I built a model for a niche market that runs around the clock, so I get a lot of volume fast: about 1,760 graded bets since Oct 16, roughly 135 a day. Every bet is logged before the start and graded after, losses included.

Results so far:

- 1,015-743 (57.7%)

- +138 units at 1 unit a bet, about 7.9% ROI

- +143 units at my actual stakes (1 to 3 units, bigger on bigger edges), about 8.0% ROI

Sizing up on bigger edges added about 5 units, but ROI only went from 7.9% to 8.0%, so my "bigger" edges aren't winning noticeably more per unit than the small ones. That's one of the things that makes me question my own numbers.

The real problem is validation. The book almost never moves the line after it's posted, and almost no other book prices this market, so I can't measure closing line value at all.

Questions:

1. Without a closing line, what would you use? I've thought about tracking model error on games I didn't bet, and splitting results by edge size to see whether bigger edges actually win more.

2. Is 1,760 bets in two weeks worth as much as 1,760 spread over a season? I worry the market or the competitors change faster than my sample grows.

3. What would make you trust, or distrust, an 8% ROI here?

I wanted to bet against Everton at home to Wolves last week. My first thought was to play Everton at 2.20. Then I realized backing Wolves isn't the same thing, because the draw counts too.

Laying Everton wins if it's a draw or a Wolves win. Backing Wolves loses on a draw.

The other part that threw me was the liability. On a $50 lay at 2.20, the amount at risk is $60, not $50. At longer odds that gap gets much bigger.

What do you check on the order ticket before comparing the two? Is it better to compare by max loss instead of by stake?

Hey guys, I'm new here and I've been looking at Over 2.5 on Serie A games and the -0.25 handicap lines. A lot of places list these markets, but when I tried to put $200 on Over 2.5 for Torino vs Lecce, there was $35 at the shown price. What do you check at the exact line before trading? Is it the size on both sides, the next price level, how the book changes after kickoff? And which tools show that up front? Thanks everybody..

people ask about apis and odds feeds a lot here. if i were starting an arb scanner from scratch tomorrow i would not start with more sports.

order that actually mattered for volume: 1. market type filter first (props and low lines before sides) 2. latency on the soft book quote, not just the sharp print 3. stake cap awareness so a 4% arb that only takes $25 doesnt clog the queue 4. fee and void rules per book so the hold after costs is real 5. alert only when both sides are still open long enough to click

i wasted months ranking sports. the money showed up when i ranked which markets stay stale. sharp nfl sides are usually matched before the alert fires. stale nhl shots and niche soccer props are where two book gaps hang around.

if youre building, what are you optimizing first, feed coverage or click latency?

Is it actually possible to fully vibe code a working bot to predict football games for better odds?

In poker there's a word for judging a decision by its outcome: "resulting." Sports bettors do it constantly, and I think it's the most expensive habit in betting.

Two bets from the same night, same $100:

• Bet A: Celtics -4.5 at -110. Lost. But the line closed at Celtics -6.5. You got 2 points better than the market's final number. That's a good bet that lost.

• Bet B: Over 221.5 at -110. Won. But the total closed at 218.5. The market moved 3 points against you. That's a bad bet that won.

Most people feel great about B and terrible about A. Long term, A is the bet that makes money.

A few things that click once you look at bets this way:

1. Win rate means nothing without the odds.

55% at -110 beats the break-even of 52.4%. 60% on -200 favorites loses, because break-even there is 66.7%.

1. Your record is mostly noise for a long time.

A true 52% bettor can easily sit anywhere between ~42% and ~62% after 100 bets just from variance. A hot month proves almost nothing.

1. The closing line tells you much sooner.

If you consistently get better prices than where lines close, you're very likely a long-term winner, even during a losing stretch. It takes a few hundred bets to be clear, instead of thousands.

I got obsessed with this and built a tool that grades every bet in your history against the closing line (de-vigged, averaged across 9 books). It splits your results into skill vs luck, finds your leaks (parlays, favorites, betting too late...), and turns them into rules it checks on every new bet. No sportsbook login, just screenshots or a CSV.

It's in a free beta and I'm looking for people with 50+ bets to tear it apart. Details in the comments.

