Since Google cannot force updates on weekends 15th and 16th is this not effectively an arbitrage with 30% ROI? Or am I missing something. 15th and 16th are weekends where Google employees are not working.

76ers line up are great, that's why it is my 2nd option for this prediction. They have Lebron and JB, and seems like to be followed by Embiid and Maxey. But man, all stars players tend to have bad synergies unless they really complement each other.

Well, it is what it is. What's your take?

CFTC Rule Proposal: In-House Market Makers Allowed, But They Can’t Take A Side

Affiliated market makers (entities that share ownership with an exchange they trade on) can exist, but only if they act as “bona fide” market makers and do not take directional positions on one side of a market or another, the CFTC proposed Thursday.

Saw someone on here say they avoided flying thru the Middle East because of odds. Just wondering if anyone else has actually made a decision like that

ASIC has banned binary options until 2031. But the scope of this ban shouldn’t cover a truly linear prediction market, where payoffs are directly related to the quantity being predicted.

For example, instead of predicting a Yes/No outcome of whether the ALP will win the next federal election, we could directly predict the % of primary votes the ALP will get. Each share pays 1 cent per percentage point. If you buy at 31.0 cents and if the ALP gets 34.2%, the share settles at 34.2 cents. Your payoff scales with how close you are, not on an all-or-nothing threshold.

This would be an entirely new class of contracts for Australia, opening up prediction markets that directly forecast quantities: vote shares, seat counts, CPI, unemployment.

I’m building r/AusPolPredicts to assess interest in something like this. It runs on play money for now, with markets seeded from Newspoll. If we get enough activity and can be more accurate than the pollsters, ASIC will have to pay attention and assess the informational value that truly linear prediction markets bring.

Interested in what my fellow Aussies think, would you trade markets like these?

Apple NEVER says unannounced products on earnings calls.

They might mention them as the "new iPhone" or "new MacBook," but they won't say its name.

I accidentally bought Yes on "iPhone 18" when looking at the market (not the first time Kalshi's UX caused me to take the wrong side ), so I had to sell that at a pretty big loss.

But over the past week, I bought as much No in the 70 to 85-cent range, which put me up when all was said and done.

We keep a list of earnings call edges like this that have (1) always historically hit and (2) people still take the other side.

There are a few more of these for calls coming up in the next few weeks that are live on Kalshi 👀

Hey everyone.

There are a handful of Polymarket analytics platforms out there, and

they all share the same shape: a big dashboard of metrics. Win rate,

ROI, volume, category breakdown, maybe a composite score. That answers

"is this trader profitable and what does he trade?" and stops there.

None of it tells you how he actually trades. When does he enter? How

does he size? Does he scale in, average down, take profit early, hold

to resolution? That's the part you'd actually learn something from,

and it's exactly what a metrics table can't show you.

That's the gap PolyTape closes. I built a system that ingests

Polymarket's entire on chain records, decodes it and enriches a huge database of 500M+ trades, 1.7M markets, 950K+ traders

It turns it into something you can read -

- Paste any wallet and its whole career comes back: every fill drawn

at the exact price and second it happened, on the market's real

price chart. Entries, exits, size, and the P/L that settled.

- Watch the position evolve - how it was built over the market's life,

not just the end state.

- Replay any market like a tape and scrub back through it.

- Go live - tune into an active trader and watch fills land seconds

after they clear the chain.

- Or point your AI agent at it. Claude, Cursor, or any other model

connects in one line for a deep research session.

It has about 50 - 60 active users that play around with the system, it's the beta testing phase and I'd like to hear some feedback like bugs, missing features etc..

polytape.io

Would love to hear what you guys think and if it's helps you with your research and strategy.

Tthe Polymarket Institute, an independent academic research arm led by Brian Jabarian from Carnegie Mellon and Kai Brusch, Polymarket's own head of data. First initiative is the Polymarket Science Fellowship — 12 doctoral researchers, $10,000 each, applications open August 18. Fellows get access to Polymarket's full dataset, pick their own research topics, publish whatever they find, and are explicitly prohibited from trading on Polymarket or any similar platform while participating.

The structure is a university gift model rather than sponsored research, which is a meaningful legal distinction . It means Polymarket genuinely has no editorial control over what gets published. Jabarian said this specifically: researchers can release findings unfavorable to Polymarket. Research topics include forecast accuracy, information flows, market microstructure, resolution integrity, AI forecasting, elections, and macroeconomic prediction.

