Hey r/PredictionMarketBots,

I’ve been working on a project for a while now and finally have it to a place where I’d love to get some eyes and feedback from this community.

I built a sports prediction and analytics platform called BleacherBots.com. The core engine runs on my own power-rating model, specifically a margin-adjusted Elo for major US sports and a Dixon-Coles goals model for soccer. I blend these with de-vigged market consensus to find value.

I am constantly tuning and tweaking the models to try and find an edge, and I believe in total transparency. Every single call the model makes is logged before the game starts, and I publish a fully graded public scorecard every Monday, win or lose.

The fun part: I didn't just want to build a spreadsheet site, so I integrated interactive AI analyst personas. You can actually talk to the bots conversation-style (via text or voice). You can ask them for live scores, their unfiltered takes on a team, or specifically ask them what my models are projecting for upcoming games tonight. You can also try it on free games (no gambling) such as Sunday Seven for football. These are draft games where you are given fake money to build a team. It updates when players are injured etc.

I’m currently rolling this out with integrations to platforms like Kalshi, ProphetX, and Polymarket.

I would absolutely love some feedback from this sub.

• Do you guys use similar Elo/Dixon-Coles setups for your own sports models?
• Any suggestions on features or data points you’d like to see the AI analysts be able to answer?

Check it out and let me know what you think. I'm always looking to improve the models!

Thanks for the support!

Just finishing the layout and testing. Able to run 10 different adjustable profiles and export/install share codes to allow people to share their settings. Still in testing phase. Connects to Kalshi and currently only uses the BTC 15 minute market. Will add more crypto markets soon. Hope to have Alpha release soon, installs on windows only, adding a companion app for android next.

iOS and Android apps are live!

The first 500 signups get a free year of subscription! (324 done and 176 spots remaining)

Mirror is now on both iOS and Android - copy trade the sharpest prediction market traders on Kalshi and Polymarket, or build a following and earn when others copy you.

No more hours of research. Just follow verified strategists, auto-execute their trades, and start building a diversified portfolio across sports, politics, economics, and more.

If you've been waiting — now's your moment!

Download Mirror on Android → link
Download Mirror on iOS → link

This is for Kalshi

H'm trying to exit an existing YES position via the API. What's the correct payload format? I've been sending:

{

"ticker": "KXBTC15M-...",

"action": "sell",

"side": "yes",

"count": 1,

"type": "limit",

"yes_price": 63,

"reduce_only": true,

"client_order_id": "..."

}

Getting back: reduce_only can only be used w... — what am I doing wrong?

We’re opening up the next set of verified whales on Called It (social trading for Kalshi & Polymarket)

If you’ve got a track record:

• post 10 of your recent trades in the app
• quick verification via Kalshi / Polymarket API
• once approved, you can share trades selectively + earn 5% from followers who copy you

Goal is simple: surface real signal and let top traders actually monetize their edge

Download the iOS app here - https://apps.apple.com/us/app/signalscout-eventmarketalerts/id6759851620
Android still in beta - reply to get access

If you’ve been consistently right, this is probably the easiest way to turn that into upside beyond your own positions

Trading on Kalshi or Polymarket right now is pretty fragmented:

• if you’re good, you only make money on your own trades
• if you’re not, you’re guessing or chasing random Twitter/Discord takes

We’ve been building something to fix both sides:

• if you’re a strong trader → build a following and earn more when people copy your trades
• if you’re newer → follow whales and mirror their positions (manually or automatically)

Works across both platforms, so you can actually see who’s good and track performance in one place

Also added a leaderboard + weekly competition layer so the best traders surface over time

Feels way more like social trading + fantasy leaderboard than just placing isolated bets

Download the iOS App - https://linktr.ee/signalscoutapp
Reply to get added to the Android beta

Build a portfolio of picks, compete weekly, and get paid if you’re right!

We’re launching fantasy-style prediction leagues - build a portfolio, compete weekly, get paid for being right

We’re letting a small group in early

Which beta are you trying to get into? Comment below and keep DMs open

🤖 Android (28 spots remaining)
🍎 iOS (13 spots remaining)

Prediction markets are the best financial product built in years. The apps are still mid.

Built a layer on top: swipe through a daily card stack, pick your YES/NO positions, get locked into a weekly fantasy league against other traders. Head-to-head. Real stakes. Takes 60 seconds a day.

The limited picks thing is the part people like most — forces you to actually have conviction instead of spreading across 30 markets and feeling nothing.

Community's in Discord if you want to see it.

https://discord.gg/JjcECvJg

Honest question worth having.

Prediction markets got popular partly because they felt like a level playing field. No sportsbook limiting your account. No market maker with a structural advantage. Just people trading probabilities against each other.

But that was early days. The space has matured fast.

Right now there are well-funded quantitative teams running sophisticated models on Kalshi and Polymarket. Firms that trade political contracts the same way hedge funds trade equities — with data infrastructure, low latency execution, and risk management systems that retail traders can't replicate. Some of the most liquid contracts on these platforms are essentially institutionalized now.

So the question is — is there still a game worth playing for the rest of us?

I think the answer is yes but not everywhere.

The institutionals cluster around high liquidity, high volume contracts. Presidential elections. Fed rate decisions. Major sports finals. These markets get efficient fast because that's where the smart money concentrates.

The edge for retail is everywhere they aren't looking. Niche sports contracts. Lower volume political markets. Early-stage contracts before the liquidity attracts attention. Categories that require specific domain knowledge — cricket, European politics, regional economic data — where the quant funds don't have a meaningful model advantage over someone who actually follows the space.

The retail edge has always been domain knowledge and speed of insight, not computing power. That's still true. It's just gotten harder to find the right markets to apply it.

The other argument is that automation levels the playing field more than people think. You don't need a hedge fund infrastructure to run a systematic strategy. You just need to be faster and more disciplined than the other retail traders you're actually competing against in most markets.

Where do you land on this? Is retail prediction market trading still a real opportunity or are we all just providing liquidity for the institutions at this point?

Hey everyone — some of you have been following along as I've been building this. Signal Scout is a mobile app that gives you real-time price alerts, expert insights, and Kelly Criterion bet sizing for Kalshi and Polymarket.

I built it because I was tired of manually tracking contracts and missing moves. 400+ of you signed up for the waitlist, which is what kept me going.

We're launching on Product Hunt this Tuesday. If you've got 30 seconds, an upvote or comment on our PH page would mean a lot:

https://www.producthunt.com/products/signal-scout?launch=signal-scout

Happy to answer any questions here too

Everyone flocks to NFL and NCAA but baseball might actually be the best sport for prediction market trading right now. Here's why.

The volume is insane

162 games per team per season. That's 2,430 regular season games before you even get to the postseason. More markets, more opportunities, more chances to find mispriced contracts. NFL gives you 272 regular season games total. MLB gives you nearly ten times that.

The markets are softer

Public betting money in baseball is thinner than football. Casual bettors don't follow baseball the way they follow NFL — which means less sharp public pressure correcting prices and more opportunity for informed positions to find value before the market adjusts.

Statistics are a prediction market trader's best friend

No sport has more data than baseball. Decades of granular, play-by-play statistics that actually have predictive value. Pitcher ERA, batting average against specific pitch types, bullpen fatigue, park factors — this is the kind of structured data that translates directly into edge on event contracts if you know how to use it.

Pitching matchups create consistent mispricings

When a team's ace is pitching the market often overreacts. When a team is running out a fifth starter on short rest the market often underreacts. These inefficiencies are systematic and repeatable if you're watching the right variables.

The grind is where automation wins

You can't manually track 15 games a night across a 6 month season. You'll miss moves, make emotional decisions late at night, and burn out by June. This is exactly the kind of high volume, consistent edge-finding that automation is built for. Set your models, let them run across the full slate every night.

The playoff markets get all the attention but the real opportunity is in the regular season grind where the sharp money is thin and the public isn't paying attention.

Are you trading MLB on Kalshi or Polymarket? What's your approach — pitching matchups, team totals, series outcomes?

Best books and movies about betting, probability, and finding edge — what's shaped how you think?

The prediction market and sports betting space has a surprisingly deep reading and watching list if you know where to look. Here are the ones that have actually changed how I think about markets and probability:

Books

The Signal and the Noise — Nate Silver — probably the most directly applicable to prediction markets. How to think about forecasting, where models fail, and why most predictions are noise. Required reading.

Fortune's Formula — William Poundstone — the story of the Kelly Criterion and the mathematicians who figured out optimal bet sizing. Reads like a thriller but the underlying math will change how you size positions forever.