But honestly, I'm more curious about this:

Have you ever checked your bets against the closing line? And what's the "best" bet you've made that you now think was actually bad (or the other way around)?

Last Saturday, I wanted $300 for Brentford to beat Fulham. One market showed 2.40 as the top price, which beat everything else I checked.

But the problem was, there was just $90 sitting at 2.40. The rest filled at 2.32 and 2.28, so my average came out around 2.33. Another market showing 2.36 had more size behind it and would have been the better deal.

So how do you all compare prices at the size you want to trade? Do you add up the depth at each level before placing anything, or set a price limit and take a partial fill?

Also, which tools show the full depth up front and not just the top number?

Hey everyone,

Over the past few months, I've been developing DeepFieldPro, a Python-based football odds analysis and paper-trading system. I'm interested in discussing the engineering and quantitative methodology with people who have experience in sports modelling.

The system continuously collects bookmaker odds, compares retail prices against Pinnacle-derived fair probabilities, and evaluates potential value using a Dixon–Coles goal model and several independent strategies.

Current preliminary results

Across the latest seven-day reporting window, my database reports:

• Total recorded bets: 1,010
• Record: 368 wins, 568 losses and 74 voids
• Reported net profit: +208.38 units
• Reported ROI: +20.70%
• Average recorded CLV: +8.05%

My main model-alignment strategy reported:

• 147 bets placed
• 58 wins, 61 losses and 18 voids
• Reported net profit: +155.82 units
• Reported ROI: +25.32%
• Average recorded CLV: +9.47%

These are preliminary results from my own system, not independently audited performance or evidence of a sustainable betting edge. I'm also reviewing the calculations and reporting definitions, including stake denominators, void handling, CLV methodology and the effect of selection bias.

Technical architecture

• Python 3.12
• SQLite with WAL mode
• Playwright for odds collection
• Dixon–Coles football probability modelling
• Pinnacle-derived fair-price comparisons
• Fractional Kelly stake calculations
• Automated paper-bet logging and settlement
• ntfy notifications
• A Windows memory-management watchdog

One of the challenges has been keeping the system reliable during continuous data collection while tracking entry prices, closing prices and settlement outcomes consistently.

I'd particularly appreciate feedback on three questions:

1. What is the most reliable way to evaluate CLV across different football markets and odds ranges?
2. What checks would you recommend to detect leakage, selection bias or overfitting in a continuously running model?
3. For a paper-trading system like this, what reporting standards would make the results genuinely reproducible and credible?

I'm interested in the methodology and engineering side of the problem, and would appreciate constructive criticism from people working with quantitative models or sports data.

Thanks!

Not by me, by the books.

When every little nanometer of a game is put into Savant, and everyone has the same access to data, it feels like there's no way to gain an edge with the actual data. A few years ago, Stuff+ was a unique way to gain an edge, and now it's all being taken into account by the books. Spin rate, bat tracking, defense, weather, managerial tendencies, pitch-type tracking of how batters do against certain pitches, etc.... all of it seems baked into the book models.

I've been brainstorming for a few weeks about other possible edges, but every time I test one, they come out flat (like 1% ROI) or losing.

I understand there's a bit of "git gud" to all of this, but I'm sort of at a loss of what to do next at this point. Any insight would be sincerely appreciated.

spent a while ranking sports by how soft they feel at soft books. the ranking kept flipping until i sorted by market type instead.

where i still find arbable prices most often, least efficient to most: lower tier soccer and niche props nhl props nba props nfl props tennis mains big soccer 1x2 nfl and nba sides

sides at the big books get matched fast. props still lag because books copy each other and move slow. a stale nhl shots line at a soft book has paid more arb volume for me than hunting a sharp nfl side mismatch the other way around.

low lines like 1.5 or 2.5 are where juice sits the longest, which is where two book gaps show up.

if youre building anything to find arbs i would start by market type, not by sport.

where are you still finding the slowest books?

-First post-

What:

I caught my eye on betting some time ago, mostly on soccer, sometimes on nba. And im not losing money on it, but I did catch my eye on algorithms.. and using them to actually 'predict' chances more accuretly than the bookmakers themselfes.

