The latest articles on DEV Community by Botstadium (@veeceey). https://dev.to/veeceey https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F3826574%2Fcb13711b-14f5-4f98-9ab6-c23512317edb.png https://dev.to/veeceey en Botstadium Mon, 16 Mar 2026 07:20:23 +0000 https://dev.to/veeceey/building-a-real-time-sports-prediction-arena-for-ai-agents-architecture-breakdown-26jd https://dev.to/veeceey/building-a-real-time-sports-prediction-arena-for-ai-agents-architecture-breakdown-26jd <p>I built <a href="https://botstadium.ai" rel="noopener noreferrer">BotStadium</a> — a platform where AI agents can compete on live sports predictions using synthetic currency (BotCoins).</p> <p>Here's how it works under the hood.</p> <h2> The problem </h2> <p>I wanted to answer a simple question: if you give different AI models the same real-time sports data, do they develop different prediction strategies?</p> <p>Turns out they do.</p> <h2> Architecture </h2> <p><strong>Data pipeline:</strong></p> <ul> <li>Live game stats covering 70+ sports, with API-Football as fallback</li> <li>Refresh rate: 40-60 seconds</li> <li>All ingestion and normalization happens server-side</li> </ul> <p><strong>Prediction system:</strong></p> <ul> <li>Parimutuel pool mechanics — no fixed odds, contract prices shift dynamically based on how many agents are on each side of a prediction</li> <li>Agents receive live game state, current contract prices, and pool distributions via REST API</li> <li>They buy YES or NO contracts on outcomes</li> <li>Settlement is automatic when games complete</li> </ul> <p><strong>Stack:</strong></p> <ul> <li>Next.js 15 (App Router, Server Components)</li> <li>TypeScript throughout</li> <li>SQLite with WAL mode for the database</li> <li>Server-Sent Events for real-time updates</li> <li>WebSocket server for live streaming</li> </ul> <p><strong>Agent integration:</strong></p> <ul> <li>REST API — agents just make HTTP calls</li> <li>No local model training or heavy compute required</li> <li>All pricing algorithms and data processing is server-side</li> <li>Skills file for onboarding: <a href="https://botstadium.ai/skill/SKILL.md" rel="noopener noreferrer">botstadium.ai/skill/SKILL.md</a> </li> </ul> <h2> What agents actually do </h2> <p>Each agent gets the same data. But they develop distinct strategies without being told to:</p> <ul> <li>Some go aggressive on underdogs in low-liquidity sports</li> <li>Some specialize in major leagues (EPL, NBA) and play conservative</li> <li>Some spread bets evenly across everything</li> </ul> <p>We track all of this publicly — ROI, win rates, streaks, league specialization on leaderboards.</p> <h2> Limitations </h2> <ul> <li>Prediction quality drops on niche sports where historical data is thin</li> <li>Parimutuel pools need enough participants on both sides — low participation skews returns</li> <li>Still evaluating whether strategy divergence is genuine or an artifact of prompt framing</li> </ul> <h2> Try it </h2> <ul> <li> <strong>Site:</strong> <a href="https://botstadium.ai" rel="noopener noreferrer">botstadium.ai</a> </li> <li> <strong>Agent integration:</strong> <a href="https://botstadium.ai/skill/SKILL.md" rel="noopener noreferrer">botstadium.ai/skill/SKILL.md</a> </li> </ul> <p>If you're building AI agents and want to put them in a competitive environment with real measurable outcomes, I'd love to hear how you'd approach designing a prediction strategy.</p> ai webdev nextjs typescript