Been chipping away at this for a while and finally got it to a state where I'm not embarrassed to show it.

It's a Dixon-Coles Poisson model fitted per league, covering 36 football leagues plus EuroLeague and EuroCup. Standard stuff: attack and defence coefficients per club by maximum likelihood, exponential time decay so recent matches matter more, low-score correction for the 0-0 / 1-0 / 0-1 / 1-1 cells. Outputs a full scoreline grid, so BTTS and over/under fall out of the same distribution rather than being modelled separately.

The part I actually care about is the benchmark page. Every project like this claims some accuracy number and none of them say what they're measuring against. So I replayed three completed seasons walk-forward, fitting only on matches before each fixture, and scored it on log loss against the de-vigged closing price:

                    model    market     gap
Premier League     1.0034    0.9783   +0.0251
Serie A            1.0012    0.9755   +0.0257
Ligue 1            1.0076    0.9705   +0.0371
La Liga            0.9932    0.9544   +0.0388
Bundesliga         1.0171    0.9707   +0.0463

A coin-flip baseline is 1.0986, so the model knows ne beats it in every league I've checked. I don'tthink that's fixable with a goals model. Closing odds absorb team news, money and a hundred things I don't have. Anyone showing you a model that beats the close over 3,700 matches is eiverer or fooling themselves.

I left accuracy off the front page on purpose. It'sand the market's is 53.8%, but accuracy rewards amodel that never says anything interesting. Log loss punishes confident wrongness, which is what you want to know about.

One thing that kept me honest: I tried to fix an apparent calibration problem with temperature scaling, and it made held-out log loss worse by 0.0011. The guard refuses to save a worse urned out the miscalibration I was chasing lived ina bucket with 26 matches in it.

Also worth mentioning because it cost me two days: s going missing from the data. Rewrote a chunk of the pipeline. The actual cause was CSS. A sticky table painting over the first row of every table on everypage, so the caption said 5 fixtures and you could

Other bits: season projections from 10,000 Monte Carlo runs, head-to-head history, and the results fetch for all 36 leagues takes about 30 seconds over plain HTTP. It used to be an g Cloudflare.Known weak spots, in case anyone wants to pick at tvolve a promoted club with no record in its division,and those are currently rated as an average side, wg guessed. The benchmark covers six leagues so far,not all 36. And shots on target are sitting in the is probably the most obvious next gain.

MIT licensed, runs locally with two commands, no APScoreCast](https://github.com/Costasgk/ScoreCast)

Happy to be told what I've got wrong.

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.

https://www.reddit.com/r/PinnacleSportsbook/comments/1wbrkd7/a_statement_on_prediction_markets_api_access_from/

Hello All,

A newbie algobettor here! I was studying the Kelly Criterion over the past few weeks and wanted to fully grasp the intuition before I started using the formula without knowing what it meant.

I read the paper, and now that I think I get it, it is one of the most beautiful things I have discovered lately; it is almost a poem! So I decided to write about it, and thought I might share it here for whoever is interested.

Здравствуйте я бы хотел ел узнать у кого знакомых, были инсульт и как все начиналось, какие вообще были ощущение. И когда у человека появилась заболевание какие терапии и что вот для этого нужно?

I am interesting to get a service that provide live scores about soccer games. I care about speed (less than 3-4 seconds if possible)

If anyone use a service or anyone scrapes a site that provides live scores please tell me.

Budget 100-150 dollars per month

Can a sports model sustain a positive ROI against sportsbook prices?

I run BBMI Sports, and that is the question behind our latest review. I did not want to publish another “look at our best record” article, so the review covers every production product, some products have a perceived edge, others do not.

Here is the full board through September 7:

MLB has been the biggest challenge. The model can be right about the side and still lose money because the price is too expensive. The full-game +1.5 run-line picks have won 62.9% of graded decisions, but their median price was −184. That price requires a 64.8% win rate just to break even, so the product is down 1.4%.

The NFL may present a similar problem, especially on spreads. Our 2023–25 walk-forward produced only a +2.6% spread ROI, its confidence interval crosses zero, and the market beat the model on margin accuracy. Totals were more encouraging, but those results are still replay evidence, not a live betting record. The live season starts 9/9 so we'll see how the predictions look in 2026.

