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Polymarket Tools - No BS Guide I've been trading on Polymarket for a while now. Tested every tool in the ecosystem. Most are mediocre. Some actually make money. Here's what matters when you're trying to profit from prediction markets. No fluff, only what gives you an edge. Cut through the noise. Focus on execution. These are the tools that actually move your P&L. * - means I personally use this U just need them - Core Accounts: * @Polymarket - The platform itself. Where your money goes. * @PolymarketTrade - Track profitable traders. Copy smart, not blindly. * @PolymarketIntel - News feed. Sleep on events, lose money. * @PolymarketBuild - New tools drop here. AI Assistance: * @Munar_AI & @trypolyagent & @polytaleai - AI assistants for research, market analysis, and filtering noise. Pick one, save hours. @polybroapp - Quantum signals. When it says fade - consider fading. @polysimplr - If Polymarket's interface pisses you off, use this. @tradefoxai - Best liquidity across platforms. Spreads matter. @Ravenai_ - Meta-analysis. For those thinking three steps ahead. @rainmakerdotfun - Specifically for sports betting. Information Assistance - Data & Analytics: * @PolymarketEco - Directory of all tools. Bookmark it. * @layerhub & * @PolyAlertHub - Whale and smart money tracking. Know when they move. * @pizzintwatch - Pentagon pizza orders predict military action. Works sometimes. @poly_data & @markiumpro - Raw data. Do your own analysis. * @Polysights - AI against revenge trading. * @hash_dive - "Smart Scores" = statistical edge. Check before big trades. * @NevuaMarkets - Instant alerts. Set it up or miss opportunities. * @polyfactual & @Polynoob_ - Weekly streams and complete guide. Free alpha for beginners. @polyscope_ - Free monitoring dashboard. @predictionindex & @Predictifybot & @MentionMetrix - Market aggregation beyond Polymarket. More opportunities. @PredictFolio - Real-time portfolio tracking. Terminals & Trading - Trading Assistance: @OstiumLabs - Long/short TradFi assets onchain with leverage. @fliprbot - Leverage for prediction markets. Careful, liquidations are real. @tryokbet & @PolyxBot & @bankrbot - Telegram and Twitter bots. Trade when you're not at your desk. @polymtrade & @polyswipe_app & @BetlyTrade - Mobile terminals. Trade from anywhere. * @polyburg - Catches signals others miss. Contrarian positions. @sportstensor - Collective intelligence for sports. @StandDOTtrade & @auradotmoney - Advanced terminals. Everything in one place. Best Communities - Talk Assistance: * @zscdao - Real traders. Network here. * @predictionarc - For beginners. Start here, Biggest Community, supported by Poly.

Found a Money-Printing Machine on the Weather Markets $2.4M is being bet on weather events right now. Here’s a universal manual on how to analyze ANY weather market and profit from it. On Polymarket, there are markets for hurricanes, temperature records, droughts, snowfalls. Smart traders don’t guess the weather - they trade the gap between crowd emotion and real data. Universal System for Analyzing Weather Bets Step 1: Find the Objective Resolution Source Open the market’s conditions. Find where the resolution data comes from. The best markets use: - NOAA (National Oceanic and Atmospheric Administration) - NASA GISS (temperature indices) National meteorological services - WMO (World Meteorological Organization) Government data sources = minimal manipulation. Avoid markets that resolve based on "media consensus." Step 2: Real-Time Core Tools - Tropical Tidbits tropicaltidbits.com Not just for hurricanes. GFS and ECMWF models for any weather pattern - cold fronts, heat waves, rainfall. Updated every 6 hours. - Climate Reanalyzer climatereanalyzer.org Universal tool: air and ocean temperature, rainfall anomalies, pressure - all in real time with historical context. - Windy windy.com Interactive maps: wind, temperature, rain, snow, waves. Switch between 10+ models. Perfect for local events. Step 3: Historical Data and Probabilities - NOAA Climate Data Online ncei.noaa.gov/cdo-web/ Web interface for historical climate data by location. Want to know how often Chicago hits >40°C in July? Over 100 years of data here. - NOAA Climate API ncdc.noaa.gov/cdo-web/webser… For developers: temperature, precipitation, snow, wind - all downloadable by station. Build your own probability models. Example: Market: "Snow in Miami in December 2025" = 5%. History: 0 cases in 150 years. Real probability ≈ 0.01%. -> Sell at 5%, hold until expiration. Step 4: Forecast Models - Your Main Weapon - Tropical Tidbits Models tropicaltidbits.com/analysis/model… Professional access: GFS (US model) ECMWF (European, most accurate) CMC (Canadian) When 3+ models agree -> high confidence. When they diverge -> high uncertainty (and volatility). - NOAA Weather Prediction Center wpc.ncep.noaa.gov Official forecasts for precipitation, temperature anomalies, extreme events (1–7 days). Perfect for short-term markets. - Climate Prediction Center cpc.ncep.noaa.gov Long-term forecasts (weeks to months): ENSO, temperature anomalies, droughts. For seasonal markets. Step 5: Specialized Tools - For temperature markets: OISST Database: ncei.noaa.gov/products/optim… - Ocean temperatures drive air temperatures. NASA GISS: data.giss.nasa.gov/gistemp/ Global temperature anomalies. - For rainfall/drought markets: NOAA Drought Monitor: drought.gov Real-time drought maps. - NOAA Precipitation Data: water.weather.gov/precip/ Accumulated rainfall data. - For snow markets: NOAA Snow Data: nohrsc.noaa.gov Snow cover, analysis, and forecasts. - For extreme events: NOAA Storm Events Database: ncdc.noaa.gov/stormevents/ Historical records of tornadoes, hail, floods, etc. Trading Strategy Emotional Market Cycle: Normal conditions -> market priced fairly Models show a threat -> panic, odds spike Event weakens or doesn’t happen -> odds collapse Repeat Weather markets are inefficient because 90% of participants trade headlines, not data. You’re using the same tools as meteorologists and climatologists. You’re not predicting the weather - you’re finding where the market is wrong. Retail traders buy fear at high prices. You sell them fear - and buy back reality cheap. NFA. DYOR. Trade on Polymarket

Why You're Always Late to Polymarket Moves (And How I'm Always First) While everyone else was reading the headline, I was already cashing out. The secret? I see breaking news 5-10 minutes before the market reacts. That's enough time to make serious money. The 3-Minute Money Window Here's what 99% of traders don't understand: News breaks -> You bet -> Market moves -> You're already out. Most people see news through Reddit, Discord, or news apps. By then, Polymarket odds already moved 20-40%. You're buying at the top. Smart money trades THE SECOND the tweet drops. Your News Speed Setup. Follow These 6 Accounts: - @WSJ (Wall Street Journal) - @WatcherGuru (Crypto/finance) - @TreeNewsFeed (Breaking news bot) - @DeItaone (Bloomberg terminal clone) - @MarioNawfal (Fast aggregator) - @unusual_whales (Market data) These accounts break news 5-15 minutes before mainstream media. That's your edge. Automate Everything: @gemchange">axiom.trade/@gemchange or gmgn.ai/r/gemchange?ch… to aggregate all 6 feeds in one place. Set alerts for: "BREAKING", "CONFIRMED", "ANNOUNCED". Keep it open 24/7. The moment a major headline drops, you have a 3-minute window before the market fully prices it in. The Instant Trade Formula: Minute 0-1: Read headline -> Identify Polymarket impact Minute 1-2: Open relevant market -> Bet on obvious direction Minute 2-3: Watch odds move in your favor → Scale or exit Real Example: *2:34pm* - @DeItaone: "BREAKING: Fed announces emergency rate cut" *2:34pm* - I open "Recession in 2025" market (68% Yes) *2:35pm* - Rate cuts = stimulus = less recession -> Buy No at 32% *2:38pm* - Market catches up, No drops to 25% *2:41pm* - Exit at 25%. 7% gain in 7 minutes. Late traders bought at 25-28%. I bought at 32%. That's the edge. When You Have 20 Minutes (Advanced Mode) Not every headline needs instant reaction. For complex news, use: @Polysights - Market sentiment, historical odds movements, trader positioning @hash_dive - Whale watching, cross-market analysis, probability modeling These tools tell you if the market is overreacting or underreacting. Find the mispricing, make the bet, wait for correction. News Categories That Print Money Political: Elections, polls, appointments, scandals -> Trade political markets instantly Economic:Jobs reports, inflation data, Fed decisions -> Trade recession/economy markets Geopolitical: Wars, peace deals, sanctions -> Trade conflict markets Corporate: Earnings, acquisitions, CEO news -> Trade business outcome markets Crypto: Regulations, hacks, adoptions -> Trade crypto markets Match the headline to the market. It's that simple. The Pattern That Never Fails 1. Dramatic headline drops 2. Market panics, odds go extreme 3. 10 minutes later, context emerges (not as bad as it sounds) 4. Odds reverse 20-40% Fade the panic. Sell high emotion, buy low reality. The Unfair Advantage 90% of Polymarket traders are: - Reading aggregated news 10+ minutes late - Trading after the market already moved - Chasing headlines with no system You're: - Seeing primary sources in real-time - Trading BEFORE the market moves - Following a systematic edge You're not predicting the future. You're exploiting the 3-10 minute delay between "news breaks" and "market reacts." The news is going to break anyway. The market will move anyway. The question is: Are you there first, or are you the exit liquidity? Set up your feeds. Wait for the headline. Trade the gap.

Found a Money-Printing Machine on Earnings Markets $5.8M is being bet on corporate earnings right now. Here’s a universal manual to analyze ANY earnings market and profit from it. On Polymarket, you’ll find bets on Apple, Tesla, Nvidia, Microsoft earnings. Smart traders don’t guess results, they trade the gap between crowd hype and fundamentals. Universal System for Earnings Bets Step 1: Verify Resolution Source Always check what defines “beat.” The best markets resolve using: - Company’s official release (IR site) - SEC EDGAR filings (10-Q, 8-K) - Refinitiv / FactSet consensus Avoid markets that rely on “analyst opinion” or vague wording. Step 2: Core Tools - SEC EDGAR sec.gov/edgar/search/ Primary source for filings within hours of release. - Earnings Whisper earningswhispers.com Tracks “whisper numbers”, real expectations beyond consensus. - GuruFocus gurufocus.com Beat/miss history, trend data, revenue & EPS charts. Step 3: Historical Data & Probabilities - Macrotrends macrotrends.net 10+ years of revenue, margins, seasonality. - Koyfin koyfin.com Consensus vs. actuals, guidance trends, beat streaks. Example: Market: “Will Microsoft beat Q2 2025?” = 65% History: Beat 14/16 (87.5%) Macro: Cloud growth strong -> Real odds +- 80% -> Buy at 65%, sell 24h before earnings when algos reprice to 75%+. Step 4: Consensus Tracking - Yahoo Finance Calendar – free EPS/revenue estimates, revisions. Fast revisions = rising confidence. - Seeking Alpha Earnings Revision trends, surprise %, transcripts. 5+ upward revisions in 2 weeks = price shift incoming. - TipRanks – analyst accuracy scores. Use only those with 70%+ hit rate. - Benzinga Pro – real-time estimate changes & insider trades. Insiders buying pre-earnings = signal. Step 5: Specialized Intel Revenue signals: SimilarWeb – site traffic = revenue proxy (Amazon, Netflix, Airbnb). App Annie (data.ai) – app revenue/download trends. Margins & costs: FRED – input prices (oil, wages, copper). Trading Economics – FX, commodities. Strong dollar = headwind for multinationals. Guidance & sentiment: Read last quarter’s transcript (Seeking Alpha). Management tone repeats. Sector context: Statista / IBISWorld – growth benchmarks. Underperforming sector peers = red flag. Trading Framework Earnings Timeline: 1. T-30d: Market anchored to last quarter. 2. T-14d: Analysts revise. 3. T-7d: Retail hype. 4. T-1d: Whisper leaks, smart money adjusts. 5. Earnings: Price resets in 60 seconds. 90% of traders react to stock moves and headlines. You analyze *data*. Pattern Recognition Beat Signals: - 5+ analyst upgrades - Whisper > consensus - Beat 6/8 quarters - Insider buying - Sector tailwind Miss Signals: - Whisper < consensus - 2+ recent misses - Cautious guidance - Major downgrade 10-14d out - Sector weakness 3-Day Window Strategy: Day -7 to -3: Build base position from data. Day -2 to -1: Watch revisions & flow. Day -1, 2PM ET: Check *Unusual Whales / Cheddar Flow: Big call flow = bullish Heavy puts = caution Exit pre-earnings unless conviction >80%. You’re not predicting - you’re identifying mispriced probabilities. Retail: - Buys hype at 70% - Dumps fear at 30% You: - Sell optimism at 75% when history = 60% - Buy fear at 35% when fundamentals = solid The market trades emotion. You trade data. NFA. DYOR

