How Big Data Analytics is Disrupting & Reshaping Scouting in Professional Football
Football clubs, like many organisations, serve the same purpose, to generate a profit and ensure their long-term sustainability in competitive environments. Scouting or player recruitment is akin to Human Resources, where identifying and recruiting the best talent can lead to teams gaining a competitive advantage. ‘Moneyball’, a term first coined by Michael Lewis, was a strategy implemented by the Oakland Athletics in the 2002 MLB season where they drafted a successful team on a limited budget utilising an analytical method known as Sabermetrics as opposed to traditional scouting methods (Lewis, 2003). This system’s success saw investing in an analytics team become standard practice within the Sports Industry, which has now disrupted and reshaped traditional player scouting and recruitment practises. Furthermore, the rise of Big Data has also impacted the Sports industry with many teams and clubs starting to see the value of data in improving performance both on and off the pitch. This article will look at various opportunities, limitations, challenges, and ethics concerning the use of Big Data and Analytics for scouting in professional Football.
The Opportunities Big Data Analytics has created for Football Clubs
The major drawback of traditional scouting strategies was that they were limited to scouts and coaches’ subjective opinions (Christensen, 2009). Players often get overlooked for various biased reasons such as their appearance, personality, age, etc. Analytics removes the subjective element by generating fact-based insights to guide informed decision-making to ensure success, both competitively and financially by finding value in players no one can see.
Gerrard (2007) describes the sporting industry as one with a high degree of resource homogeneity, allowing clubs with the most economical power to purchase the best resources (players) to develop a resource-based advantage. These clubs are the likes of Real Madrid, Barcelona, PSG, Manchester City, etc, who have the financial capacity to attract and buy the best players in the world.
Top 50 most expensive association football transfers.
Therefore, financially smaller clubs to be competitive, need to develop a knowledge-based advantage through identifying quality players at a lower per-unit cost which may come with higher risks. Moneyball can be classified as an analytical strategy where teams with limited economic power would develop a knowledge-based advantage by utilising the big data available on player performance and conducting statistical analysis to better decide and identify players before opponents do.
Big Data Analytics has also created new opportunities for smaller Football clubs to pursue alternative business models to stay viable. A club can stay commercially viable without winning trophies through utilising a Transfer Strategy Business Model. These clubs are sometimes more informally referred to as ‘Selling Clubs’. Andras and Havran (2015) defines this model as a system where a club identifies and recruits undervalued players, nurtures their development and sells them at a profit to bigger football clubs once fully developed. Financially smaller clubs regularly adopt this model since they cannot compete with the bigger clubs for trophies and instead make their profit through the Transfer Strategy to stay viable. Analytics will assist these clubs through informed decision making and value creation by identifying players with good prospects that a traditional scout wouldn’t see.
This article by Bleacher Report highlights the top Selling Clubs across the European leagues and how they earned a transfer surplus through key player’s sales.
The Limitations of Analytics in Football
Gerrard (2007) notes that gathering accurate analytical data in invasion sports is complicated due to measurement issues in team sports where players work together to achieve a specific goal such as Football. This problem is non-existent in sports like Baseball and Cricket, where batters score independently.
The three major measurement problems for invasion sports are tracking (tracking player contribution), attribution (allocating individual contribution to interdependent actions), and weighting (determining the significance of individual actions) (Gerrard, 2007).
In Football, for example, some statistics are clear and easy to understand, such as numbers of goals, assists, and clean sheets. However, a more in-depth analysis of contribution is required to understand an individual players contribution. You may have instances where a team didn’t concede, and the goalkeeper didn’t have to make a save. While the goalkeeper gets a clean sheet, how do you measure the impact of the strikers and midfielders pressing, which prevented the opposition from getting to the other half of the pitch to score?
Better analytical tools and statistics are increasingly becoming available to get a more holistic understanding of how good a player is. Tools such as SmarterScout are making it easier to see and identify player’s skills and contribution. Furthermore, better data collection technology such as implementing automated video analysis, stadium tracking sensors, image recognition software, and chip technology can address these measurement gaps and provide accurate data in the future.
Additionally, Baker, Cobely, Schorer, and Wattie (2017) notes that while analytics can provide useful insights that scouts and coaches may not be able to see, the main limitation is that it cannot predict the quality of future performances nor if their personality will be a good fit for the club. Hence, analytics is unlikely to replace traditional scouting completely. Coaches can use their experience and understanding of the game to see whether a player is viable and has the personality to become a good player. The best teams are likely to use both tools using analytics in a more supportive role.
The Main Challenges of using Analytics in Football
With the vast amount of data readily available, analytics provides football clubs with the ability to generate more significant insights to aid decision making. The key challenges faced by clubs are developing a framework where they can use Big Data and create a knowledge-based strategy in an accurate manner that can lead to value creation (Rein & Memmert, 2016). Clubs are now tasked with upskilling their traditional scouts to be more analytical in their approach and hire data scientists to make sense of their data and identify potential value-adding resources before rivals do (Burn-Murdoch, 2018).
Furthermore, another key challenge is that many traditionalists see Big Data Analytics as disruptive and it undermines the traditional way of scouting which has proven to work for decades. As seen in the movie Moneyball which covers the 2002 Oakland Athletics season, many scouts didn’t see the value nor were convinced by the insights analytics could provide. The main challenge for some teams will be to convince the board of the potential added value analytics can provide for the team’s long-term sustainability.
Ethical Considerations of using Analytics in Sports
With the advent of better tracking technologies, issues surrounding the ethical implications need discussing. Broad player data tracking does possess the risk of revealing private information and may be misused if ended up in the wrong hands (Osborne & Cunningham, 2017). It is also possible for sports analysts to come across incidental health findings of players and their family without their consent, resulting in the premature ending of an athlete’s career (Greenbaum, 2018). Furthermore, with more onfield and technological tracking tools, data is being recorded from many sources. The degree to which this data is encrypted is another concern for players should their data be leaked (Greenbaum, 2018). The use of big data in sports is relatively new. The laws and regulation are still premature in this area, leaving many loopholes for data about players being used without consent or inappropriately (Greenbaum, 2018; Osborne & Cunningham, 2017).
The Future of Analytics in Football
The rise of Big Data is likely to continue and change how we view the world, with the sporting industry being no exception. Employing an analytics team within a football club is no longer a secret and has become a regular practice. Liverpool FC one of the best teams in the English Premier League, pay top dollar for their data science team and attributed part of their historic Champions League and Premier League campaigns in 2019 and 2020 to their data use. The value data can provide is evident, and we’re only likely to see more football teams invest in analytics. Whether it be to draft the best team, identify the best strategy to win titles or to find value in players the naked eye can’t see, the use of data is endless. If you’re a data professional with a passion for sports, the football industry is looking for people like you who can use analytical skills to help teams make sense of the big data and add value to their operations.
