Evaluating the file and cluster research

I don't think the article will directly help with the bug since it talks about tennis match clustering rather than the cluster governor. However, I should at least skim it to understand the clustering logic (multi-variable, silhouette, etc.).

Next, I’ll check the script and locate the getTotalsPick function in the large file.

Searching and refining script logic

I’ll search the script for "getTotalsPick" and check the surrounding code. We need to update ctx variable names and check for any other places where cluster support uses “median2set” or “median3set,” changing them to “clusterMedian2set” and “clusterMedian3set.”

I'll also look for functionality near “winner decision” that might use cluster metrics and check for “wce” or undefined variables like “synct.” Lastly, I wonder if running a simple parsing script could help find errors.

Scanning for undefined references and cluster logic

I'll start by scanning the code for any references to "undefined" or "synct." There’s also the possibility of a function called "_syncT" I should search for. Regarding "update clusters in match winner," it seems related to how clusters influence pick outcomes, particularly with favorites and underdogs. I’ll load the file into Python and search.

import re, os, textwrap, json, math, statistics path = "/mnt/data/Tennislocks_V17.4_Cluster_Governor_PATCHED_v17.4.3.txt" with open(path, 'r', encoding='utf-8', errors='ignore') as f: data = f.read() len(data), data[:200]

Finding getTotalsPick occurrences

The file is pretty large (894k), but I’ll go ahead and search for instances of "getTotalsPick" to locate where it’s being used. I’ll focus on finding those specific occurrences to help narrow down what needs to be adjusted. It shouldn't take too long!

find getTotalsPick definition

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

Extracting relevant code around getTotalsPick

I’ll go ahead and extract the surrounding code where "getTotalsPick" appears. This should help clarify the context and give us the information we need to make modifications. I just need to focus

start = m.start()-500 end = m.start()+5000 snippet = data[start:end] snippet[:1000]

Searching for bimodalTwoSetSupport occurrences

I’m going to search the file for "bimodalTwoSetSupport" to locate any relevant blocks of code. I’ll go through the results and check if there’s anything related to the adjustments or changes needed. Let’s find those occurrences and narrow it down!

idxs = [m.start() for m in re.finditer(r'bimodalTwoSetSupport', data)] len(idxs), idxs[:5], idxs[-5:]

Extracting code around getTotalsPick

I’ve found some code inside "getTotalsPick," possibly around line 572579. I’ll extract the code around line 572550. Let’s take a closer look at that section to see what’s happening and check for any important adjustments or logic that might need to be addressed.

s = 572400 e = 572750 print(data[s:e])

Inspecting _med2 assignment

I've located the exact block. It looks like I need to update it using ctx.clusterMedian2set/3set instead. The script currently uses the _med2 variable, which seems to be from ctx.median2set. I’ll search through the getTotalsPick function for the _med2 assignments to confirm how it's defined earlier.

Find where _med2 is set within getTotalsPick by searching backwards from block.

block_start = data.rfind("var _med2", 0, 572400) block_start

print(data[571930:572120])

Adjusting variable names for ctx

The script currently uses ctx.median2set, but the user suggests it should be ctx.clusterMedian2set and ctx.clusterMedian3set instead. I need to confirm if this is indeed what’s required and make sure that the script is using the correct names. I'll search through the code for clusterMedian2set to verify.

len(re.findall(r'clusterMedian2set', data)), len(re.findall(r'clusterMedian3set', data))

Adjusting for compatibility with clusterMedian variables

It seems that ctx.clusterMedian2set exists, but median2set may still be used in some contexts. I need to check if getTotalsPick expects ctx.median2set, or if it's actually using clusterMedian2set. The plan is to adapt backward compatibility by using _med2 from ctx.clusterMedian2set first, then fall back to ctx.median2set. The same approach applies to _med3, and we'll implement changes accordingly. I'll also replace the block with user-described logic for cluster support.

sf_idxs = [m.start() for m in re.finditer(r'var _sfMed2', data)] sf_idxs[:10], len(sf_idxs)

Extracting relevant section around 573792

It looks like the block I’m interested in is around line 573792 within getTotalsPick. I’ll extract the relevant section or code surrounding this line to better understand how to adjust variables and logic accordingly. I'll move on to the next step once I confirm the details.

print(data[573730:574040])

print(data[574040:574240])

Updating code and ensuring no conflicts

I need to replace the code with _sfMed3 and adjust for clusterMedian variables. The patch notes mention something about the "Cluster governor" and "tiered p3 ladder," so I need to check that too, ensuring no conflicts. Now, I’ll search within the getTotalsPick function for any instances of ctx.median2set or ctx.median3set and locate the next function definition.

start = m.start()

find next "function " after start+1

next_m = re.search(r'\nfunction\s+[A-Za-z0-9_]+\s*(', data[start+1:]) next_pos = start+1+next_m.start() if next_m else None start, next_pos

Extracting region to search for median2set

The getTotalsPick function spans from line 567910 to 607082. I will extract this region and search within it for any references to median2set to make sure I address everything correctly within that specific function range.

totals_region = data[start:next_pos] sorted(set(re.findall(r'ctx.(median2set|median3set|clusterMedian2set|clusterMedian3set)', totals_region)))[:20], len(re.findall(r'ctx.', totals_region))

Locating clusterMedian3set reference

In the getTotalsPick region, I noticed that ctx.clusterMedian2set isn't referenced, but clusterMedian3set is. This seems unusual, so I'll need to locate where exactly clusterMedian3set is used to understand why it's appearing while the first isn't. I'll investigate further.

for m2 in re.finditer(r'ctx.clusterMedian3set', totals_region): print("pos", m2.start()) print(totals_region[m2.start()-80:m2.start()+120])

Clarifying median and cluster variables

It seems the variables aren't exactly as expected — for example, clusterMedian3setBo5 or clusterMedian4set are used instead of clusterMedian3set. The regex picked clusterMedian3set by substring, so I'll adjust to use the correct variables like clusterMedian2set and clusterMedian3set if present, and check where ctx.median2set is set in the code.

set_idxs = [m.start() for m in re.finditer(r'.median2set\s*=', data)] len(set_idxs), set_idxs[:5]

Inspecting code for median2set

It looks like median2set is set elsewhere, not clusterMedian2set. I should inspect around line 703213 for more context and details, which likely holds the key to understanding where median2set is assigned. I'll clarify this to ensure the proper variable adjustment later.

print(data[703140:703360])

Investigating clusterMedian2set assignment

It looks like the stats.median2set might be feeding into ctx, possibly through the cluster governor. However, I need to double-check if clusterMedian2set is set elsewhere, maybe within the cluster engine. I’ll search for all .clusterMedian2set assignments to clarify this.

assign_idxs = [m.start() for m in re.finditer(r'clusterMedian2set\s*=', data)] len(assign_idxs), assign_idxs[:10]

Searching for clusterMedian2set assignment

There's no direct assignment found, so I’ll look into whether it’s a property set with something like ctx['clusterMedian2set'] or through stats.clusterMedian2set. I’ll search for those references to clarify how it’s being set.

occ = [m.start() for m in re.finditer(r'clusterMedian2set', data)] occ[:10], len(occ)

Inspecting relevant areas

Now, I’ll go ahead and inspect around the points I’ve noted — checking both ctx['clusterMedian2set'] and stats.clusterMedian2set. This should help clarify how they are being set, and from there I can figure out the next steps with more confidence.

for pos in occ: print("----", pos) print(data[pos-100:pos+200])

Inspecting cluster variables and winner decision logic

It looks like there are cluster median variables created in the context at certain positions. I’ll focus on adjusting the totals pick logic to use clusterMedian2set and clusterMedian3set, with a fallback to median2set. I’ll also inspect the earlier parts of the file around position 102800, likely linked to the winner engine cluster governor.

print(data[102720:103250])

Searching for winnerDecision function

I’m trying to identify the function around position 102720, which seems to relate to the winnerDecision cluster governor. I’ll search for the function definition to confirm this and locate the full function, so I can understand how it interacts with the rest of the logic.

mwd = re.search(r'function\s+winnerDecision', data) mwd.start() if mwd else None

Inspecting winnerDecision cluster policy

The cluster references at position 102720 are likely found within the winnerDecision function. I need to update clusters for both the favorites and underdogs, which might be pointing to a logic error. I'll focus on the cluster policy part in winnerDecision, extracting the region from 100k to 106k to review it.

region = data[99000:108000]

find "cluster" key blocks

for pat in ["Cluster governor", "cluster", "cMed2", "support", "confirm", "override", "underdog", "favorite", "fav", "dog"]: print(pat, region.lower().find(pat.lower()))

Inspecting Cluster Governor

I think I need to view the section around the "Cluster governor" line. This might provide clarity on the functionality and any issues concerning how clusters are handled in the current context. I'll inspect that part thoroughly to ensure everything is in order.

start = 99000 + 1500 print(data[start:start+4000])

Reviewing Cluster Governor Logic

I'll continue examining the logic for the cluster governor, particularly how it handles favorites and underdogs. This will help ensure the correct processing, and I’ll check if any updates or extensions are needed to improve how they're managed in the current setup. Time to extend this review.

print(data[start+4000:start+8000])

print(data[start+8000:start+12000])

Inspecting winner decision logic for bugs

It seems the winner decision's cluster governor already uses clusterMedian2set and 3set effectively, using set-path for balance, but the issue might lie in how it handles favorites and underdogs. It uses thresholds and failures around adjP2set and cP3, so there might be a bug in how these clusters apply, especially for Bo5 or incorrect median application. I'll need to investigate further to fix this potential issue.