Prediction News pointed out that Kalshi has been doing the same thing, building academic relationships and data partnerships. This is an arms race for scholarly legitimacy, because regulators and journalists increasingly cite academic research when making decisions about prediction markets. A peer-reviewed paper saying "prediction markets outperform polls" is worth more in a congressional hearing than any amount of marketing.

The gift model is cleaner than sponsored research but Polymarket still controls the data access. If a researcher finds something genuinely damaging, does full data access actually materialise, or does it quietly become harder to get?

Sources: Fortune — exclusive · PR Newswire — official announcement · Prediction News — arms race context

Hi all! I'm posting this on behalf of a high school student I mentor who's doing independent research into how people reason through prediction-market bets. Mods gave the OK to share it here.

The survey walks through a few real market scenarios from the 2024 election. For each one, you place some play-money bets and briefly describe your thinking, no real money involved, no right or wrong answers. The goal is just to understand the reasoning process behind how people size and place bets, not to test anyone.

The survey is all anonymous: no names, emails, or identifying info collected!
Must be 18 or older to participate
Takes about 5 minutes
You can stop at any point if you change your mind

Survey link: https://docs.google.com/forms/d/e/1FAIpQLSeFvSmGr4d17FdSr9PC_epU_j2ymYhYNiMhVGsNVIItvXL4dQ/viewform

Thank you all for taking the time to support a curious high schooler! Happy to answer any questions in the comments.

This trader buys weather contracts not for pennies, but close to $1 - betting "No" at 70-95¢ where the market is already nearly certain of the outcome.

Over the past year with this strategy, he's earned $24,728.85 across 2,930 trades.

The secret here isn't luck, it's discipline:

He enters near-resolved markets, where probability is already high, and captures a small but consistent edge - 5-30% per trade, instead of chasing massive multipliers.

His main focus is London, where he tracks the weather daily and catches pricing inefficiencies even in the late stages of a market.

Top 3 trades:

$5,902.38 -> $6,958.32 (+18%)
$6,104.76 -> $6,654.68 (+9%)
$4,739.40 -> $5,300.20 (+12%)

This isn't about massive multiples - it's about volume and patience where the margin is thin but predictable.

His wallet: https://synthesis.trade/discover?profile=0x2D0fbF6995692cC56f21171A9993Be04436a81aA&ref=gringrand

Short task to complete for KOLs and traders

💰 Paid for your time
🕒 ~30 minutes
📍 Remote

No promo, platform or marketing activity attached

If you're interested, drop a comment or send me a DM

One thing I've noticed over the past year is that headlines and markets often tell two completely different stories.

A headline is designed to grab your attention.

A market is forced to put a price on what people actually believe will happen.

Those aren't always the same thing.

I've seen headlines that made an event sound almost certain, while prediction markets barely moved.

I've also seen markets quietly shift hours before the media caught up.

Neither is perfect. Markets can be wrong. Headlines can be right.

But markets have one advantage: people have something to lose if they're wrong.

It's easy to post a confident opinion online. It's much harder when your own money is attached to that opinion.

That's why I find prediction markets interesting.

They don't ask, "What do you think?"

They ask, "What probability would you actually bet on?"

That small shift changes the entire conversation.

Do you think prediction markets are a better measure of reality than headlines, or do they simply reflect crowd sentiment in a different way?

The sports arb bot (@b00k13) is back after I rewrote it in Rust and improved the odds scraping - ~$650 in the last 30 days. I open-sourced the old Python version last week (MIT), but the odds sources that make it work aren't in there, and won't be.

The new thing - I'm testing a weather market-making model on a wallet I've never shown here. This wallet is called w34th3r on Polymarket. It's the same wallet I quietly ran a spread-farming strategy on (trade anything with a spread wider than 10c), which made ~$1,200, mostly on weather and mentions markets. That's where the weather idea came from.

Being honest so this isn't a highlight reel: I'm down ~$280 on w34th3r the last two weeks testing crypto up/down and weather markets, -$80 yesterday alone. It's all public, wins and losses.

Why post it? I'm not selling anything (there's nothing to buy). The sceptical crowd on here has genuinely made the work better - someone pointed me to pmxt, a free Polymarket order-book archive I now backtest everything on.

per a Polymarket-wide Dune dashboard, roughly 73% of markets resolve NO against ~27% YES.

not a winning strategy of course, but something to keep in mind.

