Executive Summary

Operators and integrators in sports betting and iGaming currently spend large amounts of time and money reconciling and adapting heterogeneous sportsbook feeds — a problem faced across roughly 6,000 licensed sportsbooks and platforms that drives high integration costs, frequent settlement disputes, and fragile live-betting behavior. These pain points are acute for product, trading and operations teams who need sub-second consistency and reliable post-match reconciliation to avoid liability and revenue leakage. You could build a neutral middleware platform that ingests and normalizes disparate APIs, provides resilient aggregation with failover and observability, and applies ML-based fuzzy matching to automate event/entity mappings and reconciliation workflows. The product would expose a single developer-friendly API and a library of connectors/adapters plus SLAs for real-time consistency and settlement accuracy. This is an attractive moment: the $1.2B addressable market (6,000 platforms × ~$200k ACV) reflects both high willingness to pay and accelerating provider fragmentation as niche data suppliers proliferate and live betting volumes grow. Your competitive edge would be a combination of vendor-neutral orchestration, proven ML matching that reduces manual mapping, and enterprise-grade reliability and audit trails — but expect hard engineering work to achieve sub-second guarantees across flaky upstreams and a lengthier sales/process cycle to earn operator trust and meet regulatory/compliance demands.

Market Opportunity

Normalize and resiliently aggregate sportsbook feeds across mismatched APIs targets a $1.2B = 6,000 licensed sportsbooks & iGaming platforms × $200,000 ACV on integration, data reconciliation and orchestration services total addressable market with medium saturation and a year-over-year growth rate of 8% CAGR (industry estimates for sports betting technology demand; e.g., Statista and industry reports).

Key trends driving demand: Provider fragmentation — more niche and regional data providers are emerging, creating demand for neutral orchestration between multiple feeds.; Real-time reliability expectations — operators require sub-second consistency and robust settlement reconciliation as live betting volumes grow, raising willingness to pay for dependable middleware.; Machine learning for fuzzy matching — advances in ML make automated entity and event matching feasible, reducing manual mapping labor and increasing automation ROI..

Key competitors include Sportradar, Betgenius (Stats Perform), TheOddsAPI / odds providers (the-odds-api).

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Normalize and resiliently aggregate sportsbook feeds across mismatched APIs

Executive Summary

Market Validation

Market Opportunity

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