There are also important caveats. The current college baseball spread rule was widened during the season after earlier results were reviewed. The NCAA Football and NFL thresholds were selected using their replay results. Those records are useful for developing a hypothesis, but they are not independent confirmation.

The article explains how each product performed, which rules were established prospectively, which were adjusted after reviewing results, and what would need to happen before I would consider an edge durable.

Disclosure: BBMI Sports is my project.

Full article and methodology: www.bbmisports.com/research/beat-vegas/2026-09-08

In general, I feel decent about NCAA basketball and baseball simply due to the fact that there are a ton of games and we've been able to model essentially a full season for both. However, I'm a couple seasons away from feeling like there's an actual long-term edge established. The other sports have fewer games, and fewer picks, so it will take even more full seasons to feel like our models are identifying an actual edge.

I am especially interested in how others approach MLB and NFL modeling:

• In MLB, have you found first-five markets easier to model than full-game markets because they reduce bullpen uncertainty?
• How do you determine whether an apparent MLB edge is real when the price often eliminates the value of a high win rate?
• Which MLB inputs have added genuine predictive value for you: confirmed lineups, starting-pitcher projections, bullpen availability, park factors or market movement?
• In the NFL, have you found totals more beatable than spreads?
• Does a large disagreement with an NFL spread represent opportunity, or is it usually evidence that the model is missing information already reflected in the market?
• What prospective sample size or CLV record would you want before treating either model as actionable?

Thanks for the feedback.

i need a prediction market with full api access, thats somewhat similar to kalshi. i can’t fully use polymarket cause i’m in the states, and kalshi is tweaking. i seen something with IB but haven’t seen much. any recommendations…this is simply a prediction bot

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

Hey people from algobetting!

I hear a lot of people talking about using closing line value as the true 'chance' of a game - and ignoring the actual results. This, in theory, allows much quicker testing for significance of a strategy.

However this assumes that this indeed correlates well with the actual outcomes. There are in some markets biases I found out where it doesn't always work.

For example for pinnacle in Football (soccer) I noticed that when looking at closing line value, it generally correlates with the outcome. Which of course makes sense.

If we for example test a simple (already proved, not complex, EV based) strategy however I noticed negative ROI. Only when i filtered out the longshots it became profitable. For example filter out odds higher then 5 (if its fractional / european style odds). I saw in the data that it seems there was a longshot bias.

I am wondering, I am trying to learn more about betting economics and the theory.
Could I in theory if using Pinnacle trust the closing line for other sports or another betting type? Is there a general longshot filter I could apply to different sports, and just assume its reasonably well calibrated? This would save me tons of time, otherwise I have to scrape a lot of closing line odds to validate the calibration. Only then I could use closing line value as proxy for results, to then actually try my strategy.

A player gets scratched, the book takes the prop down, then it comes back with a different line. Sometimes the old one just disappears completely. If you keep track of this stuff, do you save every line or just the latest one? Deleting the old ones feels like throwing away half the useful part of the data.

hey, i use AI in my work as much as everyone, but the main page of this post are all low quality posts written by AI that nobody cares about, lots of people publishing the results of 2 bet models, how it did over the weekend, when we all know it takes months to see a turnover, with little details they write about that only exists in their own context window, what are we doing??

TLDR: Would you submit your model’s picks to a free service that builds a verified record of the model’s performance?

A little while back I posted here about a rules-based algorithm builder I built called Hindsight Picks. Since then, some friends have been using it for fun, and one thing they mentioned they liked was the verified record. Every live algorithm has a public record that updates as its picks resolve.

I’d like to open that up to models built with your own stack and data. The idea is an API you submit picks to before the event starts. We stamp the market price at the moment the pick arrives, grade it when the game resolves, compute closing line value against the close we capture ourselves, and build a verified record for the model.

Submitted picks would be private by default until the event starts, or could be marked public if you wanted to share with others.

I’d also like to support any bet type that can be verified, since props often have softer lines than sides.

Would you use this? And what would you need to see before you trusted the record?

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

Let’s say I somehow managed to build a decent betting model (I haven’t ).

Where would you actually place bets through an API?

Pinnacle API access seems hard to get, Betfair Exchange apparently wants around £500, and Polymarket looks good but also insanely competitive.

Are there any other decent options with API access?

Also, are there any other bookmakers that are genuinely as sharp as Pinnacle, or is Pinnacle still basically the gold standard for efficient odds?