m.start(), sec[m.start()-50:m.start()+80].replace("\n"," ")[:160])

Inspecting call at 674733 in potential TOT_GAMES_PICK function

It seems like there might not be a gapP after all. Now, there’s another call at 674733 — possibly in the TOT_GAMES_PICK function. I’ll take a look around lines 674500-675100 to confirm and thoroughly inspect what's happening in that section to make sure everything aligns.

print(new_text[674300:675300])

Patching and ensuring consistency in functions and variables

It looks like the TOTAL_GAMES_PICK function might be using pOverEval, potentially computed from a distribution. I'll check if it's blended, but it's not critical. We're updating the totals logic and removing the “Direction uses blend” comment. I also patched _totCtx.pOverBlend. Now, I need to ensure the variable pOverBlendForDiag used earlier is still in scope. After ensuring it's used in the right places, I’ll search for any remaining gapP computations throughout the file.

len(re.findall(r'.gapP\s*=', new_text)), re.findall(r'.gapP\s*=\s*[^;]+;', new_text)[:5]

Finalizing changes and ensuring consistency

Great, only one issue left. We updated matchOutcomeProbs with _moUseSetTaper, but didn’t change its other uses—like with tail governors. That's okay, it will continue using the USE_CONDITIONAL_SET_TAPER flag. Now I need to confirm the string replacements didn’t affect indentation. I’ll also double-check that we inserted _moIndependent flags correctly in matchOutcomeProbs. After confirming the marker and insert_after logic, I'll check that the syntax is valid by inspecting the function.

mo_new = extract_function("matchOutcomeProbs")

confirm contains new flags and if statements

mo_new.find("_moIndependent"), mo_new.count("if (_moUseSetTaper)"), mo_new.count("if (_moApplySet3")

Investigating issue with insertion and replacement

The insertion seems like it didn’t work, possibly because the marker wasn’t matched correctly. I see that the code was inserted after try { var _moTierInfo, not directly inside the function. Although the marker was found, the expected _moIndependent isn’t in new_text, which suggests the replacement didn’t happen due to a naming mismatch. I’ll search for _moIndependent in new_text to track this down.

new_text.find("_moIndependent"), new_text.find("independentMarkov")

Checking function extraction for _moIndependent

The _moIndependent flag exists, but it might not be inside the extraction of the matchOutcomeProbs function, possibly due to how the function was extracted. It looks like the extraction finds the first occurrence of matchOutcomeProbs, but it’s not detecting _moIndependent, even though it appears earlier in the code. I’ll check around line 275900 to see if it’s located before the function begins.

print(new_text[275920:276120])

Fixing extraction function to handle updated text

It turns out extract_function didn’t find the expected _moIndependent flag because it uses the global text variable, which still references the original file instead of the updated one. I’ll adjust extract_function to accept an input string, so it processes the latest version of the text correctly.

def extract_function_from(s, name): start = s.find(f'function {name}(') if start==-1: return None i=s.find('{', start) depth=0 for j in range(i, len(s)): if s[j]=='{': depth+=1 elif s[j]=='}': depth-=1 if depth==0: return s[start:j+1] return None

mo_new = extract_function_from(new_text, "matchOutcomeProbs") mo_new.find("_moIndependent"), mo_new.count("if (_moUseSetTaper)"), mo_new.count("if (_moApplySet3")

Checking modifications in runMatchSimulation_Exact

Okay, now that I’ve updated the extraction function, I should apply it to other checks too. Next step is to inspect the modifications made in runMatchSimulation_Exact. I'll verify if everything looks good there, ensuring that changes align with what’s needed for the task.

rms_new = extract_function_from(new_text, "runMatchSimulation_Exact") rms_new.find("_disableTailGovernors"), rms_new.count("if (_useSetTaperSim)"), rms_new.count("if (!_disableTanking)")

Checking _markovEffectiveHolds_

Now that the last step is done, it’s time to check _markovEffectiveHolds_. I’ll need to make sure everything is functioning as it should within that section, looking for any issues or necessary adjustments. Let's dive into it and confirm it's working properly.

mk_new = extract_function_from(new_text, "markovEffectiveHolds") mk_new[:400], "holdDrop" in mk_new

Adjusting and confirming function integrity

I need to confirm that the total simulation functions compile properly, since we've inserted new variables. The patch for the totals pick block now uses gamesListPriceAdj, and changes to mcDistAdj also use this. I'll double-check that gamesListPriceAdj isn't used before its declaration, which looks fine. The next step is ensuring that the new variables like safeMarketLine and tierTriggered are in scope when used, especially in the sanity rail section. Everything seems to be lined up! Now, adjusting the totals context object creation follows.

new_text.count("_pDecAdj"), [m.start() for m in re.finditer(r'_pDecAdj\b', new_text)][:10]

