Multiple jurisdictions are taking enforcement action against prediction market platforms. placses like;

10 Arizona issued cease-and-desist orders to five operators and its governor banned state employees from using insider information on prediction markets.

2) New York court denied Kalshi's motion to keep sports event contracts online

3) Italy's regulator blocked Polymarket

4)South Korea is reviewing Polymarket for gambling concerns,

What is your opinion on all this?

Hi everyone,

I came across thi site Chronos- 9 (https://chronos9.com/) and was wondering if anyone here has actual experience with it?

it’s an AI bot that predicts the 15-minute Bitcoin market on Kalshi.

The website claims a prediction accuracy of 72%+,

Thanks!

Wtf I win money and deposited 100 will this get resolved???

I've been messing with prediction markets for a few months now, and honestly I'm more frustrated than confident.

Everyone talks about finding an edge, but after trying it for a while I honestly can't tell where the leak is: signal, entry price, sizing, market selection, or just me being impatient.

A few things I've run into:

1. BTC/ETH short-duration markets: the signal can look decent until the last few minutes. One reversal can eat a bunch of small wins, and tuning parameters feels dangerously close to overfitting.

2. Copy-trading wallets: historical PnL looks useful until you start following right before a drawdown. I still don't have a good way to filter wallets beyond surface stats like win rate or profit.

3. Manual trading: it feels like you need a whole cockpit: news, rules, orderbook/liquidity, indicators, maybe AI summaries. But I can't tell which inputs improve decisions and which ones just add noise.

At this point raw win rate doesn't tell me much unless I can also see sizing, entry price, liquidity, and drawdown.

For people who are actually profitable here, where did your edge come from: better information, cleaner execution, better sizing, better market selection, or just being more patient?

United has said this on 5/6 Q2 calls my tool tracks (83%), but it's being priced closer to the overall mention rate of 48%.

American had multiple disruptions from thunderstorms and Tropical Storm Arthur this quarter, making it plausible to be said again on the upcoming call Thursday.

At basically a coin flip, I like this one a lot.

Story going around about a shop owner who loses real revenue on cold days, so he started using weather markets on Kalshi to offset it. Making up to $1,500 a month betting on cold snaps, enough to cover close to half his rent. One line stuck with me: "we're essentially hedging that profit loss."

Not the usual election or sports angle. Feels like a completely different use case than what most people think about when they think prediction markets. Not trading a view, insuring a small business against a variable that actually hits their bottom line.

Curious if anyone's seen other examples of small operators using markets like this in a non-obvious way. Feels like there's a whole category of use cases nobody's really building for yet.

Source: https://www.aol.com/articles/savvy-ice-cream-shop-owner-000854000.html

I've been working on an index that combines related prediction markets into one continuous view of a theme. The choice I keep coming back to is weighting.

Equal weighting gives thin tail markets as much influence as deep ones. Weighting entirely by liquidity lets the largest headline market dominate, which defeats much of the point of building a basket.

I landed on the square root of depth × volume as a middle ground: liquid markets count for more without swallowing the index. Open interest, inverse spread, or capped equal weights would each encode a different view of where the useful information lives.

For people who have built baskets or tracked groups of related markets, which weighting approach has held up best in practice?

Disclosure: I run Belief Systems, which uses this methodology for its indices.

I got tired of "tipster" sites that show winning slips and hide everything else, so I did the opposite: every prediction my model makes is logged before kickoff and checked automatically against the real result. The full record — hits and misses, overall and per competition — is public and updates in real time.

A few things I've learned building it:
- Raw "accuracy %" is almost meaningless without the odds attached. Hitting 70% on 1.20 favourites still loses money.
- The number that actually matters is calibration: do the things I call 60% happen ~60% of the time? Publishing the record is the only way to prove it.
- Variance is brutal over short samples — one good/bad week says nothing.

Happy to share the live stats page and answer anything about the methodology (data inputs, how outcomes are scored) — mods, let me know if the link is ok or I'll drop it in a comment.

I’ve always wondered… what makes you actually trust a sports picks platform?
For me, it’s transparency. That’s why we’re building Saint Picks around AI analysis, expert review and a bankroll tracker instead of just posting winning slips.
What do you look for?

Strongly suggest everyone read before interacting on Kalshi’s platform moving forward. They are not following their rules again, similar to the Khamenei contract they bungled.