Checking for potential misclassification in cluster valid

The match winner decision seems focused on favorites versus underdogs, yet could misclassify them due to possibly incorrect property names or an oversight of market underdog data. I need to track the ctx pass into _winnerDecision_ and verify if the cluster context names match, ensuring the counts are set properly. I'll review earlier code at positions around 544301 and 779693 to cross-check the context setup.

print(data[543950:544420])

print(data[544420:544820])

Tracing getTotalsPick function call

It looks like the context building already uses properties like sim.median2set and 3set. Now, for the totals pick, it seems to be using ctx.median2set. I’ll track where getTotalsPick is called to confirm if those properties are correctly passed into it. Time to search for "getTotalsPick(" calls.

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

Inspecting function definition call

The definition seems to be at 567919, and there are other relevant calls at 639330 and so on. I'll need to investigate the call around 639330 to see how the function is working, specifically in the context of the properties passed and whether they're being correctly handled.

for pos in calls[3:]: print("----", pos) print(data[pos-100:pos+200])

Inspecting _totCtxBet construction

The _totCtxBet context is being used and likely contains properties like clusterMedian2set. I need to check where this context is being built, which seems to be around 639330. It’s probably inside a function related to total picks in preview. I’ll open the code around that area for further inspection.

print(data[638900:639420])

Searching for _totCtxBet definition

I need to find where _totCtxBet is defined earlier in the code. To do this, I'll search within the preceding region for any mention of “_totCtxBet”. This will help me understand its properties or how it's being used, which is important for resolving the current issue.

pos = data.rfind("var _totCtxBet", 0, 639330) pos

print(data[pos:pos+500])

print(data[pos+500:pos+900])

Evaluating context usage in totals and winner decision

The current context for _totCtxBet uses median2set and median3set, while winner decision uses clusterMedian2set/3set. The user wants consistency with their sheet that uses clusterMedian2set, so the solution may involve adding both properties for backward compatibility. I think we’ll need to map these in getTotalsPick, but I’m not sure if changing context names is necessary.

Adjusting context use for consistency

The user’s asking to use ctx.clusterMedian2set/3set by default, rather than ctx.median2set. It seems their sheet may use these properties to create the context. We’ll make getTotalsPick read these values first and update places where ctx uses median2set to include clusterMedian2set. I'll also check if winnerDecision consistency needs improvement.

wd_calls=[m.start() for m in re.finditer(r'winnerDecision(', data)] len(wd_calls), wd_calls[:10]

Investigating context consistency in match winner

Looking at the definition at 94018 and calls at 543193 and 778535, it seems like the correct cluster fields are being used for match winner. The ctx object includes clusterMedian2set, indicating things are aligned. However, the user insists on updating. I'll inspect if p5setVal is being referenced incorrectly, particularly in a conditional.

Clarifying the use of p5setVal and cluster logic

p5setVal is defined within the isBo5 block and hoisted, so it's safe to use within conditions that check (isBo5 && p5setVal >= x). If isBo5 is false, it remains undefined, but the conditions remain safe. Regarding the update, I should check whether favorites and underdogs should be treated the same in certain scenarios and review other possible logic flaws.

Investigating _med2/_med3 usage in getTotalsPick

I should search within getTotalsPick for any additional uses of _med2 or _med3 logic, especially in relation to comparisons like OVER. I'll use regex to find them and check if any other part of the cluster is also using these values. This will help ensure everything is consistent.

find occurrences of _med2 and _med3 within getTotalsPick region

occ_med2 = sorted(set(m.start() for m in re.finditer(r'\b_med2\b', totals_region))) occ_med3 = sorted(set(m.start() for m in re.finditer(r'\b_med3\b', totals_region))) len(occ_med2), len(occ_med3), occ_med2[:10], occ_med3[:10]

Introducing _med3 and checking for conflicts

Currently, there's no _med3 variable, so I'll define it in our patch. However, I need to make sure it doesn’t conflict with any existing variables. I'll check around line 4065 and similar areas to ensure no issues arise and that everything remains consistent with the rest of the code.

for pos in occ_med2: print("---", pos) print(totals_region[pos-80:pos+200])

Updating narrative for clarity with median values

There are only two uses of the cluster: the twoSetSupport block and narrative with 2setMed. I’m considering updating the narrative to display the correct median depending on the side, although it’s not strictly necessary. Still, it may help avoid confusion, so I’ll search for "2setMed=" to check for occurrences.

list(re.finditer(r'2setMed=', totals_region))[:3]

Checking second occurrence of 2setMed

I found two occurrences of "2setMed=". Now, I’ll take a closer look at the second one to see if any updates are needed. I'll check the context and decide if I need to make any adjustments or clarifications to keep things accurate and clear.

print(totals_region[6700:6900])

Updating override logic and investigating "wce"

I need to update the override logic to properly display the correct median (2-set or 3-set) based on the side. Also, there's a bug where using the 2-set median for “OVER” always results in "under," so that will be patched. The user has mentioned "wce," which could be related to "win chance estimate," so I'll search for it.

wce_occ = [m.start() for m in re.finditer(r'\bwce\b', data)] len(wce_occ), wce_occ[:10]

Verifying "wce" existence

Alright, it looks like "wce" still exists even though the user thinks it's been removed. I’ll need to inspect the code around line 635250 to see how it's being used or if it’s being referenced somewhere it shouldn’t be.

print(data[635200:635650])

Confirming wce usage and searching for "synct"

It looks like "wce" is still in use, where it's referred to as "expGamesAdjusted." It wasn't removed as the user thought. Now, they've asked me to check for something called "synct," so I’ll search for the "synct" substring to investigate its presence in the code.

synct_occ=[m.start() for m in re.finditer(r'synct', data, flags=re.IGNORECASE)] len(synct_occ), synct_occ[:20]

Exploring "sync" for possible matches

I didn’t find "synct," but maybe the user meant "sync" or something like "syncTotals" instead. Since "sync" is quite a broad term, searching for its usage is a good idea. I’ll focus on where "sync" shows up and see if that leads to something relevant.

sync_occ=[m.start() for m in re.finditer(r'\bsync', data, flags=re.IGNORECASE)] len(sync_occ), sync_occ[:10]

Checking around sync usage

Okay, I’ll take a closer look around the "sync" term and its usage. It might lead me to something that fits the user’s intent. I’ll focus on finding references or instances of it throughout the code, which could give me a better understanding of what they might be referring to.

for pos in sync_occ: print("----", pos) print(data[pos-80:pos+200])

Inspecting for undefined references and errors

It seems the user is asking to check for errors related to undefined references, especially around the "synct" variable. I’ll do a quick static scan by searching for “ReferenceError” or undefined variables across the script. I'll run a basic syntax check using Node or a similar container to see if the environment handles it properly.

bash -lc node -v

Handling JS checks and incorporating PDF insights

We’ll run a node check on the JS file after modifications to catch syntax errors, but it might not catch issues like undefined references. To handle undefined issues, we’ll search for patterns like "validation" and make our modifications defensive. Regarding the PDF, I’ll make sure to clarify that it’s about tennis match clustering, explaining concepts like multi-modal clusters. I’ll cite the relevant pages too!

Modifying getTotalsPick function in Python

I need to modify the getTotalsPick function by updating how we assign values to _med2 and possibly _med3. For _med2, I’ll prefer ctx.clusterMedian2set if it’s finite; otherwise, I’ll fall back to ctx.median2set. Similarly, we’ll define _med3 to follow the same logic. Careful not to overwrite existing variable names like _med3b5 – it should be fine since ctx.median3set is not in use. I'll preserve the original behavior where needed.

Finalizing logic for _med2 and _med3 variables

For _med2, I’ll handle the fallback to NaN if ctx.median2set is not valid. Similarly, I’ll handle _med3 by preferring ctx.clusterMedian3set but falling back to ctx.median3set. I need to ensure _med3 is still useful wherever median3set is referenced in the function and check for its broader usage. Let’s search.

search for 'median3set' in getTotalsPick region

for mm in re.finditer(r'median3set', totals_region): print(mm.start(), totals_region[mm.start()-60:mm.start()+80])

Updating median values for consistency

I noticed there’s a variable med3 defined at line 11406, which needs updating to check ctx.clusterMedian3set similar to _med3. This adjustment affects various values like _med2, _sfMed2, and med3. I’ll update all references of ctx.median2set and ctx.median3set throughout getTotalsPick to prefer cluster-based values where appropriate, while ensuring the fallback is in place where it's needed.

