It is an undeniable reality that data science has been increasingly adopted in sports, and its outcomes have been prolific. What was once a relatively unknown concept two decades ago has totally transformed the industry, which is predicted to be valued at $4.4 billion by 2022 (Sri, 2021). Its popularity is due to its potential application in seemingly every aspect of sports, from scouting a new prodigy to improving a golf swing or predicting the direction of penalty kicks (source).
Laurie Shaw, a former astrophysicist, and Treasury policy adviser in the UK, made headlines in January by joining Premier League champions Manchester City (Harper, 2021). His first-team role is to lead the development of data science, in order to predict future events. Whereas, data science currently is being used to analyze past events. Therefore this development has the potential to further enhance the competitive advantage of the English Champions. The signing of such a high-profile data scientist, prompts the question; are data scientists becoming the new golden signings in the football industry? It is absolutely clear that data science is here to stay, and its impact will incrementally grow on an industrial level. What about on a more business level?
In April of 2021, Manchester City captain, Kevin de Bruyne, took the media by storm, ’Kevin De Bruyne uses data analysts to broker £83m Man City contract without an agent (McDonnell, 2021)’. The decision to go against traditional forms of negotiation in sports was previously unseen in European football. The process of negotiation in football mostly follows the hiring of an agent. These individuals are often ruthless and in combination with their strong network, often succeed in fulfilling the demands of the player, at a very high cost. Agents normally take around 5-10% of the negotiated purchasing price and salary (Hendley, 2021). In the case of Kevin de Bruyne, this figure would have exceeded 15+ million pounds.
The football star was able to innovatively leverage his impact from all the games he’s played for the club, in terms of his contributions to the club’s success over the past four years. Whilst also predicting the impact impact in the future and the economic value he will add to the organization. The case of Kevin de Bruyne was the first with a player of such magnitude, in European football. Not only was he able to gain a 30% increase in wage, he also managed to avoid the excessive costs associated with traditional forms of negotiation (Vulpen, 2021). Evidently, the application potential of data science in sports is vast. More recently, it has proved an effective tool for negotiation and will most probably be widely adopted in the future, as it is already doing so in NBA and NFL.
Sources:
Sri, T. (2021). How is big data analytics changing sports?. Selerity. Retrieved 2 October 2021, from https://seleritysas.com/blog/2021/03/27/how-is-big-data-analytics-changing-sports/.
Harper, J. (2021). Data experts are becoming football’s best signings. BBC News. Retrieved 2 October 2021, from https://www.bbc.com/news/business-56164159.
McDonnell, D. (2021). De Bruyne uses data analysts to broker £83m Man City contract without agent. Mirror. Retrieved 2 October 2021, from https://www.mirror.co.uk/sport/football/news/kevin-de-bruyne-uses-data-23870686.
Hendley, A. (2021). How much money football agents earn – and how you become one. Mirror. Retrieved 2 October 2021, from https://www.mirror.co.uk/sport/football/news/how-much-money-football-agents-14580322.
Vulpen, E. (2021). How a Soccer Player Hired Data Scientists for Contract Negotiations | AIHR Blog. AIHR. Retrieved 2 October 2021, from https://www.aihr.com/blog/kevin-de-bruyne/.
Hi Keiko, very nice article you’ve got here! Really interesting how you zoom in on the professional sports aspect of data analytics. I can only agree that with increasing technological power, more applications are found everyday for data-analytics. Of course such a big industry as the professional sports industry, like every other big industry, will move into a data driven industry I would argue.
As there are many aspects to this industry, also like many industries, I am curious what paths data science will find to play a role in. As you described scouting, technique and game decisions are already being analysed. What more is to come? Every area where there is competition, there is room for a party to improve and utilize data to realize this improvement. Which I am a fan of.
But, wouldn’t you agree that there is a limit to the use of data-science in sports? Of course, making negotiations can be way more efficient by removing the role of these way too expensive brokers, we can only agree that this is a great improvement. But when football, baseball, boxing or tennismatches are steered by data and strategies generated by computers to make the game more efficient and the chance of winning increasingly large, will this not be to loss the beauty of the sports? When the emotions and passions are no longer part of the decisions and drive of individuals, will it still be fun to see a match, or play in one?
I am curious to know what your take would be on this!
Hey Luke, thanks for your response, I am glad you liked the article. I fully agree with you that technology and data are modernising this traditional sport. And at a certain point it might start taking away the beauty of it. This is already seen with the introduction of VAR in the premier league. The intention of the technology is correct, and in essence is the way forward. As it provides a fair playing ground, but the implementation of it has caused a lot of confusion. Not only concerning players, but management and fans alike. Lets see what the future holds!
Hi Keiko,
I really like your application of data analytics in the world of professional sports, this is such an exciting new application! As data analytics is proving to be useful in an increasing amount of industries, I can imagine that the professional sports industry won’t stay behind.
Also, I thought it was great to read your explicit example about Kevin de Bruyne using data analytics to negotiate the terms of his contract. I wanted to point out to you that there are also companies that specialize in assisting clubs in finding new players with skills that are a good addition to their current teams. An example of such a company is SciSports, perhaps you would be interested in getting to know more about them! I read that they use deep learning algorithms that are able to quantify, for example, “applying pressure on an opponent” in football, so that players’ ability of this skill can be used in data analytics, even though it seems almost impossible to quantify it by humans. Personally, I think these are very exciting developments, but I wonder if companies like these will replace human scouts completely. After all, a player’s personality should be a good addition to the existing team as well, not just his football skills. I’m wondering how you feel about this!
Kind regards,
Tessa