Refactoring and adjusting the script

User wants a script fix with specific adjustments. I'll read and modify the refactor script directly, removing sandbox simHA_totals, unifying the 2% shift, and ensuring markov uses simHA. I'll set SET_CONFIDENCE_DELTA to 0.03, correct the double-dipping with applyPreSimulationAdjustments, and check syntax/undefined elements. Let's go!

Refining patch for undefined variables and improvements

I'll check for any undefined variables like tierGap and adjA/adjB, which were used in the MATCH_PREVIEW function. I’ll compare sections from both scripts and patch the refactor file. Plan includes replacing sandbox code with unified calculations, synchronizing the Markov engine, tweaking momentum settings, and ensuring applyPreSimulationAdjustments is called only once to avoid double-dipping.

Ensuring applyPreSimulationAdjustments is called once

To avoid duplicate calls to applyPreSimulationAdjustments, I’ll modify it to be idempotent by marking a flag on the player object. If the flag is set, it’ll return early; otherwise, it’ll apply the changes and set the flag. I’ll also remove any extra calls and check the syntax using Node.

Checking script syntax and compatibility

It seems like the script might reference Google Apps Script-specific items like SpreadsheetApp, which could fail if run outside its environment. I'll parse the script using Node, understanding that any undefined references wouldn't cause errors until execution. I'll scan for undefined variables and ensure proper declaration, particularly for SET_CONFIDENCE_DELTA.

from pathlib import Path, PurePosixPath ref_path = Path("/mnt/data/Tennis.God.JS.refactor.TOTALS_ENGINE_UNIFIED_FIXES.txt") v3_path = Path("/mnt/data/3.0version.txt") ref_text = ref_path.read_text(encoding="utf-8", errors="replace") v3_text = v3_path.read_text(encoding="utf-8", errors="replace") len(ref_text), len(v3_text)

import re, textwrap, math, json, sys def find_all(text, pattern): return [m.start() for m in re.finditer(pattern, text)] for pat in ["simHA_totals", "Sandbox", "SET_CONFIDENCE_DELTA", "applyPreSimulationAdjustments", "contextAdjustedSPW", "matchupAdjustedSPW", "Markov"]: idxs = find_all(ref_text, pat) print(pat, len(idxs), idxs[:5])

Inspecting sandbox occurrence

I’ve located the sandbox sections, including simHA_totals. Next, I’ll inspect the area around the first occurrence to determine how best to update the code and implement the necessary changes. I’ll review carefully so nothing gets overlooked.

def excerpt(text, pos, window=500): start = max(0, pos-window) end = min(len(text), pos+window) return text[start:end] print(excerpt(ref_text, find_all(ref_text, "Sandbox")[0], 600))

Examining match logic and tier shifts

I noticed that in the MATCH_PREVIEW function, the sandbox is defined in the pick logic. I’ll need to replace that block with the unified tier shift. The variables like hA_match seem relevant for adjusting probabilities before tier shifts, so I’ll check for tier shift application farther down by searching for "tierGap."

pos = find_all(ref_text, "var simHA_match")[0] print(excerpt(ref_text, pos, 1200))

Checking hold shift application

It looks like the hold shift is still being applied. I’ll need to look further into this to understand exactly where it’s being used. This should help me pinpoint which parts of the code are still referencing shift logic and ensure everything aligns properly.

pos2 = pos + 900 print(excerpt(ref_text, pos2, 1200))

Unifying shift logic for winner and totals engines

It looks like we need to apply a uniform 2% shift to both the winner and totals engines. Currently, the winner engine uses simHA_match and simHB_match, while the totals engine uses raw simHA_totals. I’ll adjust the logic to compute the shift with the tierGap and ensure the correct direction for adjusting values. After the shift, we’ll set the totals values properly, ensuring everything aligns with the new approach.

Refining variable usage and shift logic for clarity

We’ll streamline the logic by removing sandbox variables and updating the function calls. The runMatchSimulation_Exact should use simHA and simHB, not simHA_totals. After updating the class shift to 0.02, we'll modify the logic with simHA_match and simHB_match for total simulations, ensuring clarity and consistency without changing essential behavior. We'll also look into Markov engine adjustments.

Searching for Markov-related functions and variables

I need to locate where functions like hA_markov or holds are used, especially in dpGameMarkov or computeMarkovGamesLine. To do this, I'll search for occurrences of markov, hA_match, and simHA_match. I'll also check around the second sandbox reference where the user mentioned, looking for markovLine to see if it's related to match preview logic.

find functions that compute markov fair line for totals

patterns = ["markov", "Markov", "MARKOV", "distFair", "Fair Line", "fairLine", "markovLine", "dpMarkov", "markovGames"] for pat in ["markovFair", "markov", "MARKOV", "Dist Fair", "distFair", "MARKOV LINE", "markovLine", "markovGamesFair"]: idxs = find_all(ref_text, pat) if idxs: print(pat, len(idxs), idxs[0])

Looking for Markov-related outputs

I noticed there are 40 instances of "Markov" with a capital M, but I'll need to focus on the exact "MARKOV". I’ll search for the term "Markov/Dist Fair Line" since that might point me to where it's printed in the output. This should help me locate the relevant part quickly.

for pat in ["Markov/Dist", "Dist Fair", "MARKOV LINE", "Markov Line", "Markov Fair", "markovFair", "DP cross", "DistFair"]: idxs=find_all(ref_text, pat) if idxs: print(pat, len(idxs), idxs[:3])

Inspecting around 458k

I see around 458k, and now I need to focus on that section. Let’s check out an excerpt from here to get a better understanding. Something in the output around this point might clarify things further for me. I’ll move forward to narrow in on this.

pos_markov = find_all(ref_text, "Markov/Dist")[0] print(excerpt(ref_text, pos_markov, 800))

Updating simHA_totals in Markov computations

Alright, I noticed simHA_totals is still being referenced within the Markov totals computation. I'll need to update that. Let's take a closer look to ensure everything's aligned properly and adjust any references for consistency. I'll dive into more details to finalize this.