Implementing helpers for median variables

At the start of getTotalsPick, I’ll define helper variables _ctxMed2 and _ctxMed3, which prioritize ctx.clusterMedian2set and ctx.clusterMedian3set respectively, falling back as needed. Then, I’ll replace corresponding references in the function. I’ll also ensure to patch specific areas like the _med2 and med3 assignments, as well as any uses of ctx.median2set and ctx.median3set throughout.

totals_region.count("ctx.median2set"), totals_region.count("ctx.median3set")

Examining and updating occurrences

I need to update those 10 occurrences. First, I'll examine the context for each and look at the surrounding lines. Then, I’ll systematically update each one, ensuring the correct variable is used for consistency. It’s important that I pay close attention to context so no logic is broken.

find contexts for each occurrence

for mm in re.finditer(r'ctx.median2set', totals_region): pos = mm.start() print("median2set at", pos) print(totals_region[pos-60:pos+80].replace('\n','\n')) print() for mm in re.finditer(r'ctx.median3set', totals_region): pos = mm.start() print("median3set at", pos) print(totals_region[pos-60:pos+80].replace('\n','\n')) print()

Updating variables and ensuring consistency

The med2 and med3 variables are used for the escape valve, and I need to update them for consistency. I'll ensure any references to median2set and median3set also use the cluster median fallback. Then, I'll inspect the relevant section of the code and check for any other potential logic flaws in the cluster handling within getTotalsPick.

list lines in getTotalsPick containing 'cluster'

cluster_lines = [line for line in totals_region.splitlines() if 'cluster' in line.lower()] cluster_lines[:40], len(cluster_lines)

Inspecting for cluster governance and potential logic flaws

There seems to be a larger cluster governance layer later in the code that could also be affected by the bug. I'll need to examine this section carefully, especially how it handles bandOver and bandUnder based on med2/med3, to ensure they are calculated correctly. This may resolve the user's bug.

find section where bandOver computed

pos = totals_region.lower().find("bandover") pos

print(totals_region[pos-800:pos+2200])

Checking how diff2 is defined

It seems that diff2 is related to (med2 - line), but I need to confirm its definition. If overSupport is based on diff2 and underSupport equals -diff2, and it's reliant on med2, it could introduce bias. I'll need to trace diff2's definition to understand its role.

find where diff2 is defined earlier

for mm in re.finditer(r'\bdiff2\b\s*=', totals_region): print(mm.start(), totals_region[mm.start()-120:mm.start()+180])

Understanding the bug around cluster governance

It seems that diff2, which uses med2 (ctx.median2set) minus the line, is central to the issue. This results in the over/under bands being based solely on the 2-set median, causing a bias. There's an escape valve for 3-set medians, but the main bug is always predicting "under."

Fixing the cluster governance bands

We need to adjust the cluster governance so that the Over band uses med3 and the Under band uses med2, or a combination. We might define overSupport as med3 - line (or with margin and p3 weight), while UnderSupport could be line - med2, making them symmetrical. I’ll check the escape valve for med3 usage.

Show escape valve section and how med3 used

ev_pos = totals_region.lower().find("escape valve") print(totals_region[ev_pos:ev_pos+1800])

print(totals_region[ev_pos+1800:ev_pos+2800])

Adjusting cluster governance logic

The current logic uses diff2 (med2 - line), but the escape valve for Over uses med3 and p3, which is not enough. The user suggested using med3 for Over and med2 for Under. To fix this, I'll adjust overSupport and underSupport. I'll compute diff3 as med3 - line for Over and keep underSupport as line - med2, as they define.

Adjusting diff and band calculations

I'll patch the code by computing diffOver and diffUnder, while keeping diff2 for context. To maintain backwards compatibility, I'll store clusterDiff2 as med2-line, and add clusterDiffOver and clusterDiffUnder. Since overSupport will now use med3-line, it’s better to compute overSupport and underSupport separately rather than assuming symmetry.

Refining support and band calculations

I plan to rewrite the bandOver and bandUnder computations by adjusting the support calculations. OverSupportRaw will use med3 - line, and UnderSupportRaw will use line - med2, where a negative value indicates opposition. For match-type adjustments, I'll compute adjusted supports separately and shift them based on _govMtBandShift. Additionally, I'll compute diff2 and diff3 as med2-line and med3-line.

Updating support computations and cluster detection

I’m planning to update the assignment for med2 and med3 to use clusterMedian for consistency. The "bimodal/messy cluster detection" relies on bandOver and bandUnder, which will now behave correctly. I’ll also ensure that Bo5 band enrichment properly uses set medians, and Bo3 uses med3 for overSupport and med2 for underSupport. Lastly, I'll address margins for support as well.

Considering Over support and p3 incorporation

The band thresholds are based on the difference between med2 and line. For Over support, med3-line could be positive if the match is expected to go 3 sets. Currently, using med3 for Over support might make the support too strong if p3 is small. I might need a weighted combination of 2-set and 3-set medians to balance this out, considering p3 probability, but still need to ensure the focus remains on the structural aspects of the cluster.

Refining Over and Under support logic

Over support should factor in both p3 and med3, not just med3, as p3 plays a role in the outcome. For consistency, I’m thinking I’ll base bandUnder on med2 and bandOver on med3, potentially scaling by p3. This aligns with the user's request to validate Over picks using med3 while considering the cluster’s projection from the line.

Identifying bandOver/Under usage in rules

I'm thinking that the Over band may be used in rules, especially when engines are split or weak, to confirm predictions. In specific rules, like Rule 1, where engines align and the cluster confirms, the Over band might be involved. I should look for where "bandOver" is mentioned to check how it fits into this.

for mm in re.finditer(r'\bbandOver\b', totals_region): if mm.start() > 15000: print(mm.start(), totals_region[mm.start()-80:mm.start()+120])

Improving bandOver for Over picks

BandOver and bandUnder are used to confirm or oppose picks. If we adjust bandOver to use med3 - line, it may make Over support stronger or weaker, depending on the situation. The main issue is that Over picks are vetoed due to bandOver always being negative. A potential fix could involve a support metric considering both med2 and med3 with suitable adjustments.

Validating Over picks using cluster and p3

For Over picks, the cluster should use the 3-set median and adjust support by considering p3. If p3 is low, Over isn't likely, but the cluster shouldn't override engine probabilities. I’m thinking of using a formula for overSupport scaling it by p3, starting at 0 for low p3 and increasing as p3 rises. UnderSupport might also scale similarly.

Balancing overSupport and cluster validation

Using a more complex formula for overSupport might not be ideal—it's already set up with an escape valve for p3 in the script. I think the easiest fix is using diff3 for overSupport but adjusting for low p3. The tricky part is where both bandOver and underSupport are strong, leading to potential messy outcomes or a bimodal scenario. I'll need to dig deeper into this.

Considering bimodal match detection

For a typical match, both the Under and Over bands are strong, potentially signaling a bimodal match. This could happen when one set is below the line and the other above. To detect this, the code looks for the difference between med2 and med3, and if it's large enough, it sets the cluster as "messy." I'll need to check the code for how it detects this.

bim_pos = totals_region.lower().find("bimodal/messy cluster") print(totals_region[bim_pos:bim_pos+800])

Exploring the role of clusterMessy in rule handling

If med3 - med2 >= 12, then the cluster is marked as "messy," triggering Rule 5, where engines split and the cluster is messy. This might lean toward a pass, but engines strongly agreeing could still result in a pick. By design, clusterMessy may often lead to a pass, though additional rules also come into play. In bimodal matches, bandOver and bandUnder may both be strong, making it tricky whether the cluster opposes either side, as it depends on p3.

Refining cluster logic for better support direction

Instead of giving strong support to both sides, cluster should use p3 to decide the direction. Currently, diff2 decides direction, often favoring bandUnder, and p3 + med3 adjusts bandOver only when p3 is high. The bug lies in defaulting to under when engines pick over. To fix this, we can adjust how we evaluate OVER by using med3 for cluster support checks while keeping diff2 as the baseline. This avoids making both sides strong and keeps the logic cleaner.

Adjusting bandOver behavior with cluster support logic

We can keep bandOver based on diff2, but add a "clusterSupportForPick" variable. For OVER picks, evaluate if med3 is greater than line + margin; for UNDER picks, check if med2 is less than line - margin. This matches the user's patch. The current escape valve adjusts bandOver when p3 is high, but maybe the thresholds are too strict, preventing bandOver from ever being supportive. This could be why the cluster still vetoes every over.

Evaluating cluster checks and escape valve logic

It seems the earlier guards may be vetoing OVER picks before the cluster governance even kicks in. Fixing those could help the escape valve work properly. The user mentioned that the cluster checks mostly use the 2-set median, though the escape valve uses the 3-set median for OVER. While the escape valve was adjusted with a tiered p3 ladder, it might still be too weak, and the cluster's bimodal guard could be causing issues. It seems like Step 2 may already be fine.

Adjusting overSupport logic for better stability

To restore balance as the user suggests, we could modify overSupport to switch from med2-line to med3-line when p3 is high. The patch note mentions using a 3-set median at p3 >= 0.44, so we can apply that logic. We might set supports based on p3 to make bandOver supportive when the decider probability is high. This approach could improve stability. Let's implement: if p3 >= 0.44, use med3-line; otherwise, stick with med2-line for validation.

Implementing updated over and under logic with cluster support

I’m implementing a new approach to manage the over and under bands based on p3 and the medians. DiffOver will switch between med2 and med3 depending on p3 values. We’ll keep the escape valve intact, but with the switch at p3 >= 0.44, the band can become supportive when needed. This update better aligns with the patch notes and user expectations.