Checking tanking block changes

I’ve changed the tanking block to wrap with if (!_disableTanking), but now I need to check if the braces are placed correctly. I'll ensure the indentation and the closing brace are aligned properly. Let’s review the snippet around that block to confirm everything is in order.

extract surrounding in runMatchSimulation_Exact around if (!_disableTanking) insertion

idx = rms_new.find("if (!_disableTanking)") print(rms_new[idx-200:idx+600])

Confirming brace and blank line placement

I saw a blank line after the if statement—good, I’ll leave it. Now, I should check if the close brace is correctly inserted. I’ll confirm that by reviewing the area near the "momentum" comment to make sure everything is properly aligned and structured as it should be.

idx2 = rms_new.find("// v7.2.9: MMC-lite momentum", idx) print(rms_new[idx2-120:idx2+120])

Checking brace and conditional replacements

The close brace looks good with proper indentation, though there’s an extra newline between the braces and the comment, which is fine. Now, I’ll focus on ensuring that the braces don’t interfere with other code, like the "else" statement. I’ve replaced the conditionals as planned. Time to verify everything in rms_new.

check snippet around conditional set taper block in rms_new

rms_new.find("if (_useSetTaperSim)"), rms_new.find("if (_applySet3Sim")

Verifying matchOutcomeProbs replacement

I’ve now replaced matchOutcomeProbs similarly. It’s time to check the snippet to ensure everything’s properly updated and working as expected. Let’s make sure it integrates smoothly with the changes we've made so far!

mo_new = extract_function_from(new_text, "matchOutcomeProbs") idx=mo_new.find("if (_moUseSetTaper)") print(mo_new[idx-120:idx+120])

Checking try block insertion for matchOutcomeProbs

Okay, set3 uses the _moApplySet3, and now I need to confirm that inserting the matchOutcomeProbs didn’t accidentally break the try block structure. It was inserted right after the try { var _moTierInfo = getTournamentTier(); line, which is inside the try block. This seems fine since var is function-scoped, but I’ll double-check anyway.

Ensuring totals update consistency

I need to confirm that getTotalsPick is updated correctly. I’ve already addressed the changes to the matchOutcomeProbs flag and direction in the totals block, but there’s still some patching needed. For example, ensuring computeTotalsBlend uses relevant metrics like pOverGames. Also, I'll refine the comment on the blend and shape metrics.

Checking for syntax and undefined issues

The user requested a check for syntax and undefined problems. I’ll run node --check to verify the script. Afterward, I’ll create a new file at /mnt/data/updated_script.txt to store the results and complete the check. This should help ensure everything's in good shape.

out_path="/mnt/data/Tennislocks_V17.4_Cluster_Governor_v17.4.6_UPDATED.txt" pathlib.Path(out_path).write_text(new_text, encoding="utf-8") out_path, os.path.getsize(out_path)

Ensuring code correctness and adjustments

I need to make sure the code uses pOverBlendForDiag after the anchor to correctly assign _totCtx.pOverBlend. It's currently not updated, though _totCtx.pOverDisplay is set. I'll check if pOverBlendForDiag needs to be global, but it's within the totals section. I'll also ensure fairLineMC and priceDiscovery flags are correctly set.

bash -lc node --check /mnt/data/Tennislocks_V17.4_Cluster_Governor_v17.4.6_UPDATED.txt

Ensuring correct file check

It looks like Node's --check expects .js files. But this might specifically use ECMAScript modules, and I'm not sure how it handles .txt files. To play it safe, I’ll copy the file to a .js extension and run the check. Hopefully that clears up any issues.

bash -lc cp /mnt/data/Tennislocks_V17.4_Cluster_Governor_v17.4.6_UPDATED.txt /mnt/data/Tennislocks_V17.4_Cluster_Governor_v17.4.6_UPDATED.js && node --check /mnt/data/Tennislocks_V17.4_Cluster_Governor_v17.4.6_UPDATED.js

Checking for undefined variables and providing download link

The syntax seems fine, but now I need to check for undefined variables from the recent edits. For example, safeMarketLine is defined before the totFair block, so it’s good. I also checked the other variables like tierTriggered and pOverBlendForDiag, and they seem fine, too.

I'll provide a downloadable link via the sandbox.

Providing the updated file and citation

I’ll respond with the download link for the updated file the user requested. I’ll mention that the script was updated, and I’ll include a brief citation referencing the original file as required. No extra explanations or instructions—just the link as per the user’s request.

Confirming syntax checks and providing download

We’ve run the syntax check with no errors, and we've scanned for undefined references like _pDecAdj, which was removed. I will deliver the updated file as a .txt, providing a download link. I'll also add the required citation as a footnote, including "(website)" at the end.

Thought for 19m 35s