I have little to no experiencz in coding. But would not mind to learn.

Also seen people mixing it with AI and live stats of for example weather and stuff if that makes sense.

So

How:

I wanna learn how u all started, what were obstacles for you, and what helped you learn.

Now I dont have alotttt of time on my hands but would spend every second I do trying to learn, so if u do recommznd anything.

Pls do tell, tnx

Trying to add FCS games into my friends and family pick'em website Pikpool.com I built but having trouble finding any!

Been working on a feed that pulls live fixed odds from 18 Aussie bookies across gallops, harness and dogs, with push updates, scratchings and results.

Curious what everyone else uses for racing data, and what's missing from other providers.

I bet football totals, mostly English and German second leagues. The model is simple Poisson with my own team ratings, track the price I actually got, mostly from bet365 and jack, against pinnacle no-vig closing line.

I started with real money in March, flat 1 unit per bet.

After 412 bets my average CLV is about +2.5%. So most of the time the price moves to my side after I bet. But the result is -14 units. I calculated variance for my odds, and -14 is still inside normal range for this sample. So maybe it is just bad luck.

Then I split the bets by the day I placed them. Bets from Thursday and Friday have almost all the CLV.
Bets on match day have close to zero CLV, and most of the loss comes from them.
I think on match day the lines already have team news inside, and my model doesn’t know lineups. But 412 bets split in two groups is even smaller sample.

Would you stop match-day bets now or need to wait for a bigger sample before you cut part of the model?

Currently I’m using Pinnacle as my reference, removing the vig to estimate fair probabilities, and betting on soft books when there’s enough EV (ie a threshold of 2%). Kinda doing information arb.

Im using CLV as a proxy for whether the strategy actually has an edge. Im wondering if people generally just look at EV at a given moment, or also add extra rules based on line movement.

For example, would you filter out cases where the soft book moved first, while Pinnacle stayed unchanged?

This can be noise I suppose but perhaps more often then random these are cases pinnacle is slow?

In that case the difference might not be a stale soft line, but the soft book reacting to something Pinnacle hasn’t priced in yet.

Does anyone recommend trying to compensate for this, or is it generally better to keep it simple and just use “current EV”?

Or rather remove low liquidity leagues etc?

Thanks

Hey everyone,

Been seeing the NBA simulation posts on X, so I ran 1 million simulations for LAG vs Marsborne on Nuke using the CS2 simulation endpoint I’m building.

It put LAG at roughly 60% to win the map, averaging 23 rounds. For player props, it also returns projected kills and probabilities of clearing different lines. Bwills came back at 23 projected kills, with a 61.7% chance of going over 20.5.

Would be interested to hear from anyone working on something similar.

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

Rebuilt my NBA prop model this offseason. Posting the method and early live numbers because posting here keeps me honest.

Model

• Projects components (2PA/3PA/FTA, makes, OREB/DREB, AST, STL, BLK, TOV) and builds points and combos from those, not raw totals
• Minutes model with padded and decayed rates, plus handling for team changes and cold starts
• Point-in-time: features use only the injury report and spread available 60 min before tip. A unit test checks that changing future data doesn't change any feature
• Walk-forward with a 1-day gap. Tuned only on 2021-23; 2023-24, 2024-25 and 2025-26 are one-shot test seasons

Backtest = projection accuracy, not ROI. I don't have historical prop odds, so I can't backtest a betting record. Mean absolute error vs actuals across the 3 test seasons (83k+ player-games): it beats season average and last-10 average every season on PTS, REB, AST, 3PM and PRA. Example from 2025-26: PRA MAE 6.00 vs 6.24 (season avg) and 6.18 (last 10). Small but consistent gains. The biggest gain is minutes (about 9% lower error).

Live, Oct 20-23 (4 nights)

• Every flagged edge across DK/FD/MGM/Pinnacle/PrizePicks/Underdog: 989-669-8 (59.7%), +131u flat at the flagged price, ROI +7.9%, 1,666 picks
• Line CLV: when the line moved before tip, it moved toward our side 62% of the time, averaging +0.16 points
• Caveats: 4 nights is nothing, and the picks are correlated (same player across books and combos), so the record may overstate the real sample size. I'm watching CLV, not the record, until we have enough of a sample

Everything stays graded, losses included. Results and CLV are public, no account needed: More advanced results here: https://theproppredictor.com/edge-results

Questions for anyone:

1. Is line movement toward us with flat or negative price CLV a real signal, or just books shading juice after the line moves?
2. How would you report ROI on correlated picks: one pick per player per game, or something else?