Clarifying cluster selections in match winner for favorite and underdog

I’m considering the cluster governor logic for winners, where it uses cMed2/cMed3 but may not be applying them as expected. It looks like clusterSep uses the correct logic, but naming issues could arise with ctx properties. I'll search for misapplied cluster terms and ensure the properties are used correctly in match winner conditions for favorites and underdogs.

for pat in ["cluster supports", "cluster confirm", "cluster veto", "cluster promotes", "clusterForce", "clusterBand"]: idx = data.lower().find(pat) print(pat, idx)

Considering adjustments for underdog promotion in winner decision

In the winner decision logic, the cluster governor handles underdogs with a veto when a favorite dominates. But this may be too strict, so I’m thinking of adjusting it based on factors like cleanLane or oppSweep. The user didn’t provide full details, so I’ll make a minor tweak to balance it better. I'll also look at the dog branch to ensure the underdog promotion uses the appropriate metrics for better balance overall.

Checking for bugs in market dog classification and cluster logic

There could be an issue with how we're determining if a team is a market dog, especially with potentially incorrect market odds. The user wants to update cluster logic for both favorites and underdogs, but it might already be fine. I’ll review the match winner logic for any bias toward favorites and double-check if the metrics, like cP3 and adjP2set, are applied correctly.

wd_start = mwd.start() wd_next = re.search(r'\nfunction\s+[A-Za-z0-9_]+\s*(', data[wd_start+1:]) wd_end = wd_start+1+wd_next.start() if wd_next else None wd_start, wd_end

Counting median in the winner decision region

I'm focusing on the region between 94009 and 127927 as part of the winner decision logic. I'll count the "median2set" within this specified region to ensure everything aligns correctly for that part of the analysis. Let’s check if the counts look as expected.

wd_region = data[wd_start:wd_end] wd_region.count("median2set"), wd_region.count("clusterMedian2set"), wd_region.count("median3set"), wd_region.count("clusterMedian3set")

Considering updates to match winner cluster logic

The winnerDecision seems to be using the correct cluster names, so no immediate changes required. But since the user mentioned updating clusters for favorites and underdogs, there might be issues with thresholds causing bias. I could potentially add additional checks, like ensuring the favorite isn’t overconfirmed when there’s a bimodal match.

Updating the script for cluster compatibility

I’ll focus on the totals cluster bug first, and for the match winner, I’ll ensure cluster context names are correct. Even though we’ve previously fixed the ctx.median2set issue, I’ll add a simple fallback in case older versions of the script are used by the user. This will make the script more compatible and safe.

Updating the file and providing download

I’ll use Python to modify the file and create a new version called "Tennislocks_V17.4_Cluster_Governor_FIXED_v17.4.4.txt" for download, after making necessary string replacements. We’ll cite the original file and PDF in the response, and highlight the key modifications in a bullet list. This ensures everything is updated while maintaining clarity.

Adjusting code variables and handling updates

I’m updating the code to account for changes made to clusterMedian2set and clusterMedian3set. The goal is to fix potential NaN issues while keeping consistency in variable naming, especially with ctx.bimodalTwoSetSupport. I’ll make sure the override block is similar, adjusting for the new median variable names.

Crafting updated cluster handling with proper safeguards

I’m implementing the changes to handle clusterMedian2set and clusterMedian3set, ensuring that appropriate fallbacks to ctx.median2set and ctx.median3set are used. I’ll preserve the structure of the original function while adjusting the block starting with var _sfLine = .... I need to ensure none of the function’s logic is disrupted.

find substring to replace for severe override block in full data

sf_start = data.find("var _sfLine", 567910, 607082) sf_start

get snippet around for context

print(data[sf_start:sf_start+700])

Including reasonDetail lines

I’ll move forward by incorporating the reasonDetail lines, so the context of the decisions or logic changes is clearer. It seems important to provide that extra layer of detail for understanding how the calculations or decisions were made in this update. This should be straightforward to add!

print(data[sf_start+700:sf_start+1250])

Updating variable names and reason details

I’ll modify the block starting from "var _sfLine" just before "ctx.capTotalsToLean." I'll be replacing the initial lines and adjusting the reasonDetail to show the used median—either _sfMed2 or _sfMed3, depending on the side. I'll add variables for clarity, ensure brevity, and keep everything stable.

Updating bimodal guard

I need to locate the code snippet starting from var _side0 and ending at if (ctx) ctx.bimodalTwoSetSupport = _twoSetSupport; and then replace it. I'll search for this exact substring in the original block and apply the adjustments from there.

orig_block1 = """ var _side0 = (pO >= 0.5) ? "OVER" : "UNDER"; var _twoSetSupport = false; if (Number.isFinite(_line0) && Number.isFinite(_med2)) { if (_side0 === "OVER") _twoSetSupport = (_med2 >= (_line0 + _marg)); else _twoSetSupport = (_med2 <= (_line0 - _marg)); } if (ctx) ctx.bimodalTwoSetSupport = _twoSetSupport; """ data.find(orig_block1) != -1

Creating replacement string

I’ll preserve indentation and create a replacement string with the correct structure. The replacement code handles the cluster medians, preferring clusterMedian* names if available and falling back to median* for legacy support. It ensures proper calculations for _clusterSupport and compatibility with ctx.bimodalTwoSetSupport.

Updating the severe block

To update the severe block, I'll look for the substring starting from var _sfLine until just before if (_sfClusterOverride). It seems simpler to replace the entire block from var _sfLine to the end of if (_sfClusterOverride), but I want to match the exact substring. Let’s make sure to create a pattern for this.

orig_sf_head = """ var _sfLine = (ctx && Number.isFinite(ctx.line)) ? Number(ctx.line) : NaN; var _sfMed2 = (ctx && Number.isFinite(ctx.median2set)) ? Number(ctx.median2set) : NaN; var _sfSide = (pO >= 0.5) ? "OVER" : "UNDER"; var _sfClusterOverride = false; if (Number.isFinite(_sfLine) && Number.isFinite(_sfMed2)) { if (_sfSide === "UNDER") _sfClusterOverride = (_sfMed2 <= (_sfLine - 1.0)); else _sfClusterOverride = (_sfMed2 >= (_sfLine + 1.0)); } """ data.find(orig_sf_head) != -1

Creating substring and handling NaN

I'll replace the code with the new segment and include _sfMed3. I also need to update the reasonDetail lines that reference "2setMed=" + _sfMed2.toFixed(1). To handle the toFixed issue when _sfMed3 is NaN, I'll ensure the code gracefully handles this, as toFixed on NaN returns "NaN".

Handling NaN and updates for medians

If _sfMed3 is NaN, toFixed will return "NaN", but that’s fine. I'll update earlier references to gapLine and assignments for _med2, _sfMed2, med2, _capMed2, and med3 to prefer cluster medians, ensuring all relevant lines are patched accordingly, including direct access to ctx.median3set in the getTotalsPick region.

Considering p3 switch and med3 implementation

I’m considering whether to implement the p3 switch. If p3 >= 0.44, I'll switch to using med3 as noted in the patch. This will allow bandOver to be calculated with 3-set median in decider-likely matches. I'll ensure med3 exists and update med2/med3 to use cluster.

Updating totals region for diff and support calculations

I plan to replace some lines in the totals_region. For diff2, I’ll update the code to check for finite values before performing calculations for both med2 and line. Then, I’ll add diff3 for the med3 calculation. Also, I’ll adjust the logic for overSupport and underSupport to accommodate these changes. Lastly, I’ll update the band calculations in the match-type band adjustment block.

Modifying band support and shift logic

I’ll update the condition to evaluate both overSupport and underSupport, and adjust calculations accordingly. I need to ensure the bandOver and bandUnder use the correct computations with the updated logic. Storing diff2 and diff3 in ctx is important, along with handling band softening. We’ll also make sure symmetric escape valves for under remain unchanged and add new cluster fields like clusterSupportOverUses. Additionally, some fallback handling for medians and p values in the _winnerDecision_ region.

Ensuring robust cluster validation and context updates

We need to ensure that the clusterValid correctly uses proper sample sizes, especially for the bo5. For bo5, cP3 likely represents the probability of a 5-set series, so pDecider should reflect that. I'll check for any undefined variables and update contexts like _totCtxBet and _ctxMT, adding clusterMedian2set and clusterMedian3set aliases where necessary.

find occurrences of "median2set:" in whole file

occ = [m.start() for m in re.finditer(r'\bmedian2set\s*:', data)] len(occ), occ[:20]

Reviewing context occurrences for cluster aliases

I see there are 8 occurrences, with some in stats objects or related to ctx. One is at 544367 for the winner, another at 779771 for totals preview. It seems placing the cluster alias where ctx objects are built could be helpful—likely within getTotalsPick. I'll check the areas near 637205 and 779750 to confirm.

for pos in occ: print("----", pos) print(data[pos-120:pos+200])

Checking structural override function for ctx.median2set update

I found a function at 612452 that seems to use ctx.median2set, possibly outside of getTotalsPick. It might be part of a structural override, like getTotalsConf. Since it reads med2 = ctx.median2set, I should check if we need an update to prefer clusterMedian2set instead. I’ll look closer around 612300.

print(data[611900:612650])

Searching for ctx.median2set usage in other cluster contexts

It looks like this might be from getTotalsConf or a similar function, as it uses med2 and p2. If med2 is incorrectly named, it could cause issues, so we should patch that. Since the user asked about clusters in totals game logic, I’ll search the entire file for ctx.median2set to check for other related flaws.

occ_ctx_med2 = [m.start() for m in re.finditer(r'ctx.median2set', data)] len(occ_ctx_med2), occ_ctx_med2[:10], occ_ctx_med2[-5:]

