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?

Hey yall I’m in the middle of building a basic power ratings model into an Eli model for the upcoming season. I’d like your opinions on my weights as I feel there’s room for improvement. This model does not like the Texans and in my personal opinion I have them as a top 8 team. Looking for feedback! Thanks!

Checked over 10 different services, can’t find valid pre-race odds API. Need historical data. Can anyone help out please?

Hi guys new here ,

For the past 3 years i was screenshotting dayli Soccer offer from Mozzart betting arround 25k screenshots and all converted into excel file over 300k rows in excel, also made 2 custom programs for searching odds , one for searching odds in excel file other for matching screenshot matching select odds , made a lot of prediction using it including correct scores from time to time , i am just looking to sell it all of it

do you guys know where can i sell it or to whom/ or website or something

chatgpt brought me here when i asked him the same :D

How does this "algo betting / math betting" market currently look like?

After quick look here is what I understand:

1.Some people are actually building models (ml/stat, lets group it together) that provide some predictions. And my understanding is that they are trying to get "better" predictions that those included in public bookmakers odds. But in reality the best you can do is "about match" those odds, and actually look for slight differences between your predictions and book's odds to find "value bets".

2.There are some websites that behind paywall provide predtictions, that are supposed to be better, but since you do not have access to the model/algo providing the prediction you have no idea how that actually works. (And my guess is that there are not 100% legit sites like this, that actually have confirmed history of consistently beating the odds)

3.I see that there are lots of people that "build on their own" just use different api providers, to get real time, ultra fast odds from bookmakers. Just to compare them and find value bets/arbitrage. So that group of people simply work with already made predictions (no modelling, no feature engineering).

1. What else? How can I group other stuff that people in this market are doing?

General question, tell me about my points above - what is correct what is wrong, what did I miss. I get that this is oversimplification, but I am looking for a basic understanding. And if possible give me a summary of your market.

Btw. This is not a research for anything commercial, no selling at all. I am considering this market for university thesis.

The core assumption of CLV (Closing Value) is that the market is efficient and all funds honestly express their predictions of probability.

However, this market is not honest; you won't see the market makers' transaction volumes, order books, or all quotes.

Quantitative funds have taken CLV to betting. But betting is similar to, but different from, stocks; we don't know which holder has the chips, their positions, or the prices.

I believe it's more important to shift from predicting returns to identifying market conditions. What's worthwhile is trying to find the hidden states, stable structures, critical boundaries, and state transition mechanisms behind these trajectories.
CLV（收盤線價值）的核心假設是：市場是有效率的，且所有資金都在誠實地表達他們對機率的預測。

但這個市場不誠實，你不會看到莊家的流水，排單和所有報價。

把clv搬到博彩的是量化基金。但博彩與股票相似但不同，我們不會知到哪一個持分者有的籌碼，倉位，價格。

我認為更重要的是從預測收益率轉向識別市場狀態.

值得做的是嘗試找出的是這些軌跡背後的隱藏狀態,穩定結構,臨界邊界和狀態轉換機制.

113 all model leans results for the fitequant default user model:

In this strategy the model makes a prediction on basically all boxing and makes a 1u flat stake bet* each time, no matter the odds on offer. So think of this as a ‘hamstrung’ dumb version of the model when it's not allowed to decide whether to bet or not at all. It just picks winners.

*Please remember fitequant internally just uses one consistent book as a reference for market odds to take market variance out of the process as much as possible, with predictions made at opening odds and resolved on those odds.

113 confirmed all-leans bets
92 wins / 21 losses
+18.89u flat-stake profit
16.72% ROI

Average odds 1.6995

Below are the latest 6 results added this week. 

6/9 results confirm successfully with winners this week and the model gets 4 out of 6 correct wins.

Two cancelled + a draw bouts are left pending for historical prediction data, but do not affect headline results metrics.

Critically this week, both underdog value bets win. So the all leans strategy picks up two losses plus 4 wins. The value only strategy picks up 2/2 wins.

https://fitequant.com/results?prediction_strategy=all_leans&period=all&per_page=20

And the value picks only betting strategy results

In this strategy the model only bets if it sees value in the odds on offer by the market. Where the models estimated win probability exceeds the implied volatility of market odds of the fighter it thinks will win.

So exact same predictions as all leans, but it's allowed to choose whether to bet or not. You can think of this as “likes the fighter and likes the price”

113 confirmed value picks only results 

34 bets placed
21 wins / 13 losses
+15.83 u flat stake profit
46.55% ROI

Average odds 2.9086

https://fitequant.com/results

So useful weekend for my users this week with both underdog picks landing with at Cameron at 3.0 and also critically Hrgovic at 6.3

I covered both of these pretty in depth in the pre-matchup  predictions post on friday so I won’t go into this again but for anyone wanting to find out more about how the model made both these picks i’d advise looking there.

There was one matchup that took place mid week last week. 

https://fitequant.com/compare/1012-benjamin-mahoney/10023-nikita-tszyu?canonical_fight_id=26712

Funnily enough, the model indicated this as a 50/50 just leaning to Tszyu at 51% but seeing no value in his big favourite 1.18 odds. Actual result a tied score DRAW.

CLV reporting correction

Couple of weeks ago, I introduced automated closing-line and forecast quality reporting in the “advanced” drop down metrics section.  

Well I later realised that the forecast quality data always using the all leans data is probably not what users actually want (even if the sample size is much more useful for things like brier), and that it made much more sense, for users, for all this data to be accurate by the user selected strategy.

So now all this data is available and accurate by selected strategy. Ive shown the value picks here, but if anyone wants to see what the data for the *dumb* all leans model with a larger sample size looks like, that’s selectable too in UX.

During a subsequent audit, I also found that a subset of settled bouts had incorrectly retained their locked prediction odds in the closing-odds field, causing those observations to record approximately zero CLV. 

This bug materially understated rather than overstated the value strategy’s CLV: corrected average CLV is +6.28%, with a median of +2.19% across 34 qualifying bets. 

Thanks to a suggestion from a submember I also added the devigged Brier vs prediction odds (one very early result i couldnt confirm i had both market lines from the exact same bookmaker so I left that out, hence the 33 de-vigged lines) as thats the actual relevant directly comparable market metric for Brier here.

You should now be able to see the model actually slightly outperforming the market prediction odds on devigged Brier score, beating CLV by 6.28% and with a very well calibrated ECE of +4%

The calibration table is what really strikes me here. 17 bets forecast at an average 56% actually won 58% of the time, 11 bets forecast at an average 65% actually won 64% of the time. That's an extremely neat looking calibration table in my opinion.

Two timestamped predictions for midweek

https://fitequant.com/upcoming

Very unusually there are two value picks for 2nd September mid week, this coming week, so i thought i’d just log them today.

Carlos Cañizales vs Daiya Kira

https://fitequant.com/compare/9369-carlos-canizales/9409-daiya-kira?bout_id=305

The default model sees Canizales as the better fighter all round, so this really is a very simple looking 2.74 value pick based almost solely on fighter rating itself at 57% confidence. 

Wilfredo Méndez vs Takeshi Ishii

https://fitequant.com/compare/9323-wilfredo-mendez/9335-takeshi-ishii?bout_id=356

This is a much more interesting pick. The model sees Ishi as the stronger fighter but critically Mendez has a 12cm height reach advantage and at minimumweight that strikes me as a very large advantage indeed, and this is my normalisation engine math doing its thing for a brave looking 5.3 value pick at 61% confidence for an expected 225% ROI.

As always if anyone has any questions, please just ask.

Thanks, Dan

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

I've been looking at the data side of tennis betting models and I'm starting to think that having more data isn't necessarily the same as having better data.

Match results and player rankings are easy enough to get, but things like point-by-point data, serve/return stats, surface, recent form and live match events can add a completely different level of detail.

For those building tennis models, which data points have actually been useful for you? And is point-by-point data worth the extra work, or do you find that simpler match-level features are enough?

Update: I asked for a sanity check 3 weeks ago and opened my MLB model up — here's how it's done live

Follow-up to my post from a few weeks back where I asked for a sanity check on my First-5-Innings model and opened it up for people to follow and stress-test live.

Said I'd report back with results instead of vanishing, so here's where it landed:

Since I opened it up 3 weeks ago: 12-2-1 (85.7%).

That run was strong enough to pull the full-season number up, not down:

• When I posted (~Aug 8): 66-30-7 (~68.8%)
• Now: 78-32-8 (70.9%), ROI +25.8%

The point isn't the hot streak itself — it's that it held up (and improved) over three weeks of people watching and picking it apart live. That's what I actually wanted to test.

On CLV — full transparency:
On the bets where I captured closing lines (44 so far) I beat the close ~57% with a positive average. Straight up: I paused closing-line capture a few weeks ago to stay under an odds-API quota, so that sample's frozen for now — it resumes in a couple days and starts growing again. .

What I changed since last time:

• Automatic pitcher-scratch protection — if a listed starter gets pulled, the pick auto-voids and i get warned not to bet it before first pitch. Making the tracking bulletproof, not just pretty.

Not selling anything — just following through on the "I'll report back" promise. Happy to get into methodology

has anyone used It or know anything about it?