Oddpool

Competitive Intelligence & Product Roadmap

Normalizes prediction-market data for institutional traders.

Company Overview

Oddpool is a market-data platform that maps prediction-market contracts across venues and streams orderbook, trade, arbitrage, and macro probability data. Serving quant funds, prop shops, market makers, hedge funds, and serious traders.

Latest Intel

Zeitgeist tracks private signals to determine where the company is heading strategically.

New Intel: Oddpool is packaging canonical prediction-market symbology and tick history for institutions. If desks adopt it, Kalshi and Polymarket data starts looking like a Bloomberg-style feed budget, not a trader dashboard subscription.

What They're Building

The company's public product roadmap & what they're committed to building.

Fed Market Watch

Oddpool launched cross-venue outcome normalization for Fed rate markets, turning fragmented prediction contracts into a comparable macro probability surface.

Whale Tracking

The product tracks large prediction-market trades so serious traders can monitor flow and positioning across venues.

Public API & WebSockets

Oddpool added programmatic access for live orderbooks, trade feeds, search, and market history.

Opinion Arbitrage Support

The arbitrage scanner expanded beyond Kalshi and Polymarket to include Opinion where matching events exist across venues.

Oddbot

Oddpool launched an AI analyst that queries live Kalshi and Polymarket markets, historical moves, whale flow, and cross-venue spreads.

Founder and Key Execs

Avi Arora

Founder and CEO (former Microsoft ML researcher, Georgia Tech CS)

Ritesh Malpani

Founder and CTO (former Bloomberg software engineer, Georgia Tech BS/MS)

Founder Force Multiplier

Arora brings ML security experience from Microsoft, while Malpani brings high-throughput trading infrastructure experience from Bloomberg. That pairing fits a company whose wedge depends on reliable market data pipes and machine-readable analytics.

Funding History

2025 | Product launch with Fed Market Watch and cross-venue outcome normalization
2026 | YC Spring 2026
2026 | No disclosed priced round or lead investor found

Competitors

OddsPapi:

Provides normalized prediction-market and sportsbook-style data access, with a narrower public surface than Oddpool’s institutional archive and mapping pitch.

AhaSignals:

Tracks Polymarket and Kalshi divergences for matched events, closer to arbitrage monitoring than full market-data infrastructure.

Apify Polymarket + Kalshi Arbitrage Finder:

Offers a task-specific arbitrage scraper rather than a persistent institutional data layer.

Internal desk tooling:

Quant teams can build venue scrapers and normalizers themselves, but Oddpool sells the maintained mappings, feeds, and history as the product.

Oddpool

's Moat:

Candidate moat is proprietary data: verified cross-venue mappings and tick/orderbook history become harder to recreate as new markets, venues, and backtest needs accumulate.

How They're Leveraging AI

Oddbot Queries Live Prediction Markets

Oddbot acts like an AI analyst for prediction-market traders, answering questions over live Kalshi and Polymarket markets, historical moves, whale activity, and cross-venue spreads. The useful bit is that it sits on top of Oddpool’s mapped market data rather than a generic web corpus.

Prediction-Market Agent Evaluation Benchmarks

Oddpool’s PredictionMarketBench evaluates trading agents on historical prediction-market orderbooks and trades, including LLM tool-calling agents and rule-based strategies. It gives the company a lab for testing market reasoning before exposing agents to live workflows.

Cross-Venue Event Mapping As Data Intelligence

Oddpool appears to use structured data logic to match prediction contracts across venues that describe the same real-world outcome with different tickers, token IDs, labels, and price formats. This is less glamorous than model AI, but it is the core intelligence layer.

AI Use Overview:

Oddbot appears to be a tool-using LLM analyst over live structured market data; the distinct surface is retrieval over mapped venue data, not a proprietary forecasting model.

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