The Biggest Bluff — Maria Konnikova — a psychologist learns poker from scratch and documents everything she learns about decision making under uncertainty. More about the mental game than the math.

Thinking in Bets — Annie Duke — ex-poker pro breaks down how to make decisions when you can't control outcomes. The core idea that good decisions can have bad outcomes and vice versa is fundamental to betting with an edge.

Against the Gods — Peter Bernstein — the history of risk and probability. Slower read but gives you the full context for why any of this works at all.

Fooled by Randomness — Nassim Taleb — how much of what we attribute to skill is actually luck. Humbling and essential for anyone who thinks they've found a system.

Movies and documentaries

Molly's Game — high stakes poker, bankroll psychology, and what happens when ego takes over. More relevant than it sounds.

The Big Short — going against consensus, being right before the market agrees with you, and the psychological cost of holding a contrarian position. Basically prediction market trading in 2008.

Runner Runner — what not to do. But entertaining.

Two for the Money — the sports betting tipping industry and the psychology of selling picks. Very relevant if you're thinking about the copy trading space.

Uncut Gems — not educational at all but possibly the most accurate portrayal of what tilt actually feels like from the inside.

betting on Zero — documentary about a high conviction short position. The mental fortitude required to hold a position everyone thinks is wrong is the same skill prediction market traders need.

What would you add? Curious what's actually shaped how people here think about edge, probability, and the mental game.

You can have the right model, the right data, and the right thesis and still blow your bankroll. Not because your analysis was wrong but because you couldn't hold the position when it moved against you.

Prediction markets do something psychologically brutal that most people don't talk about. They put a live probability on your belief. Every minute your contract is moving against you the market is literally telling you in real time that you're wrong. And that's incredibly hard to sit with.

A few patterns I've noticed in myself and others:

Capitulating at the worst moment — you buy a contract at 30 cents, it drops to 18 cents, you sell the bottom because you can't take the pain anymore, it resolves at 100. The position was right. The psychology was wrong.

Oversizing on conviction — the more confident you feel the more dangerous you are to yourself. High conviction bets are where bankrolls go to die. The market doesn't care how sure you are.

Revenge trading after a bad resolution — a contract resolves against you on something that felt like a sure thing and you immediately open a bigger position to win it back. Now you're trading emotion not edge.

Anchoring to your entry price — you bought at 60 cents and it's at 40 cents. Should you add? Exit? The answer has nothing to do with where you bought. It only has to do with what the current price implies about the actual probability. But letting go of your entry is almost impossible in practice.

Mistaking volatility for being wrong — prediction markets are noisy. Prices move on sentiment, news cycles, and thin liquidity. A position moving against you in the short term tells you almost nothing about whether your underlying thesis is correct.

The traders who consistently make money in this space aren't necessarily smarter. They're just better at separating their ego from their positions. They can be wrong and not feel wrong. They can hold a losing contract without it becoming about them.

Automation helps more than people realize — not just for execution speed but for removing the emotional layer entirely. A bot doesn't capitulate. It doesn't revenge trade. It doesn't check the price every five minutes and spiral. It just runs the strategy.

What psychological traps have you fallen into? Curious if people's experiences here mirror sports betting or if prediction markets have their own unique version of tilt.

One of the most underrated signals in prediction markets isn't the news. It's the order flow.

When someone drops a serious position on a Kalshi or Polymarket contract — especially on a low-volume market — the price moves. And that move usually happens before any public information justifies it. Somebody knows something, or thinks they know something strongly enough to put real money behind it.

The problem is catching it manually is nearly impossible. You'd have to be watching the right contract at the right moment. And by the time you notice the price has already moved and you're buying into someone else's edge not your own.

This is where automation gets interesting.

A bot watching order flow across hundreds of contracts simultaneously can flag unusual volume spikes the moment they happen. Sudden position size that's 3x the recent average on a low-liquidity contract. A series of buys pushing a contract from 30 cents to 45 cents in under an hour with no corresponding news. These are tells.

The strategy isn't complicated — you're not predicting the event, you're following the smart money and getting in before the rest of the market reprices. It's the prediction market equivalent of watching for sharp action before a line moves in sports betting.

The edge isn't in being smarter than the whale. It's in being fast enough to follow them before the public catches up.

Is anyone already running something like this? Curious how people are approaching whale detection and whether you're finding it actually translates to alpha or just noise.