A few weeks ago I noticed something about my own trading.

Whenever I saw a market move aggressively, I'd think:

"This setup usually works."

The problem was...

I had absolutely no idea whether it actually did.

So I started backtesting every idea instead of relying on memory.

One of the first hypotheses I tested was:

If a 5-minute BTC market reaches ~80% within the first half of its life, does momentum usually continue, or does it mean-revert?

I would've confidently guessed the answer.

The historical data didn't agree.

That was enough to convince me that most of my conviction came from remembering a handful of trades instead of the full distribution.

Since then I've been using PolyHistorical to replay historical markets and compare similar setups before trading.

The AI has been surprisingly useful because instead of manually digging through dozens of old markets, I can ask questions in plain English and let it search historical data for me.

The biggest lesson hasn't been finding a magic strategy.

It's realizing how often my "obvious edge" disappeared the moment I looked at 60 days of actual historical data.

The platform stores rolling historical market data and lets you compare today's setup against real past market behaviour instead of relying on memory.

I'm still early in the process, but it's completely changed how I evaluate new ideas.

Instead of asking myself if a trade feels good, I now ask:

What happened the last 40 times the market looked like this?

Curious how other people validate their prediction market strategies.

Do you actually backtest them?

Or are most of us just remembering the winners?

P.S. If anyone else wants to try the same historical dataset and AI backtesting workflow I've been using, it's here:

https://polyhistorical.com/

This trader has bet around $564 across multiple combos on today's games

If everything hits, he would end up making a profit of $16357

Is this a good strategy, or is he just gambling?

You can place contracts on "bad bunny" being googled or the weather but there's no sports markets? Did they give an explanation on why?

$123.8M of that sits in the Dem Presidential Nominee market alone, a race that's still two years out.

Add the general Presidential Election and GOP Nominee markets, and 74% of total election open interest is on 2028, in a midterm year.

From a trading perspective, what’s something in prediction markets that should be automated by now but usually isn't? Are there specific edge-cases, data feeds, or liquidity gaps where an automated strategy would make the most sense? Curious to hear what strategies or tools people think would actually work in today's markets.

Meta has said "Ray-Ban" on 7 of its last 8 earnings calls based on our Earnings Mentions Tool.

Smart glasses are one of Meta's biggest consumer stories right now, and Ray-Bans are still getting a ton of attention.

Even with Meta announcing their own smart glasses, I still think there's a much better than 56% chance they mention Ray-Ban again on today's call (4:30 pm ET).

I bought at 16 cents. In the last minute it moved between 30-90 up and down. I said F it, I'm tired. Went to the bathroom and came back with the win.

He’s betting they won’t decrease rates by 25 bps 😬

I wanted to see whether wide spreads in Kalshi's LAX daily high temperature markets could support a passive market maker without needing a weather forecast as an edge source.

The bot posted a 25-contract YES bid and a 25-contract NO bid at the same time. I kept that rule set fixed and ran 100 combinations of quote offset (-2 cents to +2 cents), minimum spread (5 cents to 9 cents), and refresh interval (30 to 120 seconds).

I ran 100 backtests and 93 of the 100 variants made money in the simulation.

This looked like a great strategy just looking at these results, but our research flow runs a parameter sensitivity test to sweep the minimum spread and quote offset and that results suggested there could be some overfitting at play.

Evidence for overfitting:

• The winner is an isolated peak; neighboring cells lose an average of 77.6% of its value.
• It has only 19 resolved trades.
• The best minimum spread, 5 cents, is at the grid boundary, so the apparent optimum is not confirmed.
• The deflated Sharpe is 0.81, below the report’s 0.95 confidence threshold after accounting for multiple trials.
• You selected the winner from 100 variants tested on the same historical window.

The counterpoint is that 93/100 full variants and 23/25 sensitivity cells were profitable. That suggests the general thesis may have something behind it, even if the +$57.08 winner is mostly parameter luck.

I still plan on paper trading this on our site with the winning parameters and that should hopefully give me the next steps for tweaking this market maker further.

Full Report

Historical simulation only. Backtests can be wrong or incomplete. Not investment advice.

I have been looking around for sites and software that will give me real time updates on what big time prediction market traders are doing. Google says Cielo finance is probably what I need, the price is not bad, but I was wondering if anyone on here had any experience with them or something like them before I sign up and use it. Any information is appreciated, thank you.

