With 2 European cup titles and a record 36 Primera Liga victories, including four titles in the last five years, Sport Lisboa e Benfica (“Benfica”) is widely regarded as the most successful football club in Portuguese history . A key driver of the club’s recent success has been its heavy investment in technological infrastructure at its Caixa Futebol Campus, a training center located on the outskirts of Lisbon. Fitted with state-of-the-art sensors and GPS tracking systems, this facility is being used to create one of the largest repositories of athletic performance data in Europe today . In 2016, Microsoft Azure launched an engineering/technology partnership with Benfica to explore new ways in which the club’s copious amounts of accumulated data could be harvested and analyzed . Together, this partnership is yielding a promising data machine that could revolutionize talent management in European football.
Competitive Pressures in European Football
While football has long been the most popular sport on the planet, growing viewership rates in previously untapped markets (such as the US and China) have increased the value of broadcasting rights for the world’s top leagues. Consequently, European football clubs have become extremely lucrative investments for global entrepreneurs and investors. Along with the massive inflow of capital into the sport, the cost of acquiring and developing top talent has also risen meaningfully as some of the best players have commanded transfer fees of over €200 million in recent years. With the advent of rising costs, clubs like Benfica have shifted their focus to developing and nurturing homegrown talent. Moreover, the frothy market for young, talented football players has allowed Benfica to monetize its core capability as a talent incubator by selling its players for a profit 
Benfica’s Big Data-Driven Solution
At Caixa Futebol, players in Benfica’s three professional teams practice on sensor-laden pitches that closely track individual player movement, speed, agility accuracy, heart rate, etc. . Players’ sleep patterns and nutrition are also closely monitored, and all data is transmitted into a vast “data lake” hosted by Azure. Data scientists use these voluminous amounts of data to identify trends, patterns, and relationships between players’ habits and on-the-field performance. Coaching staff also use the insights gleaned from this data to develop personalized training programs for individual players, focusing on developing their strengths and working to improve areas of weakness . Fitness staff also use predictive analytics to determine the likelihood of player injuries, aiding in roster selection for high-profile games .
In the medium term, Benfica and Azure are exploring innovative ways of collecting and analyzing data to grow the size of the “lake” and perform a broader range of analytics that optimize team management. Capability targets include predicting future fitness and performance, which will allow the team to strategically plan its line-ups for competitive tournaments. The club is also seeking subtler, “less invasive” data collection devices that players can wear during practice to replace the current bulky sensor systems . More sophisticated monitoring equipment will also expand the data collection capabilities and minimize data integrity issues stemming from the use of elementary sensors.
What Does the Future Hold?
Going forward, it is critical to maintain the pace of investment in research and development to identify new ways to streamline data collection, and potentially expand the number of environments in which relevant data can be harnessed. While the focus is currently on monitoring player behavior during training at Caixa Futebol, Benfica and Azure should invest in hardware solutions and other technology to better track data on the pitch during actual games. This will complement current team optimization efforts by allowing for real-time analysis and data-driven decision-making during – rather than before – competitive games.
As Benfica’s data-driven machine becomes the cornerstone of the club’s talent-development strategy, several questions remain about the extent to which it can be relied on. For example, data points fed into the analytical tools are based on relatively small sample sizes when compared to the entire quality spectrum of football players. The extent to which these insights can be generalized and applied to subsequent generations of Benfica players will depend on the machine’s ability to learn and develop a constant feedback loop. Would Benfica need to expand enrollment at its academy to mitigate this? What lessons can be gleaned from the use of machine learning in more technically advanced sports? While Benfica may be one of the earlier adopters of machine-learning, to what extent will this translate into a sustainable competitive advantage, particularly as larger teams catch up to this model?
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- Sebastian Anthony. “Football: A deep dive into the tech and data behind the best players in the world” Net, 2017. Ars Technica, https://arstechnica.com/science/2017/05/football-data-tech-best-players-in-the-world/
- “The unlikely secret behind Benfica’s fourth consecutive Primeira Liga title” Net, 2017. WIRED, https://www.wired.co.uk/article/microsoft-sl-benfica
- Harry Petit. “How Benfica uses technology and data science to be one of the world’s best football clubs” Net, 2017. Daily Mail, https://www.dailymail.co.uk/sciencetech/article-4544900/How-world-s-best-football-clubs-use-data.html