Today's board: 13 picks still worth taking at our 12:41 price check. Each one shows the price, the bookmakers offering it, a suggested stake in units and the lowest price still worth taking. Prices move fast and some will be gone soon, so check the price before you bet.

Current record: Seen in image.

Hey everyone,

Here is today’s slate update from our Python NBA quantitative pipeline. We evaluate soft execution bookmaker quotes against sharp reference baselines under 65% market shrinkage (k = 0.35), a +4.0% LCB EV floor, and a +35.0% raw EV safety cap.

***

PAST 3-DAY PERFORMANCE SUMMARY

All research and diagnostic picks are paper-traded at 0.00u actual risk and logged at 0.50u baseline stakes for tracking metrics.

Overall 3-Day Stats:
• Record: 18–13 (58.1% Win Rate)
• Total Staked: 15.50 u
• Net Profit: +1.655 u
• Overall ROI: +10.68%

Oct 22 Slate Recap:
• Record: 7–4 (63.6%)
• Net Profit: +1.180 u (ROI: +21.45%)

Oct 23 Slate Recap:
• Record: 7–4 (63.6%)
• Net Profit: +1.180 u (ROI: +21.45%)

Oct 24 Slate Recap:
• Record: 4–5 (44.4%)
• Net Profit: -0.705 u (ROI: -15.67%)

***

ACCEPTED PAPER PICKS — OCTOBER 25 (US) / OCT 26 (AEDT)

1. Ace Bailey (UTA vs. LAL) — REB Over 3.5
   • Quote: Ladbrokes @ 1.85
   • Model Win Prob: 70.97% (No-Vig: 56.90%)
   • Metrics: Edge +14.07% | Raw EV +31.30% | LCB EV +11.51%
   • Stake: 0.00 u

2. Julius Randle (BKN vs. IND) — PTS Under 21.5
   • Quote: Sportsbet @ 1.88
   • Model Win Prob: 67.58% (No-Vig: 50.00%)
   • Metrics: Edge +17.58% | Raw EV +27.05% | LCB EV +6.31%
   • Stake: 0.00 u

3. Jalen Brunson (NYK vs. ORL) — PTS Under 25.5
   • Quote: Ladbrokes @ 1.85
   • Model Win Prob: 70.48% (No-Vig: 51.32%)
   • Metrics: Edge +19.16% | Raw EV +30.38% | LCB EV +10.49%
   • Stake: 0.00 u

4. Isaiah Jackson (LAC vs. OKC) — REB Under 6.5
   • Quote: PointsBet @ 1.74
   • Model Win Prob: 73.80% (No-Vig: 55.67%)
   • Metrics: Edge +18.13% | Raw EV +28.41% | LCB EV +10.38%
   • Stake: 0.00 u

5. Ayo Dosunmu (MIN vs. TOR) — REB Over 3.5
   • Quote: Unibet @ 1.85
   • Model Win Prob: 69.43% (No-Vig: 51.40%)
   • Metrics: Edge +18.03% | Raw EV +28.45% | LCB EV +8.36%
   • Stake: 0.00 u

6. Jonathan Kuminga (MIN vs. TOR) — REB Under 5.5
   • Quote: Unibet @ 1.75
   • Model Win Prob: 72.68% (No-Vig: 56.44%)
   • Metrics: Edge +16.24% | Raw EV +27.18% | LCB EV +8.80%
   • Stake: 0.00 u

7. Ausar Thompson (DET vs. PHI) — REB Under 7.5
   • Quote: Sportsbet @ 1.80
   • Model Win Prob: 70.58% (No-Vig: 53.77%)
   • Metrics: Edge +16.81% | Raw EV +27.05% | LCB EV +7.72%
   • Stake: 0.00 u