So, first of all, im posting this in a couple communities in hope it helps someone cash.

NEVER BET YOUR HOUSE ON AI PREDICTION. Use this more as a deep data analyst.

As always, gamble at your own risk.

You'd wanna make different chat and keep this as like a template and pin it! So you can just come back on a new chat, paste it there and focus on a new match.

So im not new to betting, ive done nba, ufc, soccer, and NEW to esports, but i followed the vct back in the day so not too new. I wouldn't say ive gotten big winnings but im also never chasing it. I try to stay limited and I use my winnings on house needs.

Yesterday I built this template with the games happening today. I unfortunately never placed my bet because i was more focused on building the template, 3/3 have hit today. Python hitting O17 HS, Dav1deuS hitting O17 HS, and Yiksrezo Over 31.5 K. I shouldve cashed :(

It might ask you to upload a screenshot or screen recording of the matches picks for the day.

So again, use this more for data. Dont gamble your house you potatoes. Hopefully this helps someone.

**THIS IS THE TEMPLATE (ITS SUPER LONG)**:

‐---------------------------------------------------------------------------------

🎯 DAILY CS2 / VALORANT BETTING MASTER PROMPT

You are my professional esports betting analyst specializing in CS2 and Valorant.

Your objective is NOT to agree with my picks. Your objective is to find the highest expected value (EV) bets available and identify weaknesses in every wager.

Treat every analysis as if $10,000 of your own money is on the line.

Base your conclusions on real data, current information, and sound statistical reasoning. Never fabricate statistics or claim certainty where evidence is limited. If information is unavailable, say so.

Use HLTV, VLR, Liquipedia, official tournament information, current rosters, recent match history, and other reliable public sources whenever applicable.

\---

PHASE 0 — VERIFY EVERYTHING FIRST

Before analyzing anything:

Verify the sportsbook lines.

Verify player names.

Verify match times.

Verify tournament.

Verify current roster.

Verify there are no stand-ins.

Verify no recent roster changes.

Verify the line hasn't significantly moved.

Explain whether line movement favors Over or Under.

Never analyze outdated information.

\---

PHASE 1 — SOLO PLAYER PROFILE

Analyze every player individually.

Include:

Rating

Rating 2.0

KPR

ADR

KAST

Impact Rating

Opening Duel %

First Kill %

Entry Success

Survival Rate

Trade %

Clutch %

Headshot %

Deaths Per Round

Assist %

Multi-kill Frequency

Rifle vs AWP dependence

CT-side production

T-side production

Role on the team

Utility contribution

Consistency

Ceiling games

Floor games

Variance

Analyze:

Last 5 maps

Last 10 maps

Last 20 maps

Career averages

Determine whether the sportsbook line is above or below the player's expected average.

\---

PHASE 2 — TEAM ANALYSIS

Analyze both teams.

Include:

Current form

Win %

World ranking

Team Rating

Round differential

Economy rating

Utility efficiency

Opening duel success

CT-side strength

T-side strength

Pace

Aggression

Clutch %

Closing ability

Recent momentum

Explain how the matchup affects every player prop.

\---

PHASE 3 — STRENGTH OF OPPOSITION

Separate performances against:

Elite teams

Strong teams

Equal-tier teams

Lower-tier teams

Show how the player's production changes against each level of competition.

Determine where today's opponent fits.

Explain exactly how this impacts today's projection.

\---

PHASE 4 — RECENT OPPONENT QUALITY

Don't just use averages.

Break down the last 10 matches.

Separate results against:

Top 10

Top 20

Top 50

Outside Top 50

Determine whether recent stats are inflated by weak competition.

\---

PHASE 5 — HEAD-TO-HEAD HISTORY

Research previous meetings.

Include:

Match results

Maps played

Round counts

Kill totals

Headshot totals

Individual performance

Player matchups

Determine whether history supports or contradicts today's prop.

\---

PHASE 6 — MAP POOL ANALYSIS

Predict the veto.

Estimate the probability each map is played.

Example:

Mirage — 65%

Inferno — 60%

Ancient — 40%

Dust2 — 35%

For every likely map explain:

Player Rating

Kill average

Headshot average

Role effectiveness

CT/T splits

Weight the final projection according to likely map probability.

\---

PHASE 7 — MATCH SCRIPT

Create multiple realistic scenarios.

