From Moneyball to Machine Learning: Data Analytics and AI in Sports Contracts

The use of data analytics in sport, pioneered by the Oakland Athletics Major League Baseball team, and depicted in the movie “Moneyball”, has fundamentally changed how players are scouted, valued, and utilised. What began with a then revolutionary approach of using data analytics models to drive recruitment decisions has since spread across the sports industry. Two decades later, the ‘Moneyball’ approach looks like just the beginning with data analytics now mainstream across all professional sports, shaping decisions from recruitment and injury prevention to sponsorships and fan-engagement.

This article is the first article in our series on the impact of AI on sports, focussing on the negotiation of player contracts, while a subsequent post will focus on sponsorship deals and fan engagement.

Age of Analytics

Data analytics has long become an indispensable tool in the negotiation of player contracts. Where deals were once shaped by reputation and subjective judgment, every major professional sport has now embraced analytics. It has now become standard practice for professional football teams to use metrics like Expected Goals (xG), Expected Assists (xA) and contributions in high-value areas of the pitch to assess a player’s true worth. Other sports like basketball have also embraced efficiency metrics like true shooting percentage and player efficiency ratings, while Formula 1 teams rely on analytics from various datasets such as telemetry data to refine car and driver performance.

Players and agents have also successfully leveraged such information to negotiate improved contractual terms, often employing specialist data analysts to use comparative analysis against peer players to benchmark their value. In some cases, data has even guided players in identifying new teams or leagues where their playing style would be better suited, helping to maximise both career longevity and financial return. Teams, in turn, rely on the same insights to find the right players for a coach’s system, assess long-term potential and mitigate risks before committing to significant transfer fees/trade assets or longer-term contracts.

While this may appear to be rapid progress, the next frontier is already underway: artificial intelligence (AI). Valued at $1.2bn in 2024, the AI in sport sector is expected to grow 14.7% year on year to reach $4.7bn by 2034. Unlike traditional analytics, AI can process vast datasets, identify hidden patterns, and make predictive assessments that go beyond what humans-or spreadsheets-can manage. AI is not only capable of enhancing player performance analysis, but is also beginning to reshape the way commercial transactions in the sports industry, such as player contracts are being negotiated.

AI Era

Machine learning models can help forecast an athlete’s career trajectory, estimate commercial value, and predict injury risks and general risk factors related to performance, which are packaged in datasets that can be used in contract negotiations. For instance, AI tools can simulate how a player might perform in different tactical systems, project how contract terms could impact a team’s wage structure, or estimate the commercial return an athlete might bring through fan engagement and sponsorship.

Sporting organisations are increasingly using AI to analyse large sets of data and replace more subjective player evaluations, eliminating research time, travel cost and making evaluations more reliable and consistent. Basketball teams for example use AI models to assess load management data and playing time sustainability, which help structure contracts around availability guarantees and performance-based incentives. This has allowed contract negotiations to move beyond a debate over past statistics and into a forward-looking discussion of value, risk, and opportunity. In short, AI is transforming negotiations into data-rich bargaining between the various stakeholders (organisations, agents, players and commercial partners), all of whom are now armed with insights that reduce uncertainty and create value on and off the field.

While the role of AI in contracts is promising, legal and ethical questions also loom large. Negotiations must still be conducted in fairness and good faith, but the use of AI-driven insights raises privacy issues in relation to personal data of players such as biometric and performance data and transparency issues in relation to the fairness of reliance on predictive AI models. A sector agnostic risk (which the sports industry is no exception to) is the replication of bias in training data by AI models and the perpetuation of such outcomes.  

Athletes are increasingly likely to call for requirements that their personal information will not be misused and the creation of guardrails in relation to reliance on AI models to ensure fairness and avoid bias in the contract negotiation process.  Regulators in turn will need to adapt to these new uses and attempt to create clear rules on accountability in relation to the use of personal data. Clear guidelines with regard to the use of AI models which preserve fairness and avoid bias in the negotiation process and the protection of personal data will be key to building trust between players, sports organisations, and fans.

Future Considerations

The evolution from Moneyball’s statistical revolution to today’s AI-driven negotiations represents a shift in the business of sport. While data analytics has already empowered athletes to showcase their worth and teams to make smarter, more risk-aware decisions, AI is now taking this even further, offering predictive insights and negotiation tools that can model performance, return, and risk.

When AI models are deployed with input from all stakeholders and in line with established guidelines, there is an opportunity to create a negotiation environment where athletes, sports organisations, and sponsors alike are better informed to strike fair, future-proof deals, reducing uncertainties and crucially optimising efficiencies in performance.

Looking ahead, it is clear that the use of data analytics and AI is already transforming and will further transform the process of negotiating contracts into evidence-based, forward-looking agreements, where value, risk and opportunity are better understood, reshaping the balance of power between players, agents, and sports organisations.