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Football/soccer analytics – the search for competitive advantages

I have earlier written articles about the best ‘pathway’ or the best strategies to winning in football (soccer in the US). For example, I have explored how winning the UEFA Champions League is a function of how strategy creates a vital level of cohesion and thus understanding within a team. Data plays a central role in helping to facilitate such scenarios in the world of football. Football is the most complex sport. Therefore, it is crucial to let data assist in shaping a good foundation for decision making on and off the playing field.

The importance of qualifying data

Football performances thrive when they are surrounded by competent ‘researchers’ of the game. To optimize performances in football you want these so-called researchers to take the role of the players, the coaches, the sporting directors, the physical trainers, the sports medicine staff and so on to simply show a more in-depth interest for the development of the game. If that part is established, it is significant that the football club cultivates this interest in association with the club’s strategic foundation. In that sense, it is imperative to figure out how a football club’s identity and strategy should be aligned with data analytics. For instance, what are the most important technical or physical data points for FC Barcelona, which is differentiated from the needs of other clubs with different capabilities? The data points must be aligned with the playing philosophy and style of the club and therefore by the club’s approach to talent identification, recruitment and management.

Open source approaches and knowledge management

Meeting and talking to Javier Fernández and others from FC Barcelona in relation to learning from Javier’s presentation about Expected Possession Value during MIT’s Sloan Sports Analytics Conference yesterday provides an example of FC Barcelona’s ‘open source approach’ to data in football. This perspective is interesting in that the real value of data analytics in football comes down to more than just access to data and how the club is able to crunch data. From my perspective, there is no vision without execution. So, I see that the real value is hugely associated with the applicability and the club having a culture where knowledge sharing is at the forefront. This should be given a high priority from top management in recognizing that optimal knowledge sharing leads everyone to become better at what they do, which in the end increases the quality of work.

Another interesting point emphasizes that contextual factors matter, which is a key learning point when working with football data analytics as the dynamics of the game changes rapidly. For instance, I had an interesting chat with Chris Loxston from FIFA, who presented some interesting points from analysis concerning the 2018 FIFA World Cup in Russia where there was a trend of ‘compact defending’. The trend showed that teams really prioritized to defend the areas outside the penalty box with many players and short distances between the players within a line or between lines, e.g., see the full report here. The increased focus on ‘compact defending’ was one example of how the FIFA World Cup and the execution of play on the pitch had developed over the span of the four years since the 2014 World Cup.

Below, you will find a podcast, which I did today during the Sloan Sport Analytics Conference with Laurie Shaw, who is a Visiting Fellow at Harvard University’s Data Science Initiative and behind the football analytics blog EightyFivePoints. Recently, he played a central role in starting a new football analytics group at Harvard University. Laurie and I have started to look into fields of research collaboration to see if we can produce valuable knowledge, which accounts for football analytics from a more holistic perspective.

To facilitate the search for competitive advantages even further, I wrote a book chapter with Daniel Rascher from University of San Francisco, which came out in November 2018, in which we cover some relevant context examples of football analytics. We present how and why the increased influence of technology and data in football, e.g., the case of sports tracking data, can produce meaningful interactions between sporting and business performances of football clubs and thereby help to drive capitalization by adding a meta-layer to the traditional sports economic models. You can access the book chapter here.

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