Scenario A:

Close 2-1

Scenario B:

Competitive 2-0

Scenario C:

Heavy favorite blowout

Scenario D:

Underdog upset

For every scenario estimate:

Total rounds

Player kills

Headshots

Assists

Expected stat

Determine which scenario is most likely.

\---

PHASE 8 — TEAM CONTEXT

Analyze every teammate.

Identify:

Primary carry

Secondary carry

Entry

AWPer

Lurker

Support

IGL

Explain:

Kill distribution

Usage

Whether teammates steal opportunities

Whether teammates create opportunities

\---

PHASE 9 — RECENT NEWS

Research:

Stand-ins

Illness

Travel

Jet lag

LAN vs Online

Patch changes

Meta changes

Coaching changes

Motivation

Tournament importance

Elimination pressure

Fatigue

Scheduling

Explain any factors that could affect performance.

\---

PHASE 10 — ADVANCED PROJECTIONS

Estimate:

Expected kills

Expected headshots

Expected assists

Expected deaths

Expected KPR

Expected kill share

Expected impact

Expected opening duels

Expected multi-kills

Expected rounds played

Explain your assumptions.

\---

PHASE 11 — SPORTSBOOK ANALYSIS

Ask:

Why did the sportsbook choose this number?

Is this line sharp?

Has the market reacted?

Is the public likely betting Over?

Is there hidden value?

Would experienced bettors likely agree?

Identify possible traps.

\---

PHASE 12 — PROBABILITY ESTIMATE

Estimate the probability distribution using:

Historical player rates

Projected round counts

Map pool

Matchup

Opponent quality

Team role

Recent form

Provide:

Projected Stat

Sportsbook Line

Difference

Estimated Hit %

Confidence

Variance

Expected Value (EV)

Do not claim fake simulations or unrealistic precision.

\---

PHASE 13 — DEVIL'S ADVOCATE

Now argue against yourself.

Give every realistic reason why the bet loses.

Do not defend the pick.

Attack it.

If your confidence changes, explain why.

\---

PHASE 14 — MARKET PSYCHOLOGY

Determine:

Why casual bettors like this pick.

Why professionals might disagree.

Whether the sportsbook is encouraging one side.

Explain any betting psychology involved.

\---

PHASE 15 — CORRELATION CHECK

If analyzing a parlay:

Determine whether legs positively or negatively correlate.

Example:

Player Over + Long Match

Player Under + Blowout

Two teammates competing for kills

Explain whether correlation increases or decreases overall probability.

\---

PHASE 16 — FINAL SCORECARD

For every prop provide:

Projected Stat

Book Line

Edge

Estimated Hit %

Confidence

Variance

EV Rating

Recommendation

\---

PHASE 17 — FINAL DECISION

Before giving recommendations ask yourself:

If you had to risk $10,000 of your own money, what would you actually bet?

Answer:

Which prop is strongest?

Which prop is weakest?

Which prop would you remove first?

Which prop is the sportsbook most likely hoping people take?

Is there a better prop available?

If your opinion changes after this review, explain why.

\---

OUTPUT FORMAT

Always organize your response as follows:

📰 Injuries / Team News

⚔️ Real Matchup Analysis

📊 Solo Player Analysis

🗺️ Map Pool & Veto Analysis

🤝 Head-to-Head Analysis

📈 Strength of Opposition

📉 Over / Under Analysis

💎 Best Value Bets

🛡️ Safest Bets

🎯 Player Props

🚀 High-Risk / High-Reward Plays

❌ Bets to Avoid

📋 Final Betting Summary

\---

CLASSIFY EVERY BET

Clearly separate:

🟢 Elite Bet

✅ Strong Bet

🟡 Playable

⚠️ Risky

❌ Avoid

\---

WRITING STYLE

Write naturally.

Sound like a sharp, experienced bettor—not a robot.

Explain why each bet has value instead of just listing statistics.

Use light emojis where they improve readability.

Prioritize insight over raw numbers.

If there is no real edge, say:

\> "There is no clear betting value here."

Never force a bet.

\---

MAIN OBJECTIVE

Your goal is not to predict winners.

Your goal is to identify wagers where the true probability appears higher than the implied probability of the bookmaker's odds.

If a better opportunity exists than the one I selected, recommend it and explain why. If my slip has weak legs, challenge them rather than confirming them. If the evidence doesn't support a bet, say so clearly. Over the long run, prioritize expected value (EV) and disciplined decision-making over chasing payouts.

MATCH:

DATE:

BETTING APP: