u/Clean_Reference_9927

Bookmaker odds (and prediction markets) imply probabilities that sum to more than 1 because of an embedded margin. I want the latent probabilities behind the distortion. A few things I can't resolve cleanly.

1. Model choice for removing the margin. Proportional normalization, Shin (a latent proportion z of informed traders), and the power/log method impose different unobservable structures and give materially different estimates on short prices, enough to flip the sign of a downstream signal. Since you never observe the true p, only realized 0/1 outcomes and a later sharper price, is there a principled basis to discriminate between these models, or is it identifiability-limited and I should just report sensitivity across all three?
2. Pooling under a missing low-bias reference. I anchor to one near-efficient source when available; when it's absent I take the median of the other sources' de-vigged probabilities. But those sources are strongly correlated (several are effectively clones), so the median behaves like a median of correlated estimators: it looks precise while carrying little independent information. How would you estimate an effective number of independent sources and down-weight accordingly, and is abstaining the more defensible choice when the low-bias anchor is gone?
3. Combining a trusted low-variance estimator with a correlated ensemble. When the reference IS present, precision-weighting it against the consensus assuming independence is clearly wrong. Is there a clean correlation-aware pooling or shrinkage approach for one low-variance source plus many correlated higher-variance ones?
4. Validation target. I grade earlier estimates against the closing price (a later, sharper estimate), not realized outcomes. Under a proper scoring rule, is "tracks the later estimator" a coherent target, or does it conflate calibration with just chasing a second estimate? And what does the selection bias look like when you only get a validation point on markets that reach a close?

(Aside that turned out to matter: my reference source silently dropped out of my data feed for months and the pipeline substituted the fallback the whole time while still labeling outputs "reference-anchored." The values populated fine, so nothing looked wrong. I only caught it after storing a per-observation flag for whether the reference actually contributed. Log provenance, not just values.)

Bookmaker odds (and prediction markets) imply probabilities that sum to more than 1 because of an embedded margin. I want the latent probabilities behind the distortion. A few things I can't resolve cleanly.

1. Model choice for removing the margin. Proportional normalization, Shin (a latent proportion z of informed traders), and the power/log method impose different unobservable structures and give materially different estimates on short prices, enough to flip the sign of a downstream signal. Since you never observe the true p, only realized 0/1 outcomes and a later sharper price, is there a principled basis to discriminate between these models, or is it identifiability-limited and I should just report sensitivity across all three?
2. Pooling under a missing low-bias reference. I anchor to one near-efficient source when available; when it's absent I take the median of the other sources' de-vigged probabilities. But those sources are strongly correlated (several are effectively clones), so the median behaves like a median of correlated estimators: it looks precise while carrying little independent information. How would you estimate an effective number of independent sources and down-weight accordingly, and is abstaining the more defensible choice when the low-bias anchor is gone?
3. Combining a trusted low-variance estimator with a correlated ensemble. When the reference IS present, precision-weighting it against the consensus assuming independence is clearly wrong. Is there a clean correlation-aware pooling or shrinkage approach for one low-variance source plus many correlated higher-variance ones?
4. Validation target. I grade earlier estimates against the closing price (a later, sharper estimate), not realized outcomes. Under a proper scoring rule, is "tracks the later estimator" a coherent target, or does it conflate calibration with just chasing a second estimate? And what does the selection bias look like when you only get a validation point on markets that reach a close?

(Aside that turned out to matter: my reference source silently dropped out of my data feed for months and the pipeline substituted the fallback the whole time while still labeling outputs "reference-anchored." The values populated fine, so nothing looked wrong. I only caught it after storing a per-observation flag for whether the reference actually contributed. Log provenance, not just values.)

Disclosure up front: this is my project, so I'm tagging it as self-promotion per the subreddit rules.

A few weeks ago I was trying to build a historical closing line value dataset and realized I couldn't find one that combined de-vigged fair probabilities, closing values, and game outcomes across multiple sports. Everything I found was either limited to one sport, only included raw odds, or couldn't be redistributed because of licensing.

