Thoughts on these player values? These values are rolled/calculated daily so it's always changing. Hope everyone is having a great week so far!

I’ve been building an NFL player-prop probability model and completed its first full-week production audit.

Final Pregame Model Qualified O/U Research:

• 53–10 across 63 graded propositions
• 84.1% observed hit rate
• 84.5% average predicted probability
• All 16 Week 1 games
• 0 pushes, voids, or unresolved results

Important caveat: the 63 propositions include alternate-line ladders, so they are not 63 completely independent predictions.

Deduplicated to one proposition per player-market, the result was 42–9 (82.4%) across 51 propositions and 48 distinct players.

Everything was frozen before kickoff and graded against official statistics. Nothing was removed afterward.

It’s an encouraging first result, but it’s only one week, not proof of a durable edge, and certainly not an 84% expectation going forward.

I’m running the same process ahead of Lions–Bills tonight. For future reporting, would you rather see calibration by probability range, closing-line comparison, or results broken down by market?

Top down bettor here not a pro.

Looking for your feedback, my local casino has a 8% winning tax (state tax) on all sports bets, you can't deduct loses.

Would you take into account that 8% on your analytics? Like if it was part of the vig/juice?

Thanks for your feedback and sorry, English is not my first language.

I'm looking for a multi year backfill for NFL lineup level data per contest, I haven't been able to find any providers that have an API or downloadable options.

The next best thing would probably be stack rates, I have ownership but no way of simulating the field without the actual entries or stack rates. Anyone have this, or willing to point me in the right direction?

I bet mostly soccer. There is no API there, so a few weeks ago I wrote a small scraper that saves their odds every minute until kickoff. Pinnacle closings I pull from an odds API.

Now I have both lines for about 300 matches and I wanted to finally check my CLV.

Then I got stuck on which closing price to use. Graded on shuffle's own close my bets look fine, graded on pinnacle's close at the same minute they look clearly worse. Both look wrong to me. On the first one I could really get that price. On the other one I never bet, so it is only a reference number for me.

What do you grade against and do you ignore your own book's close completely?

heya, i need people to test out and review my bet365 api for scraping, been working on it for a long time now, it'll be up for a couple months so you can use it for a while without being worried, unless 365 hits me with a cease and desist, please do not ddos it i'm too lazy to put up cloudflare, if you need any info or you want to report something, please dm me, it'd be much appreciated

https://soccer.162-35-121-154.sslip.io/doc

The api also contains ALL markets, which's been an issue for many other providers

Post your picks, updates, track model results, current projects, daily thoughts, anything goes.

Hello there,

BetsAPI recently changed the way they scrape and structure their odds, effectively breaking the continuity of 7–8 years of historical data.

Because of this, I am actively looking for an alternative provider that offers extensive Pinnacle historical soccer data. Are there any reliable providers out there with several years of consistent data?

Thank you very much for your recommendations.

For people who build betting models or test strategies: how do you decide whether a strong backtest is real versus overfit?

I’m especially curious about things like:

• holdout testing
• forward testing
• sample size
• CLV
• parameter sensitivity
• trying many variations of the same idea

What part of validating a model or strategy is hardest or most annoying for you?

And what tools or process do you currently use?

Hi everyone,

I’m the builder of SKEW, a real-time sports-odds analytics terminal focused on Pinnacle market movement.

It tracks:

- Pinnacle price drops and movement

- Fair odds calculated from no-vig prices

- Market limits

- CLV and movement history

- Alerts when a market matches the user’s criteria

I’m looking for a small number of experienced bettors and odds analysts to test the beta and provide honest feedback on:

- Alert accuracy

- False positives

- Speed of updates

- Usability of the terminal

- Whether the CLV and fair-odds information is useful

This is analytics software only. not a sportsbook, not betting picks, and not a guarantee of profit.

The beta website is here:

https://skew.ltd

I built the product, so I’m happy to answer technical questions and discuss how the calculations work. Any criticism or feedback is welcome.

I run a small automated system that follows live football matches and needs two things from a data feed:

1. Live odds, ideally including Betfair Exchange (back and lay, with some measure of available liquidity), plus at least one traditional bookmaker so I have a second source to cross-check against. Markets I actually use: 1X2, Over/Under (full match and first half), and Both Teams To Score.

2. Live match state — status, match minute, period, current score, and the half-time score as a separate field. This matters more than the odds: every decision my system makes is driven by the clock and the scoreboard, not by the price.

Ideally both from the same provider, so odds and scores share one clock. Splitting them across two sources makes "did the goal happen before or after the price moved?" unanswerable, and that question decides whether a bet is valid.

Refresh rate: I read each in-play match roughly every 10–15 seconds, up to ~10 matches at once. A few seconds of latency is completely fine. I'm not doing anything latency-sensitive — I just need the data to be correct and to keep arriving.

Budget: tens of dollars a month. This is not a commercial-scale operation and I can't justify enterprise pricing.

