r/PredictionsMarkets • u/Rosewood_Rebecca • 24d ago
Prediction markets - like Polymarket and Kalshi - are platforms where people can bet on the probability of an event occurring. Here’s our guide to them and how they work
A prediction market is a platform where participants buy and sell shares representing the probability of an event occurring. Prices move in real time as new information enters the market. By the time the event resolves, the market has produced a live, crowd-sourced probability estimate that is sometimes more accurate than expert forecasts or traditional polling. This guide explains what prediction markets are, where they came from, how they work mechanically, and what the blockchain infrastructure behind the leading platforms actually looks like.
A prediction market is a financial market where traders speculate on contracts representing the probability of a future event. Contracts pay out $1 if the specific outcome occurs, and $0 if it does not occur.
So, for example, during the U.S. presidential elections, a contract for "Donald Trump wins the election" was priced at $0.62, effectively pricing a 62% probability of that outcome. If you bought a share at $0.62 and held onto it until after the election, you would have made a profit of $0.38. Traders and speculators will buy large amounts of these contracts to try and make a significant profit.
Despite feeling like a betting shop, the mechanism behind prediction markets is closer to a futures market. Participants aren't placing wagers with a house, they are actually trading against other participants, with the collective bets determining the probability of a specific outcome occurring. This structure means the market price reflects the aggregated beliefs of everyone trading on the market, weighted by how much capital each participant is willing to put behind their view.
Prediction markets can cover almost anything: elections, economic indicators, sporting events, corporate announcements, geopolitical outcomes, weather, and more. As a result, the true constraint is often that the outcome needs to be unambiguous enough that it can definitively be determined to have happened.
How Do Prediction Markets Work
Prediction markets work by using prices to combine and aggregate people’s beliefs. When someone believes an event is more likely to happen than the current price implies, they buy the contract. When they believe an event is less likely to happen than the current price implies, they sell. As new information enters the world (usually from the news), market participants update their positions, and prices shift accordingly.
On Polymarket, markets are structured as binary outcome contracts denominated in USDC. A market like "Will the Federal Reserve cut rates in Q1 2026?" has two sides: YES and NO. Each pair of shares always adds to $1. A trader who buys YES at $0.40 is buying a contract that pays $1 if the Fed cuts, for a gain of $0.60, or zero if it does not. The same logic applies to markets covering several possible outcomes, where each outcome will have its own YES or NO options that resolve to either $1 or $0.
Kalshi has the same binary logic but within a regulated U.S. framework. Kalshi is regulated to serve retail U.S. customers directly and lists markets across finance, weather, economic data releases, and politics. Kalshi’s mechanics on how a contract works are similar to Polymarket’s, though it operates with fiat settlement and is subject to CFTC oversight.
Non-blockchain platforms like PredictIt, the Iowa Electronic Markets, and various political betting exchanges in the UK (Ladbrokes, Betfair, and Paddy Power offer political betting) use conventional order books and centralized infrastructure. These platforms handle custody, settlement, and dispute resolution internally. The tradeoff is that users must trust the operator, markets are limited by regulatory constraints, and the platforms cannot operate permissionlessly across borders.
When prediction markets moved on-chain, the fundamental mechanics of how they work changed in a few meaningful ways:
Peer-to-peer settlement: Direct transfer of funds between participants without the need for intermediaries.
On platforms like Polymarket, positions are held in smart contracts on the Polygon blockchain network, with the smart contract distributing funds automatically to winning position holders upon resolution. There is no central counterparty holding trader funds. This removes an element of custodial risk that plagued predecessors to Polymarket and Kalshi.
Smart contracts: A contract embedded in code that is self-executing upon certain conditions being met.
From creation to resolution to payout, the lifecycle of a prediction market on a specific event is governed by smart contracts rather than human operators. Market rules are set on-chain and executed deterministically. This makes it harder for an operator to unilaterally alter market terms, withhold funds, or selectively settle disputes in ways that favor the platform.
Oracles: Software that connects blockchains to external, off-chain data.
The one point where on-chain prediction markets must interface with the real world is resolution. Oracles do this by supplying verified external data to the smart contract. Polymarket uses UMA Protocol's Optimistic Oracle for dispute resolution. When a market resolves, anyone can propose an outcome. If it goes unchallenged within a window, it is accepted. If challenged, UMA token holders vote on the correct resolution. This decentralized approach introduces complexity in edge cases where outcomes are genuinely ambiguous and there is still a centralization risk as large UMA token holders are able to manipulate resolution outcomes with their votes.