So I started building my own derived dataset.

Each row contains things like:

• Sport
• Market
• Selection
• Event time
• Opening fair probability
• Closing fair probability
• De-vig method
• Whether the fair line came from a sharp reference (Pinnacle) or a consensus fallback
• Edge at entry
• Closing line value (CLV)
• Beat close (yes/no)
• Final result

A couple notes:

• This does not include raw sportsbook odds or sportsbook names since my data provider doesn't allow redistributing that data.
• The dataset only contains derived metrics like fair probabilities and CLV.
• "Beat the close" is meant as a research metric, not proof that a bet was good or profitable.

One thing I think is useful is the anchor field. You can separate observations that were generated from a sharp market reference from ones that used a consensus fallback instead of treating them as the same thing.

I'm planning to keep expanding this over time as more sports and markets are added.

Project: https://edgedesksports.com

I'd really appreciate feedback from people who work with betting or forecasting datasets.

• Are there any columns you'd want added?
• Is there another derived metric that would make this more useful for research?
• Has anyone found a comparable open dataset that covers multiple sports?

I'm recovering latent probabilities from margin-inflated odds (bookmaker and prediction-market prices sum to more than 1). I rolled the three standard de-vig methods in base R to understand the internals rather than lean on a package. Posting the code for a critique, and I have two R-specific questions.

r

implied <- function(dec) 1 / dec                       # decimal odds -> implied (with margin)

# 1) proportional / multiplicative
devig_prop <- function(dec) { q <- implied(dec); q / sum(q) }

# 2) power method: find k such that sum(q^k) = 1
devig_power <- function(dec) {
  q <- implied(dec)
  k <- uniroot(function(k) sum(q^k) - 1, c(0.5, 10))$root
  q^k
}

# 3) Shin (1993): latent proportion z of informed money
devig_shin <- function(dec) {
  q <- implied(dec); S <- sum(q)
  pz <- function(z) (sqrt(z^2 + 4 * (1 - z) * q^2 / S) - z) / (2 * (1 - z))
  z <- uniroot(function(z) sum(pz(z)) - 1, c(1e-6, 0.5))$root
  pz(z)
}

dec <- c(1.5, 3.0, 7.0)                                 # a 3-way market, overround ~1.143
sapply(list(prop = devig_prop, power = devig_power, shin = devig_shin),
       function(f) round(f(dec), 4))
#      prop   power   shin
# [1,] 0.5833 0.6218 0.6092
# [2,] 0.2917 0.2760 0.2864
# [3,] 0.1250 0.1022 0.1044

The favorite ranges 0.583 to 0.622 depending on method, which is enough to flip a downstream signal, so method choice isn't cosmetic.

Questions:

1. Idiom / robustness. I'm leaning on uniroot for Shin and the power method. Is there a more numerically stable approach near the boundaries (the (1 - z) denominator in Shin gets touchy as z approaches its bracket, and the power root can be sensitive on lopsided books)? I know the implied package exists and covers these; I built my own to learn, but if you'd trust its solvers over a hand-rolled uniroot, I'd like to hear why. Also curious whether people vectorize this across thousands of markets with apply/purrr::map or push it to matrix ops.
2. Pooling correlated estimators. When I don't have a single trusted reference, I currently take the median of several sources' de-vigged probabilities. But those sources are correlated (some are effectively clones), so the median looks precise while carrying little independent information. Is there an R idiom or package for correlation-aware pooling / effective-sample-size weighting rather than a naive median? I've looked at metafor for correlated effects but I'm not sure it maps cleanly onto "combine N non-independent probability estimates for the same event."

Reproducible above, happy to share more. Mostly want the R crowd's read on 1 and 2.

Bookmaker odds (and prediction markets) imply probabilities that sum to more than 1 because of an embedded margin. I want the latent probabilities behind the distortion. A few things I can't resolve cleanly.

