Beyond Moneyball: Data Analytics Empowers Players in the Age of Complex Sports Contracts

At the outset of this article, I want to make it clear that I have supported Manchester United ever since I got into football, in no small part, thanks to David Beckham. And it is with hope I write this piece, that with the transfer window open, the club I have supported for nearly 20 years now, builds a winning team. Note to my US audience: for the purposes of this post, football = soccer (as it should always be).

Data analytics in sports is nothing new. The 2011 film Moneyball, does a brilliant job of showing how modern data analytics and sports married. In fact, my alma mater, Stevens Institute of Technology , offers a MS-MBA dual degree in Sports Technologies and Digital Transformation degree in conjunction with Universidad Europea with a guaranteed internship with Real Madrid Next .

But so far, this has always been from the team’s point of view. One player however, has shown how data analytics can be used to turn the tables around. Kevin DeBruyne, Captain and mid-fielder for Manchester United ’s long-time rival Manchester City Football Club , turned to two respected lawyers and several leading football analytics service providers to provide context around his numbers, instead of a football agent. In doing so, not only did he manage to get an amazing deal for himself and show other players it can be done, but also pretty much make football agents obsolete.

Although DeBruyne used data analytics prominently, he is not the only one. Players like Marcus Rashford, Martin ?degaard, Ollie Watkins, and Hwang Hee-chan are strategically leveraging data to highlight their contributions and negotiate favourable contracts. And this is becoming necessary for players as the increasing insertion of clauses and the rise of free agents have added complexity to negotiations. ?degaard?recently became the highest-paid player at Arsenal with a contract extension until 2028, by highlighting his importance in the team through an important metric: his goals minus his Expected Goals (xG) which showed a figure of plus five in the 2022-23.

Data analytics firms such as have a key role to play when they offer bespoke reports. Their value lies not only in statistical comparisons but in providing objective assessments tailored to each player's objectives. It is not about making a bad player look good but objectively showcasing strengths and revealing areas for improvement. For example, their report analysing DeBruyne’s performance, and benchmarking it against other players at his position added a layer of financial insight to the process.

However, smaller clubs and lower leagues are still unable to deploy such technologies/applications primarily due to cost considerations of using such powerful tools. But, the landscape is evolving and services for data analytics will become cost effective for them soon enough.

Data analytics is not merely a trend but a game-changer in sports contracts. It empowers players, enhances negotiations, and provides a deeper understanding of a player's worth. The future promises increased sophistication, with players becoming more adept at leveraging data to secure favourable deals. In the US, we are witnessing how women’s sports, be it the NCAA or the WNBA, are improving with respect to funding, pay and infrastructure, by using data analytics to make their case.

Share your thoughts! How do you think data analytics will impact sports contracts in the future? Please read the brilliant article from Gregg Evans (which I referred to while writing this article), linked in the comments below.

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