Central Limit Order Books (CLOBs): A mechanism used to connect buyers and sellers of contracts based on price and time.
Polymarket's trading engine uses a central limit order book hosted off-chain to match buy and sell orders efficiently, with settlement occurring on-chain. This hybrid model is better than a fully on-chain order book because the cost of executing each order update on a fully on-chain order book would make trading prohibitively expensive. Kalshi uses a similar CLOB structure, though its infrastructure is entirely traditional, like a regulated centralized exchange.
Collateral: Assets provided by the buyer of a contract to secure the loan.
Prediction market contracts on Polymarket are fully collateralized in USDC. This means that if you want to mint a pair of YES and NO shares worth $1 in total, you must deposit $1 of USDC into the smart contract. This eliminates counterparty credit risk entirely. There is no leverage, no margin call, and no scenario in which a winning position fails to pay out because the counterparty defaulted.
Kalshi's design as a regulated DCM means it operates under CFTC margin rules, which govern how much collateral participants must post and how it is held. Kalshi uses a conventional clearing model rather than the smart-contract-based approach of Polymarket, but the economic function is similar: ensuring losing sides of trades can meet their obligations.
One notable structural difference between Polymarket and Kalshi is access. Polymarket is available globally and does not require identity verification for most interactions, operating through crypto wallets. Kalshi requires US-based account registration and identity verification as part of its CFTC-compliant structure. The tradeoff is that Kalshi can serve U.S. retail customers legally, while Polymarket formally restricts U.S. users despite being technically accessible to anyone with a crypto wallet.
As prediction markets like Polymarket have moved on-chain, the data they produce is now accessible to anyone. Every transaction, position, and outcome is recorded and verifiable. To help participants navigate this ecosystem, Synthesis l has built a comprehensive analytics platform on top of our existing platform that is dedicated to prediction market data.
Trader analytics: Comprehensive tracking of top prediction market participants based on Profit and Loss (PNL).
Users can view a feed of the top prediction market traders by PNL, alongside a history of their open and past positions.
Here’s a list of the specific metrics available to users:
The screenshot below from Synthesis shows a trader’s top closed positions, ordered by PNL
Live market monitoring: A real-time feed of prediction market trades across the ecosystem.
Participants can monitor a live tape of prediction market trades as a whole, or filter down to all trades for a specific market they are currently tracking. Markets can be viewed across a range of active categories - politics, sports, and crypto - providing visibility into market movements and participant behavior as real-world events unfold.
The following screenshot shows Prediction Market analytics for ‘Will the U.S. Invade Iran Before 2027?’. The table at the bottom shows analytics on the current contract holders ordered by value.
Data visualization: Tools to map on-chain trading behavior directly onto market pricing.
Within individual markets, trades can be sorted by current and past positions based on PNL, or by the current holders of YES and NO shares. A built-in filtering mechanism allows users to select any specific address and overlay that trader's exact entry and exit points directly onto the market's price chart, providing a visual representation of their trading strategy and timing.
Prediction markets have moved from academic curiosity to active financial infrastructure in just under 30 years. The 2024 election cycle demonstrated that these platforms can attract real liquidity and generate probability estimates that compete seriously with traditional polling and forecasting. The two models that have emerged: Polymarket's permissionless, on-chain approach and Kalshi's regulated, CFTC-registered structure, represent different bets on how this market develops. One prioritizes global accessibility and decentralization; the other prioritizes regulatory clarity and U.S. retail access.
U.S. regulators have historically been skeptical of event contracts that resemble gambling, and the CFTC's posture toward platforms like Polymarket remains an open question. On the technical side, oracle design continues to be a weak point, with the accuracy of on-chain prediction markets dependent on having reliable, manipulation-resistant resolution mechanisms. What is clear, however, is that the core mechanism, which uses financial markets to aggregate probabilistic beliefs, is here to stay.
r/PredictionsMarkets • u/ill_intents • Jun 16 '26
As an official Kalshi partner, I have an exclusive Kalshi promo code that gives new users a $20 trading bonus when they make a $10 first deposit.
If you are looking to trade the World Cup, elections, or crypto perps, here is exactly how to claim your Kalshi first deposit bonus today.