1. Model choice for removing the margin. Proportional normalization, Shin (a latent proportion z of informed traders), and the power/log method impose different unobservable structures and give materially different estimates on short prices, enough to flip the sign of a downstream signal. Since you never observe the true p, only realized 0/1 outcomes and a later sharper price, is there a principled basis to discriminate between these models, or is it identifiability-limited and I should just report sensitivity across all three?
2. Pooling under a missing low-bias reference. I anchor to one near-efficient source when available; when it's absent I take the median of the other sources' de-vigged probabilities. But those sources are strongly correlated (several are effectively clones), so the median behaves like a median of correlated estimators: it looks precise while carrying little independent information. How would you estimate an effective number of independent sources and down-weight accordingly, and is abstaining the more defensible choice when the low-bias anchor is gone?
3. Combining a trusted low-variance estimator with a correlated ensemble. When the reference IS present, precision-weighting it against the consensus assuming independence is clearly wrong. Is there a clean correlation-aware pooling or shrinkage approach for one low-variance source plus many correlated higher-variance ones?
4. Validation target. I grade earlier estimates against the closing price (a later, sharper estimate), not realized outcomes. Under a proper scoring rule, is "tracks the later estimator" a coherent target, or does it conflate calibration with just chasing a second estimate? And what does the selection bias look like when you only get a validation point on markets that reach a close?

(Aside that turned out to matter: my reference source silently dropped out of my data feed for months and the pipeline substituted the fallback the whole time while still labeling outputs "reference-anchored." The values populated fine, so nothing looked wrong. I only caught it after storing a per-observation flag for whether the reference actually contributed. Log provenance, not just values.)

Basic idea: price the whole board, strip the bookmaker margin to get a fair line, then flag where the market disagrees. Either with a sharp reference (Pinnacle) or with itself across books. The part I actually care about is that the engines stay separate and labelled. You always know if a number came from a sharp anchor, a model, or book disagreement. I don't blend them into one score because I don't trust a composite number I can't take apart.

Anything you log gets graded against the closing line, since CLV is the only honest proof a price was good once you're past small samples.

Two things I'm genuinely not sure about and wanted this sub's read on:

• How do you de-vig when Pinnacle hasn't priced a market yet? Right now I fall back to consensus median and flag it, but that feels weak.
• How do you weight multi-book disagreement against a single sharp reference?

Happy to get into any of the internals. It's a live tool (EdgeDesk) if anyone wants to see it running, I'll drop the link in the comments to respect the sub, but mostly here for the methodology talk.

I've been working on this project for the better part of the last year because I got tired of opening five or six different websites every time I wanted to research a bet.
The goal wasn't to build another "AI lock" service or sell impossible win rates. There are already enough people claiming they hit 80% of their bets. Reality tends to file a complaint against those numbers eventually.
Instead, I built a platform that puts the research in one place so I can make my own decisions.
Some of the features include:
📈 Sharp vs. public market movement
📉 Closing Line Value (CLV) tracking
📊 Historical betting trends
🏆 Team and player statistics
🚑 Injury information
📚 Research dashboards across multiple sports
📅 Historical matchup data
⚡ Live odds monitoring
The homepage is free to browse, while the full research dashboards are part of a subscription because the data providers and infrastructure aren't free.
This isn't meant to replace your own judgment. It's built to help you spend less time hunting for information and more time actually analyzing bets.
I'd really appreciate honest feedback from this community. If there's a feature you'd want to see, something that's missing, or something you think could be improved, let me know. I plan on continuing to build it based on what bettors actually want.
https://edgedesksports.com

Over the past several months, I've been building a sports betting research platform because I got tired of jumping between multiple sites just to research one wager.
Rather than trying to create another "AI lock" or sell guaranteed picks, I wanted to build a research tool centered around market data and decision-making.
Current features include:
Closing Line Value (CLV) tracking
Sharp vs. public market movement
Historical odds and betting trends
Team and player statistics
Injury tracking
Research dashboards across multiple sports
Historical matchup data
Market movement visualization
The philosophy is simple: provide as much relevant information as possible in one place so users can make their own decisions instead of blindly following picks.
The homepage is free to explore, while the full research dashboards require a subscription because of the ongoing costs of data infrastructure and APIs.
I'm not here to claim I have a 70% win-rate model. I'm looking for honest feedback from people who enjoy sports analytics and quantitative betting research.
If you had access to a platform like this, what analytics or datasets would you consider "must-have" before paying for it?
I'd appreciate any constructive criticism, whether it's about the features, workflow, or analytics themselves.

