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/.
It will indeed be very interesting to see how and to what extent hiring data scientists will enhance teams’ competitive advantage in discovering new talents. I see one main critical issue in this approach to talent scouting: we have already seen in basketball, a sport where data science is widely used to scout new young players, that many times the first choices in the NBA draft become so-called “busts”. These quantitative scouting methods, based on scoring averages, passing success rate, rebounds, +/- and so on, often fail because the numbers that players put up in college do not necessarily occur due to great talent, but rather to temporary physical advantage, poor opponents and other factors. It still seems to me that in terms of sports decision, successful talent-scouts should look at more “traditional” aspects of the game like technique, flair, positioning and personality. These features are often hardly quantifiable and thus cannot be addressed efficiently through numerical data analysis.
However, data science does promise great improvements- for players contracts and for football clubs e.g. in terms of revenue and expense management. Anyhow, the right managers need to be hired at the top of the hierarchy to ensure a mindful application of these technologies.