Last updated 2/7/2021
This was written as part of my thesis on predictive models for transfer decisions
Intro
Football has faced an increase in professionalization in the last 20 years that nobody could have predicted, lots of the innovations come at the sporting level with better exercise routines as a way for athletes pouring millions into achieving peak performance, however, one of the trends from the last years is a shift towards data when taking decisions both in the sporting direction and in the preparation for matches. In a quest for peak performance and profit, data is becoming a central part of the sport, taking ideas from Baseball’s Moneyball and sporting science.
Teams And athletes are turning to big data to increase their chance of making correct decisions on every aspect of the game from the club office to the pitch. Over the last 10 years, this has become less of an innovation or a well-guarded secret and has become the norm.
Brentford
Brentford F.C. is a football club based in West London that has recently gained promotion to the Premier League via playoffs after 70 years with no first division football. They have gained fame in the last years as a disruptor club in football, by using big data to aid in the scouting process and to analyse the opposition teams. In the last 10 years, they have been promoted from League One, England’s third division to the topflight, ever since investor Matthew Benham came to the club and decided to do things differently to traditional football clubs. According to Deloitte, promotion to the Premier League comes with a two-hundred and eighty million-pound generation bonus over the next 5 seasons which allows clubs to develop massively and gives them a fair fighting chance at staying in the elite league. Brentford is not an outlier team in English football club, first, relegation is easy in a competitive league like the Championship (the second tier) however, they have avoided relegation every year since they promoted in 2015 whilst always generating profit through transfer activity. Teams in England tend to throw money at problems, the prime example of this is Manchester City that has a four-hundred- and ninety-six-million-pound net spend in the same period. In addition to sporting and financial results, Brentford has also achieved a style of play that is attractive to the fans.
One of the cornerstones of their success is the use of big data when making club decisions. In the scouting and signing department one of the most prominent examples of this is forward Ollie Watkins who was signed in 2017 from Exeter City and who played for three seasons at Brentford, scoring 49 goals in 143 appearances and winning the Championship player of the season in 2020. This secured a transfer for the player to Premier League team Aston Villa with a net profit of a reported thirty million pounds. This king of signings of lesser known, undervalued players is the norm at Brentford and has become a staple of their transfer windows, at Brentford they have a stock market approach to the transfer market, by seeing players as appreciating assets with both a sporting and economic return. Another use of big data at the club is when scouting opposition teams, they do this to find the opposition’s hidden weaknesses in order to exploit them and obtain positive results from difficult games, few things are left to chance or traditional scouting alone.
Finally, and continuing with their disruption of traditional football, Brentford have closed their development academy in favour of a more American system where all the talent is developed elsewhere brought in, and a B team or development team is formed. This decision comes as a result of the little sporting and financial returns on investment a football academy brings for a small club, as bigger clubs come and take away all the young talent before they sign professional contracts. Specially for the B team signings, the club focuses on rejects from other bigger clubs who will never break into the first team there, players from lower divisions and foreign players from less competitive leagues where the price tag of players is smaller, this allows the club to bring undervalued players both economically and athletically and give them a stage to develop both as players and as assets. At Brentford they believe that narrowing the gap between the humble clubs and clubs like Manchester City comes from doing things differently, by playing their advantages and exploiting the opposition’s inefficiencies.
Brentford are aware they are playing an infinite game and therefore make signings according to their own values; this increases the odds of profit and sporting success in the long run. They are fully aware that big data is just an aid, a way to lower the risk not a predictor machine, chairman Rasmus Ankersen has been quoted saying: “Data can tell you where to look but cannot exactly who to pick.” This mindset has in conclusion allowed them to disrupt football, make profit and have sporting returns which allows to have both shareholders and fans happy.