Been messing around building my own research setup, mostly because I got tired of “edges” that looked great and turned out to be nothing once the line closed. Tracking CLV instead of my record was a humbling wake-up call lol. Curious what you all would actually want. Not picks, research. What’s the thing you wish existed that would genuinely help you bet sharper?

I'm a Navy veteran (ITS on the USS Montana) and I've been helping some friends in the mortgage industry look at their office setups.

Coming from a background where 2-second lag is a system failure, I’m genuinely confused by what I'm seeing. These platforms feel like they're running on 20-year-old legacy code.

For the people using these daily: Is the lag usually during the initial file load, or is it specifically when you're trying to run credit and upload docs? I'm trying to figure out if there's an actual fix for the latency or if the industry is just stuck with "spinning wheels" as a standard feature in 2026.

What’s the worst technical glitch you’ve dealt with lately that almost killed a deal?

Posting because this is a data-layer problem I've spent serious time on, and I haven't seen it discussed cleanly anywhere.

Most quant infrastructure for event-driven trading assumes a simple mapping: event E happens to ticker T, so T's price reaction is what you model. For corporate actions and earnings, that mapping is usually clean because the SEC filer is the listed entity. For FDA biotech catalysts, it isn't, and the failure mode is structural, not occasional.

The setup. A drug has multiple economic stakeholders: the original developer, the company that licensed it for commercial rights, royalty holders from the original IP, and partners in specific geographies. When the FDA issues a decision on that drug, every public PDUFA calendar I've found lists exactly one ticker, usually whichever entity the calendar provider's scraper found first in the press release. That ticker becomes "the PDUFA stock."

The problem is that ticker is frequently not the entity with primary economic exposure. Sometimes it's the licensee. Sometimes it's a royalty holder whose upside is capped at a milestone payment. Sometimes the listed ticker has no exposure at all and the calendar is just wrong.

Concrete example from this month. MGNX (MacroGenics) shows up on every PDUFA calendar I've checked for an April 29 decision on TZIELD pediatric expansion. MGNX is not the sponsor. Sanofi is. TZIELD was originally MacroGenics' program, licensed to Provention Bio in 2018, and Sanofi acquired Provention in 2023. MGNX retained royalty and milestone rights. Per the 10-K, MGNX's economic exposure to the April 29 decision is a single milestone payment within a remaining schedule on the order of low hundreds of millions, paid out over time as commercial sales tiers hit. SNY is a $100B+ market cap and a pediatric label expansion barely registers. MGNX is a $200M cap and the milestone, if material, is a fraction of market cap and not a clean read because the per-event tier amount isn't disclosed.

If your model treats this as "MGNX has a binary catalyst on April 29," your sizing, your IV expectations, your post-decision expected move, all of it is wrong. The actual question is "what's the tier amount, what's the probability of approval, and what's the present value of that single milestone discounted for time and execution risk." Different model, different result.

This generalizes. LGND was the royalty holder on TVTX's sparsentan. When TVTX got the FSGS expansion approved on April 13, LGND moved off a passive royalty interest. Calendars that listed only TVTX missed the LGND opportunity entirely; calendars that listed LGND but treated it as a primary filer mispriced both the upside and the timing.