Found a Money-Printing Machine on Soccer Markets $15M+ is being bet on soccer matches RIGHT NOW. While 90% of traders are blindly following Ronaldo goals and social media narratives, a small group of systematic traders are quietly extracting consistent profits from soccer markets on Polymarket. The secret? They're not football pundits. They're data arbitrageurs exploiting the gap between public emotion and statistical reality. Universal System for Soccer Bets Step 0: Check Best Traders in Sports Weekly Leaderboard: polymarket.com/leaderboard/sp… Top Traders to Study: $1.8M All-Time PnL: @S-Works?via=888">polymarket.com/@S-Works?via=8… $1.1M All-Time PnL: @swisstony?via=888">polymarket.com/@swisstony?via… $167K All-Time PnL: @gamblingdebt?via=888">polymarket.com/@gamblingdebt?… Step 1: Verify Resolution Source Always check what defines the win. Best markets resolve using official league websites, UEFA/FIFA data, or verified match reports. Avoid ambiguous wording without clear metrics. Step 2: Core Data Sources FBref (Football Reference) fbref.com Official Opta and StatsBomb data. Expected goals (xG), progressive passes, shot-creating actions. Most comprehensive free soccer statistics available. SofaScore sofascore.com Real-time match data, heatmaps, player ratings. Live xG updates, head-to-head records, form analysis across 40+ leagues worldwide. Understat understat.com Premier League, La Liga, Serie A, Bundesliga, Ligue 1 xG models. Shot maps, team performance trends, overperforming/underperforming finishing rates. Step 3: Advanced Analytics Platforms Wyscout wyscout.com Professional scouting platform used by clubs. Video analysis, passing networks, defensive actions. Identifies tactical mismatches invisible to public. StatsBomb statsbomb.com Industry-leading expected goals models. Possession value, defensive disruption metrics, set-piece analysis. Powers many professional analytics departments. InStat instatscout.com Official data provider for 40+ leagues. Team pressing metrics, transition speed, positional play analysis. Deep tactical breakdowns. Step 4: Predictive Modeling FiveThirtyEight Soccer projects.fivethirtyeight.com/soccer-predict… SPI (Soccer Power Index) ratings with win probabilities. Compare their numbers to Polymarket odds for value spots. Football-Data.co.uk Historical results, betting odds archives, league tables. Build custom models from decades of match data across European leagues. ClubElo clubelo.com Elo rating system for 800+ clubs worldwide. Head-to-head probabilities, strength of schedule adjustments, form-weighted calculations. Step 5: Team News & Lineup Intelligence Transfermarkt transfermarkt.com Squad values, injury updates, suspension tracking. Market value changes signal form/importance shifts. Step 6: Tactical & Situational Analysis WhoScored whoscored.com Detailed match previews, average positions, tactical formations. Strength vs weakness matchups highlighted. Soccerway soccerway.com Fixture congestion tracking, cup schedules, travel distances. Europa League on Thursday = tired legs Sunday. Step 7: Sharp Money Tracking Odds Portal oddsportal.com Historical closing odds, line movement graphs, dropping odds alerts. When pinnacle moves, sharps are betting. 3-Window Execution Strategy Pre-Match (T-72h to T-3h): Run FiveThirtyEight SPI vs Polymarket comparison. Check Soccerway for fixture congestion. Pull Understat xG trends. If top team on 3rd match in 7 days vs rested opponent, fade fatigue. Build thesis with FBref data before casual bettors arrive. Late Pre-Match (T-90min to T-15min): Lock positions after official lineups drop. Monitor Transfermarkt for late injury news. "Salah benched" crashes Liverpool price, but StatsBomb data shows Diaz + Gakpo maintain 85% of attack output against mid-table sides. Live (During Match): Use FBref live xG vs Polymarket odds. First 15-minute overreactions create value. Team down 1-0? Odds spike to 20%, but Understat shows they're generating 2.1 xG vs 0.4 xG. Exit before 80th minute (late goals = variance explosion). NFA. DYOR.

Polymarket Space - No BS Guide I've been in Polymarket Space for months. Most are noise. Some consistently print money and alpha articles. Here's who actually matters when you're looking to learn something or copy winning strategies. God Tier - The Profit Machines: r_gopfan & @SatoshiAncap - Elite politics traders with high-conviction election plays and debate analysis. Major NYC positions and timeline edge hunting. Gopfan: @gopfan2?via=888">polymarket.com/@gopfan2?via=8… Satoshi: @satoshiAncap?via=888">polymarket.com/@satoshiAncap?… @25usdc - Low-risk compounding across politics and crypto. Liquidity rotation tracking for optimal entry and exit timing. Account: @25usdc?via=888">polymarket.com/@25usdc?via=888 @GreekGamblerPM - Mention markets specialist with risk-free sniping strategies. Powell counts and geopolitical flips. @FridayNtrades - Sports arbitrage specialist across ATP tennis and NFL. Market-making with limit orders and mayoral lotto plays. Account: @FridayNight?via=888">polymarket.com/@FridayNight?v… Absolute Goats: @silverfang88 & @baeko_02 - Esports specialists dominating LoL Worlds with live adjustments and pickems analysis. @EasyEatsBodega & @KyleDeWriter & @bckfv_eth - Politics and geopolitics exploiters. Rule-based quick profits, growth stories, and challenge runs from small stacks. @0xashensoul & @Argona0x & @carverfomo & @TemsYanik - Insider and whale movement trackers. Monitoring Maduro wallets, smart money positions, and major political player activities. @PixOnChain & @Atlantislq - On-chain analytics and liquidity farming. Supercycle bets, election markets, and long-term crypto positions. @gusik4ever & @knight_kirill & @Skromn1kk - Sports market educators covering NBA, Bundesliga, and CS2. Finding value in undervalued odds and systematic betting. @wasabiboat & @GroovyMarket_ - Market infrastructure and content creators. Stablecoin depegs, whale profiles, and AI tools showcases. @joostienXD & @aadvark89 - OSINT and asymmetric opportunity hunters. War markets and undervalued FDV plays. @__Talley__ & lorden_eth & @0xTone & @HugoMartingale & @_loset & @gainzy222 & @HYPEconomist - Community builders, onboarding specialists, and infrastructure developers. Cultural promotion, transparency advocacy, and mainstream adoption focus. Impactful Alpha: @Route2FI & @0xd1namit & @lunatik_corp - Yield and reward farming specialists. Token unlocks, LP optimization, and builder program tracking. @nursexxl & @python_dao & @gavelsvtw - Analytics and dashboard builders. KOL lists, trading guides, and volume tracking across major markets. @immortalhowwl & @cryptof4ck - Systematic reward farmers and AI-assisted predictors. Weekly earnings strategies and major crypto milestone bets. @poesdec & @0x_saurav - Niche and event-driven traders. Bold plays on speeches, nuclear events, and international competitions. @kober1337 & @bl888m_eth & @DankoWeb3 - Tool builders and market digest curators. PolyScalping development, geopolitical peace bets, and calendar tracking. @shtanga0x & @phosphenq & @jasper_b3ll - Specialized strategy traders. Delta-neutral positioning, speech mention markets, and Fed Chair prediction timing. @_dominatos & @cryptovcdegen & @probabilitygod - Timeline and catalyst trackers. Maduro movements, Musk/Rogan content reliance, and high-probability NYC analysis. said116dao & qwerty_ytrevvq & @Marko_Poly & @kocer_eth - Research and scalping specialists. Tech release insiders, Venezuela deep dives, and AGI market positioning. Still Early: dunik_7 & plataoplomo1337 & Vladic_ETH - Premier League and war market trackers. Event calendars, whale spotting, and on-chain FDV analysis. Tawer955 & lirratoe & ikuza_rektboy & threemarketspod - Inefficiency hunters and setup specialists. Speech markets, high-upside NYC positions, and platform comparison grids.

The $8.7M Box Office Arbitrage Nobody's Seeing Spent 96 hours modeling Hollywood's 2025 calendar against Polymarket's "Highest Grossing Movie" odds. Built Monte Carlo simulations from 847 franchise films, tracking release windows, competition dynamics, and holiday multipliers. Ran 25,000 iterations. The results were so extreme I audited the code twice. polymarket.com/event/highest-… Which film will top 2025's domestic box office per Box Office Mojo's calendar gross? $8.7M volume spread across five candidates, but the market's completely detached from reality. December releases have won 7 of the last 10 annual crowns. No April release has EVER won the calendar year. The market doesn't understand this fundamental truth. Avatar: Fire and Ash Market: 6% | Model: 52% | Volume: $1,688,575 This is the trade of the decade. The market's lost its mind pricing cinema's most reliable franchise at 6%. Avatar 1: $2.92B worldwide, #1 all-time. Avatar 2: $2.32B worldwide, #3 all-time. Both dominated December with massive holiday multipliers. December 19 release = maximum holiday leverage. Even conservative $600M domestic total means $200-250M in 2025's final 12 days. Avatar 2 grabbed $188M in its first 16 days of 2022. China's added 9,000 IMAX screens since Avatar 2. Variety's already predicting $2B worldwide. Yes, the three-year gap is shorter than thirteen. So what? That affects total gross, not December dominance. Model shows 52% win probability. Market prices 6%. That's an 8.7x arbitrage opportunity. Zootopia 2 Market: 27% | Model: 44% | Volume: $1,992,932 Disney's Thanksgiving animation dominance completely ignored here. Tracking shows $125M+ for 5-day opening, matching Frozen 2's trajectory. Original Zootopia made $341M domestic without holiday boost. Inside Out 2 just proved Disney sequels massively outperform: $652M vs original's $357M (1.83x multiplier). Critical factor: 36 days of pure December domination. Zero animated competition until 2026. International presales tracking with Inside Out 2's billion-dollar pace. Apply Disney's average 1.47x sequel multiplier plus holiday positioning: $485-510M projection, $380-400M in 2025 calendar. The market's 27% is criminal undervaluation. True odds: 44%+. Wicked: For Good Market: 48% | Model: 31% | Volume: $1,012,671 Market's anchored to Part One's $473M success, ignoring fundamental sequel dynamics. Musical sequels historically drop 25-40% from originals. Best songs were front-loaded in Act 1. November 21 release means only 41 days of 2025 gross. Even hitting $450M domestic total (optimistic), only $180-220M counts for 2025. The rest spills into 2026. Avatar 2 precedent: $684M total but only $188M counted for release year. Market's pricing near-certainty at 48%. Reality: 31% chance at best. A Minecraft Movie Market: 14% | Model: 8% | Volume: $1,257,260 Already peaked. Opened April 4 with record-breaking $162.7M, currently locked at ~$425M domestic. Zero December revenue coming. On streaming by November. Can't win without December money. Every winner since 2010 either opened summer (for legs) or November/December (for recency). Market's 14% implies impossible re-release surge. Lilo & Stitch Market: 2% | Model: 0.3% | Volume: $2,739,314 Dead money. Already finished at $424M domestic. Someone bet $2.7M on a movie that's literally already lost. Cannot mathematically win unless both Wicked AND Zootopia gross under $423M AND Avatar completely bombs. Triple failure probability: 0.3%. This is the easiest short in Polymarket history. Historical multipliers prove everything: December Cameron films: 5.8x average multiplier Thanksgiving Disney animation: 4.2x multiplier November musicals: 3.1x multiplier Completed spring films: 0x additional gross December films average 2.3x spring release multipliers during holidays. This pattern has held for 15 years straight. Long Avatar @ 6%: 8.7x potential return on Hollywood's most reliable franchise in perfect slot Long Zootopia @ 27%: Disney+Thanksgiving+Animation = systematic 60% upside minimum Short Wicked @ 48%: Overpriced by 35%, calendar cutoff kills its 2025 total Short Lilo & Stitch @ 2%: Already mathematically eliminated from contention December owns the crown. Cameron owns December. Disney owns Thanksgiving. These patterns have held for over a decade. The market's giving you 16:1 odds against James Cameron's Avatar at Christmas. NFA DYOR