print(excerpt(ref_text, pos_markov+500, 1200))

Updating Markov to use unified simHA

Markov references simHA_totals, but we'll replace it with simHA_match (or the simHA variable for consistency). We’ll remove simHA_totals, and fallback to simHoldA when needed. Since simHA_match is defined earlier in the MATCH_PREVIEW, I'll set the variables to simHA_match and simHB_match after adjustments for synchronization.

find "simHoldA" occurrences near earlier section

idxs = find_all(ref_text, "simHoldA") print(len(idxs), idxs[:5]) print(excerpt(ref_text, idxs[0], 300))

Unifying simulation holds for consistency

Inside the MATCH_PREVIEW, there's an early simulation using hA and hB, which may already be adjusted. Later, simHA_match gets set. The goal is to unify both the match outcome and total simulations by ensuring they use the same holds. We’d adapt the simTotals call to use simHA_match while keeping consistency with simHoldA/hB.

Tracing hA and hB adjustments

The match simulation uses simHoldA = clamp(hA) and simHoldB = clamp(hB), where hA and hB likely come from adjustments, possibly from BTMS. I need to trace their origin, maybe from getAdjustedHold. I'll search for the hA_match assignment to clarify if it aligns with hA/hB or is pre-tier shift data.

idxs = find_all(ref_text, "hA_match") print(len(idxs), idxs[:5]) print(excerpt(ref_text, idxs[0]-200, 600))

Understanding Computation of hA_match

hA_match is likely computed from SPW adjusted with class-aware and possibly tier-aware shifting. But I’m unsure about that. There’s also computeHoldFromSPW, which uses certain parameters. I will search for references to “var hA =” to better understand how hA/hB are derived, probably from getAdjustedHold().

find first occurrence 'var hA =' near hA_match context

segment_start = ref_text.rfind("function MATCH_PREVIEW", 0, idxs[0]) segment_start

Searching for MATCH_PREVIEW function

I can't find a function name with that string, but maybe there's a function called MATCH_PREVIEW(...) defined somewhere. I'll search for it directly to see if that's where the logic is being handled or if there's a different name for that function in the code.

idx = ref_text.find("function MATCH_PREVIEW") idx

Locating hA_match definition and context

Hmm, it looks like hA_match is used at 256k but occurs before the MATCH_PREVIEW function is defined. This suggests there might be other code earlier in the file, like MATCH_PICK, that defines hA_match. I'll search for the closest function definition earlier and try extracting more context around that.

pos_hA_match = idxs[0] print(excerpt(ref_text, pos_hA_match-1200, 1200))

Identifying sandbox function in MATCH_PICK

The function computeCoreContext computes hA_match and hB_match, which are used in MATCH_PREVIEW. However, a sandbox around 382k directly uses hA_match and hB_match, not through computeCoreContext. This sandbox is likely inside MATCH_PICK. I’ll search for function boundaries around this position to confirm.

def find_prev_function(text, pos): # find preceding 'function ' occurrence idx = text.rfind("function ", 0, pos) return idx, text[idx:idx+80] idx, snippet = find_prev_function(ref_text, find_all(ref_text,"Sandbox")[0]) idx, snippet

Locating sandbox functions and cross-checking match preview

It looks like the sandbox around 382k is likely inside the FINAL_BET_SUMMARY function, which might be used by MATCH_PREVIEW. I found a second sandbox at 439723, possibly also within MATCH_PREVIEW. I'll check to ensure the patch is applied consistently across all relevant places.

idx2, snippet2 = find_prev_function(ref_text, find_all(ref_text,"Sandbox")[1]) idx2, snippet2[:100]

Identifying sandbox patches and variable usage

It seems both FINAL_BET_SUMMARY and possibly MATCH_PREVIEW contain sandboxes. I'll need to patch both instances to ensure all sandbox segments are addressed. Let's start by inspecting the first sandbox region in FINAL_BET_SUMMARY to better understand how variables like simHA_totals are being used so I can adjust them properly.

pos_func = idx print(excerpt(ref_text, pos_func, 600))

Looking into sandbox region

I need to scroll further and examine the area around the sandbox segment more closely. By doing this, I can gather a better understanding of how it's structured and how to adjust everything correctly. This will help me focus on the necessary changes for patching the issue effectively.

print(excerpt(ref_text, find_all(ref_text,"simHA_match")[0]-300, 900))

Inspecting second sandbox in MATCH_PREVIEW

Yes, the first sandbox was in FINAL_BET_SUMMARY, and we'll apply the patch there. I also need to make sure that the Markov line is aligned with those changes. Now, there’s another sandbox around position 439,723 within MATCH_PREVIEW; I’ll inspect the area around that position to confirm where adjustments are needed.

pos_sb2 = find_all(ref_text,"Sandbox")[1] print(excerpt(ref_text, pos_sb2-400, 900))

Patching sandbox totals and unifying variables

In the sandbox totals, simHoldA and simHoldB are used, and then simHA & simHB come into play for the winner, with tier penalties applied later. I'll patch it by removing simHA_totals and applying the 2% shift. Since hA/hB are already calculated, this should simplify things. I'll focus on unifying at the class shift, using tierGap * 0.035. Let's locate the exact area for these changes and proceed.

find after pos_sb2 where classShift computed

pos_class = ref_text.find("var classShift", pos_sb2) print(pos_class, excerpt(ref_text, pos_class-200, 600))

Adjusting classShift and patching totals logic

We'll set the classShift to tierGap * 0.02 and apply it to both the totals and winner. This means we’ll adjust simHA and simHB and remove simHA_totals. We also need to ensure the totals simulation uses simHA & simHB (not raw simHoldA/B). After this, totals won’t rely on the sandbox. Now, I need to locate where simHA_totals is used later in MATCH_PREVIEW and check around position 439723.

show occurrences after pos_sb2

for i,idx in enumerate(find_all(ref_text,"simHA_totals")): if idx>pos_sb2-1000 and idx<pos_sb2+30000: print(i,idx, ref_text[idx-40:idx+40].replace("\n"," "))

