Former NBA Analytics Executive Makes Case for Data Science Hall of Fame Recognition

A veteran sports analytics leader who helped build data operations for two NBA franchises is arguing that the architects of modern sports analytics deserve formal recognition alongside the players and coaches who benefit from their work.

Ben Alamar, who served as an analytics executive with the Oklahoma City Thunder and Cleveland Cavaliers before authoring "Sports Analytics: A Guide for Coaches, Managers, and Others," made the case during a February 18 podcast with Wharton faculty members Cade Massey, Eric Bradlow, and Abraham Wyner. The discussion, published by Knowledge at Wharton, covered NBA tanking incentives, draft reform proposals, and the broader evolution of data-driven team building.

The timing reflects a broader tension in professional sports front offices: as analytics departments grow in influence and budget, the question of how to credit—and compensate—the people building the models has become increasingly fraught. Finance leaders in sports organizations face similar challenges to CFOs in other industries: how do you value the person who built the forecasting model versus the executive who acted on it?

Alamar's Hall of Fame argument centers on a familiar corporate dilemma. The coaches and general managers who implement analytics-driven strategies often receive public credit and lucrative contracts, while the data scientists and analysts who developed the underlying insights remain largely anonymous. It's the same dynamic that plays out when a CEO announces a "data-driven transformation" without mentioning the team that built the infrastructure.

The podcast also addressed tanking—the practice of deliberately fielding weak teams to secure better draft picks—which Alamar and the Wharton professors framed as a rational response to misaligned incentives in the NBA's current draft system. For finance-minded listeners, the parallel is obvious: when you reward failure (losing teams get better draft position), you get more failure. The league has attempted various lottery reforms, but Alamar discussed why structural incentives remain problematic.

The conversation touched on draft reform proposals, though the source material does not detail specific alternatives discussed. What's clear is that league economics—where a single superstar can transform a franchise's valuation by hundreds of millions of dollars—create extreme incentives to game the system. CFOs of sports franchises must balance short-term revenue losses from fielding weak teams against the potential long-term payoff of landing a generational talent.

Alamar's dual perspective as both a former team executive and an author gives him credibility on both sides of the analytics divide. He's lived the reality of presenting data to skeptical coaches, navigating front-office politics, and watching other people take credit for insights his team generated. His book serves as a practical guide for managers trying to implement analytics without the organizational dysfunction that often accompanies it.

The broader question his Hall of Fame proposal raises is one that resonates beyond sports: in an era where data science drives competitive advantage, how do organizations properly recognize and retain the people building the models? Sports franchises, with their public-facing results and celebrity executives, simply make the tension more visible than it is in a typical corporate finance department.

For CFOs managing their own analytics teams, the lesson is straightforward: if you want to keep your best data scientists, you need to solve the credit problem before they leave for somewhere that will. The NBA is just figuring this out. Most finance departments are still a few years behind.