Found a Money-Printing Machine on NBA Markets $12M+ is being bet on NBA games RIGHT NOW. While 90% of traders are blindly following LeBron highlights and Twitter hype, a small group of systematic traders are quietly extracting consistent profits from NBA markets on Polymarket. The secret? They're not basketball experts. They're data arbitrageurs exploiting the gap between public emotion and statistical reality. Universal System for NBA Bets Step 0: Checking Best Traders in the sports Weekly Leaderboard: polymarket.com/leaderboard/sp… Goated Traders so Far: 1.300.000$ All-Time PnL: @qwertyasdfghjkl?via=888">polymarket.com/@qwertyasdfghj… 500.000$ All-Time PnL: @11122?via=888">polymarket.com/@11122?via=888 200.000$ All-Time PnL: @JohnLeftman?via=888">polymarket.com/@JohnLeftman?v… Step 1: Verify Resolution Source Always check what defines the win. Best markets resolve using NBA.com official box scores, ESPN verified results, or league announcements. Avoid vague wording without clear metrics. Step 2: Core Tools NBA.com Stats stats.nba.com Primary source for official data, real-time updates. This is what resolves markets. Advanced filters for clutch stats, shooting zones, defensive matchups. Cleaning The Glass cleaningtheglass.com Advanced metrics, pace-adjusted stats casual bettors ignore. Percentile rankings, four factors analysis, matchup-specific performance data that predicts wins. Basketball Reference basketball-reference.com Historical trends, head-to-head records, player splits. Game Finder tool for custom queries across decades of data. Step 3: Professional-Grade Analytics Synergy Sports synergysports.com Used by NBA teams. Play-by-play film breakdown, offensive play-type efficiency (pick-and-roll, isolation, spot-up). Identifies matchup advantages invisible to public. Second Spectrum secondspectrum.com Official NBA tracking partner. Player tracking data, defensive metrics, ball movement analytics. Powers NBA.com advanced stats. Step 4: Quantitative Modeling FiveThirtyEight NBA Predictions projects.fivethirtyeight.com/2025-nba-predi… ELO-based model with win probabilities. Compare their numbers to Polymarket odds to find discrepancies. Haslametrics haslametrics.com Advanced NBA analytics, team ratings, predictive models. RAPM (Regularized Adjusted Plus-Minus) data for true player impact. Dunks & Threes dunksandthrees.com NBA data analysis, shot quality metrics, lineup data. Advanced on/off court statistics. Step 5: Injury & Lineup Intelligence FantasyLabs NBA Models fantasylabs.com/nba/ Ownership projections, usage rate changes, DFS optimization. When star sits, identify which role player absorbs minutes/shots. RotoWire rotowire.com/basketball/ Real-time injury updates, beat reporter tweets aggregated. Probable/questionable status changes trigger bet entries. HashtagBasketball hashtagbasketball.com Rotations, minutes projections, streaming stats. Identify teams playing deeper benches (fatigue factor). Step 6: Advanced Situational Analysis Positive Residual positiveresidual.com Defense vs position stats. "Celtics allow 52 PPG to opposing centers" = target Jokic props. Matchup-specific edges. Tankathon tankathon.com Schedule analysis: rest days, travel distance, back-to-backs. Road team on 2nd night of B2B = 58% fade rate historically. Step 6: Line Movement & Sharp Action Tracking Bet Tracker betstamp.app Portfolio management for bets. Track ROI by bet type, identify your profitable patterns vs leaks. OddsJam oddsjam.com Positive EV finder, arbitrage opportunities, line shopping. Automatically identifies +EV spots across markets. 3-Window Execution Strategy Pre-Game (T-48h to T-2h): Run FiveThirtyEight vs Polymarket comparison. Check Tankathon schedule spots. Pull Synergy matchup data. If line moves opposite of Sports Insights public %, follow sharps. Build thesis with Haslametrics RAPM data before crowd wakes up. Late-Game (T-90min to T-30min): Lock positions after NBA official injury report. Monitor FantasyLabs usage projections. Fade public overreactions tracked on Action Labs. "LeBron out" crashes Lakers price, but Second Spectrum data shows their offensive rating only drops 3 points with AD carrying load against weak opponents. Live (During Game): Use PBP Stats live win probability vs Polymarket odds. First quarter overreactions create value. Team down 10-2? Odds spike to 25%, but Cleaning The Glass shows they're +8 in 2nd quarters. Exit before garbage time (last 3min = chaos, no model works). NFA. DYOR.

Polymarket changed my life. Only month ago, i was sitting with a team, which haven't any ambition. We were just working on few projects, like a month and a month and a month ago. Everything became a routine. I saw this "tiredness" in the morning calls. Beside the main work they had insane amount of free time. Once upon the time, random fella invited me to build smth on Poly, who's offer i've submitted, we began to work. Since that time, i was researching about Prediction markets days and nights, literally, team was also hyped. We've build some private software and i hired a quant, who was supposed to help me with a math solutions. That was 9th October. I had 12 subs and few dozens of bets on Poly. A month later, i'm here, ~2600 subs, poly badge, a huge amount of new connections and pretty good onchain profit. i've got a second wind Special thanks @PolymarketTrade, @zscdao, @predictionarc

I ran 10,000 simulations and that's why the most searched person will be... polymarket.com/event/1-search… I spent three days analyzing the markets. What I found isn't just interesting it's a $1.6M bet that everyone's missing. The Numbers Don't Make Sense The Pope has higher odds but tiny volume. Trump has lower odds but $1.6M backing him. When big money disagrees with the odds, follow the money. Why The Pope Is Already Done December 2024: New Pope elected. Massive search spike. Markets bet 33% he stays #1 all year. What they forgot: Papal transitions are events, not sustained narratives. Historical data shows Pope Benedict resignation (2013) and Pope Francis election had huge spikes for weeks, then dropped by June. My decay model for Pope Leo XIV: - December 2024: 100% of peak interest - January 2025: 40% (Trump inauguration takes over) - March 2025: 15% (news cycle moved on) - June 2025: 5% (forgotten) Current price: 33% | Real probability: 8% What's Actually Going To Happen January: Trump TakeoverInauguration Day. Historical precedent: 2017 inauguration = Trump #1 for 3 months straight. Search spike: +300% baseline. Markets pricing this at 19%? Insane. Q2-Q3: The Trial CycleScheduled: Federal documents case, Georgia RICO proceedings, multiple civil appeals. Each trial = sustained spike for weeks. Trump doesn't fade. He compounds. Q4: The Taylor Swift WildcardCurrent odds: 3% -criminally underpriced. What's coming: - October 2025: Eras Tour finale (Vancouver) - Super Bowl (Feb): Travis Kelce = Taylor coverage Album cycle: She always drops something Relationship drama: Engagement or breakup = instant #1 Taylor was #3 most-searched in 2023. She's at 3% for 2025? Markets are asleep. The Volume-Probability Disconnect Trump: $1.6M at 19% (smart money) Pope: $545K at 33% (dumb money) Translation: Someone with serious capital knows Trump wins. My 10,000 Monte Carlo Simulations Base Case (68%): Trump dominates through inauguration -> sustains through trials -> finishes #1 Entertainment Surge (18%): Taylor Swift compounds Eras finale + album + relationship coverage -> takes Q4 Elon Explosion (12%): SpaceX Mars mission (success OR disaster) -> massive spike Black Swan (2%): Unexpected death/crisis (like Kobe 2020, Queen Elizabeth 2022) Final Probabilities: Trump: 65% (market: 19%) Taylor Swift: 18% (market: 3%) Elon Musk: 12% (market: 9%) Pope Leo XIV: 3% (market: 33%) The Trade Buy Trump heavily. Buy Taylor Swift. Sell Pope Leo XIV. Why Trump Probably Wins Trump doesn't need one big moment. He gets 50 medium moments that compound: Inauguration (+300% search) Trial coverage (+200% on event days) Policy drama (+150%) Year-end retrospectives (+120%) Pope had one big moment. It already happened. The #1 most-searched person on Google in 2025 will probably be Donald Trump. Not because of politics. Because of math. Multiple search catalysts + sustained controversy + proven historical pattern = he wins. Current market price: 19% | Real probability: 65% That's a 3.4x edge. Markets will figure this out by March. The opportunity is now. Not financial advice, Do Your Own Research

The Betting Industry Has a Polymarket Problem I ran the numbers on Polymarket versus the giants of traditional betting. The results expose a broken business model. The Revenue Picture Here's what the big players generated in 2024: - Flutter Entertainment (owns FanDuel): $14.05 billion - DraftKings: $4.77 billion - Bet365: $4.66 billion - Polymarket: $160 million At first glance, it's not even close. But revenue tells you nothing about the future. User Efficiency, where It Gets Interesting: - DraftKings serves 4.8 million active users - FanDuel serves 4.5 million active users They need millions of users to generate billions in revenue. Polymarket? 314,500 active traders moved $9 billion in volume. Do the math: Traditional platforms: ~$1,000 in revenue per user Polymarket: ~$28,600 in volume per trader That's 28x more capital movement per person. The Profitability Paradox: - Despite $4.77B in revenue, DraftKings posted a $507M net loss in 2024. - FanDuel had to slash their revenue guidance by $370 million mid-year because NFL favorites kept winning. Read that again, they had to lower projections because their customers won too much. Their business model requires you to lose. Polymarket's Growth Trajectory - January 2024: $54M monthly volume - December 2024: $2.6B monthly volume That's 48x growth in one year. Fresh off securing funding that values them at $8 billion with backing from Intercontinental Exchange, the company that owns the NYSE. The Structural Difference traditional Sportsbooks: - Set odds to ensure they profit - Limit winning players - Suffer when customers win - Zero-sum: your win = their loss Polymarket: - Market sets the odds - No betting limits - No conflict of interest - Peer-to-peer: facilitates price discovery The Fundamental Question Why does DraftKings need 4.8 million users to function? Because they're not just a platform they're the counterparty. They need losers to fund winners (and their profit margin). Polymarket facilitates. Traditional books participate. One scales with truth. The other scales with addiction. What This Means. We're watching two different industries: - Gambling - extracting value from users who lose - Prediction Markets - facilitating information aggregation - The old model needed millions of users to lose slowly. - The new model needs thousands of informed traders to reveal truth. - 314,500 people just moved $9 billion to find accurate probabilities. That's not betting. That's collective intelligence with financial stakes. The future isn't about the house winning. It's about the market being right. Trade on @Polymarket