Patching simulation calls and sandbox updates

We need to ensure all uses in the simulation call, Markov totals, and structuralCoinFlip are updated to use the unified variables like simHA, simHB, or simHoldA. We'll patch them accordingly. Also, earlier sandbox occurrences in FINAL_BET_SUMMARY are at 382514 and 384308. I need to inspect those areas now.

for idx in find_all(ref_text,"simTotals = runMatchSimulation_Exact(simHA_totals"): print(idx, ref_text[idx-80:idx+80].replace("\n"," "))

Fixing regex issue and handling occurrences

The find_all method uses a regex pattern, but our string contains parentheses and causes an error. To fix this, we can either escape special characters or use re.escape. I'll go with text.find loops or re.escape. Also, I need to locate occurrences of "runMatchSimulation_Exact(simHA_totals".

pat = "runMatchSimulation_Exact(simHA_totals" for idx in find_all(ref_text, re.escape(pat)): print(idx, ref_text[idx-60:idx+80].replace("\n"," "))

Patching occurrences and fixing momentum constant

I’ve found three occurrences—one in FINAL_BET_SUMMARY and two in MATCH_PREVIEW, possibly related to highN vs fallback. I'll patch them to use simHA and simHB. I also noticed references in Markov and structuralCoinFlip using simHA_totals. I’ll patch those too. Next, I need to focus on the momentum constant. Let's check around SET_CONFIDENCE_DELTA.

pos = find_all(ref_text,"SET_CONFIDENCE_DELTA")[0] print(excerpt(ref_text, pos-200, 500))

Clarifying delta value and searching SET_CONFIDENCE_DELTA

Okay, the default delta values for WTA and ATP are 0.025 and 0.020, but the user seems to want a global delta of 0.03. They also mentioned a 0.06 shift in version 3.0, but it seems that was considered too high. Maybe there's a different constant elsewhere in the simulation. Let’s search for "SET_CONFIDENCE_DELTA" and take a look at all the occurrences of "0.06".

show context for each occurrence of SET_CONFIDENCE_DELTA

for idx in find_all(ref_text, "SET_CONFIDENCE_DELTA")[:6]: print("----", idx) print(excerpt(ref_text, idx-120, 280).replace("\n"," "))

Investigating possible delta values and SET_CONFIDENCE_DELTA

The deltas mentioned in the current script are 0.020 and 0.025, not 0.06. But I'm wondering if there's a separate momentum setting in the winner engine outside runMatchSimulation_Exact. The user mentioned 0.03 globally, but earlier they referenced 0.06. Let’s search for instances of 0.06 near “setDelta” to clarify.

find occurrences of "setDelta" string

idxs = find_all(ref_text, "setDelta") print("setDelta occurrences", len(idxs)) print(excerpt(ref_text, idxs[0]-200, 400))

search for "0.06" near SET_CONFIDENCE_DELTA assignment

import itertools for m in re.finditer(r"SET_CONFIDENCE_DELTA[^;]*0.0?6", ref_text): print("found", m.start())

Clarifying delta setting and momentum options

There are no direct references to 0.06 in the current script, but the user wants to set the delta to 0.03 globally. Our script uses mild defaults, which may be different from what they intended. Since delta can be overridden in the configuration, I need to check if momentum is disabled for totals in the simulation. The current call suggests momentum might still be on with the default values.

Inspecting delta settings in match simulation

I think the user might be overriding the delta value within propConfig, potentially setting it to something larger, like 0.06, for winners. I'll look at the runMatchSimulation_Exact function signature to confirm how propConfig.setDelta is being used and whether it's affecting winners. Searching for "setDelta" might help further.

find setDelta: occurrences

for m in re.finditer(r"setDelta\s*:\s*([0-9.]+)", ref_text): print(m.start(), m.group(1), excerpt(ref_text, m.start()-80, 160).replace("\n"," "))

Looking at disableSetMomentum in propConfig

It seems there's no direct setDelta literal in the code, but maybe a variable elsewhere is setting it. I'll search for disableSetMomentum in propConfig to see if that's affecting how momentum is applied globally, especially in context with the settings for delta control.

for pat in ["disableSetMomentum", "setDelta", "disableMomentum", "momentum"]: idxs = find_all(ref_text, pat) print(pat, len(idxs))

Figuring out delta adjustments for totals

It looks like disableSetMomentum is only mentioned in comments and the simulation function, with no active calls that would actually turn it off. The user asked for the momentum to be optional off for totals, but I’m considering if they want a change to the default delta for both ATP and WTA to match their global request for 0.03. However, increasing it might go against their goal.

Considering user's momentum request in script

It seems the current script might not align with the user’s request exactly. Maybe they’re referring to a different momentum in the winner engine, not SET_CONFIDENCE_DELTA. However, the user still wants the delta globally set to 0.03, and I need to follow those instructions while considering any updates or patches to the versions they might have used.

Evaluating delta impact and momentum behavior

I’m concerned that increasing delta to 0.03 might have the opposite effect, but the user thinks 0.03 is the sweet spot compared to 0.06. Since it’s still higher than the current values, I’ll adjust it. I’ll also check if the momentum is consistent and not disabled for totals simulations.

locate runMatchSimulation_Exact definition and inspect for totalsOnly check

pos_def = ref_text.find("function runMatchSimulation_Exact") pos_def

Extract portion around totalsOnly usage

segment = ref_text[pos_def:pos_def+12000]

find totalsOnly in this segment

idxs = [m.start() for m in re.finditer("totalsOnly", segment)] idxs[:10], len(idxs)

show code around first totalsOnly check

for off in idxs[:3]: print("---", off) print(segment[off-150:off+250])

Inspecting delta in runMatchSimulation_Exact

The first two instances at lines 121 and 140 are likely related to parsing options with the _totalsOnly variable being set from opts.totalsOnly. This doesn’t seem to disable momentum. I’ll take a closer look at the earlier parts of the runMatchSimulation_Exact function to analyze where delta is assigned.

find SET_CONFIDENCE_DELTA assignment within segment

m = re.search(r"SET_CONFIDENCE_DELTA\s*=", segment) m.start() if m else None

Investigating delta assignment in simulation process

It looks like the delta assignment occurs earlier than I thought, possibly around 157k, but it seems to involve a different function, maybe something like runMatchSimulation_Exact_OLD. The runMatchSimulation_Exact at line 410k might be a wrapper or a different function. I’ll take a closer look around pos_def.

print(ref_text[pos_def:pos_def+400])

Locating delta assignment in runMatchSimulation_Exact

It seems like the runMatchSimulation_Exact function doesn’t contain the delta assignment after all. I’ll search for "SET_CONFIDENCE_DELTA" again, but maybe the delta assignment happens earlier in runMatchSimulation instead of runMatchSimulation_Exact. I'll look for "delta" inside the runMatchSimulation_Exact function to locate it.

find within runMatchSimulation_Exact function code length maybe huge; extract until end by finding next "function " after pos_def

next_func = ref_text.find("\nfunction ", pos_def+10) pos_def, next_func

rm_code = ref_text[pos_def:next_func] len(rm_code)

rm_code.find("SET_CONFIDENCE_DELTA"), rm_code.find("setDelta"), rm_code.find("disableSetMomentum")

Searching for delta in simulation functions

It looks like runMatchSimulation_Exact doesn’t apply momentum directly. Maybe momentum is handled in another function like simulateMatch. As for SET_CONFIDENCE_DELTA, it could be in a different simulation function, such as runMatchSimulation. I’ll search for the definition around 157k and check the previous function.

idx, snip = find_prev_function(ref_text, find_all(ref_text,"SET_CONFIDENCE_DELTA")[0]) idx, snip

Clarifying SET_CONFIDENCE_DELTA and momentum

Looks like SET_CONFIDENCE_DELTA is used in the matchOutcomeProbs function, which handles Markov chain match outcome probabilities, not in the simulation. The user wants to change the default to 0.03 globally. I’ll change it there instead of runMatchSimulation_Exact. But I also need to check runMatchSimulation_Exact for any momentum-related terms.

for pat in ["momentum", "CONFIDENCE", "dynHA", "set", "delta"]: if pat=="set": continue print(pat, rm_code.lower().find(pat.lower()))

Checking momentum in runMatchSimulation_Exact

I realized that runMatchSimulation_Exact likely contains momentum, but it might not be in the SET_CONFIDENCE_DELTA variable. It could be using a different term like SET_MOMENTUM_DELTA. I’m going to check the code around momentum in rm_code to confirm and make sure I have the correct variables.

pos_mom = rm_code.lower().find("momentum") print(rm_code[pos_mom-200:pos_mom+500])

Standardizing momentum with SET_DELTA_MP

It looks like runMatchSimulation_Exact uses SET_DELTA_MP for momentum with values like 0.025/0.020. This could be what the user means by SET_CONFIDENCE_DELTA. To keep things consistent, I’ll set this to 0.03 across both the winner and totals simulations, and adjust mmcLite accordingly.

Standardizing momentum across simulations

To standardize momentum, I’ll ensure that mmcLite is consistent for both totals and winner simulations. Currently, the totals simulation in FINAL_BET_SUMMARY has mmcLite: false, while earlier match simulations may use mmcLite: true. I’ll check for the usage of mmcLite in the runMatchSimulation_Exact.

locate runMatchSimulation_Exact calls with mmcLite

for m in re.finditer(r"runMatchSimulation_Exact$[^;]+?$", ref_text): s = ref_text[m.start():m.end()] if "mmcLite" in s: print(m.start(), s[:200].replace("\n"," "))

Unifying momentum and simulation adjustments

I’ll set SET_DELTA_MP and SET_CONFIDENCE_DELTA to 0.03 in runMatchSimulation_Exact and matchOutcomeProbs to align momentum across simulations. I will also patch both functions, making sure Markov probabilities are consistent. Additionally, I'll check for "totals-only MC sims" constants and address the double-dipping issue with applyPreSimulationAdjustments.

app_idxs = find_all(ref_text,"applyPreSimulationAdjustments") for idx in app_idxs: print("----", idx) print(excerpt(ref_text, idx-200, 400).replace("\n"," "))

Investigating double-dipping in adjustments

I see that the applyPreSimulationAdjustments function is defined at position 74659 and is called at 247459 through contextAdjustedSPW. The double-dipping likely happens when matchupAdjustedSPW calls applyPreSimulationAdjustments or uses contextAdjustedSPW, which already did. I’ll check the code around position 248073.

pos_matchup = find_all(ref_text, "function matchupAdjustedSPW")[0] print(excerpt(ref_text, pos_matchup, 1000))

Continuing investigation later

I’ll pick this up later to check if applyPreSimulationAdjustments is being applied again in the code. I'll need to confirm if it’s being reused elsewhere, like in the matchupAdjustedSPW or any similar functions. The double-dipping could affect the consistency.

Search within matchupAdjustedSPW function body for applyPreSimulationAdjustments

start = pos_matchup end = ref_text.find("\nfunction ", start+10) matchup_code = ref_text[start:end] matchup_code.count("applyPreSimulationAdjustments"), matchup_code.count("applyPreSimulationAdj")

Checking full function for double-dipping

The matchupAdjustedSPW uses contextAdjustedSPW, which has already applied applyPreSimulationAdjustments. However, the user mentioned the possibility of double-dipping. It seems like applyPreSimulationAdjustments might get called again on variables like pA_adj, so I’ll check the bottom of the full function to confirm.

print(matchup_code[-800:])

Investigating double-dipping in function calls

It looks like matchupAdjustedSPW isn’t re-applying the adjustment, so there’s no double-dipping there. The user's warning suggests another function might apply applyPreSimulationAdjustments again, possibly inside a loop. I’ll check lines 700, 2336, and other potential calls, using regex to locate them.

Find all occurrences of 'applyPreSimulationAdjustments(' with parentheses

occ=[] for m in re.finditer(r"applyPreSimulationAdjustments\s*(", ref_text): occ.append(m.start()) occ, len(occ)

Investigating potential double-dipping issue

It seems double-dipping might not be an issue after all. Although line 210400 is a comment, it may have been captured by the regex due to the string. The user mentions multipliers being applied twice at lines 700 and 2336. It looks like contextAdjustedSPW could be responsible for the double application, multiplying by the WTA 0.97 multiplier twice. I’ll need to check contextAdjustedSPW to confirm.

pos_caspw = find_all(ref_text, "function contextAdjustedSPW")[0] start=pos_caspw end=ref_text.find("\nfunction ", start+10) caspw_code = ref_text[start:end] print(caspw_code[:1200])

Search within caspw_code for TOUR_MULTS or 'WTA' or '0.97'

for pat in ["TOUR", "WTA", "0.97", "applyPreSimulationAdjustments"]: print(pat, caspw_code.find(pat))

Locating multiplier usage and confirming double-dipping

It seems the WTA multiplier applies to SurfaceWeights and not tour multipliers within applyPreSimulationAdjustments itself. Double-dipping could happen there, but not when applying WTA and the SurfaceWeights multiplier together. The user’s concern is likely about earlier versions. I’ll search for “0.97” in the file to track multiplier usage.

idxs = find_all(ref_text,"0.97") print(len(idxs), idxs[:10]) print(excerpt(ref_text, idxs[0]-80, 200))

Searching for tour multiplier usage

I found the 0.97 multiplier in comments or related to multipliers, and it appears around TOUR_MULTS. However, there might be other instances of multipliers. Double-dipping could still be an issue if applyPreSimulationAdjustments is called twice, possibly once in contextAdjustedSPW and again in computeHoldFromSPW. I’ll search for getTourMults( to locate other occurrences.

idxs=find_all(ref_text,"getTourMults(") print(len(idxs), idxs[:10])

Fixing regex issue with escaping parentheses

I noticed the regex issue caused by parentheses. To handle that, I should use re.escape(), which will properly escape special characters like parentheses. This will make sure the regex works as intended and avoid any errors in the matching process. Let’s go ahead and apply that fix.

idxs=find_all(ref_text, re.escape("getTourMults(")) len(idxs), idxs[:10]

Examining tour multipliers and Markov engine interactions

There’s something around 162493 within matchOutcomeProbs — likely related to the Markov engine. I wonder if it applies the tour multipliers. But since applyPreSimulationAdjustments also calls getTourMults, there might be double application if matchOutcomeProbs adjusts SPW. However, hold probabilities are separate from SPW, so maybe tour multipliers are only applied to hold? Let's carefully check applyPreSimulationAdjustments to be sure.

pos_apply_def = ref_text.find("function applyPreSimulationAdjustments") print(excerpt(ref_text, pos_apply_def, 1000))

Checking for double application of adjustments

It seems applyPreSimulationAdjustments multiplies by the tour multiplier and applies various adjustments like surface, indoor, CPI, etc. But it doesn’t track idempotence. In contextAdjustedSPW, tour or surface multipliers aren’t included initially, so no double application there. However, I’m checking if applySurfaceAdjustment or tour multipliers are mistakenly applied twice through other calls. Double-checking the logic for lines 700 and 2336 seems necessary, but it doesn't seem like it's happening inside matchupAdjustedSPW.

Ensuring applyPreSimulationAdjustments is called once

Adding idempotence is an option to ensure applyPreSimulationAdjustments isn’t applied twice, but could complicate things. This function is used for both players and other functions, and caching might break things when different playerRank or SPW values are used. Adding an alreadyPreSimAdjusted flag might help, but current calls don’t support it. A simpler idea is to remove applyPreSimulationAdjustments from contextAdjustedSPW and let later code handle it, but this would affect existing expectations, so it’s not a good change.

Avoiding double application of tour multiplier

To avoid double-dipping the tour multiplier, we could remove the application in applyPreSimulationAdjustments if it’s already applied elsewhere. However, there's the possibility that another function like readSPW applies this multiplier earlier. I’ll need to check the readSPW function to verify if it’s already incorporating the tour multiplier.

pos_readspw = ref_text.find("function readSPW") pos_readspw

readspw_code = ref_text[pos_readspw:ref_text.find("\nfunction ", pos_readspw+10)] print(readspw_code[:600])

print(readspw_code[600:1400])

Ensuring single application of tour multiplier

The readSPW function doesn’t apply the tour multiplier, so applyPreSimulationAdjustments is the only place it's applied. We can safely ignore the warning but should add a comment saying we've verified single application. For added clarity, we might look into handling any potential double-counting of the multiplier in applyCPIAdjustment.

Ensuring one call of applyPreSimulationAdjustments

In applyPreSimulationAdjustments, the sequence is fine since the applyCPIAdjustment expects the tour multiplier to be applied already. There's only one call to it, so we don't need to make changes there, just a comment to be thorough. As for the "getTotalsPick" change, it’s optional, so we’ll leave that as is.

Implementing user request and syntax check

The user wants their refactor script fixed with syntax and undefined issues tackled. We’ll address the fixes they’ve requested and also ensure the script is free of errors. First, we'll remove simHA_totals and related elements in both function blocks, then use a unified 2% tier shift as requested.

Modifying variables for tier shift and simplification

In the current implementation, variables like simHA_match and simHB_match are defined, with totals being updated and used for calculations. We're simplifying this by renaming and keeping track of the tier shift as 0.02 for simHA and simHB. We'll remove simHA_totals and rename appropriately to minimize overall changes, while still maintaining clarity.

Simplifying variables in FINAL_BET_SUMMARY

We’ll simplify the code by keeping the simHA_match and simHB_match variable names, while removing simHA_totals and simHB_totals. The tier shift factor will be updated to 0.02, modifying simHA_match and simHB_match. In the simTotals call, we'll replace the totals variables with the match variables. Later, for Markov calculations, we’ll suggest replacing simHA_totals with simHA_match if it's in scope.

