TxODDS and gameplAI Partner to Automate College Basketball Pricing

A close-up of a sportsbook mobile app interface displaying real-time, granular player prop bets for a college basketball game, powered by gameplAI automation.

TxODDS, a leader in low-latency sports data, has announced a partnership with AI pricing specialist gameplAI to launch a new automated modelling and pricing solution for the US college basketball market. The collaboration integrates TxODDS’ live scores feed into gameplAI’s automation stack, enabling operators to create and update markets with greater speed and precision.

Addressing the Demands of College Basketball

The new product is designed to handle the specific challenges of college basketball, such as frequent lineup changes, short turnarounds, and intense in-game momentum shifts. By combining real-time data with AI models tailored for college sports, the solution supports rapid market creation and deeper player-centric pricing.

Thomas McGrath, Chief Revenue Officer at TxODDS, emphasized the synergy between the two companies. “We’re excited to see TxODDS Scores powering gameplAI’s breakthrough product for US college sports,” McGrath said. “College sports is one of the most dynamic and fast-growing categories in the US, and our best-in-class data is perfectly suited to support the modelling precision and automation gameplAI is bringing to market”.

Closing the Gap with Professional Sports Markets

Donal Barron, Chairman of gameplAI, highlighted the need to elevate college sports betting products to the level of professional sports. “TxODDS scores gives us the reliability, speed and data granularity we require to develop a suite of sports betting products for college sports, commensurate in quality with those available for professional U.S. sports,” Barron stated. “This partnership enables us to offer operators a powerful automated solution, at a time when popularity of betting on college sports betting is accelerating rapidly”.

The partnership aims to help operators launch differentiated markets without increasing manual trading overhead, focusing on modelling, player-prop automation, and micro-market creation.

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