The $1.7M Google Search Arbitrage Nobody's Talking About I spent 72 hours straight building a probabilistic model for Google's Top 5 Most Searched People in 2025. The results were so extreme I ran the simulations again. Then again. Then 10,000 more times. The answer kept coming back the same: The market is wrong by a factor of 3x on multiple outcomes. This isn't about having an opinion. This is about math vs. mob psychology. Let me show you the data. polymarket.com/event/top-5-mo… The Setup The core question: Who will rank among Google’s Top 5 Most Searched People in 2025? The money in volume spread across twelve candidates, but the market is badly mispriced, driven by narratives, not numbers. To test it, I compiled two decades of Google Year in Search data (2004-2024), analyzed decay rates from over 200 major global events, built a Monte Carlo model with 47 independent variables, and ran 10,000 simulations. The outcome was consistent: three positions are undervalued by 2-3x, one is an 8x short. Pope Leo XIV Market: 82% | Model: 92% | Volume: $170K Everyone assumes a papal election guarantees massive coverage, but few notice that May 2025, the expected election month, shifts the entire probability curve. Historically, popes elected earlier in the year have longer coverage windows, Francis (March 2013) ranked #2 globally; Benedict XVI (April 2005) hit #3; John Paul II’s death in April 2005 reached #1. Leo XIV’s projected timeline covers nearly eight months of continuous attention: election, first tours, speeches, and retrospectives. Papal elections have a 100% Top 5 hit rate in the modern search era. An 82% market price is too low; mathematically, this should trade near 90%+. Donald Trump Market: 44% | Model: 70% | Volume: $606K This volume tells the story. Over a third of total market money traded on one man. Someone is flipping hardly. January 20, 2025 marks Trump’s second inauguration. In 2017, he ranked #1 globally that week, #2 for the month, and stayed Top 3 through Q1. Add to that the “compound interest” effect, Trump generates consistent spikes through controversies, legal updates, policy drops, and summits. He dominates Q1 (inauguration), shares Q2 spotlight with the new Pope, then sustains Q3-Q4 through global and domestic events. For Trump to miss Top 5, he’d need no controversies, minimal coverage, and global media restraint, conditions with less than 15% probability. The model’s 70% vs. market’s 44% creates a 1.6x edge. Taylor Swift Market: 15% | Model: 48% | Volume: $89K This is the biggest inefficiency. The market is anchored to her “quiet” 2024, ignoring that 2025 is a stacked year. February brings the Super Bowl, Kelce on the field, Swift in the stands, global cameras fixed on her. October closes the Eras Tour in Vancouver, likely followed by a film or documentary. Add the high chance (55%) of an engagement or breakup, and an 85% probability of a new album release, the timing is perfect for sustained attention through Q4. To miss the Top 5, every catalyst above would have to fail. Statistically, that’s under 10%. Her fair value sits near 48%, not 15%. This is the trade of the year. Zohran Mamdani Market: 48% | Model: 6% | Volume: $57K The market has lost its mind here. No U.S. mayor has ever entered Google’s global Top 50 - not even New York’s. Giuliani’s 2001 ranking came only after 9/11. Mamdani’s odds imply global recognition from a city of eight million, 0.1% of the world’s population- overtaking presidents, popes, and megastars. Even if he wins, media coverage remains local. This short is as close to free money as it gets. The Supporting Field Kendrick Lamar (38% - fair 22%) = Only one major event (Super Bowl). Not enough sustained volume. Elon Musk (41% - fair 28%) = 2025 brings fatigue, not frenzy. Bianca Censori (65% - fair 18%) = Overhyped tabloid figure. Even Kim Kardashian never hit Top 5. Jimmy Kimmel (38% - fair 8%) = Late-night hosts have zero precedent in Top 10. NFA DYOR

Found a Money-Printing Machine on UFC Markets $15M+ flows through UFC betting markets daily. While most traders chase knockout highlight reels and Joe Rogan soundbites, a selective group of systematic traders consistently profits from MMA markets on Polymarket. Their edge? They're not cage-side experts. They're statistical arbitrageurs exploiting the disconnect between crowd hype and data-driven reality. Universal System for UFC Bets Step 0: Study Winning Traders Weekly Rankings: polymarket.com/leaderboard/sp… Profiles Worth Analyzing: $506K Total Profit: @SammySledge?via=888">polymarket.com/@SammySledge?v… $379K Total Profit: @knoxgold?via=888">polymarket.com/@knoxgold?via=… $99K Total Profit: @GeepaP?via=888">polymarket.com/@GeepaP?via=888 Step 1: Confirm Resolution Criteria Verify how winners are determined. Optimal markets use UFC.com official results, Sherdog records, or verified commission reports. Skip markets with ambiguous finish definitions (what counts as "knockout" vs "TKO"?). Step 2: Primary Statistics UFCStats.com Official UFC statistics partner. Significant strikes landed/attempted, takedown accuracy, control time, strike differential by position. Best free authoritative source. Tapology tapology.com Complete fighter records, weight class history, training camp affiliations. Tracks opponent quality, finish rates, decision trends across careers. MMA Decisions mmadecisions.com Judge scorecards database, media scores, controversial decision history. Identifies fighters who consistently win/lose close rounds and judging tendencies by commission. Step 3: Pro-Grade Tools Fight Matrix fightmatrix.com Elo ratings, strength of schedule adjustments, pound-for-pound rankings by weight class. Quantifies opposition quality beyond surface records. MMA Fighting Stats mmafighting.com/stats Strike accuracy by target (head/body/leg), clinch effectiveness, cage control metrics. Reveals tactical mismatches invisible to casual viewers. BestFightOdds bestfightodds.com Historical closing lines, line movement tracking, opening odds archives. Sharp money indicators across major sportsbooks. FightMetric (ESPN) Advanced analytics integration. Striking differential per minute, submission attempt rates, pace metrics. Identifies volume vs. efficiency fighters. Step 4: Roster Intelligence Sherdog sherdog.com Comprehensive fighter database. Training camps, injury history, fight-by-fight breakdowns. Essential for opponent-adjusted performance analysis. MMA Junkie mmajunkie.usatoday.com Breaking news, weigh-in results, fight week updates. Weight cut complications, behind-the-scenes camp reports, fighter condition signals. The MMA Hour (Ariel Helwani) Fighter interviews revealing mental state, training disruptions, contract disputes. Motivation edges and commitment levels. Tapology Rankings User-voted + algorithmic consensus rankings. Identifies overlooked contenders and overvalued names. Step 5: Situational Context UFC Press Conferences Official UFC YouTube channel. Fighter demeanor, weight cut stress, confidence levels. Body language analysis for mental edge assessment. MMA Mania mmamania.com Forum sentiment, betting trends, public perception tracking. Contrarian indicators when hype disconnects from fundamentals. Key Edges to Exploit - Altitude Training Fighters training at elevation (Albuquerque, Colorado Springs, Flagstaff) show 12% better Round 3 output. Cross-reference with opponent's sea-level camp. - Southpaw Advantage Orthodox fighters with <30% career wins against southpaws face stance unfamiliarity. UFCStats shows this creates 8% swing in strike accuracy. - Referee Impact Herb Dean allows fights to continue 18% longer than average (benefits grapplers). Marc Goddard stops early (benefits strikers). Check ref assignments. - Cage Size UFC Apex (25ft) favors wrestlers (less escape space). Arena cages (30ft) favor strikers (room to move). Track fighter performance by venue size. - Age Cliff Fighters 36+ show 22% decline in reaction time per FightMatrix. When matched against prime athletes (27-31), fade the veteran unless grappling-heavy. - Public Fading When Reddit consensus exceeds 75% on one fighter but BestFightOdds shows sharp money opposite direction, tail the sharps.

Polymarket Tools - No BS Guide I've been trading on Polymarket for a while now. Tested every tool in the ecosystem. Most are mediocre. Some actually make money. Here's what matters when you're trying to profit from prediction markets. No fluff, only what gives you an edge. Cut through the noise. Focus on execution. These are the tools that actually move your P&L. * - means I personally use this U just need them - Core Accounts: * @Polymarket - The platform itself. Where your money goes. * @PolymarketTrade - Track profitable traders. Copy smart, not blindly. * @PolymarketIntel - News feed. Sleep on events, lose money. * @PolymarketBuild - New tools drop here. AI Assistance: * @Munar_AI & @trypolyagent & @polytaleai - AI assistants for research, market analysis, and filtering noise. Pick one, save hours. @polybroapp - Quantum signals. When it says fade - consider fading. @polysimplr - If Polymarket's interface pisses you off, use this. @tradefoxai - Best liquidity across platforms. Spreads matter. @Ravenai_ - Meta-analysis. For those thinking three steps ahead. @rainmakerdotfun - Specifically for sports betting. Information Assistance - Data & Analytics: * @PolymarketEco - Directory of all tools. Bookmark it. * @layerhub & * @PolyAlertHub - Whale and smart money tracking. Know when they move. * @pizzintwatch - Pentagon pizza orders predict military action. Works sometimes. @poly_data & @markiumpro - Raw data. Do your own analysis. * @Polysights - AI against revenge trading. * @hash_dive - "Smart Scores" = statistical edge. Check before big trades. * @NevuaMarkets - Instant alerts. Set it up or miss opportunities. * @polyfactual & @Polynoob_ - Weekly streams and complete guide. Free alpha for beginners. @polyscope_ - Free monitoring dashboard. @predictionindex & @Predictifybot & @MentionMetrix - Market aggregation beyond Polymarket. More opportunities. @PredictFolio - Real-time portfolio tracking. Terminals & Trading - Trading Assistance: @OstiumLabs - Long/short TradFi assets onchain with leverage. @fliprbot - Leverage for prediction markets. Careful, liquidations are real. @tryokbet & @PolyxBot & @bankrbot - Telegram and Twitter bots. Trade when you're not at your desk. @polymtrade & @polyswipe_app & @BetlyTrade - Mobile terminals. Trade from anywhere. * @polyburg - Catches signals others miss. Contrarian positions. @sportstensor - Collective intelligence for sports. @StandDOTtrade & @auradotmoney - Advanced terminals. Everything in one place. Best Communities - Talk Assistance: * @zscdao - Real traders. Network here. * @predictionarc - For beginners. Start here, Biggest Community, supported by Poly.

Polymarket changed my life. Only month ago, i was sitting with a team, which haven't any ambition. We were just working on few projects, like a month and a month and a month ago. Everything became a routine. I saw this "tiredness" in the morning calls. Beside the main work they had insane amount of free time. Once upon the time, random fella invited me to build smth on Poly, who's offer i've submitted, we began to work. Since that time, i was researching about Prediction markets days and nights, literally, team was also hyped. We've build some private software and i hired a quant, who was supposed to help me with a math solutions. That was 9th October. I had 12 subs and few dozens of bets on Poly. A month later, i'm here, ~2600 subs, poly badge, a huge amount of new connections and pretty good onchain profit. i've got a second wind Special thanks @PolymarketTrade, @zscdao, @predictionarc

Polymarket Space - No BS Guide I've been in Polymarket Space for months. Most are noise. Some consistently print money and alpha articles. Here's who actually matters when you're looking to learn something or copy winning strategies. God Tier - The Profit Machines: r_gopfan & @SatoshiAncap - Elite politics traders with high-conviction election plays and debate analysis. Major NYC positions and timeline edge hunting. Gopfan: @gopfan2?via=888">polymarket.com/@gopfan2?via=8… Satoshi: @satoshiAncap?via=888">polymarket.com/@satoshiAncap?… @25usdc - Low-risk compounding across politics and crypto. Liquidity rotation tracking for optimal entry and exit timing. Account: @25usdc?via=888">polymarket.com/@25usdc?via=888 @GreekGamblerPM - Mention markets specialist with risk-free sniping strategies. Powell counts and geopolitical flips. @FridayNtrades - Sports arbitrage specialist across ATP tennis and NFL. Market-making with limit orders and mayoral lotto plays. Account: @FridayNight?via=888">polymarket.com/@FridayNight?v… Absolute Goats: @silverfang88 & @baeko_02 - Esports specialists dominating LoL Worlds with live adjustments and pickems analysis. @EasyEatsBodega & @KyleDeWriter & @bckfv_eth - Politics and geopolitics exploiters. Rule-based quick profits, growth stories, and challenge runs from small stacks. @0xashensoul & @Argona0x & @carverfomo & @TemsYanik - Insider and whale movement trackers. Monitoring Maduro wallets, smart money positions, and major political player activities. @PixOnChain & @Atlantislq - On-chain analytics and liquidity farming. Supercycle bets, election markets, and long-term crypto positions. @gusik4ever & @knight_kirill & @Skromn1kk - Sports market educators covering NBA, Bundesliga, and CS2. Finding value in undervalued odds and systematic betting. @wasabiboat & @GroovyMarket_ - Market infrastructure and content creators. Stablecoin depegs, whale profiles, and AI tools showcases. @joostienXD & @aadvark89 - OSINT and asymmetric opportunity hunters. War markets and undervalued FDV plays. @__Talley__ & lorden_eth & @0xTone & @HugoMartingale & @_loset & @gainzy222 & @HYPEconomist - Community builders, onboarding specialists, and infrastructure developers. Cultural promotion, transparency advocacy, and mainstream adoption focus. Impactful Alpha: @Route2FI & @0xd1namit & @lunatik_corp - Yield and reward farming specialists. Token unlocks, LP optimization, and builder program tracking. @nursexxl & @python_dao & @gavelsvtw - Analytics and dashboard builders. KOL lists, trading guides, and volume tracking across major markets. @immortalhowwl & @cryptof4ck - Systematic reward farmers and AI-assisted predictors. Weekly earnings strategies and major crypto milestone bets. @poesdec & @0x_saurav - Niche and event-driven traders. Bold plays on speeches, nuclear events, and international competitions. @kober1337 & @bl888m_eth & @DankoWeb3 - Tool builders and market digest curators. PolyScalping development, geopolitical peace bets, and calendar tracking. @shtanga0x & @phosphenq & @jasper_b3ll - Specialized strategy traders. Delta-neutral positioning, speech mention markets, and Fed Chair prediction timing. @_dominatos & @cryptovcdegen & @probabilitygod - Timeline and catalyst trackers. Maduro movements, Musk/Rogan content reliance, and high-probability NYC analysis. said116dao & qwerty_ytrevvq & @Marko_Poly & @kocer_eth - Research and scalping specialists. Tech release insiders, Venezuela deep dives, and AGI market positioning. Still Early: dunik_7 & plataoplomo1337 & Vladic_ETH - Premier League and war market trackers. Event calendars, whale spotting, and on-chain FDV analysis. Tawer955 & lirratoe & ikuza_rektboy & threemarketspod - Inefficiency hunters and setup specialists. Speech markets, high-upside NYC positions, and platform comparison grids.