Patching occurrences of ctx.median2set and ctx.median3set

I found 14 occurrences, some within getTotalsPick, and others around 579226, possibly in a different function. We should patch each of these to prefer clusterMedian2set. I’ll also list the positions for ctx.median3set to ensure it’s handled as well.

occ_ctx_med3 = [m.start() for m in re.finditer(r'ctx.median3set', data)] len(occ_ctx_med3), occ_ctx_med3[:10]

Examining ctx.median2set occurrences outside getTotalsPick

It looks like there are two occurrences of ctx.median2set, both within the getTotalsPick region. We need to focus on positions beyond that and ensure that clusterMedian2set is used instead. I'll look at each position to understand the context better and patch accordingly.

for pos in occ_ctx_med2: print("----", pos) print(data[pos-80:pos+120].replace('\n',' '))

Inspecting additional cluster-related checks

There are other checks related to clusters outside getTotalsPick, especially around positions like 610588 and 614264. These might be within getTotalsConf or getTotalsGrade functions. I need to inspect these areas closely to make sure we're using the cluster medians correctly and patch any issues.

print(data[609900:611000])

Inspecting potential bug in confidence function

It looks like I've found another function that handles totals confidence labeling and uses _cfMed2 for cluster saves. This has the same bug, using med2 for both sides. I need to patch it to use med3 for "OVER", similar to the earlier fix. It’s likely part of clusterConfirmsPick, which I’ll inspect.

print(data[613950:614650])

print(data[614650:615000])

Updating cluster confirmation issues

There's a bug where cluster confirms are using med2 for both cases. I need to patch this to use med3 for 'OVER' and med2 for 'UNDER'. To do this, I’ll audit any cluster confirm logic in the totals function and search for patterns where the side equals "OVER". I'll also inspect the structural overrides and other relevant blocks, ensuring systematic fixes. I'll consider updating areas like getTotalsPick as well!

Updating cluster confirmation with correct settings

It seems there are adjustments needed in functions like getTotalsConf, where _cfMed2 is used. I'll update this to use med3 where necessary, adjusting variable names too. Similarly, in the totals confidence tier function, I'll fix the double use of _ccMed2 and patch any other related blocks. I'll search for patterns like if (side === "OVER") to ensure the right changes are applied.

Find all blocks where side is checked and med2 is used

pattern = r'if\s*$\s*side\s*===\s*"UNDER"\s*$[^\n]\n\s(?:else\s*)?if\s*$\s*side\s*===\s*"OVER"\s*$' matches = list(re.finditer(pattern, data)) len(matches)

Checking for additional cluster confirms logic

I need to check if there are any other instances where clusterConfirms uses things like _cfSide. Searching for patterns like _cfSide === "UNDER" should help identify other places needing updates. I'll look into these to ensure we're handling them correctly.

matches = list(re.finditer(r'_cfSide\s*===\s*"UNDER".?_cfSide\s===\s*"OVER"', data, flags=re.S)) len(matches)

Searching for potential cluster logic issues

I’ll search for patterns like ">= (_cfLine + 1.0" to identify additional logic that might need adjustments. This will help ensure we're addressing any places where similar fixes are required, especially in relation to handling cluster conditions. Let's try searching for that.

for pat in ["_cfMed2", "_ccMed2", "bimodalTwoSetSupport", "_twoSetSupport"]: print(pat, data.count(pat))

Implementing patches and modifications

I’ll patch occurrences of the median2set pattern in the cluster logic — focusing on those used for cluster confirm support. Changes include replacing specific variables, updating expressions, and patching assignments like _med2, _sfMed2, and others referencing ctx.median2set. I'll use regex substitution for accuracy and to minimize errors. Let’s proceed carefully with these updates.

Implementing helper function for assignment replacement

I'll define a helper function to replace variable assignments for X = (ctx && Number.isFinite(ctx.median2set)) ? Number(ctx.median2set) : NaN; with cluster support. This will handle different spacing and replace patterns for both median2set and median3set assignments. I'll also ensure parentheses around nested ternaries to avoid confusion, and avoid modifying comments.

Patching cluster band computations

I'll proceed with patching the getTotalsPick region next. First, I’ll target the lines where overSupport and underSupport are being set with Number.isFinite(diff2) and replace them with the new code block. I need to confirm that these lines exist before making the changes.

orig_support = """ var overSupport = Number.isFinite(diff2) ? diff2 : NaN; var underSupport = Number.isFinite(diff2) ? -diff2 : NaN;

var bandOver = bandSided(overSupport, overStrongBand, overSoftBand); var bandUnder = bandSided(underSupport, underStrongBand, underSoftBand); """ data.find(orig_support) != -1

Replacing computation block with proper indentation

I’m going to replace the existing block, making sure the indentation matches (two spaces). I’ll also include updates for p3, med3, and diff3. I need to locate the exact original substring for the adjustment block that uses _govMtBandShift and replace it with the new version accordingly.

orig_mtshift = """ if (_govMtBandShift !== 0 && Number.isFinite(overSupport)) { var adjOverSupport = overSupport + _govMtBandShift; var adjBandOver = bandSided(adjOverSupport, overStrongBand, overSoftBand); var adjBandUnder = bandSided(-adjOverSupport, underStrongBand, underSoftBand); if (adjBandOver === "SOFT" && bandOver === "NEUTRAL") bandOver = "SOFT"; if (adjBandUnder === "SOFT" && bandUnder === "NEUTRAL") bandUnder = "SOFT"; if (adjBandOver === "STRONG" && bandOver === "SOFT") bandOver = "STRONG"; if (adjBandUnder === "STRONG" && bandUnder === "SOFT") bandUnder = "STRONG"; } """ data.find(orig_mtshift) != -1

Updating cluster support metrics and replacements

I need to update the cluster support metrics in the code. Specifically, I'll replace the existing snippet that stores ctx.clusterDiff2, ctx.clusterBandOver, and others, adding diff3 and related metric updates. I'll find and replace this exact snippet to ensure everything is captured correctly.

orig_ctx_store = """ if (ctx) { ctx.clusterDiff2 = diff2; ctx.clusterBandOver = bandOver; ctx.clusterBandUnder = bandUnder; ctx.bo5PathSignal = _bo5PathSignal; } """ data.find(orig_ctx_store) != -1

Updating functions with new metrics

I’ll update the functions _cfClusterSaves and _confClusterConfirms by replacing the relevant blocks. For _cfClusterSaves, I’ll modify the code to include diff3 and corresponding metrics, such as _cfMed3, ensuring it handles undefined values and follows the severe override pattern for consistency.

Updating _confClusterConfirms and winnerDecision

I’ll replace the logic in _confClusterConfirms to factor in the cluster preference and add _ccMed3. I’ll also adjust the conditions for "UNDER" and "OVER" on both medians as needed. For _winnerDecision_, I’ll do something similar, modifying the relevant lines for cMed2 and cMed3. After that, I'll continue with updates in Python.

import re

new_data = data

1) Replace bimodal guard block

replacement_block1 = """ var _side0 = (pO >= 0.5) ? "OVER" : "UNDER"; // Cluster medians: prefer clusterMedian* names, fall back to legacy median* names var _med3 = (ctx && Number.isFinite(ctx.clusterMedian3set)) ? Number(ctx.clusterMedian3set) : ((ctx && Number.isFinite(ctx.median3set)) ? Number(ctx.median3set) : NaN); var _clusterSupport = false; if (Number.isFinite(_line0)) { // OVER needs the 3-set median to be cleanly over the line if (_side0 === "OVER" && Number.isFinite(_med3)) { _clusterSupport = (_med3 >= (_line0 + _marg)); } // UNDER needs the 2-set median to be cleanly under the line else if (_side0 === "UNDER" && Number.isFinite(_med2)) { _clusterSupport = (_med2 <= (_line0 - _marg)); } } if (ctx) ctx.bimodalTwoSetSupport = _clusterSupport; // kept name for compatibility """ new_data = new_data.replace(orig_block1, replacement_block1)

2) Replace severe/fragile override head

replacement_sf_head = """ var _sfLine = (ctx && Number.isFinite(ctx.line)) ? Number(ctx.line) : NaN; var _sfMed2 = (ctx && Number.isFinite(ctx.clusterMedian2set)) ? Number(ctx.clusterMedian2set) : ((ctx && Number.isFinite(ctx.median2set)) ? Number(ctx.median2set) : NaN); var _sfMed3 = (ctx && Number.isFinite(ctx.clusterMedian3set)) ? Number(ctx.clusterMedian3set) : ((ctx && Number.isFinite(ctx.median3set)) ? Number(ctx.median3set) : NaN); var _sfSide = (pO >= 0.5) ? "OVER" : "UNDER"; var _sfClusterOverride = false;

text
if (Number.isFinite(_sfLine)) {
  if (_sfSide === "UNDER" && Number.isFinite(_sfMed2)) {
    _sfClusterOverride = (_sfMed2 <= (_sfLine - 1.0));
  } else if (_sfSide === "OVER" && Number.isFinite(_sfMed3)) {
    _sfClusterOverride = (_sfMed3 >= (_sfLine + 1.0));
  }
}

""" new_data = new_data.replace(orig_sf_head, replacement_sf_head)

