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POTENTIAL ANTITRUST ISSUES WITH NIL GO'S ALGORITHMIC DETERMINATIONS OF NIL "FAIR MARKET VALUE".

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Bibliographic Details
Title: POTENTIAL ANTITRUST ISSUES WITH NIL GO'S ALGORITHMIC DETERMINATIONS OF NIL "FAIR MARKET VALUE".
Authors: EHRLICH, SAM C.1, VAN DYCK, KATHERINE2, PHILLIPS, TYLER3
Source: Journal of College & University Law. Apr2026, Vol. 51 Issue 1, p89-130. 42p.
Subject Terms: *College sports, *College athletes' salaries, *College sports management, Antitrust law, Market value, Time-based pricing, Price regulation
Abstract: The House Settlement introduces a new regulatory architecture for college sports: Universities may now share revenue directly with athletes but only within a capped system accompanied by centralized oversight of third-party name, image, and likeness (NIL) transactions. The newly formed College Sports Commission (CSC), working with Deloitte, has operationalized these rules through NIL Go, a data-driven platform that evaluates whether athlete deals fall within a permissible "reasonable range of compensation." This article situates NIL Go within a broader set of industries turning to algorithmic tools to structure markets, including rental housing and hospitality, where shared pricing systems have recently come under judicial antitrust scrutiny. By comparing the CSC's model to these emerging forms of algorithmic coordination, the article identifies the key questions NIL Go raises for universities implementing the settlement and considers how developments in algorithmic-pricing litigation may shape future governance of athlete compensation. [ABSTRACT FROM AUTHOR]
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Database: Education Research Complete
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Abstract:The House Settlement introduces a new regulatory architecture for college sports: Universities may now share revenue directly with athletes but only within a capped system accompanied by centralized oversight of third-party name, image, and likeness (NIL) transactions. The newly formed College Sports Commission (CSC), working with Deloitte, has operationalized these rules through NIL Go, a data-driven platform that evaluates whether athlete deals fall within a permissible "reasonable range of compensation." This article situates NIL Go within a broader set of industries turning to algorithmic tools to structure markets, including rental housing and hospitality, where shared pricing systems have recently come under judicial antitrust scrutiny. By comparing the CSC's model to these emerging forms of algorithmic coordination, the article identifies the key questions NIL Go raises for universities implementing the settlement and considers how developments in algorithmic-pricing litigation may shape future governance of athlete compensation. [ABSTRACT FROM AUTHOR]
ISSN:00938688