{"id":36769,"date":"2018-11-13T22:36:49","date_gmt":"2018-11-14T03:36:49","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/big-data-in-sports-how-s-l-benfica-is-using-machine-learning-to-build-a-european-football-powerhouse\/"},"modified":"2018-11-13T22:36:49","modified_gmt":"2018-11-14T03:36:49","slug":"big-data-in-sports-how-s-l-benfica-is-using-machine-learning-to-build-a-european-football-powerhouse","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/big-data-in-sports-how-s-l-benfica-is-using-machine-learning-to-build-a-european-football-powerhouse\/","title":{"rendered":"Big Data in Sports: How S.L. Benfica is Using Machine Learning to Build a European Football Powerhouse"},"content":{"rendered":"

 <\/p>\n

Benfica<\/strong><\/p>\n

With 2 European cup titles and a record 36 Primera Liga victories, including four titles in the last five years, Sport Lisboa e Benfica (\u201cBenfica\u201d) is widely regarded as the most successful football club in Portuguese history [3]. A key driver of the club\u2019s 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 [1]. In 2016, Microsoft Azure launched an engineering\/technology partnership with Benfica to explore new ways in which the club\u2019s copious amounts of accumulated data could be harvested and analyzed [2]. Together, this partnership is yielding a promising data machine that could revolutionize talent management in European football.<\/p>\n

Competitive Pressures in European Football<\/strong><\/p>\n

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\u2019s 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 \u20ac200 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 [1]<\/p>\n

Benfica\u2019s Big Data-Driven Solution<\/strong><\/p>\n

At Caixa Futebol, players in Benfica\u2019s three professional teams practice on sensor-laden pitches that closely track individual player movement, speed, agility accuracy, heart rate, etc. [3]. Players\u2019 sleep patterns and nutrition are also closely monitored, and all data is transmitted into a vast \u201cdata lake\u201d hosted by Azure. Data scientists use these voluminous amounts of data to identify trends, patterns, and relationships between players\u2019 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 [1][3]. Fitness staff also use predictive analytics to determine the likelihood of player injuries, aiding in roster selection for high-profile games [3].<\/p>\n

In the medium term, Benfica and Azure are exploring innovative ways of collecting and analyzing data to grow the size of the \u201clake\u201d 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, \u201cless invasive\u201d data collection devices that players can wear during practice to replace the current bulky sensor systems [1]. More sophisticated monitoring equipment will also expand the data collection capabilities and minimize data integrity issues stemming from the use of elementary sensors.<\/p>\n

What Does the Future Hold?<\/strong><\/p>\n

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 \u2013 rather than before \u2013 competitive games.<\/p>\n

Open Questions<\/strong><\/p>\n

As Benfica\u2019s data-driven machine becomes the cornerstone of the club\u2019s 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\u2019s 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?<\/p>\n

(Word count: 754)<\/p>\n

Sources:<\/p>\n

    \n
  1. Sebastian Anthony. \u201cFootball: A deep dive into the tech and data behind the best players in the world\u201d Net, 2017. Ars Technica<\/em>, https:\/\/arstechnica.com\/science\/2017\/05\/football-data-tech-best-players-in-the-world\/<\/a><\/li>\n
  2. \u201cThe unlikely secret behind Benfica’s fourth consecutive Primeira Liga title\u201d Net, 2017. WIRED<\/em>, https:\/\/www.wired.co.uk\/article\/microsoft-sl-benfica<\/a><\/li>\n
  3. Harry Petit. \u201cHow Benfica uses technology and data science to be one of the world’s best football clubs\u201d Net, 2017. Daily Mail<\/em>, https:\/\/www.dailymail.co.uk\/sciencetech\/article-4544900\/How-world-s-best-football-clubs-use-data.html<\/a><\/li>\n<\/ol>\n

     <\/p>\n","protected":false},"excerpt":{"rendered":"

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