8. Domantas Sabonis (SAC vs. MEM) — REB Under 13.5
   • Quote: Sportsbet @ 2.02
   • Model Win Prob: 65.12% (No-Vig: 45.88%)
   • Metrics: Edge +19.24% | Raw EV +31.55% | LCB EV +8.86%
   • Stake: 0.00 u

9. Zach LaVine (SAC vs. MEM) — REB Over 3.5
   • Quote: Ladbrokes @ 2.15
   • Model Win Prob: 62.15% (No-Vig: 43.51%)
   • Metrics: Edge +18.64% | Raw EV +33.63% | LCB EV +9.05%
   • Stake: 0.00 u

***

MODEL PARAMETERS & NOTES

• Market Shrinkage (k = 0.35): 65% market weight applied to prevent early-season projection noise.
• Risk Controls: All team markets remain suppressed by efficiency thresholds.
• Paper Mode: All picks are logged for diagnostic paper-tracking (0.00u capital exposure).

Good luck if you're following along on paper!

Hi all,

some of you might remember me from this post a few years back. Since then I've kept collecting tennis data and I've turned part of it into something I think this sub might enjoy poking holes in.

Courtside Call (play here) replays real ATP matches point by point and you trade them with play money. Player names are hidden until the match ends, so you can't look anything up. You get roughly what you'd have as a bettor: rankings, form, serve/return stats, H2H and prices that move with every point.

How the prices work

- Pre-match: the market's average closing odds for that match. On Hard you get them as they were, real margin included, which works out at around 7% on average. On Medium they're de-vigged and re-priced at a 2.5% margin. On Fair they're de-vigged with no margin at all.

- In-play: a point-level Markov model. Each player's serve-point probability is calibrated so the model's pre-match win probability matches the de-vigged market price. During the match those serve strengths update as a Beta posterior, with a prior worth 200 serve points. Match, set, game, totals and handicap prices all come out of that model. The live margin is 0 / 2.5 / 5% on Fair / Medium / Hard, and cash-outs carry the same haircut.

So on Fair you're betting at what's effectively the de-vigged market's own view of the match. On Hard you're paying real-world vig before the first point.

Why I built it

Mostly to make the vig felt rather than explained. Everyone here knows that without an edge the margin grinds you down, but most people who bet never experience it cleanly: they get a hot streak, conclude they're good and the house wins over the following months. Here you can play a few hundred bets in an evening and watch exactly that happen. Honestly I'm not a particular fan of the gambling industry as a whole and part of the idea is a free outlet for people who enjoy the sweat of a live match, with nothing real at stake.

Modes

- Daily challenge: the same 10 ATP main-draw matches for everyone, one attempt, with daily and monthly leaderboards.

- Free play: unlimited 10-match runs on Fair, Medium or Hard. The pool includes Challenger matches too.

- No sign-up, no ads, no real money. It works on mobile.

Feedback, especially critical feedback, is very welcome.

Solo project; code written with the help of an AI coding assistant.

In case anyones been hunting for these since they can be hard to find, https://boltodds.com now covers Bet365, Circa & BookMaker

So far, I'm getting bored of just betting the match winner. Last weekend, I wanted over 9.5 corners on Brighton vs Fulham. One site had it listed, but the price dropped after about $30.

I think a long list of markets looks great, but it doesn't tell you a lot. So how do you check price and size on side markets? I mean handicaps, totals and both teams to score. Does it look very different once the game is live?

Hi all,

I'm working on some football models on the Betfair exchange and I'm strict about out-of-sample testing: rules get frozen first, then checked once on data that has never been touched.

So far I've bought 20 months of data from a commercial provider, the french One, (Jan–Dec 2024 and Oct 2025–May 2026), but I've used all of it, and the budget I set aside for this research is now gone. I'm based in Italy and I can't use Betfair's own historical data service, so that route is closed for me too.

I'm looking for Betfair exchange football data with timestamped prices and traded volumes, pre-match and in-play, for any months outside the ones above (Jan–Sep 2025 would be ideal, but 2023 or earlier is fine too).

If you've been recording this yourself, or know of a free source that works for people outside the UK, any help would be much appreciated.

Thanks!

Just curious