3) Patch the reasonDetail "2setMed=" line in severe override

orig_reason_line = '" 2setMed=" + _sfMed2.toFixed(1) + " line=" + _sfLine.toFixed(1) +' if orig_reason_line in new_data: new_reason_line = '" " + ((_sfSide === "OVER") ? "3setMed=" : "2setMed=") + ((_sfSide === "OVER") ? _sfMed3.toFixed(1) : _sfMed2.toFixed(1)) + " line=" + _sfLine.toFixed(1) +' new_data = new_data.replace(orig_reason_line, new_reason_line)

4) Update ctx.median2set assignments with fallback to clusterMedian2set, for specific local vars (var <name> = ... Number(...))

pattern_med2_num = re.compile(r'var\s+(\w+)\s*=\s*ctx\s*&&\s*Number\.isFinite\(ctx\.median2set)\s*?\sNumber$ctx\.median2set$\s:\s*NaN;') def repl_med2_num(m): varname = m.group(1) return f'var {varname} = (ctx && Number.isFinite(ctx.clusterMedian2set)) ? Number(ctx.clusterMedian2set) : ((ctx && Number.isFinite(ctx.median2set)) ? Number(ctx.median2set) : NaN);' new_data = pattern_med2_num.sub(repl_med2_num, new_data)

Also handle cases without Number(): var med2 = (ctx && Number.isFinite(ctx.median2set)) ? ctx.median2set : NaN;

pattern_med2_plain = re.compile(r'var\s+(\w+)\s*=\s*ctx\s*&&\s*Number\.isFinite\(ctx\.median2set)\s*?\sctx.median2set\s:\s*NaN;') def repl_med2_plain(m): varname = m.group(1) return f'var {varname} = (ctx && Number.isFinite(ctx.clusterMedian2set)) ? Number(ctx.clusterMedian2set) : ((ctx && Number.isFinite(ctx.median2set)) ? Number(ctx.median2set) : NaN);' new_data = pattern_med2_plain.sub(repl_med2_plain, new_data)

5) Update ctx.median3set assignments similarly (only those with Number())

pattern_med3_num = re.compile(r'var\s+(\w+)\s*=\s*ctx\s*&&\s*Number\.isFinite\(ctx\.median3set)\s*?\sNumber$ctx\.median3set$\s:\s*NaN;') def repl_med3_num(m): varname = m.group(1) return f'var {varname} = (ctx && Number.isFinite(ctx.clusterMedian3set)) ? Number(ctx.clusterMedian3set) : ((ctx && Number.isFinite(ctx.median3set)) ? Number(ctx.median3set) : NaN);' new_data = pattern_med3_num.sub(repl_med3_num, new_data)

6) Replace over/under support band block in totals cluster governance

replacement_support = """ // OVER support: use 3-set median when the decider path is meaningfully alive; otherwise fall back to 2-set median. // UNDER support: always uses the 2-set median. var diff3 = (Number.isFinite(line) && Number.isFinite(med3)) ? (med3 - line) : NaN;

var overSupport = NaN; var underSupport = NaN; if (Number.isFinite(line)) { if (Number.isFinite(p3) && p3 >= 0.44 && Number.isFinite(diff3)) overSupport = diff3; else if (Number.isFinite(diff2)) overSupport = diff2; if (Number.isFinite(diff2)) underSupport = -diff2; }

var bandOver = bandSided(overSupport, overStrongBand, overSoftBand); var bandUnder = bandSided(underSupport, underStrongBand, underSoftBand); """ new_data = new_data.replace(orig_support, replacement_support)

7) Replace match-type band adjustment block

replacement_mtshift = """ if (_govMtBandShift !== 0 && (Number.isFinite(overSupport) || Number.isFinite(underSupport))) { var adjOverSupport = Number.isFinite(overSupport) ? (overSupport + _govMtBandShift) : NaN; var adjUnderSupport = Number.isFinite(underSupport) ? (underSupport - _govMtBandShift) : NaN;

text
var adjBandOver = _bandSided_(adjOverSupport, overStrongBand, overSoftBand);
var adjBandUnder = _bandSided_(adjUnderSupport, underStrongBand, underSoftBand);

if (adjBandOver === "SOFT" && bandOver === "NEUTRAL") bandOver = "SOFT";
if (adjBandUnder === "SOFT" && bandUnder === "NEUTRAL") bandUnder = "SOFT";
if (adjBandOver === "STRONG" && bandOver === "SOFT") bandOver = "STRONG";
if (adjBandUnder === "STRONG" && bandUnder === "SOFT") bandUnder = "STRONG";

} """ new_data = new_data.replace(orig_mtshift, replacement_mtshift)

8) Replace ctx store block

replacement_ctx_store = """ if (ctx) { ctx.clusterDiff2 = diff2; // (med2 - line) ctx.clusterDiff3 = diff3; // (med3 - line) ctx.clusterSupportOver = overSupport; ctx.clusterSupportUnder = underSupport; ctx.clusterSupportOverUses = (Number.isFinite(p3) && p3 >= 0.44 && Number.isFinite(diff3)) ? "3set" : "2set"; ctx.clusterBandOver = bandOver; ctx.clusterBandUnder = bandUnder; ctx.bo5PathSignal = _bo5PathSignal; } """ new_data = new_data.replace(orig_ctx_store, replacement_ctx_store)

9) Patch _cfClusterSaves block (severe+fragile saves in totals tier function)

Find exact old snippet

old_cf_snip = """ var _cfLine = (ctx && Number.isFinite(ctx.line)) ? Number(ctx.line) : NaN; var _cfMed2 = (ctx && Number.isFinite(ctx.median2set)) ? Number(ctx.median2set) : NaN; var _cfSide = (side === "OVER" || side === "UNDER") ? side : ((pTmp >= 0.5) ? "OVER" : "UNDER"); if (Number.isFinite(_cfLine) && Number.isFinite(_cfMed2)) { if (_cfSide === "UNDER") _cfClusterSaves = (_cfMed2 <= (_cfLine - 1.0)); else _cfClusterSaves = (_cfMed2 >= (_cfLine + 1.0)); } """ if old_cf_snip in new_data: new_cf_snip = """ var _cfLine = (ctx && Number.isFinite(ctx.line)) ? Number(ctx.line) : NaN; var _cfMed2 = (ctx && Number.isFinite(ctx.clusterMedian2set)) ? Number(ctx.clusterMedian2set) : ((ctx && Number.isFinite(ctx.median2set)) ? Number(ctx.median2set) : NaN); var _cfMed3 = (ctx && Number.isFinite(ctx.clusterMedian3set)) ? Number(ctx.clusterMedian3set) : ((ctx && Number.isFinite(ctx.median3set)) ? Number(ctx.median3set) : NaN); var _cfSide = (side === "OVER" || side === "UNDER") ? side : ((pTmp >= 0.5) ? "OVER" : "UNDER");

text
  if (Number.isFinite(_cfLine)) {
    if (_cfSide === "UNDER" && Number.isFinite(_cfMed2)) {
      _cfClusterSaves = (_cfMed2 <= (_cfLine - 1.0));
    } else if (_cfSide === "OVER" && Number.isFinite(_cfMed3)) {
      _cfClusterSaves = (_cfMed3 >= (_cfLine + 1.0));
    }
  }

""" new_data = new_data.replace(old_cf_snip, new_cf_snip)

10) Patch _confClusterConfirms block

old_cc_snip = """ var _ccMed2 = (ctx && Number.isFinite(ctx.median2set)) ? Number(ctx.median2set) : NaN; var _ccLine = (ctx && Number.isFinite(ctx.line)) ? Number(ctx.line) : NaN; var _ccIsWhole = (ctx && ctx.isWholeLine === true); var _ccMargin = _ccIsWhole ? 1.0 : 0.75; if (Number.isFinite(_ccMed2) && Number.isFinite(_ccLine)) { if (side === "UNDER") _confClusterConfirms = (_ccMed2 <= (_ccLine - _ccMargin));

else if (side === "OVER") _confClusterConfirms = (_ccMed2 >= (_ccLine + _ccMargin)); } """

note the snippet includes a blank line and newline before else. We'll be more flexible with regex

Let's do regex replacement for that block around _ccMed2 usage

cc_pattern = re.compile( r'var _confClusterConfirms = false;\sif ctx && side {\svar _ccMed2 = .?;\svar _ccLine = .?;\svar _ccIsWhole = .?;\svar _ccMargin = .?;\sif Number\.isFinite\(_ccMed2 && Number.isFinite$_ccLine$) {\sif $side === "UNDER"$ _confClusterConfirms = _ccMed2 <= \(_ccLine - _ccMargin);\selse if $side === "OVER"$ _confClusterConfirms = _ccMed2 >= \(_ccLine \+ _ccMargin);\s*}\s*}', flags=re.S ) def cc_repl(match): return """var _confClusterConfirms = false; if (ctx && side) { var _ccMed2 = (ctx && Number.isFinite(ctx.clusterMedian2set)) ? Number(ctx.clusterMedian2set) : ((ctx && Number.isFinite(ctx.median2set)) ? Number(ctx.median2set) : NaN); var _ccMed3 = (ctx && Number.isFinite(ctx.clusterMedian3set)) ? Number(ctx.clusterMedian3set) : ((ctx && Number.isFinite(ctx.median3set)) ? Number(ctx.median3set) : NaN); var _ccLine = (ctx && Number.isFinite(ctx.line)) ? Number(ctx.line) : NaN; var _ccIsWhole = (ctx && ctx.isWholeLine === true); var _ccMargin = _ccIsWhole ? 1.0 : 0.75;

text
if (Number.isFinite(_ccLine)) {
  if (side === "UNDER" && Number.isFinite(_ccMed2)) _confClusterConfirms = (_ccMed2 <= (_ccLine - _ccMargin));
  else if (side === "OVER" && Number.isFinite(_ccMed3)) _confClusterConfirms = (_ccMed3 >= (_ccLine + _ccMargin));
}

}""" new_data, n_cc = cc_pattern.subn(cc_repl, new_data)

n_cc

Patching winnerDecision cluster medians fallback

I’m replacing the cMed2 and cMed3 lines to include fallbacks to ctx.median2set and ctx.median3setBo5. I’ll also check if clusterPDecider needs to fallback to ctx.pDecider. I'll focus on the medians, counts, and probabilities, using regex for substitution. Let's proceed with this update.

winnerDecision fallback patches

new_data = new_data.replace( 'var cP3 = (ctx && Number.isFinite(ctx.clusterPDecider)) ? Number(ctx.clusterPDecider) : NaN;', 'var cP3 = (ctx && Number.isFinite(ctx.clusterPDecider)) ? Number(ctx.clusterPDecider) : ((ctx && Number.isFinite(ctx.pDecider)) ? Number(ctx.pDecider) : NaN);' )

new_data = new_data.replace( 'var cMed2 = (ctx && Number.isFinite(ctx.clusterMedian2set)) ? Number(ctx.clusterMedian2set) : NaN;', 'var cMed2 = (ctx && Number.isFinite(ctx.clusterMedian2set)) ? Number(ctx.clusterMedian2set) : ((ctx && Number.isFinite(ctx.median2set)) ? Number(ctx.median2set) : NaN);' ) new_data = new_data.replace( 'var cMed3 = (ctx && Number.isFinite(ctx.clusterMedian3set)) ? Number(ctx.clusterMedian3set) : NaN;', 'var cMed3 = (ctx && Number.isFinite(ctx.clusterMedian3set)) ? Number(ctx.clusterMedian3set) : ((ctx && Number.isFinite(ctx.median3set)) ? Number(ctx.median3set) : NaN);' )

bo5 medians fallback

new_data = new_data.replace( 'var cMed3b5 = (ctx && Number.isFinite(ctx.clusterMedian3setBo5)) ? Number(ctx.clusterMedian3setBo5) : NaN;', 'var cMed3b5 = (ctx && Number.isFinite(ctx.clusterMedian3setBo5)) ? Number(ctx.clusterMedian3setBo5) : ((ctx && Number.isFinite(ctx.median3setBo5)) ? Number(ctx.median3setBo5) : NaN);' ) new_data = new_data.replace( 'var cMed4 = (ctx && Number.isFinite(ctx.clusterMedian4set)) ? Number(ctx.clusterMedian4set) : NaN;', 'var cMed4 = (ctx && Number.isFinite(ctx.clusterMedian4set)) ? Number(ctx.clusterMedian4set) : ((ctx && Number.isFinite(ctx.median4set)) ? Number(ctx.median4set) : NaN);' ) new_data = new_data.replace( 'var cMed5 = (ctx && Number.isFinite(ctx.clusterMedian5set)) ? Number(ctx.clusterMedian5set) : NaN;', 'var cMed5 = (ctx && Number.isFinite(ctx.clusterMedian5set)) ? Number(ctx.clusterMedian5set) : ((ctx && Number.isFinite(ctx.median5set)) ? Number(ctx.median5set) : NaN);' )

bo5 probabilities fallback

new_data = new_data.replace( 'var cP3b5 = (ctx && Number.isFinite(ctx.clusterP3setBo5)) ? Number(ctx.clusterP3setBo5) : NaN;', 'var cP3b5 = (ctx && Number.isFinite(ctx.clusterP3setBo5)) ? Number(ctx.clusterP3setBo5) : ((ctx && Number.isFinite(ctx.p3setBo5)) ? Number(ctx.p3setBo5) : NaN);' ) new_data = new_data.replace( 'var cP4 = (ctx && Number.isFinite(ctx.clusterP4set)) ? Number(ctx.clusterP4set) : NaN;', 'var cP4 = (ctx && Number.isFinite(ctx.clusterP4set)) ? Number(ctx.clusterP4set) : ((ctx && Number.isFinite(ctx.p4set)) ? Number(ctx.p4set) : NaN);' ) new_data = new_data.replace( 'var cP5 = (ctx && Number.isFinite(ctx.clusterP5set)) ? Number(ctx.clusterP5set) : NaN;', 'var cP5 = (ctx && Number.isFinite(ctx.clusterP5set)) ? Number(ctx.clusterP5set) : ((ctx && Number.isFinite(ctx.p5set)) ? Number(ctx.p5set) : NaN);' )

winnerDecision counts fallback

new_data = new_data.replace( 'var cN2 = (ctx && Number.isFinite(ctx.clusterN2set)) ? Number(ctx.clusterN2set) : 0;', 'var cN2 = (ctx && Number.isFinite(ctx.clusterN2set)) ? Number(ctx.clusterN2set) : ((ctx && Number.isFinite(ctx.n2set)) ? Number(ctx.n2set) : 0);' ) new_data = new_data.replace( 'var cN3 = (ctx && Number.isFinite(ctx.clusterN3set)) ? Number(ctx.clusterN3set) : 0;', 'var cN3 = (ctx && Number.isFinite(ctx.clusterN3set)) ? Number(ctx.clusterN3set) : ((ctx && Number.isFinite(ctx.n3set)) ? Number(ctx.n3set) : 0);' )

Some may not exist; that's ok.

11) Optional: add alias clusterMedian2set/3set into totals ctx objects where only median2set/3set exist.

We'll patch a compact object at 804891 (one-line _totCtx) and the multi-line at 637205.

For multi-line, insert after median3set line.

We'll do simple targeted replacement of the literal in those objects.

new_data = new_data.replace( 'median2set: (simTotals && Number.isFinite(simTotals.median2set)) ? simTotals.median2set : NaN,\n median3set: (simTotals && Number.isFinite(simTotals.median3set)) ? simTotals.median3set : NaN,', 'median2set: (simTotals && Number.isFinite(simTotals.median2set)) ? simTotals.median2set : NaN,\n median3set: (simTotals && Number.isFinite(simTotals.median3set)) ? simTotals.median3set : NaN,\n // Aliases for cluster governor (some sheets/functions use clusterMedian* naming)\n clusterMedian2set: (simTotals && Number.isFinite(simTotals.median2set)) ? simTotals.median2set : NaN,\n clusterMedian3set: (simTotals && Number.isFinite(simTotals.median3set)) ? simTotals.median3set : NaN,' )

For one-line _totCtx object at 804891, add aliases by replacing substring

new_data = new_data.replace( 'median2set: _totSim.median2set, median3set: _totSim.median3set,', 'median2set: _totSim.median2set, median3set: _totSim.median3set, clusterMedian2set: _totSim.median2set, clusterMedian3set: _totSim.median3set,' )

len(new_data), new_data[:200]

Checking for modification integrity

I’ve made the changes and increased the length, but I need to ensure nothing else got affected. I’ll run a quick check to count occurrences of ctx.median2set to make sure nothing else was unintentionally altered—there could be some remnants left intentionally, and I want to confirm. Let’s do this sanity check.

new_data.count("ctx.median2set"), new_data.count("ctx.clusterMedian2set"), new_data.count("ctx.median3set"), new_data.count("ctx.clusterMedian3set")

Verifying direct comparisons with med2

Now that we've added fallback lines, we may have more occurrences of ctx.median2set, but it's okay as long as it's part of ternaries and similar structures. I need to make sure there are no leftover direct comparisons to med2 in functions like getTotalsPick or cluster-related blocks. I'll search for patterns involving === "OVER") _.*Med2 >=.

search for cluster support comparisons using med2 with OVER in totals-related functions

patterns = [ r'===\s*"OVER")\s_[A-Za-z0-9]Med2\s>=', r'else\s_confClusterConfirms\s*=\s*(_ccMed2\s*>=' ] for pat in patterns: found = re.search(pat, new_data) print(pat, bool(found), found.group(0) if found else None)

Verifying compilation of getTotalsPick

Alright, now I need to check if any of the modifications caused issues with indentation or duplication. After that, I’ll verify if getTotalsPick still compiles properly. Since the code is in a .txt file but written in JS, I’ll write it to a file and run a node --check on it.

out_path = "/mnt/data/Tennislocks_V17.4_Cluster_Governor_FIXED_v17.4.4.txt" with open(out_path, "w", encoding="utf-8") as f: f.write(new_data) out_path

bash -lc node --check /mnt/data/Tennislocks_V17.4_Cluster_Governor_FIXED_v17.4.4.txt

Handling file extension issues for node check

It seems like node --check might expect a .js file, and it uses the loader based on the file extension, so .txt wouldn’t work directly. I think I'll just copy the file to a .js temporarily to avoid any issues and then run the check.

bash -lc cp /mnt/data/Tennislocks_V17.4_Cluster_Governor_FIXED_v17.4.4.txt /mnt/data/tmp_check.js && node --check /mnt/data/tmp_check.js

Checking for undefined issues

Since there’s no output, the syntax seems fine. Now, I want to check for potential "undefined issues," like with variable names. I'll scan for occurrences of ReferenceError. I’ll also search for validation to see if any issues persist from the recent update, as the patch notes mentioned it was fixed.

Find potential undefined variables used without declaration? Hard. We'll search for 'synct' and 'wce' etc.

"synct" in new_data.lower(), len(re.findall(r'\bwce\b', new_data)), len(re.findall(r'\bvalidation\b', new_data))

Checking validation occurrences

"Validation" pops up 28 times, likely tied to variable names, not necessarily undefined. To specifically check for undefined variables, I’ll search for "ReferenceError," but it seems there’s nothing indicating troubling errors. I’ll make sure to keep a close eye on that just in case.

"ReferenceError" in new_data, "is not defined" in new_data

Searching for undefined references

It looks like some strings might be referring to previous patch notes instead of direct code. To focus on undefined variables, I’ll search specifically for "ReferenceError" usage. This should help me pinpoint any potential issues related to undefined variables, but it might not capture notes.

pos = new_data.find("ReferenceError") new_data[pos-80:pos+120]

Reviewing cluster logic and ensuring accuracy

The user wants to ensure the cluster logic is correct, even after adding the fallback. We'll check the PDF they shared, but we already have the relevant excerpts. The paper focuses on clustering using BERT and KMeans, while our cluster governor focuses on a different method. Still, the concept of clustering and matching types should align. I'll verify everything and provide the updated script for download as requested.

Reviewing cluster logic for consistency and potential bugs

I've made updates to cluster confirmations and starred favorites/underdog fallbacks, but I'm unsure if the user expects further edits. I'll check if there’s any misinterpretation in the cluster logic, especially around variable names like clusterMedian. I might also review the _winnerDecision_ governor, as it seems the Bo5 calculation could miss certain sets, specifically 3-set ones. I'll verify everything just to be thorough.

Finalizing summary and download link

I need to summarize the changes, including fixes to cluster logic, and create a downloadable link for the updated script. I'll cite the original script (file0) and the PDF (file1) using the respective website references. The download link will be provided, and I'll double-check the variable names like clusterMedian2set and clusterMedian3set to ensure consistency.

Ensuring variable scope and cluster governance

I need to verify that the diff3 variable is properly scoped within the function, especially for the ctx.store block, which should now include this. Also, I must check that the bo5 logic still works correctly without breaking, ensuring diff2 is used correctly in later stages.

Testing cluster confirm behavior and dependencies

I need to ensure no old behavior remains in the cluster confirm logic. I'll run a quick test by calling getTotalsPick with dummy data and stubbing missing functions like clamp01. Although the script uses SpreadsheetApp, it should be safe to run without triggering undefined references. A lightweight check with Node will help.

Testing getTotalsPick with dummy data

I need to test the function getTotalsPick, using a minimal setup with values like pOverNoPush = 0.55, ctxt.line = 22.5, clusterMedian2set = 19, clusterMedian3set = 29, and pDecider = 0.45. I'll run the script in Node evaluation to check if it still returns "LEAN OVER" as expected, with just the necessary variables supplied.

bash -lc node - <<'NODE' const fs=require('fs'); const vm=require('vm'); const code=fs.readFileSync('/mnt/data/tmp_check.js','utf8'); const sandbox={console}; vm.createContext(sandbox); vm.runInContext(code,sandbox); // Build a minimal ctx resembling totals sim let ctx={ line:22.5, clusterMedian2set:19.0, clusterMedian3set:29.0, pDecider:0.46, bestOf:3, isWholeLine:false, fairLine:23.8, }; let pick=sandbox.getTotalsPick(0.56, ctx); console.log('pick:', pick); console.log('reason:', ctx.reason); console.log('detail:', ctx.reasonDetail); console.log('bandOver:', ctx.clusterBandOver, 'bandUnder:', ctx.clusterBandUnder, 'supportOverUses:', ctx.clusterSupportOverUses); NODE

Verifying band strength and cluster behavior

The function returns "OVER" and confirms the cluster, which seems plausible. The previous bug that may have vetoed "OVER" appears fixed. However, both bandOver and bandUnder are STRONG, possibly due to underSupport being -diff2 (3.5) exceeding the strong band. I’ll test with lowered p3 values like 0.2 to see if behavior changes.

bash -lc node - <<'NODE' const fs=require('fs'); const vm=require('vm'); const code=fs.readFileSync('/mnt/data/tmp_check.js','utf8'); const sandbox={console}; vm.createContext(sandbox); vm.runInContext(code,sandbox); let ctx={line:22.5,clusterMedian2set:19.0,clusterMedian3set:29.0,pDecider:0.20,bestOf:3,isWholeLine:false,fairLine:22.8}; let pick=sandbox.getTotalsPick(0.52, ctx); console.log('pick:', pick); console.log('bandOver:', ctx.clusterBandOver, 'bandUnder:', ctx.clusterBandUnder, 'supportOverUses:', ctx.clusterSupportOverUses); NODE

Updating patch notes and fixing accessibility

The fix works, but now I need to make sure the updated file is accessible. It might be a good idea to update the patch note at the top of the script to reflect the recent changes. I’ll add a new line, though it’s not strictly required. This update will include details about the fixes applied.

Inserting patch note update

I’ll locate the “PATCH NOTE (2026-03-01):” section in the comments and add the new update right after it. I’ll scan for this specific section and quickly insert the necessary changes. This will help clarify the recent modifications without interrupting the flow.

note = """ * PATCH NOTE (2026-03-01 v17.4.4):

• • Totals cluster governor: fixed OVER support checks to use 3-set medians (clusterMedian3set/median3set),
• while UNDER checks continue to use 2-set medians (clusterMedian2set/median2set).
• • Updated all totals-side cluster confirmation / save gates (bimodal guard, severe/fragile override,
• confidence cluster-confirm flag) to be side-aware (OVER=3-set, UNDER=2-set).
• • Added compatibility aliases so totals ctx accepts both median* and clusterMedian* naming.
• • Winner cluster governor: added safe fallbacks to legacy ctx naming (no logic change).

""" insert_after = " * PATCH NOTE (2026-03-01):" pos = new_data.find(insert_after) pos

Inserting patch note update after 2026-03-01 section

I'll scan for the end of the patch note starting with "2026-03-01," specifically after "switches to 3-set median at p3>=0.44." I'm aiming to insert the update just after that block, looking for the "*/" that ends it. Once I locate the comment section around position 9246, I’ll make the change.

print(new_data[pos:pos+400])

Insering patch note just before closing delimiter

To update the patch note, I'll look for the closing comment delimiter, */, which comes right after a blank line, and insert the new note before it. I'll specifically target the position just before the \n*/ and the following section, // ========================== TENNIS MODEL, to place the update.

marker = "\n*/\n\n// ========================== TENNIS MODEL" idx = new_data.find(marker) idx

Inserting the note before the marker

I’ll go ahead and insert the new patch note just before the marker, which I’ve identified. This will ensure that the note is placed exactly where it should be in the code, right before the relevant section begins. Let’s get that done!

new_data2 = new_data[:idx] + "\n" + note + new_data[idx:]

write again

with open(out_path, "w", encoding="utf-8") as f: f.write(new_data2)

syntax check again

bash -lc cp /mnt/data/Tennislocks_V17.4_Cluster_Governor_FIXED_v17.4.4.txt /mnt/data/tmp_check2.js && node --check /mnt/data/tmp_check2.js

Updating variable and verifying file length

Alright, I'll update the variable from new_data to new_data2 for clarity and future reference. After that, I need to make sure the file's length is correct and aligns with expectations. Once everything checks out, I can proceed with confidence. Let's do this!

len(new_data2)

Providing download links and citing sources

I’ll provide the download link as needed and also mention that we read the PDF file. In the PDF, I can highlight its focus on clustering match types using transformer + k-means, including silhouette coefficient. I'll reference specific pages (1, 2, 4) and clarify changes, including fixing bugs in other functions like confidence cluster confirm. I’ll cite files appropriately.

Updating match winner clusters and script

I’ll mention the changes to match winner clusters with a fallback to handle both naming conventions—no logic change. If further tuning is needed, we'd need examples. We'll also address the "wce" presence (still there) and provide the relevant path. A basic syntax check was completed (node parse), with notes included. Regarding the script update, I’ll avoid blocking and offer the file for download. Optional: I may include a short patch snippet.

Listing cluster-related fixes and match winner update

Here’s a summary of cluster-related logic fixes:

• Updated bimodal guard and overrides to use 3-set median where needed.
• Fixed issues with Totals confidence and cluster governance flags.
• Adjusted cluster governance to use the 3-set median when p3 >= 0.44.
• Ensured backward compatibility with naming in match winner clusters.

The user requested an update for favorites and underdogs; we only added naming fallback for now.

Final update on winner cluster logic and final steps

In the current script, the winner cluster governor already uses clusterMedian*. We've added fallback and confirmed it handles both favorites and underdogs. No additional bugs were found in the winner cluster logic, and we didn’t adjust thresholds. We quickly reviewed logic for potential bias—nothing obvious. We also verified that 'wce' is still present and used. Now, I’ll prepare the final version with a download link and include the proper citations. I’ll update the user: “I updated your uploaded script...”

Thought for 20m 49s