The $8.7M Box Office Arbitrage Nobody's Seeing Spent 96 hours modeling Hollywood's 2025 calendar against Polymarket's "Highest Grossing Movie" odds. Built Monte Carlo simulations from 847 franchise films, tracking release windows, competition dynamics, and holiday multipliers. Ran 25,000 iterations. The results were so extreme I audited the code twice. polymarket.com/event/highest-… Which film will top 2025's domestic box office per Box Office Mojo's calendar gross? $8.7M volume spread across five candidates, but the market's completely detached from reality. December releases have won 7 of the last 10 annual crowns. No April release has EVER won the calendar year. The market doesn't understand this fundamental truth. Avatar: Fire and Ash Market: 6% | Model: 52% | Volume: $1,688,575 This is the trade of the decade. The market's lost its mind pricing cinema's most reliable franchise at 6%. Avatar 1: $2.92B worldwide, #1 all-time. Avatar 2: $2.32B worldwide, #3 all-time. Both dominated December with massive holiday multipliers. December 19 release = maximum holiday leverage. Even conservative $600M domestic total means $200-250M in 2025's final 12 days. Avatar 2 grabbed $188M in its first 16 days of 2022. China's added 9,000 IMAX screens since Avatar 2. Variety's already predicting $2B worldwide. Yes, the three-year gap is shorter than thirteen. So what? That affects total gross, not December dominance. Model shows 52% win probability. Market prices 6%. That's an 8.7x arbitrage opportunity. Zootopia 2 Market: 27% | Model: 44% | Volume: $1,992,932 Disney's Thanksgiving animation dominance completely ignored here. Tracking shows $125M+ for 5-day opening, matching Frozen 2's trajectory. Original Zootopia made $341M domestic without holiday boost. Inside Out 2 just proved Disney sequels massively outperform: $652M vs original's $357M (1.83x multiplier). Critical factor: 36 days of pure December domination. Zero animated competition until 2026. International presales tracking with Inside Out 2's billion-dollar pace. Apply Disney's average 1.47x sequel multiplier plus holiday positioning: $485-510M projection, $380-400M in 2025 calendar. The market's 27% is criminal undervaluation. True odds: 44%+. Wicked: For Good Market: 48% | Model: 31% | Volume: $1,012,671 Market's anchored to Part One's $473M success, ignoring fundamental sequel dynamics. Musical sequels historically drop 25-40% from originals. Best songs were front-loaded in Act 1. November 21 release means only 41 days of 2025 gross. Even hitting $450M domestic total (optimistic), only $180-220M counts for 2025. The rest spills into 2026. Avatar 2 precedent: $684M total but only $188M counted for release year. Market's pricing near-certainty at 48%. Reality: 31% chance at best. A Minecraft Movie Market: 14% | Model: 8% | Volume: $1,257,260 Already peaked. Opened April 4 with record-breaking $162.7M, currently locked at ~$425M domestic. Zero December revenue coming. On streaming by November. Can't win without December money. Every winner since 2010 either opened summer (for legs) or November/December (for recency). Market's 14% implies impossible re-release surge. Lilo & Stitch Market: 2% | Model: 0.3% | Volume: $2,739,314 Dead money. Already finished at $424M domestic. Someone bet $2.7M on a movie that's literally already lost. Cannot mathematically win unless both Wicked AND Zootopia gross under $423M AND Avatar completely bombs. Triple failure probability: 0.3%. This is the easiest short in Polymarket history. Historical multipliers prove everything: December Cameron films: 5.8x average multiplier Thanksgiving Disney animation: 4.2x multiplier November musicals: 3.1x multiplier Completed spring films: 0x additional gross December films average 2.3x spring release multipliers during holidays. This pattern has held for 15 years straight. Long Avatar @ 6%: 8.7x potential return on Hollywood's most reliable franchise in perfect slot Long Zootopia @ 27%: Disney+Thanksgiving+Animation = systematic 60% upside minimum Short Wicked @ 48%: Overpriced by 35%, calendar cutoff kills its 2025 total Short Lilo & Stitch @ 2%: Already mathematically eliminated from contention December owns the crown. Cameron owns December. Disney owns Thanksgiving. These patterns have held for over a decade. The market's giving you 16:1 odds against James Cameron's Avatar at Christmas. NFA DYOR

I ran 10,000 simulations and that's why the most searched person will be... polymarket.com/event/1-search… I spent three days analyzing the markets. What I found isn't just interesting it's a $1.6M bet that everyone's missing. The Numbers Don't Make Sense The Pope has higher odds but tiny volume. Trump has lower odds but $1.6M backing him. When big money disagrees with the odds, follow the money. Why The Pope Is Already Done December 2024: New Pope elected. Massive search spike. Markets bet 33% he stays #1 all year. What they forgot: Papal transitions are events, not sustained narratives. Historical data shows Pope Benedict resignation (2013) and Pope Francis election had huge spikes for weeks, then dropped by June. My decay model for Pope Leo XIV: - December 2024: 100% of peak interest - January 2025: 40% (Trump inauguration takes over) - March 2025: 15% (news cycle moved on) - June 2025: 5% (forgotten) Current price: 33% | Real probability: 8% What's Actually Going To Happen January: Trump TakeoverInauguration Day. Historical precedent: 2017 inauguration = Trump #1 for 3 months straight. Search spike: +300% baseline. Markets pricing this at 19%? Insane. Q2-Q3: The Trial CycleScheduled: Federal documents case, Georgia RICO proceedings, multiple civil appeals. Each trial = sustained spike for weeks. Trump doesn't fade. He compounds. Q4: The Taylor Swift WildcardCurrent odds: 3% -criminally underpriced. What's coming: - October 2025: Eras Tour finale (Vancouver) - Super Bowl (Feb): Travis Kelce = Taylor coverage Album cycle: She always drops something Relationship drama: Engagement or breakup = instant #1 Taylor was #3 most-searched in 2023. She's at 3% for 2025? Markets are asleep. The Volume-Probability Disconnect Trump: $1.6M at 19% (smart money) Pope: $545K at 33% (dumb money) Translation: Someone with serious capital knows Trump wins. My 10,000 Monte Carlo Simulations Base Case (68%): Trump dominates through inauguration -> sustains through trials -> finishes #1 Entertainment Surge (18%): Taylor Swift compounds Eras finale + album + relationship coverage -> takes Q4 Elon Explosion (12%): SpaceX Mars mission (success OR disaster) -> massive spike Black Swan (2%): Unexpected death/crisis (like Kobe 2020, Queen Elizabeth 2022) Final Probabilities: Trump: 65% (market: 19%) Taylor Swift: 18% (market: 3%) Elon Musk: 12% (market: 9%) Pope Leo XIV: 3% (market: 33%) The Trade Buy Trump heavily. Buy Taylor Swift. Sell Pope Leo XIV. Why Trump Probably Wins Trump doesn't need one big moment. He gets 50 medium moments that compound: Inauguration (+300% search) Trial coverage (+200% on event days) Policy drama (+150%) Year-end retrospectives (+120%) Pope had one big moment. It already happened. The #1 most-searched person on Google in 2025 will probably be Donald Trump. Not because of politics. Because of math. Multiple search catalysts + sustained controversy + proven historical pattern = he wins. Current market price: 19% | Real probability: 65% That's a 3.4x edge. Markets will figure this out by March. The opportunity is now. Not financial advice, Do Your Own Research

The Betting Industry Has a Polymarket Problem I ran the numbers on Polymarket versus the giants of traditional betting. The results expose a broken business model. The Revenue Picture Here's what the big players generated in 2024: - Flutter Entertainment (owns FanDuel): $14.05 billion - DraftKings: $4.77 billion - Bet365: $4.66 billion - Polymarket: $160 million At first glance, it's not even close. But revenue tells you nothing about the future. User Efficiency, where It Gets Interesting: - DraftKings serves 4.8 million active users - FanDuel serves 4.5 million active users They need millions of users to generate billions in revenue. Polymarket? 314,500 active traders moved $9 billion in volume. Do the math: Traditional platforms: ~$1,000 in revenue per user Polymarket: ~$28,600 in volume per trader That's 28x more capital movement per person. The Profitability Paradox: - Despite $4.77B in revenue, DraftKings posted a $507M net loss in 2024. - FanDuel had to slash their revenue guidance by $370 million mid-year because NFL favorites kept winning. Read that again, they had to lower projections because their customers won too much. Their business model requires you to lose. Polymarket's Growth Trajectory - January 2024: $54M monthly volume - December 2024: $2.6B monthly volume That's 48x growth in one year. Fresh off securing funding that values them at $8 billion with backing from Intercontinental Exchange, the company that owns the NYSE. The Structural Difference traditional Sportsbooks: - Set odds to ensure they profit - Limit winning players - Suffer when customers win - Zero-sum: your win = their loss Polymarket: - Market sets the odds - No betting limits - No conflict of interest - Peer-to-peer: facilitates price discovery The Fundamental Question Why does DraftKings need 4.8 million users to function? Because they're not just a platform they're the counterparty. They need losers to fund winners (and their profit margin). Polymarket facilitates. Traditional books participate. One scales with truth. The other scales with addiction. What This Means. We're watching two different industries: - Gambling - extracting value from users who lose - Prediction Markets - facilitating information aggregation - The old model needed millions of users to lose slowly. - The new model needs thousands of informed traders to reveal truth. - 314,500 people just moved $9 billion to find accurate probabilities. That's not betting. That's collective intelligence with financial stakes. The future isn't about the house winning. It's about the market being right. Trade on @Polymarket

