sssorryMaker

sssorryMaker / ai-polymarket-sportspicker

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48
0
69% credibility
Found Feb 24, 2026 at 4 stars 12x -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
AI Summary

ML-powered platform that predicts sports outcomes on Polymarket betting markets using historical data, market sentiment analysis, and hybrid models with real-time accuracy tracking via web and mobile apps.

How It Works

1
🔍 Discover AI Sports Predictor

You stumble upon this free tool that uses smart patterns from past games to guess winners on popular betting sites like Polymarket.

2
đź“– Follow Simple Setup

Read the friendly guide to grab free sports stats from public sources – no fancy setup needed.

3
🧠 Train Prediction Brains

Use easy online notebooks to teach the AI with real game history, all free on Google Colab.

4
▶️ Get Live Game Picks

Hit run to see instant predictions for today's big sports events with confidence scores.

5
📱 Open Beautiful Dashboard

Launch the web app to view picks, track wins, and watch accuracy stats update live.

🏆 Bet Smarter, Win More

Celebrate as your picks hit 85%+ accuracy and you make confident choices on sports bets.

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Star Growth

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AI-Generated Review

What is ai-polymarket-sportspicker?

This Python platform pulls live sports betting markets from Polymarket and uses three ML-powered models—historical, sentiment, and hybrid—to predict outcomes like NBA or NFL winners. It tracks prediction accuracy in real-time via a Supabase backend, serving results through a Next.js web dashboard and React Native mobile app. Developers get deployable predictions without building data pipelines from scratch.

Why is it gaining traction?

It integrates directly with Polymarket's CLOB API for fresh markets, blending historical stats, market sentiment, and hybrid ensembles to edge out basic odds checkers. Real-time accuracy logging lets users validate picks instantly, and free Colab notebooks mean zero-cost model training on ESPN data. The full-stack setup—web, mobile, DB—saves weeks of boilerplate for betting prototypes.

Who should use this?

Polymarket traders seeking ML edges on sports bets like NFL spreads or soccer props. Data scientists tweaking prediction models for accuracy benchmarks. Full-stack devs prototyping real-time betting apps with Supabase and React Native.

Verdict

Worth forking for Polymarket ML experiments—deploy predictions fast and monitor accuracy live. At 18 stars and 0.7% credibility score, it's early-stage with thin tests and docs; add CI and examples to hit production.

(187 words)

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