The structural fix. I built a drug_assets table that maps each drug to four columns of ticker exposure: parent_ticker (original developer), licensee_tickers (commercial rights holders), royalty_tickers (passive economic interest), partner_ticker (geographic or co-development partner). Every event in my pipeline joins through this table before scoring. Each ticker that surfaces get a ticker_role tag, primary, licensee, royalty, and the scoring layer treats them differently. Royalty exposure on a partner's PDUFA is a different model than primary commercial exposure, and the position sizing has to reflect that.

The engineering pieces that surfaced:

Sponsor name resolution is the bottleneck. ClinicalTrials.gov v2 returns lead sponsor as free text. "Pfizer Inc.", "Pfizer, Inc.", "Pfizer Pharmaceuticals", and "Pfizer Limited" are all the same parent, and the API doesn't normalize them. I wrote a tokenizer that strips legal-form suffixes and matches on the first remaining significant token. Catches roughly 92% automatically; the rest are manual map entries. The same pattern shows up in EDGAR filer name fields and FDA Orange Book extracts. Without normalization, the join doesn't work, and the whole layer collapses. Anyone who's worked on corporate actions infrastructure recognizes this as the same problem in a different domain.

The mapping has to be updated on M&A. TZIELD moved Provention → Sanofi in 2023. The drug assets row had to update or every TZIELD event from 2024 forward would point at the wrong parent. Acquired entities, name changes, and ticker changes (BGNE→BONE this year as one example) all require maintenance. There's no clean public feed for this, I run a watcher off SEC 8-K Item 1.01 and 2.01 filings and triage by hand. It works but feels brittle.

Polygon's per-ticker endpoints 404 hard on delisted, renamed, and CIK-only tickers. I added a regex validator that rejects anything starting with a digit (CIKs leak in from upstream sources) or anything outside ^[A-Z]{1,5}(?:[.-][A-Z]{1,2}|\d)?$. Cut error log noise meaningfully. The bulk snapshot endpoint silently returns nothing for the same tickers, so the validator runs upstream of any per-ticker fetch. Standard data hygiene for any equity dataset built on third-party feeds.

The valuation question on royalty/licensee tickers is genuinely hard. Even with clean mapping, what's the right model? For a primary filer, the expected move is a function of PoA, market cap, and historical post-approval volatility distribution. For a royalty holder, the expected move is a function of the milestone tier amount (often undisclosed in the public license) and the present value of the royalty stream. Different model entirely. I'm using a heuristic discount based on a cap-weighted milestone-to-market-cap ratio, but I don't think it's right and I'd take input from anyone who's modeled royalty equity exposure cleanly.

The questions I'd actually like input on:

1. For corporate-actions-style mapping problems with non-stationary ground truth (M&A, name changes, license restructures), is there a cleaner pattern than the watcher + manual triage approach? I'm essentially running a CDC pipeline over SEC 8-K filings, and it works but I keep wondering if I'm reinventing something well-understood.
2. Anyone modeled royalty equity exposure to a partner's binary event in a generalizable way? The literature on milestone payment valuation is thin, and most of what I've found is buy-side internal.

A week ago, I posted about MGNX being a royalty ticker on TZIELD's pediatric expansion, with SNY as the actual filer. A lot has changed on the scanner since then, and April 23 ran two binaries, so worth an update going into Wednesday's MGNX PDUFA.

April 23 ran two binaries.

SNY Sarclisa for myeloma. Scanner called APPROVE 74%, STN 10. Approved. Stock did +2%. The STN layer was the interesting part. Most calendars had this as a high-volatility setup. Sarclisa is a Sanofi flagship; the move was already discounted, and a +2% close on approval is exactly what a STN score of 10 means in practice. STN is scored 0 to 100 on an inverted 80-baseline, with higher = more sell-the-news risk, so 10 sits at the very bottom of the range. Not a sell-the-news crash, not a moonshot, just absorbed. 2-for-2 on the SNY event, PoA right, STN right.