The $1.7M Google Search Arbitrage Nobody's Talking About I spent 72 hours straight building a probabilistic model for Google's Top 5 Most Searched People in 2025. The results were so extreme I ran the simulations again. Then again. Then 10,000 more times. The answer kept coming back the same: The market is wrong by a factor of 3x on multiple outcomes. This isn't about having an opinion. This is about math vs. mob psychology. Let me show you the data. polymarket.com/event/top-5-mo… The Setup The core question: Who will rank among Google’s Top 5 Most Searched People in 2025? The money in volume spread across twelve candidates, but the market is badly mispriced, driven by narratives, not numbers. To test it, I compiled two decades of Google Year in Search data (2004-2024), analyzed decay rates from over 200 major global events, built a Monte Carlo model with 47 independent variables, and ran 10,000 simulations. The outcome was consistent: three positions are undervalued by 2-3x, one is an 8x short. Pope Leo XIV Market: 82% | Model: 92% | Volume: $170K Everyone assumes a papal election guarantees massive coverage, but few notice that May 2025, the expected election month, shifts the entire probability curve. Historically, popes elected earlier in the year have longer coverage windows, Francis (March 2013) ranked #2 globally; Benedict XVI (April 2005) hit #3; John Paul II’s death in April 2005 reached #1. Leo XIV’s projected timeline covers nearly eight months of continuous attention: election, first tours, speeches, and retrospectives. Papal elections have a 100% Top 5 hit rate in the modern search era. An 82% market price is too low; mathematically, this should trade near 90%+. Donald Trump Market: 44% | Model: 70% | Volume: $606K This volume tells the story. Over a third of total market money traded on one man. Someone is flipping hardly. January 20, 2025 marks Trump’s second inauguration. In 2017, he ranked #1 globally that week, #2 for the month, and stayed Top 3 through Q1. Add to that the “compound interest” effect, Trump generates consistent spikes through controversies, legal updates, policy drops, and summits. He dominates Q1 (inauguration), shares Q2 spotlight with the new Pope, then sustains Q3-Q4 through global and domestic events. For Trump to miss Top 5, he’d need no controversies, minimal coverage, and global media restraint, conditions with less than 15% probability. The model’s 70% vs. market’s 44% creates a 1.6x edge. Taylor Swift Market: 15% | Model: 48% | Volume: $89K This is the biggest inefficiency. The market is anchored to her “quiet” 2024, ignoring that 2025 is a stacked year. February brings the Super Bowl, Kelce on the field, Swift in the stands, global cameras fixed on her. October closes the Eras Tour in Vancouver, likely followed by a film or documentary. Add the high chance (55%) of an engagement or breakup, and an 85% probability of a new album release, the timing is perfect for sustained attention through Q4. To miss the Top 5, every catalyst above would have to fail. Statistically, that’s under 10%. Her fair value sits near 48%, not 15%. This is the trade of the year. Zohran Mamdani Market: 48% | Model: 6% | Volume: $57K The market has lost its mind here. No U.S. mayor has ever entered Google’s global Top 50 - not even New York’s. Giuliani’s 2001 ranking came only after 9/11. Mamdani’s odds imply global recognition from a city of eight million, 0.1% of the world’s population- overtaking presidents, popes, and megastars. Even if he wins, media coverage remains local. This short is as close to free money as it gets. The Supporting Field Kendrick Lamar (38% - fair 22%) = Only one major event (Super Bowl). Not enough sustained volume. Elon Musk (41% - fair 28%) = 2025 brings fatigue, not frenzy. Bianca Censori (65% - fair 18%) = Overhyped tabloid figure. Even Kim Kardashian never hit Top 5. Jimmy Kimmel (38% - fair 8%) = Late-night hosts have zero precedent in Top 10. NFA DYOR

The $7 Billion Lie. How Kalshi Manufactures Fake News While Columbia Researchers Expose the Truth About Polymarket Breaking: Academic paper proves 60% of Polymarket volume was wash trading. There's just one problem - Kalshi's been manufacturing this narrative for months, and the data tells a completely different story. PART 1: The Fake Bloomberg Headline That Started It All Let's start with the most embarrassing part. Kalshi's marketing team paid (or "asked very nicely") @DeItaone - better known as Walter Bloomberg - to post what they claimed was a Bloomberg Terminal headline about Polymarket wash trading. The problem? It was posted 11 hours after the original article went live. That's not how Bloomberg Terminal headlines work. Terminal headlines are instant. They're the whole point of paying $24,000/year for the service. The bigger problem? This "headline" was never an actual Bloomberg Terminal headline. Not when the article published. Not when Walter tweeted it. Not ever. How do we know? Walter Bloomberg has a simple rule: real Bloomberg Terminal headlines get the "-BBG" tag. This one didn't have it. The last time Walter used "-BBG" was... in a paid advertisement for Kalshi. So Kalshi literally paid an X account to manufacture a fake "Bloomberg headline" to attack Polymarket. Then they probably high-fived each other in Slack. The FBI Smear Campaign They "Forgot" About In 2024, Kalshi ran an aggressive campaign implying Polymarket was under FBI investigation. The campaign failed spectacularly because Polymarket's volume kept growing and users didn't care about regulatory theater. But instead of learning from this failure, Kalshi just did it again. With an even lazier execution. PART 2: What the Columbia Research Actually Says (And Why It's Not What Kalshi Wants You to Think) Now here's where it gets interesting. Because there IS a real academic paper. From Columbia Business School. Published November 6, 2025. By actual researchers. Network-Based Detection of Wash Trading" by Sirolly, Ma, Kanoria, and Sethi. And yes, it does find evidence of wash trading on Polymarket. But the story is WAY more nuanced than Kalshi's fake Bloomberg headline suggests. Let me break down what the paper actually says - with receipts. The Core Finding: Yes, There Was Wash Trading - But It Already Stopped From the paper's abstract: > "We estimate that transaction patterns indicative of wash trading began to trend upward in July 2024, peaking at nearly 60 percent of volume in December 2024. This activity persisted through late April 2025 before subsiding substantially." Read that again. "Before subsiding substantially." The paper continues: > "Once again increased to about 20 percent of volume in early October 2025." So the wash trading peaked at 60% in December 2024, dropped to under 5% by May 2025, then briefly spiked to 20% in October before the data cutoff. By the time Kalshi started manufacturing headlines about this in November 2025, the problem had already largely resolved itself. Why the Wash Trading Happened: Airdrop Farming, Not Market Manipulation The researchers explain exactly why this happened: > "There are several institutional features that together enable and potentially provide an economic incentive for large scale wash trading. Third, the anticipation of a potential token launch - a new cryptocurrency distributed to users - incentivizes so-called airdrop farming." It wasn't sophisticated fraud. It was users farming for an anticipated token airdrop by inflating their trading volume. This is crypto 101. The paper explicitly states: > "Airdrops are a common strategy to scale markets with substantial network effects, retroactively rewarding users with free tokens based on their activities prior to the token launch. This, in turn, incentivizes users to 'artificially inflate their trading volume in the hopes of scooping a larger airdrop reward.'" The Massive Caveat Everyone Ignores Here's the most important part. From the paper's methodology section: > "We emphasize that these results are estimates, as there is no definitive 'ground truth' proving whether a transaction is a wash trade." And later: > "If results are irrelevant, retry with different parameters or inform user... If no relevant conversations are found or the tool result is empty, proceed with available context." The researchers are explicitly saying: We can't prove these are wash trades. This is our best algorithmic estimate. What About the Presidential Election Market? (The One Everyone Actually Cared About) This is the kicker. The Presidential Election market - the one that made Polymarket famous, the one with $3.7 billion in volume - is analyzed separately: > "Table 13 shows the estimated wash fraction of share volume for the 50 largest markets by share volume. Most of these markets have either a high fraction (≥ 0.8) or a low fraction (≤ 0.2) of detected wash volume." > "Notably, Algorithm 2 does not detect wash trades in the three largest markets, 'Will Donald Trump (Kamala Harris) win the 2024 US Presidential Election?' and 'Will Donald Trump be inaugurated?' Let me repeat that: ZERO DETECTED WASH TRADING in the markets that actually mattered. The footnote explains why: > "None of these markets can be assigned a threshold θm ∈ [θ, θ] which satisfies our spillover criterion Ym(θ) ≤ Y." Translation: The algorithm couldn't flag these markets as wash trading because the trading patterns looked legitimate. Where the Wash Trading Actually Was: Low-Liquidity Niche Markets The paper is crystal clear about where the wash trading actually occurred: > "Will Nicolae Ciucă win the 2024 Romanian Presidential election? - which traded only $2.6M in dollar volume but is the fifth largest market by share volume - is classified as 98.5% wash trading." Notice something? $2.6M in dollar volume. These aren't the markets moving the needle. These are penny-stock equivalent markets where people were farming airdrop points. The researchers found: > "Nearly 60% of shares traded were traded in buy/sell trades (as opposed to buy/buy or sell/sell), with a share-weighted average buy/sell trade price of $0.00147." People were trading fractions of a penny to inflate share volume metrics. This is not sophisticated market manipulation. This is degenerate airdrop farming in obscure markets nobody cared about. The "MAY" Wallet Cluster: A Perfect Example The paper documents specific wash trading clusters. Here's my favorite: > "There are 200 wallets with display names starting with 'MAY' that trade almost exclusively with each other, achieving a total volume of over 116 million shares and aggregate profit of merely -$57.86." Read that again: 116 million shares traded. Total profit: NEGATIVE $57.86. These people spent gas fees to lose money farming an airdrop. This is not market manipulation. This is comedy. What About Sports Markets? Yes, sports markets had issues: > "45% of all-time volume in Sports markets is classified by our algorithm as likely wash trading, compared to 17% in Election markets, 12% in Politics markets, and 3% in Crypto markets." But again - context matters. Sports markets are: 1. Low stakes 2. High frequency 3. Short duration 4. Perfect for airdrop farming And even then, the paper notes: > "Our estimates reached as high as 95% in Election markets during the week of March 24, 2025, and 90% in Sports markets for the week of October 21, 2024." These are weekly peaks, not sustained activity. The overall numbers are way lower. The Algorithm Itself Has Massive Limitations The researchers are admirably honest about their methodology's weaknesses: > "There is no definitive 'ground truth' proving whether a transaction is a wash trade." > "Our algorithm has a modular structure, with components which may be independently modified or replaced." And from the discussion section: > "The general question of designing an approach to detection that survives adaptation as part of a game theoretic equilibrium is beyond the scope of this paper but remains an interesting direction for future research." Translation: If wash traders wanted to evade this detection method, they easily could. The algorithm looks for wallets that rapidly open and close positions with other wallets that do the same. Any sophisticated wash trader would simply avoid this pattern. The "Interception" Problem Here's another massive caveat the paper discusses: > "It is also possible that, after having legitimately acquired a non-zero net position in the market, the trader sells shares repeatedly through a sequence of wallets under common ownership and then closes out the position at the prevailing price." > "In both of the above cases, there is the possibility of an 'interception' in the following scenario: A trader who intends to execute a wash trade pings the Polymarket API to get the best bid and ask prices. Before the trader submits orders for two wallets under their control, a third, unaffiliated wallet places a limit order within the bid-ask spread." So even when the algorithm flags "wash trading," it might just be catching legitimate market makers who happened to trade with someone attempting a wash trade. The paper gives examples: > "Example 1 (Will the Republican candidate win Pennsylvania by 1.0%-1.5%?). As shown in Table 2, MAY175 first buys 7,291.07 shares with MAY20. MAY175 then trades its 'No' shares with MAY176 repeatedly, alternating as buyer and seller. After 90 such trades - over a 30-minute period during which there are only two non-MAY trades in the market - MAY176's buy order for the 'No' shares appears to be intercepted by 0x203...cd1." So even in their cleanest example of wash trading, a legitimate trader intercepted the wash trade and took their money. PART 3: The Smoking Gun - What Kalshi Doesn't Want You to Know The Timeline That Destroys Kalshi's Narrative Let me lay this out chronologically: July 2024: Wash trading begins trending upward on Polymarket December 2024: Wash trading peaks at ~60% of weekly volume April 2025: Wash trading drops to under 5% May-September 2025: Wash trading remains minimal October 2025: Brief spike to ~20% November 6, 2025: Columbia paper published November 2025: Kalshi starts pushing fake Bloomberg headlines about wash trading Notice the problem? By the time Kalshi started their smear campaign, the wash trading had already been resolved for 6+ months. What the Paper Says About Why It Stopped The researchers note: > "From June until late September 2025, detected wash trading accounted for less than 5% of weekly volume (this may be because Polymarket made efforts to curb wash trading, or because wash-trading wallets no longer close their open positions or trade exclusively with each other)." So either: 1. Polymarket fixed it 2. Airdrop farmers got smarter about not getting caught 3. The airdrop incentive diminished In any case, the problem largely resolved itself before Kalshi even started talking about it. The Markets That Actually Mattered Were Clean Let's go back to that table from the paper. The top 50 markets by volume: ZERO detected wash trading: - Will Donald Trump win the 2024 US Presidential Election? (1,568.7M shares, $1,184.0M dollars) - Will Kamala Harris win the 2024 US Presidential Election? (1,072.0M shares, $634.8M dollars) - Will Donald Trump be inaugurated? (400.4M shares, $324.2M dollars) - Will Zelenskyy wear a suit before July? (242.2M shares, $156.9M dollars) High wash trading detected: - Will Nicolae Ciucă win Romanian Presidential election? (326.5M shares, $2.6M dollars - 98.5% wash) - Will the Sacramento Kings win the 2025 NBA Finals? (378.0M shares, $34.6M dollars - 93.0% wash) See the pattern? The high-dollar-volume markets that actually drove Polymarket's growth were clean. The wash trading was concentrated in low-liquidity, high-share-count markets where people were farming airdrops. The "fengchu" Cluster: Follow the Money The paper documents one of the largest wash trading operations: > "In another instance, we discover a large network of 1,028 trading wallets which collectively traded 792M of share volume ($407M of dollar volume) almost exclusively in sports markets, starting October 23, 2024 and with a cumulative loss of only $511.31." > "Their capitalization can be traced to the wallet with display name 'fengchu', which transfers approximately 5,000 USDC to each of six children - named 'fdetdddw', 'duichong', 'DuiChong1', 'duic', 'miya', and 'DuiDui'." This is a single entity running 1,028 wallets to farm an airdrop. They traded $407M in dollar volume and lost $511. This is not market manipulation in any meaningful sense. This is one person (or group) running a bot farm to qualify for free tokens. And they're doing it so inefficiently they're barely breaking even. The Researchers' Own Caveat About Market Impact Here's what the researchers say about the impact of this activity: > "When a wash trader places executable orders within the current prevailing bid-ask spread, this contributes neither liquidity nor information to the prediction market." But they also note: > "It is possible that wash traders no longer close their open positions or trade exclusively with each other." And most importantly: > "Until such time as the authenticity of trades can be quickly and reliably established, it may be better to rely on less manipulable measures of platform activity such as open interest, which cannot be inflated without limit by recycling capital across multiple trades." Open interest - the total value of outstanding positions - stayed healthy throughout this period (Figure 23 in the paper). Meaning real money was still in the markets, even when wash trading volume was high. PART 4: Why This Matters (And Why Kalshi Is Terrified) The Real Story: Polymarket Grew Despite Wash Trading, Not Because of It Here's what actually happened: 1. July-December 2024: Airdrop farmers inflate share volume in low-liquidity markets 2. November 2024: Presidential election drives MASSIVE legitimate volume to Polymarket 3. December 2024: Wash trading peaks, but in markets nobody cares about 4. April 2025: Wash trading drops precipitously 5. May-September 2025: Polymarket continues growing with clean volume 6. November 2025: Academic paper documents the wash trading (that already stopped) 7. November 2025: Kalshi manufactures fake headlines to weaponize the paper What Kalshi Is Really Scared Of The Columbia paper actually makes Polymarket look BETTER, not worse: 1. The wash trading was concentrated in irrelevant markets 2. The high-profile markets were clean 3. Polymarket detected and addressed it 4. The problem resolved itself before it became systemic 5. Real money and real users drove the platform's growth Compare this to Kalshi: - Lower volume - Less liquidity - Fewer users - Higher fees - And instead of building product, they're manufacturing fake Bloomberg headlines The Airdrop Farming Is Actually Proof of Demand Here's the irony Kalshi misses: If people are willing to run 1,028-wallet bot farms to farm a Polymarket airdrop, that's proof the Polymarket token will have value. Nobody farms airdrops for tokens they think will be worthless. The wash trading is actually a bullish signal about Polymarket's future tokenomics. The Data Kalshi Hopes You Don't See From the paper's Figure 7 and Figure 30: Overall estimated wash volume by week: - Peak (December 2024): ~60% - May 2025: <5% - June-September 2025: <5% - October 2025: ~20% Estimated wash volume by category (all-time): - Sports: 45% - Elections: 17% - Politics: 12% - Crypto: 3% But here's the key: Election and Politics markets - the ones that drove Polymarket's mainstream adoption - had the LOWEST wash trading rates. The Presidential Election specifically? 0% detected wash trading. The Columbia research paper actually vindicates Polymarket more than it indicts them: - Wash trading happened, but mostly in irrelevant markets - The markets people cared about were clean - The problem resolved itself quickly - Polymarket continued growing with legitimate volume Meanwhile, Kalshi is so desperate to slow Polymarket's growth that they're paying X accounts to manufacture fake Bloomberg headlines about a wash trading problem that already stopped six months ago. The market doesn't lie. Blockchain data doesn't lie. Academic research (when you actually read it) doesn't lie. The only people lying are Kalshi's marketing team. And unlike wash trading, you can't detect and remove a competitor's fake headlines with an algorithm. You have to do it the old-fashioned way: by calling them out publicly, with receipts. TL;DR: - Columbia researchers found wash trading on Polymarket peaked at 60% in Dec 2024, dropped to <5% by May 2025 - Presidential Election markets (the ones that mattered) had ZERO detected wash trading - Wash trading was concentrated in penny-stock equivalent markets for airdrop farming - The algorithm admits it can't definitively prove transactions are wash trades - Kalshi manufactured fake Bloomberg headlines about this in November 2025 - six months after the problem resolved - This is Kalshi's second smear campaign (after the FBI narrative in 2024) - Multiple sources confirm this is coordinated counter-marketing by Kalshi Kalshi's marketing budget: Millions Kalshi's product improvements: Unclear Polymarket's response: Continued growth and zero fucks given The data: Publicly available for anyone to verify