De Bruyne
Kevin de Bruyne is a midfielder who plays for Manchester City and the Belgian national team and as of 26/05/2021 is worth 100 million € on player valuation website Transfermarkt. It has been reported that he used big data to show his influence on the game, using analytics company Analytics FC, to broker a four hundred thousand pound a week contract. By illustrating his worth using a tangible method he has obtained leverage to be remunerated relative to his game. This is the most famous case of a player using big data to negotiate a deal because of the novelty use of data in the negotiation and because it did not include the figure of an agent, a traditional figure in sports negotiations. The combination of both together with the notoriety of the player who has won the PFA’s player of the year for two years in a row has made this singing big news and influential in football. This contract negotiation may serve as a trail blazer for football stars to change the way player club negotiations are undergone in the future as both the player and the club benefit from data and the absence of intermediaries.
Analytics FC is a scouting agency that explore the player’s past and present and even project the future of a player, it was founded in 2015 by a former Brentford B coach. They use an algorithm that calculates a player’s contribution value. Firms like this are increasingly being used in contract negotiations.
Memphis
Memphis Depay is another case study of a player using data in their favour. Back in 2015 Memphis was a wonderkid from the Netherlands playing at PSV Eindhoven looking for a move to more competitive football. After having a couple options on the table, Memphis chose to go to the Premier League to play for Manchester United, a club with lots of history but no direction after former manager Sir Alex Ferguson Retired. The move was not beneficial to the developing player, 21 years old then, who ended up playing less and worse than expected, after just one season and a half there, he chose to leave Manchester United. Trying to avoid joining another club that did not fit his style of play and where he could not develop as a player, Memphis enlisted the help of data analytics company Scisports who aided him in his move to Olympique Lyonnais. The transfer to this club, seemed as a step back for Memphis, going to a less competitive league and has come to be known as the first ‘laptop transfer’, according to Scisports, they made a player report for Memphis which helped Memphis find clubs where the conditions were optimal for his style of play and for his development.
The company were able to give Depay several teams from which he chose Olympic Lyonnais, where he was transferred for a reported fee of twenty-two million pounds. The move proved to be spot on for Memphis who at Olympic Lyonnais, has helped the historic club maintain relevancy and has broken several goalscoring records. This step back to a place where his style of play is favoured has earned him a move this summer to FC Barcelona, which according to some sources has been his dream move since he was an infant. The use of big data has allowed Memphis to find a place where his athletic attributes could be better exploited, where he has been able to improve his game and where a very lucrative move to a title contender club has been made possible. Lots of players undergo this problem where they make a bad move early in their careers and never bounce back, big data has helped Depay save a career that looked like it had peaked in 2017.
Evolution of Metrics
The data used to gain insight to inform these decisions comes from digital cameras on the pitch which follow every player and the gathering of around 10 data points per second per player on the pitch. Clubs are making a race for big data and throwing stack of cash to start-ups for a chance to get the upper hand over the competition. These start-ups crunch up the collected data and come up with models that allow the club to predict future performance. The metrics that are collected keep evolving with every season a couple years ago for example a common stat for a player was assists, the passes that they give that end in a goal, today, the stat is key passes, where the player gives a final pass which results in an attempt on goal. Stats like this let the analysis not be so result based and investigate the actual game and the opportunities created. This allows the scout or manager analyse numerically the performance of their players not just the results, they can extract how they are performing relative to the quality of the chances they create some players waste lots of chances some bank on a very small number of chances. Results are not always a direct result of performance or potential.
Final Thoughts
The use of big data in football has seen an increasing trend because it gives teams an upper hand that might translate to millions in profit, this is the reason that teams are flocking to it. As an aid in the transfer market or in the analysis of opposition big data is a great tool but it must be coupled with traditional scouting for optimum effect. Using this tool gives an edge in the long run, over a long sample of transfers and with the impatience of the sport, a big data approach has been discarded after a single player becoming a flop. Over the next years more “laptop transfers” will occur this will be a chance for smaller teams to be successful through taking risks that bigger teams with higher stakes cannot afford.