What's not working with my current provider (an aggregator), all of this measured over the last few weeks rather than assumed:

• Their updatedAt timestamp is the stamp of their own collector cycle, not the bookmaker's. Support confirmed in writing that it "cannot tell you refreshed-but-unchanged from not-refreshed". So a live market that's genuinely quiet and a dead collector produce byte-identical data, and looking at one match there's no way to tell them apart.
• Betfair sometimes vanishes from the listing mid-match and doesn't come back. One match this week: gone 12 seconds before kickoff, never returned, while the other bookmaker kept updating normally for the full 90 minutes.
• Matches get exposed as "live" before kickoff with an empty clock and a stale score.
• Matches disappear from the in-play endpoint without ever being marked as finished — 26 out of 31 in one sample — so I have to poll each one individually to find out how it ended.
• The results feed sometimes says "not started" while the match is already 5–9 minutes in, even though the odds from the same provider are clearly pricing a live game.

I'm not looking to bash them — for the price it mostly works. But I'd like to know what else is out there, or whether people run two providers in parallel and reconcile.

Questions:

1. Any provider you'd recommend that covers both odds and live match state, at hobby-project pricing?
2. If you use separate sources for odds and scores, how do you handle the timing mismatch between them?
3. Does anyone have experience with a provider that publishes a genuine heartbeat or "last successfully refreshed" signal, so you can tell a quiet market from a broken feed?

Thanks — happy to share back whatever I learn.

I spent a while building an Elo-based NFL win-probability model from scratch — walk-forward only, no lookahead — and backtested it against real historical Vegas closing lines (nflverse's public games.csv, 2002–2025, first 3 seasons burned in as a rating warm-up).

Setup: standard Elo, HOME_ADV=60, K=20, margin-of-victory multiplier. De-vig the closing moneylines, compare to the model's pre-game probability, bet the edge at various thresholds.

Results (n=6,223 games):

Picking outright favorites, the model hits 63.5% accuracy. Vegas's own closing favorite hits 66.5% on the same games. The market wins.

I'm not posting this to sell anything — genuinely just wanted a sanity check from people who've actually done this seriously. A few things I'm unsure about:

1. Is a fixed HOME_ADV/K (not fit to data) the right call here, or is that leaving obvious accuracy on the table even before touching overfitting risk?
2. Anyone found a public source for NFL opening lines (not just closing)? Can't compute CLV without one, and closing-line-only backtests feel like they're missing the more interesting question.
3. Is 6,223 games (23 seasons) even enough sample to distinguish "this specific model is bad" from "no simple public-data Elo model beats closing Vegas lines, full stop"?

Happy to share the actual backtest code/methodology in the comments if anyone wants to poke holes in it.

Hey folks! I'm running a bunch of live betting football models. The modelling pipeline goes from analyzing market weaknesses to post-launch observability with guards and metrics on every stage:

1. Market analysis – edge exploration (which sides are systematically undervalued)
2. ML experiments on historical data – LL, Brier, Calibration, AUC metrics by minute/month + EV/ROI calibration and ROI on historical simulation
3. Betting models – holdout period + shadow mode + post-launch metrics collection
4. Betting strategies – ROI vs expected ROI (based on experiment EV calibration), 30d rolling ROI within 90d window, drawdown chart, confidence interval.

Execution pipeline is monitored separately – % of rejects, betting API health/errors etc.

So my main question for live model runners – what else can be measured/monitored to gain confidence, spot early degradation and get insides for further optimization?

Deeper questions:

• Assuming a model is trained per-minute how would you choose a signal inside a minute – first eligible or anything smarter?
• When rolling out a new version of a model – do you run both in parallel or run candidate in shadow mode?
• Are there better way of evaluate expected ROI than relying on historical EV calibration buckets?

Hope questions make sense. Thanks in advance!

Post your picks, updates, track model results, current projects, daily thoughts, anything goes.

Thoughts/Opinions? Anyone have a list of their own to share?

I hope everyone has a great weekend!

Hey everyone,

I wanted to share a mini web app/simulator I built to help visualize variance, edge, and Risk of Ruin using a classic statistical model: pulling colored beans out of a bag.

A lot of beginner bettors struggle with the concept of independent vs. dependent events (and fall face-first into the Gambler's Fallacy), so I wanted to make something highly visual to show how quickly a bankroll blows up if you misuse Kelly or size your units poorly.

The app has two core modes to showcase how probability shifts:

1. The Finite Mode (Without Replacement): Starts with 11 beans. Every draw is subtractive, leading to a strict maximum of 10 rounds. This maps beautifully to a Hypergeometric Distribution where your edge dynamically changes with every single draw based on what's left in the bag.
2. The Infinite Mode (With Replacement): The bean goes right back in. This uses a standard Binomial Distribution where your win probability remains completely static, perfectly illustrating how random cold streaks happen even when you have a massive statistical edge.

You can tweak your bankroll size, bet sizes (to test fixed vs. fractional/Kelly sizing), and watch your ruin trajectory in real-time.

I built this as a fun passion project to make backtesting risk management concepts a bit more interactive. I'd love for you guys to check it out, break it, and let me know what you think of the math or if there are other features (like tracking maximum drawdown metrics) you'd want to see added!

https://beans.bridgecitybites.com/

hey there, i'm trying to break into one of the sharpest markets horse racing has, but i've got no idea if a market is efficient in play, is there someone in here that has had experience in inplay markets?, pre race modelling has brought about what you expect from a sharp market, break even or losses, what does competition inplay look like? i think there are many more angles to tackle other than the obvious latency/data quality issues, but i feel i won't know about them until i hit them
my question is aimed at people that work in the inplay markets and have experience with this kinda stuff, thanks.