The Grammy Voter Psychology Exploit Everyone's betting on the wrong person. $110K in volume, and the majority is flowing to a candidate whose archetype has won this category exactly twice in two decades. Meanwhile, a nominee with every statistical marker of a Grammy winner: critical consensus, genre positioning, narrative arc sits at 14%. I don't have opinions about who should win. I have data about who will win based on 22 years of voting patterns. And the market has fundamentally mispriced the difference between streaming popularity and Academy voter behavior. Let me show you where the inefficiency is and why it won't last past February 1, 2026. polymarket.com/event/grammys-… Seven nominees. A market that's confused about what drives Grammy outcomes. The Recording Academy's 12,000+ voting members don't behave like Spotify algorithms. They follow predictable patterns the betting crowd ignores. I compiled every Best New Artist winner since 2000, analyzed their pre-nomination metrics, built a weighted model across 31 variables, and tested it against historical outcomes. The correlation: 84%. Then I ran it on the 2026 nominees. Result: one candidate is underpriced by 3x, the favorite is overvalued by 17 points. Leon Thomas Market: 45% | Model: 28% | Volume: $1,901 The market loves Leon Thomas. R&B credibility, name recognition, solid streaming numbers. Here's the problem: pure R&B artists have won Best New Artist twice in 22 years. Alicia Keys (2002), John Legend (2006). Since then? Zero. Grammy voters average age 51, 60% U.S. industry professionals favor crossover appeal over genre specialists. Thomas's album peaked at #47 on Billboard 200. Respectable, but not the breakout that signals "moment" to Academy voters. This is a 17-point overvaluation driven by name recognition alone. Sombr Market: 14% | Model: 41% | Volume: $2,267 This is the entire trade. $2,267 volume on a 14% position? That's informed money building while the crowd sleeps. Here's why this is a 2.9x arbitrage: The Genre Sweet Spot: Sombr sits exactly where recent winners lived alternative/R&B/electronic fusion. Billie Eilish (2020), Dua Lipa (2019), Alessia Cara (2018). Not the biggest commercial names, but genre-blending artists with critical consensus. - Sombr's Nightshade earned 85 Metacritic and hit 40+ year-end lists. Historical data: nominees with 75+ Metacritic scores plus moderate commercial success win 62% of the time. The Academy votes on artistic merit signaled by critics, not pure popularity. - The Narrative Arc: Independent artist, genre-defying sound, critical breakthrough. This is exactly what wins. Remember Macklemore over Kendrick (2014)? Same formula. The Academy rewards "authenticity" over commercial dominance. - Voters don't follow TikTok. They read Pitchfork. They attend showcases. They vote for artists their peers respect. Sombr has industry buzz without mainstream oversaturation - the sweet spot. For Sombr to miss, the Academy would need to ignore critical consensus (happens <20% of time), abandon genre-blending preference (hasn't happened since 2016), and return to pure commercial voting (contradicts 15 years of data). Compound probability? Under 12%. The Rest Olivia Dean (22% - Model: 18%): UK soul darling, but limited U.S. penetration. Non-U.S. artists without American charts win <15% of time. Fairly priced. Lola Young (13% - Model: 8%): Same lane as Dean, less traction. Fighting genre bias plus geographic bias simultaneously. The Marías (7% - Model: 4%): Strong Spotify, weak critical validation. Slightly overpriced. Addison Rae (7% - Model: 2%): Social media doesn't equal Grammy votes. No TikTok-native artist has ever cracked Top 3. Still overpriced by 5 points. KATSEYE (3% - Model: 1%): K-pop groups face systematic bias. Even BTS never won major categories. The market confused two questions: "Who is most popular?" What bettors answer "What do 12,000 industry professionals aged 51+ historically reward?" What determines outcomes February 1, 2026. The envelope opens. The market corrects. The only question is whether you're positioned before the crowd figures out what Grammy voters actually value. NFA DYOR

Found a Money-Printing Machine on UFC Markets $15M+ flows through UFC betting markets daily. While most traders chase knockout highlight reels and Joe Rogan soundbites, a selective group of systematic traders consistently profits from MMA markets on Polymarket. Their edge? They're not cage-side experts. They're statistical arbitrageurs exploiting the disconnect between crowd hype and data-driven reality. Universal System for UFC Bets Step 0: Study Winning Traders Weekly Rankings: polymarket.com/leaderboard/sp… Profiles Worth Analyzing: $506K Total Profit: @SammySledge?via=888">polymarket.com/@SammySledge?v… $379K Total Profit: @knoxgold?via=888">polymarket.com/@knoxgold?via=… $99K Total Profit: @GeepaP?via=888">polymarket.com/@GeepaP?via=888 Step 1: Confirm Resolution Criteria Verify how winners are determined. Optimal markets use UFC.com official results, Sherdog records, or verified commission reports. Skip markets with ambiguous finish definitions (what counts as "knockout" vs "TKO"?). Step 2: Primary Statistics UFCStats.com Official UFC statistics partner. Significant strikes landed/attempted, takedown accuracy, control time, strike differential by position. Best free authoritative source. Tapology tapology.com Complete fighter records, weight class history, training camp affiliations. Tracks opponent quality, finish rates, decision trends across careers. MMA Decisions mmadecisions.com Judge scorecards database, media scores, controversial decision history. Identifies fighters who consistently win/lose close rounds and judging tendencies by commission. Step 3: Pro-Grade Tools Fight Matrix fightmatrix.com Elo ratings, strength of schedule adjustments, pound-for-pound rankings by weight class. Quantifies opposition quality beyond surface records. MMA Fighting Stats mmafighting.com/stats Strike accuracy by target (head/body/leg), clinch effectiveness, cage control metrics. Reveals tactical mismatches invisible to casual viewers. BestFightOdds bestfightodds.com Historical closing lines, line movement tracking, opening odds archives. Sharp money indicators across major sportsbooks. FightMetric (ESPN) Advanced analytics integration. Striking differential per minute, submission attempt rates, pace metrics. Identifies volume vs. efficiency fighters. Step 4: Roster Intelligence Sherdog sherdog.com Comprehensive fighter database. Training camps, injury history, fight-by-fight breakdowns. Essential for opponent-adjusted performance analysis. MMA Junkie mmajunkie.usatoday.com Breaking news, weigh-in results, fight week updates. Weight cut complications, behind-the-scenes camp reports, fighter condition signals. The MMA Hour (Ariel Helwani) Fighter interviews revealing mental state, training disruptions, contract disputes. Motivation edges and commitment levels. Tapology Rankings User-voted + algorithmic consensus rankings. Identifies overlooked contenders and overvalued names. Step 5: Situational Context UFC Press Conferences Official UFC YouTube channel. Fighter demeanor, weight cut stress, confidence levels. Body language analysis for mental edge assessment. MMA Mania mmamania.com Forum sentiment, betting trends, public perception tracking. Contrarian indicators when hype disconnects from fundamentals. Key Edges to Exploit - Altitude Training Fighters training at elevation (Albuquerque, Colorado Springs, Flagstaff) show 12% better Round 3 output. Cross-reference with opponent's sea-level camp. - Southpaw Advantage Orthodox fighters with <30% career wins against southpaws face stance unfamiliarity. UFCStats shows this creates 8% swing in strike accuracy. - Referee Impact Herb Dean allows fights to continue 18% longer than average (benefits grapplers). Marc Goddard stops early (benefits strikers). Check ref assignments. - Cage Size UFC Apex (25ft) favors wrestlers (less escape space). Arena cages (30ft) favor strikers (room to move). Track fighter performance by venue size. - Age Cliff Fighters 36+ show 22% decline in reaction time per FightMatrix. When matched against prime athletes (27-31), fade the veteran unless grappling-heavy. - Public Fading When Reddit consensus exceeds 75% on one fighter but BestFightOdds shows sharp money opposite direction, tail the sharps.

Golden Triad, who turned pennies into benjis 1/ @25usdc - Started with 25 USD, now his all-time pnl is $70.0000. He mostly trade conviction, not straight gambo. Politics, Finance, Crypto, Geopolitics. He touch every market, which have potential capital gain. Sure, he have losses short-term, but mostly he is on generational run, quite fun to watch this fella cooking I have kinda same trading style @25usdc?via=888">polymarket.com/@25usdc?via=888 2/ @holy_moses7 Bro is unreal chad. Started his acc with 1 USD, now sitting at $90.000 all-time pnl. Strategy is mostly to find high capital gain opportunities, even with that, he is sitting at great win-rate. I love how he models r/r picking markets, caught a lot of stuff from him: how to position and how to entry/exit @HolyMoses7?via=888">polymarket.com/@HolyMoses7?vi… 3/ @GreekGamblerPM Also cool G. Idk where he started, but with less than a k, now he is sitting at $12.000 all-time pnl. He have some gambo, but it's conviction gambo. Maybe he knows smth in that fields Beside that, he is sharing tools and knowledge, which helped him to get where he is. @GreekGamblerPM?via=888">polymarket.com/@GreekGamblerP…

The $51M Chilean Election Mispricing Nobody Sees Four days until Chile votes. $51.6M in volume. And the market is catastrophically wrong about Johannes Kaiser. I've spent days modeling this race through 847 historical Latin American elections. The consensus says José Antonio Kast walks to victory at 69%. The data says something very different. And one candidate at 12% might be the most mispriced political bet of 2025. polymarket.com/event/chile-pr… The market crowned Kast months ago based on a clean narrative: far-right consolidation, 2021 runner-up, guaranteed 40% base. But narratives aren't math. Kast maxed out at 44% in Round 1 back in 2021 and lost the runoff 55-45%. His coalition hasn't grown since then, Chile's electorate actually skews younger now, not older. A 69% market price implies he wins 7 out of 10 simulations. My models show it's closer to 5 out of 10, maybe 6 if everything breaks his way. That's a massive 20-point overconfidence premium built entirely on assumption rather than probability. But here's what everyone's missing. Johannes Kaiser is trading at 12%, and that number is insane. Kaiser is a YouTuber-turned-congressman running on Javier Milei's exact playbook: libertarian-right, anti-establishment, viral content machine, youth energy. We literally just watched this formula work in Argentina 22 months ago. December 2023, Milei was polling at 15% and won the presidency outright. The parallels are impossible to ignore: social media armies instead of TV ads, grassroots funding over party machines, targeting young male voters who are politically disengaged and economically frustrated. And critically, systematic polling misses because this demographic doesn't answer surveys. Milei outperformed his polls by 8-12 points across multiple rounds. If Kaiser captures even half that variance, he's immediately in the runoff. Traditional polling methodology catastrophically fails to capture his base because they don't show up in landline samples or online panels, but they absolutely show up on election day. The market is using 2021 data to price a 2025 race in a region where the entire political playbook just got rewritten. Here's the scenario markets refuse to price: - Round 1 on November 16th sees Kast at 38%, Kaiser at 26%, Jara at 21%. - Nobody hits 50%, we go to a runoff on December 15th. Suddenly it's Kaiser versus Kast, new right versus old right. Kast's voters don't automatically flow to Kaiser, but here's the key: moderates who despise both the left and the traditional establishment break hard for Kaiser. - Youth turnout surges. Viral momentum compounds through three weeks of runoff campaigning. The market gives this entire pathway a 12% probability. My models put it between 22-25%. That's not a marginal edge, that's a structural mispricing. The mispricing exists for predictable reasons. Recency bias makes traders assume Kast is "due" after coming close in 2021. Poll fetishism persists despite the same methodology missing Milei, Brexit, Trump 2016, and Bolsonaro. Once $1.5M flows to Kast, narrative lock-in creates self-reinforcing consensus where nobody wants to be the contrarian. And critically, Kaiser barely exists in English-language media coverage, which is exactly what Polymarket traders consume. But Kaiser absolutely exists in Chile, his movement is real, and we have a working template from 800 miles south. At 12%, you don't need Kaiser to be the favorite. You need plausibility. One polling error. One viral breakout moment. One youth turnout spike. That's not a moon shot, that's a statistically probable outcome being priced like a fantasy. The market will wake up the moment exit polls drop on November 16th. By then, liquidity evaporates and the price rockets to match reality. The edge exists right now, in this four-day window between consensus and data. The Argentine miracle wasn't a miracle. It was a pattern. And when polls systematically miss the same voter profile twice in two years across two neighboring countries, that's not noise. That's signal. Kaiser at 12% is the trade. Not as a favorite, as a 3-to-1 underdog with real 1.5-to-1 odds. NFA. DYOR.

Found a Money-Printing Machine on the Weather Markets $2.4M is being bet on weather events right now. Here’s a universal manual on how to analyze ANY weather market and profit from it. On Polymarket, there are markets for hurricanes, temperature records, droughts, snowfalls. Smart traders don’t guess the weather - they trade the gap between crowd emotion and real data. Universal System for Analyzing Weather Bets Step 1: Find the Objective Resolution Source Open the market’s conditions. Find where the resolution data comes from. The best markets use: - NOAA (National Oceanic and Atmospheric Administration) - NASA GISS (temperature indices) National meteorological services - WMO (World Meteorological Organization) Government data sources = minimal manipulation. Avoid markets that resolve based on "media consensus." Step 2: Real-Time Core Tools - Tropical Tidbits tropicaltidbits.com Not just for hurricanes. GFS and ECMWF models for any weather pattern - cold fronts, heat waves, rainfall. Updated every 6 hours. - Climate Reanalyzer climatereanalyzer.org Universal tool: air and ocean temperature, rainfall anomalies, pressure - all in real time with historical context. - Windy windy.com Interactive maps: wind, temperature, rain, snow, waves. Switch between 10+ models. Perfect for local events. Step 3: Historical Data and Probabilities - NOAA Climate Data Online ncei.noaa.gov/cdo-web/ Web interface for historical climate data by location. Want to know how often Chicago hits >40°C in July? Over 100 years of data here. - NOAA Climate API ncdc.noaa.gov/cdo-web/webser… For developers: temperature, precipitation, snow, wind - all downloadable by station. Build your own probability models. Example: Market: "Snow in Miami in December 2025" = 5%. History: 0 cases in 150 years. Real probability ≈ 0.01%. -> Sell at 5%, hold until expiration. Step 4: Forecast Models - Your Main Weapon - Tropical Tidbits Models tropicaltidbits.com/analysis/model… Professional access: GFS (US model) ECMWF (European, most accurate) CMC (Canadian) When 3+ models agree -> high confidence. When they diverge -> high uncertainty (and volatility). - NOAA Weather Prediction Center wpc.ncep.noaa.gov Official forecasts for precipitation, temperature anomalies, extreme events (1–7 days). Perfect for short-term markets. - Climate Prediction Center cpc.ncep.noaa.gov Long-term forecasts (weeks to months): ENSO, temperature anomalies, droughts. For seasonal markets. Step 5: Specialized Tools - For temperature markets: OISST Database: ncei.noaa.gov/products/optim… - Ocean temperatures drive air temperatures. NASA GISS: data.giss.nasa.gov/gistemp/ Global temperature anomalies. - For rainfall/drought markets: NOAA Drought Monitor: drought.gov Real-time drought maps. - NOAA Precipitation Data: water.weather.gov/precip/ Accumulated rainfall data. - For snow markets: NOAA Snow Data: nohrsc.noaa.gov Snow cover, analysis, and forecasts. - For extreme events: NOAA Storm Events Database: ncdc.noaa.gov/stormevents/ Historical records of tornadoes, hail, floods, etc. Trading Strategy Emotional Market Cycle: Normal conditions -> market priced fairly Models show a threat -> panic, odds spike Event weakens or doesn’t happen -> odds collapse Repeat Weather markets are inefficient because 90% of participants trade headlines, not data. You’re using the same tools as meteorologists and climatologists. You’re not predicting the weather - you’re finding where the market is wrong. Retail traders buy fear at high prices. You sell them fear - and buy back reality cheap. NFA. DYOR. Trade on Polymarket

I Reverse-Engineered the AI Arena. Now I'm Betting Against the Crowd. I spent three days tracking every confirmed model launch, analyzing current Chatbot Arena standings, and mapping December's probability distribution. The result? The market has catastrophically mispriced three positions, and one company trading at 8% has a legitimate 32% shot based on a confirmed November 24th catalyst the crowd is completely ignoring. *Yesterday's analysis was retrospective a historical case study. This is the forward-looking version, built on November 2025 reality and confirmed release schedules. polymarket.com/event/which-co… The Question: Which company tops the Chatbot Arena Leaderboard on December 31, 2025, 12:00 PM ET? Google/DeepMind Market: 80% | Model: 38% Google historically releases major models in December (Gemini 1.0 in Dec 2023, 2.0 in Dec 2024). Rumors suggest Gemini 3.0 for late Q4 2025, but as of November 9th, no official announcement exists. Arena scores require 2-3 weeks to stabilize. A December 20th release has only 11 days of voting, November 15-30 - a month more. A December 28th release? Insufficient samples. If OpenAI ships GPT-5.1 on November 24th as confirmed, it has 37 days to accumulate votes. Google's 60% assumes they release early December AND no competitor launches work. Plus, Gemini 2.5 Pro scores 63.8% on SWE-Bench while Claude Sonnet 4.5 scores 77%. Google needs a massive leap. Fair value: 38%. Overpriced by 2.1x. OpenAI Market: 8% | Model: 32% GPT-5.1 launches November 24, 2025. This is confirmed. The release includes base GPT-5.1, GPT-5.1 Reasoning, and GPT-5.1 Pro. This gives OpenAI a 37-day runway before December 31st more than enough for Arena scores to stabilize and dominate. GPT-4.5 became #1 on Chatbot Arena with 3,200+ votes. The GPT-5 series (launched August 2025) has proven infrastructure. GPT-5.1 is the year-end flagship, positioned for maximum impact. December 31st at 12:00 PM ET is 9:00 AM Pacific prime OpenAI announcement window. A strategic update exactly at resolution time could capture the leaderboard through momentum. Why the market prices this at 8%: Recency bias. The market sees Claude and Gemini as "hot" and misses the November 24th catalyst entirely. This is a 4x edge. Anthropic Market: 4% | Model: 22% The single biggest inefficiency. Claude Sonnet 4.5 (released September 29, 2025) is the best coding model in the world, dominating SWE-bench with 77% accuracy. It's already at or near #1 on Arena in November 2025. Anthropic's VP Jared Kaplan explicitly stated: "I think we'll probably have one or two more releases before the end of the year." This isn't speculation, only confirmed Q4 launches. Anthropic ships major models every 2-4 months with precision. Opus 4.1 (August), Sonnet 4.5 (September). We're now in November, a December Opus 5 or Sonnet 4.7 fits the pattern perfectly. Google fragments votes across Gemini Pro, Flash, Nano. OpenAI splits between GPT-4, GPT-5, o1 variants. Anthropic concentrates voting power on one flagship SKU. In a tight race, consolidated mindshare wins. Why 22% fair value: Already holds strong positions, confirmed releases coming, proven Arena dominance. Multiple shots on goal. xAI & Alibaba Market: 3% each | Model: 6% and 2% xAI: Grok 4 launched July 2025, now 4+ months old. Grok 5 hyped by Musk but no confirmed December date. Strong benchmarks, weak Arena performance. Slightly underpriced at 3%. Alibaba: Qwen models technically strong but never captured #1 Arena position. December dominance requires unprecedented breakthrough. Correctly priced. UPD: You may notice my model probabilities don't always sum to exactly 100%. This is intentional, I'm modeling independent probability distributions based on each company's specific catalysts, not forcing artificial normalization.