Updating variable usage in MATCH_PREVIEW

In MATCH_PREVIEW, we’ll eliminate the sandbox totals inputs and update the tier shift factor to 0.02. Instead of defining simHA_totals and simHB_totals, we'll use simHA and simHB, creating unified versions that are used consistently. To keep compatibility, we'll update references, including in the Markov code, to use simHA/simHB. This will require updating the MATCH_PREVIEW section, including all relevant calls like runMatchSimulation_Exact. We’ll also check for any other sections relying on simHA_totals.

Preparing patch for substitutions and consistency

Alright, we’ve identified the need to replace simHA_totals and simHB_totals in the file. We’ll check the Markov section, where _holdA_tot_mp uses these variables, and update them to simHA and simHB if they exist.

Additionally, we’ll standardize momentum by setting SET_DELTA_MP = 0.03 and similarly adjust the confidence delta. We’ll also ensure that the totals and winner simulations use consistent delta values.

Finally, we’ll make sure that we update specific blocks to keep everything aligned.

find_all(ref_text, "0.035"), len(find_all(ref_text,"0.035"))

Targeting classShift occurrences

There are 8 occurrences we're looking at, but not all are inside classShift. I need to focus on changing only those within sandbox blocks, likely around positions 383611 and 441031. I'll also check around position 303 for other constants to make sure we’re addressing the right places.

for idx in find_all(ref_text,"0.035"): print(idx, excerpt(ref_text, idx-100, 200).replace("\n"," "))

Updating simulation variables and calls

I’ll adjust the classShift occurrences at 383611 and 441031. Other occurrences of 0.035 are for ace baseline, so no changes there. For the Markov section in MATCH_PREVIEW, I'll replace simHA_totals and simHB_totals with the updated simHA and simHB. I'll apply the same adjustments to the structuralCoinFlip call and totals simulation, ensuring proper use of the correct variables.

Patch FINAL_BET_SUMMARY for unified variables

In FINAL_BET_SUMMARY, I’ll rename simHA_match and simHB_match to simHA and simHB or create aliases to ensure consistency. After eliminating the sandbox variables, I'll update relevant calls like runMatchSimulation_Exact, replacing simHA_totals with simHA_match as needed, and adjust the class shift to 0.02.

for idx in find_all(ref_text,"simHA_totals")[:3]: print(idx, excerpt(ref_text, idx-100, 220).replace("\n"," "))

Updating various components and ensuring consistency

In the Markov section and structural coinflip, I’ll patch the momentum update by replacing SET_DELTA_MP with 0.03, while adding a comment. Also, I’ll adjust SET_CONFIDENCE_DELTA to 0.03 in matchOutcomeProbs. Finally, I’ll ensure all references to simHA_totals are replaced, including the two calls in MATCH_PREVIEW and Markov variables.

import re patched = ref_text

1) Remove sandbox totals vars in FINAL_BET_SUMMARY and MATCH_PREVIEW. We'll remove lines declaring simHA_totals/simHB_totals.

patched = re.sub(r"\n\s*// Sandbox totals inputs[^\n]\n\svar simHA_totals\s*=\s*[^;]+;\s*\n\svar simHB_totals\s=\s*[^;]+;\s*\n", "\n", patched)

The above might remove both blocks; check remaining occurrences of "Sandbox totals inputs"

remaining_sandbox = len(re.findall(r"Sandbox totals inputs", patched)) remaining_simHA_totals = patched.count("simHA_totals") remaining_simHB_totals = patched.count("simHB_totals") remaining_sandbox, remaining_simHA_totals, remaining_simHB_totals

Updating classShift values in tier shift blocks

I’ll change the classShift multipliers in the tier shift blocks for both the final bet summary and match preview. Specifically, I’ll replace var classShift = tierGap * 0.035; with 0.02, updating comments to reflect the 2.0% factor instead of 3.5%. I’ll use regex for these replacements.

patched = re.sub(r"(//\s*)3.5% hold shift per tier gap.(.?\n\svar classShift\s*=\stierGap\s*\s*)0.035\s*;", r"\g<1>2.0% hold shift per tier gap.\g<2>0.02;", patched)

another comment with 7.0% etc; also might use different comment line. let's just replace any 'var classShift = tierGap * 0.035;'

patched = re.sub(r"var classShift\s*=\stierGap\s*\s0.035\s;", "var classShift = tierGap * 0.02;", patched)

confirm those old patterns removed

"0.035" in patched[383000:384000], "0.035" in patched[440500:441500]

Ensuring untouched 0.035 occurrences

We need to make sure other occurrences of 0.035 remain unchanged for uses like the ace baseline, as we didn’t remove them. I’ll double-check those positions to confirm they’re unaffected and ensure consistency across the code.

ensure we didn't alter ace baseline etc? Should remain 0.035 at other positions.

sum(patched.count(x) for x in ["0.035"])

Unifying totals simulation calls

The original code had 8 occurrences of 0.035, with two related to classShift. Now, we’re left with 6. I’ll unify the totals simulation calls, replacing simHA_totals and simHB_totals with the correct variables. Inside FINAL_BET_SUMMARY, we’ll use simHA_match and simHB_match for holds, while in MATCH_PREVIEW, it’ll be simHA and simHB. We need to check the variable names in those specific call contexts. Then, I’ll patch using local replacements at the necessary points.

Replace the specific call within FINAL_BET_SUMMARY by using nearby variable names

patched = patched.replace("runMatchSimulation_Exact(simHA_totals, simHB_totals, bo", "runMatchSimulation_Exact(simHA_match, simHB_match, bo") patched = patched.replace("runMatchSimulation_Exact(simHA_totals, simHB_totals, bestOf", "runMatchSimulation_Exact(simHA, simHB, bestOf")

Now update other references: markov section uses simHA_totals variable tests.

patched = patched.replace("typeof simHA_totals !== 'undefined' && Number.isFinite(simHA_totals)) ? simHA_totals", "typeof simHA !== 'undefined' && Number.isFinite(simHA)) ? simHA") patched = patched.replace("typeof simHB_totals !== 'undefined' && Number.isFinite(simHB_totals)) ? simHB_totals", "typeof simHB !== 'undefined' && Number.isFinite(simHB)) ? simHB") patched = patched.replace("Number.isFinite(simHA_totals)) ? simHA_totals", "Number.isFinite(simHA)) ? simHA") patched = patched.replace("Number.isFinite(simHB_totals)) ? simHB_totals", "Number.isFinite(simHB)) ? simHB")

may overreplace in other context; we'll check quickly.

Replace structural coinflip call:

patched = patched.replace("isStructuralCoinFlip(_pvAbsDiff, _pvDecider, simHA_totals, simHB_totals, tourLabel)", "isStructuralCoinFlip(_pvAbsDiff, _pvDecider, simHA, simHB, tourLabel)")

Replace any remaining simHA_totals/simHB_totals in Markov section and elsewhere with simHA/simHB.

patched = patched.replace("simHA_totals", "simHA") patched = patched.replace("simHB_totals", "simHB")

After replacements, check for any remaining mentions

patched.count("simHA_totals"), patched.count("simHB_totals")

Checking for undefined simHA variables

I replaced all occurrences of simHA_totals with simHA, but now I wonder if this affected the FINAL_BET_SUMMARY Markov section. I removed simHA_totals declarations there, and replacing simHA globally could cause undefined references, especially if other parts of the code were still expecting simHA_totals. I need to check for those references carefully.

Verifying variable name changes and scope conflicts

We should check for instances of var simHA = to see if any simHA_totals remain in FINAL_BET_SUMMARY, though we already removed it. In MATCH_PREVIEW, we’ve already updated the fallback expression and handled simHA and simHB replacements. We should ensure no old comments, like "Sandbox totals inputs," remain. Also, we need to confirm that class shifts in FINAL_BET_SUMMARY are set correctly to 0.02, and that simHA_match is updated properly.

Synchronizing Markov engine and standardizing momentum

We’ve already updated Markov to use simHA and simHB for tier-shifted holds, but we need to ensure this is reflected in FINAL_BET_SUMMARY too, if applicable. Meanwhile, we're updating the momentum settings by changing the SET_DELTA_MP and SET_CONFIDENCE_DELTA to 0.03 (for WTA/ATP), which will require patching via regex in functions like runMatchSimulation_Exact and matchOutcomeProbs.

patched2 = patched

Patch SET_DELTA_MP in runMatchSimulation_Exact

patched2 = re.sub(r"var\s+SET_DELTA_MP\s*=\s*$tourLabel\s*===\s*'WTA'$\s*?\s0.025\s:\s0.020\s;\s*//[^\n]*", "var SET_DELTA_MP = 0.03; // Standardized set-to-set momentum (unified for winners + totals)", patched2)

Patch matchOutcomeProbs SET_CONFIDENCE_DELTA default

patched2 = re.sub(r"\(_tourMO\s*===\s*'WTA'\s*?\s0.025\s:\s*0.020)", "0.03", patched2)

Ensure clamp still ok

Count occurrences of 0.025 and 0.020 in those contexts? We don't want to remove elsewhere (e.g., other uses)

patched2.count("SET_DELTA_MP = 0.03"), "((_tourMO" in patched2[154000:158000]

Standardizing momentum and Markov consistency

We’ve replaced the momentum delta for matchOutcomeProbs and runMatchSimulation_Exact to 0.03. Now, let’s ensure consistency: Markov should use the same variables for simHA and simHB. We also standardized the momentum for totals and winners. We should keep the toggles organized and ensure no penalties are incorrectly applied when _totalsOnly triggers. Lastly, the hold shift factor has been unified with classShift set to 0.02.

Confirming Markov consistency across functions

We’ve updated the Markov/Dist function in the preview to use simHA and simHB. But there could still be Markov computations elsewhere, like getTotalsPick, using different holds like simHoldA and simHoldB—which is probably fine. I'll check for any instances of matchOutcomeProbs with hA or hB to double-check.

find matchOutcomeProbs calls in patched2

calls=[] for m in re.finditer(r"matchOutcomeProbs(", patched2): calls.append(m.start()) len(calls), calls[:10]

Validating total cross-check and Markov consistency

At function 155197, we define the function, but later calls should use unified holds for totals cross-check in preview, which seems patched correctly. I'll confirm that simHA_totals was removed and that "Sandbox" comments were properly cleared. In FINAL_BET_SUMMARY, I'll check if simHA_match is well-defined and syntax is correct by running a Node parse.

patched_path = Path("/mnt/data/Tennis.God.JS.refactor.UNIFIED_REALITY_PATCHED.txt") patched_path.write_text(patched2, encoding="utf-8") patched_path.as_posix(), patched_path.stat().st_size

Running Node for syntax verification

I realized that node -c won’t work, so I’ll instead run a script using node -e to check the syntax. It’ll read the file in UTF-8, execute the code, and confirm with "ok." While memory-heavy, it should be fine. I’m running it in the container for this test.

bash -lc node -e "const fs=require('fs'); const code=fs.readFileSync('/mnt/data/Tennis.God.JS.refactor.UNIFIED_REALITY_PATCHED.txt','utf8'); try{ new Function(code); console.log('PARSE_OK'); }catch(e){ console.error('PARSE_ERR', e.message); process.exit(1);}"

Checking for undefined variables

The syntax is fine. Now, I want to check for any undefined variables we might have inadvertently introduced. I'll search for "simHA" since we replaced "simHA_totals" in FINAL_BET_SUMMARY. If any code references simHA without it being declared, I'll double-check if it's defined earlier, such as in MATCH_PREVIEW.

Extract FINAL_BET_SUMMARY function body and check occurrences

pos_fbs = patched2.find("function FINAL_BET_SUMMARY") pos_fbs_end = patched2.find("\nfunction ", pos_fbs+10) fbs_code = patched2[pos_fbs:pos_fbs_end] fbs_code.count("simHA"), fbs_code.count("simHA_match"), fbs_code.count("simHB_match")

Listing simHA occurrences

I need to check the code for any instances of simHA. It's possible that some of these should have been replaced with simHA_match or something else. I'll list all occurrences of simHA to ensure everything is consistent and nothing was missed during the update.

show lines containing 'simHA' in fbs_code

lines=fbs_code.splitlines() for i,l in enumerate(lines): if "simHA" in l: print(i+1, l.strip()[:200])

Checking simHA in MATCH_PREVIEW function

Great, no stray simHA references. Now, I need to ensure simHA is properly defined and used within the MATCH_PREVIEW function. I’ll extract that section of the code and verify that everything is set up correctly. This should clear up any potential issues.

pos_mp = patched2.find("function MATCH_PREVIEW") pos_mp_end = patched2.find("\nfunction ", pos_mp+10) mp_code = patched2[pos_mp:pos_mp_end] mp_code.count("var simHA"), mp_code.count("simHA"), mp_code.count("simHB")

Verifying Markov/Dist uses simHA, simHB

First, I’ll check for any leftover "simHoldA" references and update comments if needed. Then, I want to ensure the Markov section is correctly using simHA and simHB, not "simHold." I’ll locate the relevant Markov/Dist snippet to verify this and confirm everything’s in place.

pos_markov2 = patched2.find("Fair Total Line (Markov/Dist)") pos_markov2

print(excerpt(patched2, pos_markov2-700, 900))

Verifying totals simulation uses simHA and simHB

Now, I’ll ensure that the totals simulation call in the match preview correctly uses simHA and simHB. I’ll check around the old call location (around line 4415) to confirm that these variables are being used as expected, replacing any previous references.

show excerpt near totals sim call in match preview

m = re.search(r"simTotals\s*=\srunMatchSimulation_Exact(simHA,\ssimHB", patched2) m.start() if m else None

print(excerpt(patched2, 441160, 500))

Checking fallback runMatchSimulation call

Next, I’ll verify the fallback runMatchSimulation call, particularly the lowN aspect, to ensure it's functioning as expected. This needs to align with the changes made earlier, and I’ll check for consistency within the code, making sure the fallback logic is properly implemented.

m = re.search(r"runMatchSimulation_Exact(simHA,\ssimHB,\sbestOf,\s*_tlPropIters", patched2) m.start() if m else None

print(excerpt(patched2, m.start()-120, 360))

Checking structural coinflip replacement

Now, I’ll focus on verifying the structural coinflip call. I need to ensure that it has been properly replaced throughout the code. I'll find the relevant references and make sure any old calls are updated, so everything is aligned with the new structure.

idx = patched2.find("isStructuralCoinFlip") print(excerpt(patched2, idx-100, 240))

Locating MATCH_PREVIEW call

I need to find the call within MATCH_PREVIEW and ensure it’s placed earlier in the code. I’ll search for its occurrence so that I can work on repositioning or confirming it's correctly placed. Once located, I’ll continue making sure the flow is correct for the simulation.

for m in re.finditer(r"isStructuralCoinFlip(", patched2): if m.start()>430000 and m.start()<470000: print("call at", m.start(), patched2[m.start():m.start()+120].replace("\n"," "))

Verifying momentum and patching updates

We updated SET_DELTA_MP and default match outcome probabilities to 0.03 but need to ensure propConfig overrides work. For the tour-specific adjustments, they might be fine. Next, I’ll check if the Markov cross-check uses the same delta as MC. I also need to confirm that we restored getTotalsPick to v3.0 logic, as requested. Lastly, I’ll verify there's no leftover "TOTALS_MONTE_CARLO_SIMS" constant and provide the sandbox file path as a downloadable link.

Providing the download link and clarification on citations

We'll provide the sandbox file link in the response, as requested. No need for citations because we're not referencing file content via file_search. The user only asked for a refactor and for syntax issues to be checked, so no citations necessary for simply sharing the patched file.

Documenting the patch and summary of edits

I've validated the file parses and ensured there are no leftover "simHA_totals" references. I'll update the patch note header at the top of the script, adding today's date and a summary of the changes made. I'll insert the new patch note after the previous entry to maintain clarity.

patched3 = patched2

Insert patch note after first PATCH NOTE block if exists

insert_note = """\n * PATCH NOTE (2026-02-21):\n * - Unified totals + winner holds (removed totals sandbox; single reality for preview).\n * - Dampened BT-SBM tier hold shift from 3.5% -> 2.0% per tier gap (applies to ALL engines).\n * - Synchronized Markov/Dist totals to use the same sim holds as Monte Carlo.\n * - Standardized set-to-set momentum to 0.03 in BOTH MC + Markov engines.\n"""

Find the end of the first PATCH NOTE comment block (*/ after)

m = re.search(r"/**[\s\S]?PATCH NOTE\s$2026-02-19$:[\s\S]*?*/", patched3) if m: block = m.group(0) # insert before closing */ block2 = block.replace("\n */", insert_note + "\n /") patched3 = patched3[:m.start()] + block2 + patched3[m.end():] else: # prepend patched3 = "/**" + insert_note + "/\n" + patched3

Write final file

patched_path2 = Path("/mnt/data/Tennis.God.JS.refactor.UNIFIED_REALITY_PATCHED.v1.txt") patched_path2.write_text(patched3, encoding="utf-8") patched_path2.as_posix(), patched_path2.stat().st_size

bash -lc node -e "const fs=require('fs'); const code=fs.readFileSync('/mnt/data/Tennis.God.JS.refactor.UNIFIED_REALITY_PATCHED.v1.txt','utf8'); try{ new Function(code); console.log('PARSE_OK'); }catch(e){ console.error('PARSE_ERR', e.message); process.exit(1);}"

Validating the "simHA_totals" removal

I'll ensure that there are no remaining references to "simHA_totals" in the file. This should be a quick sweep to confirm everything is clean and up to date. If any are found, I’ll remove them before finalizing the changes.

patched3.count("simHA_totals"), patched3.count("Sandbox")

Thought for 16m 11s