6 football clubs who are making great use of data analytics
Football data analytics has become increasingly important in recent years, particularly in player recruitment and sales. Football clubs can analyze data on potential players to determine their suitability for the team, considering factors such as age, position, physical attributes, and performance statistics.
This information can help clubs make more informed decisions when signing new players, reducing the risk of expensive transfers that do not deliver close to desired results. Using data analytics in this way, football clubs can improve the quality of their squads and increase their chances of success on the pitch in line with their philosophy.
From a financial perspective, data analytics can also be valuable in helping football clubs generate revenue through player sales. Clubs can analyze data on the performance of their players, identifying those who are most likely to attract interest from other teams. By selling these players at the right time and price, clubs can generate significant revenue, which can be reinvested in the team to support organic growth. This approach can be particularly effective for smaller clubs, who may have different financial resources than their larger counterparts.
Listed below are football clubs that have used data analytics to gain valuable insights on players, thus enabling them to compete with their wealthier counterparts in the league:
#1 Brighton & Hove Albion Football Club
Brighton & Hove Albion are a prime example of how data analytics can be used to gain a competitive edge in the Premier League. The club has invested heavily in data analytics and has a dedicated team of experts who work to gather and analyze data on player performance, injuries, and other key factors. Using this data to inform their decisions, Brighton have been able to sign players who are well-suited to their style of play and who have the potential to perform at a high level.
One example of how the Seagulls used data analytics to their advantage is how they signed midfielder Moises Caicedo. The club's data analytics team identified Caicedo as a player with high potential based on his performance data, physical attributes, and other factors. The team then used this information to convince the club's management to sign Caicedo, even though he was relatively unknown then. Since joining Brighton, Caicedo has become a key player, delivering outstanding performances and attracting attention from other Premier League clubs.
#2 Brentford Football Club
Brentford is renowned for its data-driven player recruitment and development approach. The club has an innovative approach to data analytics, using advanced statistical models and machine learning algorithms to identify promising young players who other clubs may have overlooked. By analyzing data on player performance, physical attributes, and other factors, Brentford has signed high-performing players at lower costs than their competitors, giving them a competitive edge in the Premier League.
One example of how Brentford have used data analytics to their advantage is in the signing of striker Ivan Toney. The club's data analytics team identified Toney as a player with high potential based on his performance data, physical attributes, and other factors.
They also identified that Toney was undervalued in the transfer market and represented a great opportunity for the club. Brentford signed Toney for a fraction of the cost other Premier League clubs would have had to pay for a player of his caliber. Since joining Brentford, Toney has become the top scorer in the Championship and helped the club secure promotion to the Premier League.
#3 Rayo Vallecano CF
Spanish side Rayo Vallecano too have used data analytics to gain a competitive advantage over other La Liga teams. The club's dedicated data analytics team gathers and analyzes data on player performance, physical attributes, and other key factors.
This data is used to inform the club's decision-making processes, from player recruitment and development to tactical strategies on the pitch. By leveraging data analytics, Rayo Vallecano has optimized its performance and competed with other La Liga teams.his data is used to inform the club's decision-making processes, from player recruitment and development to tactical strategies on the pitch. By leveraging data analytics, Rayo Vallecano has optimized its performance and competed with other La Liga teams.
One example of how Rayo Vallecano has used data analytics to its advantage is in the signing of left-back Esteban Saveljich. The club's data analytics team identified Saveljich as a player with high potential based on his performance data, physical attributes, and other factors. They also identified that Saveljich was undervalued in the transfer market and represented a great opportunity for the club.
Rayo Vallecano signed Saveljich for a relatively low transfer fee, and he quickly established himself as a key player in the team. Saveljich's performances helped the club secure promotion to La Liga in the 2020-2021 season, and he has continued to be a standout player in the top flight.
#4 Napoli
Napoli made significant strides in their optimal usage of data analytics in their decisions in the transfer market. The club's dedicated data analytics team uses their analysis to guide the club in various decision-making processes, from player recruitment and development to tactical strategies on the pitch. By leveraging data analytics, Napoli have optimized their performance and competed with other Serie A teams.
One example of how Napoli have used data analytics to their advantage is in the signing of forward Victor Osimhen. The club's data analytics team identified Osimhen as a player with high potential based on his performance data, stats and physical attributes. Napoli did sign Osimhen for a significant transfer fee, but their data-driven approach gave the club that extra bit of confidence in making their decision.
Since joining Napoli, Osimhen has quickly established himself as one of the best strikers in the world.
#5 Union Berlin
Union Berlin's dedicated data analytics team gathers and analyzes data on player performance. This data is used to inform the club's decision-making processes, from player recruitment and development to tactical strategies on the pitch. By leveraging data analytics, Union Berlin has optimized their performance in Bundesliga and are in the top four at the beginning of March 2023.
One example of how Union Berlin has used data analytics to their advantage is the signing of forward Taiwo Awoniyi. They identified that Awoniyi was undervalued in the transfer market and represented a great opportunity for the club. Union Berlin signed Awoniyi on an initial loan, and he quickly established himself as a key player in the team. Awoniyi's performances helped the club secure their highest-ever Bundesliga finish in the 2020-2021 season, finishing 7th on the league table.
#6 RC Lens
French outfit RC Lens has been using data analytics to help the all departments at the club in various aspects of the game, including player performance, training routines, scouting, recruitment, and in-game tactics. Using data analytics, RC Lens has made data-driven decisions and optimized its resources, resulting in improved player performance and better results on the field.
The club uses a data analytics platform to analyze players from various leagues, including Ligue 1, before identifying potential transfer targets. This platform helps the club to identify players who fit their playing style and have a high potential to perform well on the field.
This led RC Lens to sign players such as Gaël Kakuta and Jonathan Clauss. After failing to make it to the top four of Ligue 1 last season, RC Lens look one of the favourites to gain a Champions League spot this season.