GRCE GTx-104 IV nimodipine for aneurysmal subarachnoid hemorrhage (NCT05995405). Scanner called APPROVE 72%, STN 10. CRL. Stock did roughly -50%. PoA wrong, STN wrong. Honest miss. Worth saying what the scanner did flag and what it didn't. It flagged an infinite runway anomaly (999-month implied runway with no commercial capex near the PDUFA, which usually means a sponsor that hasn't started building inventory or a sales force). It flagged the first-time approver. It did not flag the manufacturing risk that landed in the CRL. The CMC layer rated this as low risk because nimodipine is a small molecule with mature manufacturing. The CRL was specifically about the IV reformulation manufacturing, not the API. That's a real gap. 483 inspection history doesn't catch a sponsor's first commercial-scale fill-finish run. Adding that as a separate flag for first-time approvers with novel formulations of approved APIs is the fix.

Track record stands at 9 of 11 resolved. REPL and GRCE are the two misses. Both were APPROVE calls that turned into CRLs. Both involved manufacturing-adjacent risk; the CMC layer didn't surface. That's a pattern worth fixing rather than a single-event fluke.

Now MGNX Wednesday. The thesis is the same as last week. MGNX is not the filer; Sanofi is. TZIELD (teplizumab-mzwv) pediatric age expansion from 8+ down to 1+ for Stage 2 T1D is on the FDA's desk on April 29. PETITE-T1D phase 4, n=23 open label, 89.6% no progression to Stage 3 at 1 year, no new safety signals. EU already approved the same expansion as Teizeild in January. Priority review granted.

MGNX's economic exposure is the milestone ladder from the 2018 Provention Bio license (Sanofi bought Provention in 2023 and inherited the obligation). Per FY25 10-K, up to $330M remaining in regulatory and commercial milestones over time. Not revenue, not commercial infrastructure. One milestone payment if approved on Wednesday, amount not disclosed in the redacted version of the license.

Scanner output for the MGNX row this week: PoA 74% STN 88 of 100 (inverted 80-baseline, so 88 sits in the high band, scoring engine is weighting first-time-approver dynamics and royalty-recipient milestone uncertainty more heavily this week than last) Dilution MINIMAL, no ATM, no active shelf Net Edge 53 Innovation 45 of 100 Cash runway into late 2027 per the 10-K

The reason the catalyst page tags ROYALTY EXPOSURE VIA SNY at the top is the same reason I posted last week. Most calendars don't flag this. They show MGNX with an April 29 PDUFA and let the reader assume it's MacroGenics' own filing. It isn't. The MacroGenics-owned narrative on this ticker is Margenza (margetuximab) for HER2+ breast cancer, accelerated approval back in 2020, totally different drug. Every AI tool I've tested on MGNX hallucinates the Margenza story when you ask about April 29, including the AI summary on my own catalyst page, until I added the ticker role layer to the prompt.

What changed on the scanner since last week: Drug name resolver is now in production. Pulls verified drug, indication, and NCT number from SEC filings and matches against an internal drug assets table. GRCE resolved cleanly to nimodipine / GTx-104 / NCT05995405 with HIGH confidence the day before the CRL. MGNX still shows Margenza in some legacy views because the drug assets entry for teplizumab needs the royalty tickers array updated, fixing today. STN correctness threshold for the low band moved from "move > +20%" to "move >= -5%". The old threshold would not have credited SNY's +2% as an STN win, even though a score of 10 correctly predicted no crash. New threshold matches what a low STN actually means. Manual override map for the 17 indication-classifier mislabels (score-events defaults unrecognized strings to "Infectious," known bug). Long-term fix is a separate classifier function with its own table, next sprint.

Same question as last week. Does anyone have the milestone tier breakdown from the original 2018 Provention Bio license exhibit? Public filings show $330M remaining cap. The redacted exhibit doesn't show the per-event amounts. If you've read an unredacted version in an older S-1 or proxy, I'd like to put the schedule on the catalyst page.

Not telling anyone what to trade. Classifying a setup that's still wrong on most calendars three days before the event. I trade my own book off this and post resolutions when they hit, including the misses.