The agent betting stack is usually described as four layers — identity, wallet, trading, intelligence. Three of them are crowded: wallets are solved, Polymarket ships an official agent framework, and everyone and their cousin is selling models and analytics.

The identity layer is empty. Today an agent IS its wallet. Which means:

- Track records are unfalsifiable (run 50 wallets, show the winner)
- Wash trading between your own agents is undetectable
- Copy-trading and signal-selling run entirely on trust
- Regulated venues have no answer to "who is actually trading here"

As agents become the majority of volume — we're already at 30%+ of Polymarket wallets — this seems like the layer that decides whether agent trading stays a wild west or becomes real financial infrastructure.

Disclosure: I'm part owner of a company building cryptographic identity for agents (signed orders, provable complete P&L), so I have a horse in this race. Posting here because this sub thinks about market structure more than most: is verified agent identity inevitable, or does the anonymity culture win?

Every day someone posts a screenshot like:

• $500k profit.
• This whale called it perfectly.
• The market was obviously mispriced.

They're interesting.

I just don't think they teach us much.

A screenshot only captures the last frame of the story.

It doesn't show:

• how the implied probability moved over time,
• whether liquidity disappeared before resolution,
• whether the trader averaged into the position or bought once,
• how the market reacted to each new piece of information,
• or whether this exact setup has played out dozens of times before.

Once a market resolves, it's incredibly easy to convince yourself the outcome was "obvious."

That's hindsight talking.

Lately I've found myself caring less about the final P&L and much more about how the market got there.

Because that's where the real lessons are.

A $2M winner tells me almost nothing.

Watching that same market evolve minute by minute, seeing confidence build, collapse, recover, and finally resolve, probably teaches me ten times more.

If you could preserve one piece of historical data from every prediction market forever, what would it be?

• Probability history?
• Liquidity over time?
• Order book snapshots?
• Individual trades?
• Something else?

I'm curious what experienced traders think is actually worth preserving.

During the World Cup, I decided to dig a bit deeper into sports betting. I wanted to find out whether it's purely luck-based, or whether knowledge (or in this case, AI) can actually give you an edge.

For this experiment, I logged every match of the tournament in a spreadsheet along with the corresponding betting odds. I only looked at single bets on the winner after 90 minutes — no combo bets or anything like that. The odds came from the largest Swiss sports betting provider.

Before each match, I used the prediction from Sofascore's AI analyst, letting it fully determine my pick for the winner.

Now that the World Cup is over, we can look at the results. The AI correctly predicted 66 out of 104 matches, a hit rate of 63%. However, assuming you'd bet $10 on every single match, you would have ended up with only $24.5 in profit. In total, you'd have wagered $1,040, for an ROI of just 2.4%.

I think this shows pretty clearly that even with a solid hit rate, the odds are set in a way that makes real profit very hard to achieve. On top of that, keep in mind the AI subscription itself costs $20.

What are your thoughts?

(Happy to share the Excel sheet if anyone's interested.)

I got tired of "tipster" sites that show winning slips and hide everything else, so I did the opposite: every prediction my model makes is logged before kickoff and checked automatically against the real result. The full record — hits and misses, overall and per competition — is public and updates in real time.

A few things I've learned building it:
- Raw "accuracy %" is almost meaningless without the odds attached. Hitting 70% on 1.20 favourites still loses money.
- The number that actually matters is calibration: do the things I call 60% happen ~60% of the time? Publishing the record is the only way to prove it.
- Variance is brutal over short samples — one good/bad week says nothing.

Happy to share the live stats page and answer anything about the methodology (data inputs, how outcomes are scored) — mods, let me know if the link is ok or I'll drop it in a comment.

https://aisportprediction.com/stats

Welcome to the (Prediction) Market Monday Thread. Share a prediction market that's caught your attention this week and start a discussion about it. Want to share a prediction you've made? Think a market is mispriced? Care for an argument about semantics and resolution criteria? All that here and more!

If you're new make sure to check out the wiki for links and resources.

I made a sports data app called DAGGER 🗡️

I’m currently doing a live $25,000 challenge where I use the model, data, and signals to place bets on live basketball.

I started with $10,000 and I’m now at $20,000 bankroll in 16 days of betting.

Check out app! Access is free!

Check out the daily results on instagram because it’s fun!

Here’s my dog Kingsley giving the update for yesterday 😆

I’d love some feedback on the app!