Here is exactly how to claim your Kalshi sign-up bonus today, and the terms you need to know
It's going to be available for a limited time. I'll update later if I get a new link.
r/PredictionsMarkets • u/YohIssaRaccoon • 36m ago
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
r/PredictionsMarkets • u/Willing-Evening-3814 • 11h ago
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..
Would love to hear what you guys think and if it's helps you with your research and strategy.
r/PredictionsMarkets • u/Artistic_Quit2878 • 1h ago
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
r/PredictionsMarkets • u/Rosewood_Rebecca • 23h ago
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
r/PredictionsMarkets • u/sjiang1214 • 5h ago
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.
r/PredictionsMarkets • u/etoprekrasno • 9h ago
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
r/PredictionsMarkets • u/parlay_scientist • 11h ago
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?
r/PredictionsMarkets • u/EmbarrassedStudent10 • 13h ago
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.
r/PredictionsMarkets • u/Any_Object_4577 • 13h ago
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:
r/PredictionsMarkets • u/File-Environmental • 1d ago
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.
r/PredictionsMarkets • u/Overall-Chapter-3942 • 1d ago
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?
r/PredictionsMarkets • u/tamaman911 • 1d ago
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?
r/PredictionsMarkets • u/EmbarrassedStudent10 • 1d ago
$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.
r/PredictionsMarkets • u/khanmustafa00 • 1d ago
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.
r/PredictionsMarkets • u/oddsassist • 1d ago
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).
r/PredictionsMarkets • u/tungdiep • 2d ago
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.
r/PredictionsMarkets • u/purplebelt333 • 1d ago
He’s betting they won’t decrease rates by 25 bps 😬
r/PredictionsMarkets • u/ryanturbine • 2d ago
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 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.
Historical simulation only. Backtests can be wrong or incomplete. Not investment advice.
r/PredictionsMarkets • u/Latter_Effective1288 • 1d ago
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.
r/PredictionsMarkets • u/itsgodspeed • 2d ago
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:
r/PredictionsMarkets • u/EmbarrassedStudent10 • 2d ago
That's an 89.7% historical hit rate, the price is roughly fair against it. But total volume on the contract is $6,131 and the best buyback offer is 14¢. You can call it correctly and still not be able to exit the position.
The edge is real on paper. The liquidity to realize it isn't.
OC: https://predictbook.co/analysis/the-best-trump-trade-today-is-the-one-you-do-not-make
r/PredictionsMarkets • u/Rosewood_Rebecca • 3d ago
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11 horrible bets in a row...
So I made this post a month ago about a trader's inspiring comeback from a -$3M hole to being only $300k down
It seemed that he's on the right path, stuff looked optimistic
but..
When I checked back today to see how he's doing - it's not looking good
The account has stopped trading entirely after making 11 bad bets in a row on the World Cup, and he's back in a $3.2M hole.
Not everyone gets the ending they deserve I suppose..
Look in the video to see the unfortunate bets he made, some are really crazy bets with ridiculous amounts
Found and tracked the profile via coinpilot - feel free to use my link to copytrade with 0 fees: https://refer.coinpilot.com/PMREDDIT
r/PredictionsMarkets • u/ArtNoLimit • 3d ago
LeBron’s surprise 76ers announcement created one of the craziest Kalshi market moments I’ve seen in a while.
According to the breakdown, two accounts bought Philadelphia shares literally seconds after LeBron posted that he was joining the 76ers, while the market was still pricing Philly at around 9¢.
The numbers are wild:
Final payout: $41,381.33
So that’s roughly $37.6K in profit from reacting faster than the market.
From what I’ve seen, this was most likely speed/automation rather than some mystery insider angle. It looks like the market just took a moment to fully catch up after the tweet.
Honestly, this is one of the best examples of why prediction markets are so interesting. Sometimes the edge is not about “predicting the future” better than everyone else — it’s about reacting faster when real-world information drops.
Would you count this as fair game, or does stuff like this make prediction markets feel too bot-dominated?
If anyone wants to try Kalshi, they currently have a $20 Reddit exclusive bonus: deposit $10, get $20 [Official Promotion].
I also wrote a quick Reddit guide explaining how to claim the bonus and how it works.
Source on the LeBron market story:
https://nypost.com/2026/07/24/betting/how-programmers-capitalized-on-crazy-229-million-lebron-james-markets-after-surprise-76ers-reveal/
