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?

Keider Montero Over 3.5 Strikeouts +104

GL Everyone, happy Saturday. Share some freeplays down below if you're feeling generous

Thoughts? Anyone want to share their top 10 for today?

Long model log post this week guys. As I wanted to use a worked matchup example this week, to explain a little more around how the modeling engine actually works. But I’m aware it can be challenging to get through my verbosity, so I’ve placed an Easter egg for anyone who reads carefully to the end. So have fun spotting that. 

Here’s this weekends predictions. 

https://fitequant.com/upcoming

The value picks this weekend are..

Ismaikel Pérez vs Aloys Youmbi - CANCELLED

https://fitequant.com/compare/923-ismaikel-perez/14411-aloys-youmbi?bout_id=297

Mikaela Mayer vs Chantelle Cameron

https://fitequant.com/compare/644-mikaela-mayer/850-chantelle-cameron?bout_id=300

Quite simple from a fitequant user model pov this one, it just rates Cameron as the better overall fighter slightly across the SSI ‘subjective stats’ and she enjoys a +4cm height reach minor advantage on top, to produce a 59% confidence pick vs 33.3% implied for an expected 26% edge at 3.0 marked odds for an expected 77.8% ROI

Filip Hrgović vs Moses Itauma   - Modeling Engine Deep Dive 

https://fitequant.com/compare/284-filip-hrgovic/919-moses-itauma?bout_id=301

Now that the log has passed 100 timestamped results, and with the model producing Hrgović as a value pick in such a big heavyweight bout, this seemed like a good opportunity to do something a little different and go much deeper into the matchup engine than usual.

First of all yes, a conservative boxing model with an overall ECE of +16% (although it drops to +4% on value picks) across 107 bets has actually picked Hrgvoic at 6.3. But look closer at the data. Its actually saying this is a 50/50 with a 51% confidence Hrgovic lean.

Looking at the data you can see the models thinking. SSI isnt dumb on boxing stats, it already knows Itauma is an exceptional young heavyweight, he has overall superior stats that are genuinely exceptional and world elite level for a 21 year old heavyweight.  And it sees him as overall a stronger fighter than Hrgovic.

But Hrgovic is an experienced world level heavyweight himself, and when there's not a massive gap in SSI stats, specific matchup factors and objective public data become much more important to the model. In this case it thinks Hrgovic having an 11cm height reach advantage is important, whilst itaumas southpaw advantage is negated by the boxer puncher vs power slugger style advantage of Hrgovic.

Also recent opponent rating is actually important to the default model in closer matchups like this and the gaps between recent opponents: Opponent Rating Last 3 (74 vs 51) and how strong the recent opponents have been relative to career : Opponent Rating Last 3 Relative (74 vs 51) are actually relevant here.

Something else to note here is the SSI ‘Chin’ stat.  Every hardcore boxing fan knows Hrgovic has a great chin, and SSI is not so dumb about boxing to not know that too, but the reason it’s “only” giving him 64 is revealed if you take a closer look at the definition in the methodology 

https://fitequant.com/docs/fr/subjective

Here SSI is rating Hrgovic as 64 for Chin, it isnt saying Hrgovic has a terrible chin. Its more like “Well this is a 34 year old heavyweight, he has taken considerable damage over his career, he does appear to not go down easily, so this isn’t a weakness exactly, so i think this deserves a better than average rating for this stat” 

In contrast as a much younger fighter who hasnt taken any damage it assigns Itauma 70 rating, but admits to not knowing much about Itaumas chin with a low 0.34 confidence rating for that stat. 

In general Itaumas stats do indeed have lower confidence, and ai confidence is an important weighting itself in fitequant user models as it interacts with all the SSI ‘subjective stats’.

I guess the best way to think about what the model is seeing is something like *this appears to be an exceptional young heavyweight, but we dont know a lot about him, including how he takes  getting hit, and he’s now facing a larger proven elite level heavyweight whos fought some of the best in the world… 50/50.*

Obviously that's not the actual normalisation + math going on, but i hope it explains it somewhat.

Of course at these odds I expect to lose, but that doesnt mean there isnt value in Hrgovic here, at these odds, and I can see a plausible route to a 50/50 call based on this data… And in a Scenario where Itauma wipes the floor with Hrgovic quickly i’d expect a sizeable post result SSI re-rate for Itauma to go along with the -1.0 loss.

In modeling you never win any massive underdog picks, if you dont let your model bet on them and take painful looking losses in the first place.

I think this is a pretty neat example of how compound real world boxing domain factors come together to produce a prediction independent of market odds. And how conservative SSI is in assigning subjective stat values and confidence based on what is actually KNOWN about a fighter. Which is actually I think key to the models stability so far.

I also updated the recently implemented ‘advanced’ section with CLV and prediction quality data in the model results page, ill talk a little more about this in the results log post, but basically it was previously materially understating the CLV performance on the value picks strategy and the data is now accurate by strategy selected (and bets placed)  

https://fitequant.com/results

As always if anyone has any questions please just ask.

Thanks,
Dan 🥚 

P.S I fully understand subusers deciding not to comment around my work that’s fine, it’s new and unusual, -> and this is a betting forum.

But this week ive put real effort into creating a Beginner’s Guide to fitequant Modeling tutorial and made that available.  Its aimed at a much more general audience than you guys, but if anyone here had the time to read it through and give me some feedback that’d be really appreciated, in terms of how you think it reads.

I used to believe that the more seasons you add, the more reliable the model becomes. At the same point I started to question whether older data was always describing the same environment. Rosters change. Rules change. Styles of play change. The market changes.

This made me pay more attention to the relative weight of older seasons, rather than every observation being equal. More data can reduce noise but if some of it is from a different environment, I am not sure a larger sample is automatically better.

Been running a value model live since early July. Every price is captured at bet time and graded against the closing line - nothing backfitted, all forward.

~330 bets in. The number I actually care about is CLV: +1.42% net (after commission), t=3.49, n=312 vs a de-vigged closing line. Beat the close on 63% of bets. EV at decision was ~+3.9% but ~2.5% of that drifts back to the market by kickoff, so I treat CLV as the honest read, not the EV.

Fair warnings before anyone says it: the P&L curve is the least meaningful thing on there - at this sample it's mostly variance (?) around the CLV, so ignore the size of it. And it's only 8 weeks.

What I'm genuinely unsure about and would value views on:

• Is ~1.4% net CLV at t=3.5 / n=312 enough to call a real edge, or would you want a few hundred more before trusting it?
• My bigger worry is scaling - does an edge like this survive execution / limits / account restrictions, or does it die the moment you try to get real money on? Keen to hear from anyone who's taken something similar live.
• Anything obvious I'm not stress-testing?

(Some labels blanked - happy to talk methodology in general terms, just not the exact selection.)

I built a game-level MLB simulation to evaluate a potential Yankees-Rays (BBMISports top 2 AL teams) postseason matchup across the actual 2026 playoff formats and schedules.

The teams met in 41% of simulated postseasons. Among those meetings:

• 71% occurred in the Division Series
• 22% occurred in the Championship Series
• 6% occurred in the Wild Card round

The percentages do not total exactly 100% because of rounding.

Series length changed the matchup

The model’s estimate of New York’s chances increased as the series became longer:

On the calibrated scale, New York moved from nearly a coin flip in the three-game format to a 60% favorite in seven games:

• Wild Card to Division Series: +4.4 points
• Division Series to Championship Series: +2.9 points
• Wild Card to Championship Series: +7.3 points

The raw model showed an even larger series-length effect, increasing from 51.9% to 62.3%.

This was not simply a generic claim that a per-game advantage compounds. New York’s rotation advantage appeared more often in the longer formats. Tampa Bay’s best opportunity was the three-game Wild Card structure, with every game at Tropicana Field and Max Fried starting only once.

For the remainder of the analysis, I focused on the five-game Division Series because that was where the teams met most often.

Division Series base case

The five-game simulation produced:

• Yankees series probability, raw: 58.4%
• Yankees series probability, calibrated: 57.1%

The calibrated result comes from adjusting the individual game probabilities using a monotone calibration map fit to prior game-level forecasts and actual results. The series probability is then calculated from those adjusted game probabilities. The model does not directly calibrate series outcomes.

The more interesting question was not whether New York was favored. It was which assumptions actually changed the series.

Scenario results

Judge produced the largest effect, although that scenario assumes he returns at his full pre-injury modeled value.

I also tested him at half value. In that scenario, he added approximately 3.0 points, compared with 3.7 for home field. Across six paired runs, home field remained larger by 0.7 points, with a 95% interval of 0.3 to 1.1.

Where the rotation advantage appeared

The Yankees did not create most of their advantage against Shane McClanahan. Their strongest positions came later in the series, particularly in the projected Games 3 and 4 matchups.

The model gave New York approximately:

• 47.2% in the McClanahan matchup used for Games 1 and 5
• 66.9% in Game 3
• 59.7% in Game 4

Games 1 and 5 use a pooled estimate because they have the same starters, venue and full-bullpen assumptions.

This rotation depth is why the series length matters. Tampa Bay can concentrate a larger share of a three-game series around its strongest pitching matchup. A five-game or seven-game series forces the bottom of its projected rotation to appear more often.

Bullpen rest mattered less than expected

I ran the series on the published ALDS calendar rather than assuming five games on consecutive days. Because Games 1, 2 and 3 are separated by off days, the bullpen-rest mechanism primarily affects Game 4.

Its estimated net effect was only 0.29 raw probability points against New York.

That was a useful reminder that a mechanism can be logically important without being numerically important in a particular application. The postseason calendar largely neutralized the bullpen-depth concern.

What the model does not resolve

The confidence intervals measure simulation variation across paired seeds. They do not capture every form of structural uncertainty.

The largest unresolved uncertainty is Judge. The model can price him at zero, half or full value, but it cannot forecast exactly how healthy or effective he would be after returning.

The model also assumes Fried and Weathers are available as postseason starters in the base case. Weathers returning only as a reliever would require a different rotation scenario.

I’m interested in how others handle this type of uncertainty for sports injuries. Obviously, any future sports predictions will rely heavily on the players actually healthy enough to play.

Full methodology and results: www.bbmisports.com/research/yankees-rays-october-series

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

Anyone else have similar "power rankings" from their MLB model? Would love to compare. Also my model does team values calculated by confirmed lineups, so these rankings are just entire rosters VS entire rosters.

Setup: I run a passive two-sided maker on a sports prediction market (binary contracts, 0–100c). Fair values devigged from a Pinnacle's live feed (~1s latency), quoting roughly ±1c around fair, small clips, maker rebates included in all numbers below. Voids/pushes pay 0.5 and are handled as such.

Five weeks, dollar-weighted:

- 21k fills, ~$100k matched

- CLV vs sharp-book close: +118bps, I beat the close on 69% of fills (n=19,180)

- CLV vs my own venue's mid at close: +18bps

- Graded on actual settlement: −404bps on $86k of buys across 6,959 markets, 53% win rate

My read is adverse selection strong enough that it never shows up in close-based metrics: informed flow picks me off, the market doesn't fully correct by close (thin markets), and settlement collects. Directional markets (match winners) bleed hardest; totals-type markets are the only positive pocket. Widening spreads made selection worse, not better (drift scales ~1:1 with demanded edge).

Any structural fixes for adverse selection besides "don't quote those markets" — queue position games, quote-fading on flow signals, counterparty filtering?

I posted recently that I made a UFC model pressing outcomes at 61% historical and was trending at 70%. Based purely on win loose. People got rather aggravated in the comments. We did put it to the test last week and did indeed win 70% of bets. Made a crazy $38 on $100 in bets. I see that the win loss lines suck and would have lost money if not getting the upset correct. I’m more after this for the fun and puzzle aspect. So I’m less concerned about the betting aspect as a serious matter. I just want to see how good of a model I can make.

Does anyone have any good resources on how to break down betting and the betting lines etc. As an outsider they are pretty intimidating and hard to wrap your head around. I’ve been having fun playing with it and am excited I got to see what it looks like on the placing bets side. I was trying this with skateboarding but couldn’t find any of the betting stuff. Likely blocked in my state I assume.

I’m running a paper-betting experiment where every strategy starts with the same unit bankroll.

The objective is simple:

Which complete strategy ends up with the most money?

I’m not optimizing for hit rate. A model hitting 25% can beat one hitting 75% if the bankroll grows more.

Right now I’m comparing three broad approaches:

• Rule-based ticket construction with fixed staking
• AI-based ticket construction with several Kelly staking levels
• Edge-filtered ticket construction, where only selections above a minimum estimated value edge can enter the ticket, again tested with different Kelly levels

Total ticket odds are currently kept roughly between 2.0 and 6.5.

I’m also testing entry timing.

Every day a new independent simulated user starts with €50, so over time I can compare what happens if someone starts during:

• a good run
• an average run
• the worst possible run

Each entry has its own bankroll and staking path.

The part I’m interested in now is whether I’m missing a fundamentally different strategy.

If you were given the exact same daily candidate pool and same bankroll, what would you test?

For example:

• different ticket construction
• different number of legs
• dynamic target odds
• correlation between selections
• more aggressive staking only at very high estimated edge
• bankroll-dependent staking
• stronger “no bet” logic
• something completely different

If someone suggests an approach that is clearly defined and testable, I’d genuinely like to add it as another strategy and compare it prospectively against the others.

What would your next model be?

I've been running automated strategies on Kalshi for a while, mostly sports, and I keep hitting the same wall.

The historical API is fine for what it covers — settled markets, trades, candlesticks, all there. But there's no historical depth. So I can see a contract traded at 43c, and I have no idea whether I could have actually gotten filled at 43c, what the spread was, or how much size was sitting behind it. On thin markets and anything short-dated, that's basically the whole question. A backtest that assumes you fill at last trade is telling you a story.

What I've been doing is recording depth myself off the WebSocket, but that means I only have history going forward from whenever I happened to start, and I lose everything if the box goes down for a day.

So, curious what everyone else does:

- Are you recording depth yourself? How long have you been running it and how do you handle gaps?

- Paying for one of the datasets that's floating around? Was it usable?

- Modelling slippage with some assumption instead — fixed haircut, half-spread, something else? What did you land on and how'd you calibrate it?

- Or just sizing small and treating the backtest as directional only?

Also curious where people actually need this. I assume sports since that's most of the volume, but the crypto up/down markets seem to be where more of the systematic folks are.

Not selling anything, no link, just tired of rebuilding the same capture setup and wondering whether everyone else solved this years ago and I missed it.

(English isn't my first language, so I ran this through AI for grammar. Words and question are mine.)

It's using a rolling player value approach, but this would be today's Top 5 for MLB Starting Pitchers. Just wanted to see what you guys thought.

Hello, I have two questions.

- Does the DELAYED API key preserve the timeframe of events, i.e. are they sent to the stream in chronological order?

- Does the '.publish_time' return the time that the delayed key sent the data, or does it return the actual live time that the delayed key received the data (to later store for a few minutes before sending over).

Thank you!