Football has undergone a technological revolution in the last few years regarding how clubs use data analytics.

The days when clubs relied solely on scouts' intuition and old scouting methods are long gone.

Advanced statistics have become part of every club that helps them decide on player recruitment.

A turn of events has made football the day's science, with precision and plan coming to the surface.

Let's explore how data analytics is the future of player recruitment in football.

Free bets and betting enthusiasts also benefit from using data analytics in football.

More stats mean a more data-driven understanding of player and match outcomes.

The Rise of Data Analytics in Football 

The merging of football and data analytics started to gain momentum in the early 2000s, when football was first tied to data analytics.

However, it really took off after the success of Michael Lewis's book Moneyball, which described how the Oakland Athletics used an analytical approach to baseball.

The book's success inspired football clubs, especially Premier League ones, to examine how the numbers will further improve the team's performance and player acquisitions.

Teams like Liverpool, Manchester City, and Brentford were the first to take this step, and they hired a mix of statisticians, data scientists, and experts to really "dig deep" into the numbers.

The increasing use of analytics in player recruitment is mainly because of the risk-mitigating quality of the approach.

Traditional scouting heavily relied on observation. Though valuable, observation is still often biased in the scouts' eyes, and thus, inconsistencies are bound to show up.

Analytics can objectively measure performance, physical attributes, and even a player's future value.

Key Metrics Used in Player Recruitment

Data-driven recruitment is not limited to just the direct contribution of a player to goals or assists.

In-depth statistics offer all types of parameters that narrow down a player's impact.

Some of the most important metrics are:

Expected Goals (xG):

Expected Goals is a statistical model that assesses the probability of a shot being scored based on variables such as the distance of the shot, the angle, and the type of assist.

xG aids clubs in assessing the extent to which a player is a goal scorer and how dependent they are on the quality of the chances they get or do not get.

Pass Completion Percentage:

This indicator allows clubs to judge players' ability to de-ball under opponent teams' defensive pressure.

It's especially significant for midfielders who must provide the ball ahead toward attacking.

Progressive Passes and Carries:

Forward play is the skills a player possesses to pass and carry the ball in a forward direction, which is a decisive factor for evaluating players, mainly midfielders and defenders.

Progressive passes and carries are the skills through which a player can lead the play effectively, either by passing or dribbling.

Defensive Actions:

These figures, such as the number of interceptions, the positivity of correct tackles, and the pressures supplied, are valid when distinguishing defenders and defensive midfielders who can destroy opponents' attacks phenomenally.

Physical Data:

Monitoring a player's distance on the pitch, the number of sprints per game, and their top speeds can be important in judging whether a player can perform at the physical level required for elite football.

Player Value Estimation:

Clubs employ data analysis to determine a player's potential value in the future.

This is highly beneficial in scouting when the observed talents are young and might become key players on the field and in the transfer market.

Real-Life Examples: Clubs Using Analytics for Recruitment

One of the best examples of a football club using data analytics to build a team is Liverpool FC.

Under Jürgen Klopp and Michael Edwards, they invested heavily in data to find undervalued players who fit Klopp’s high press.

Key signings like Mohamed Salah, Sadio Mané, and Andrew Robertson were players with specific stats that matched Liverpool's needs and turned the club into a title-winning team.

Brentford FC is another club that uses data to find hidden gems in lower leagues and undervalued players from across Europe.

Brentford’s scouting is almost entirely data-driven, and they reaped the rewards with promotion to the Premier League in 2021.

Manchester City are also leading the way with data analytics in recruitment.

Their analytics team works with traditional scouting to use extensive metrics to ensure that new signings fit into Pep Guardiola’s complex system.

Players like Rodri and Ruben Díaz were analyzed for their passing, tactical awareness and ability to execute the high defensive line Guardiola likes.

Data-Driven Recruitment Benefits and Drawbacks

Using data in football recruitment has many advantages.

Firstly, it helps clubs make better decisions and reduces the risk of big money transfers.

With transfer fees going through the roof, the stakes are high, and data can help you avoid costly mistakes.

Also, data gives clubs the ability to find undervalued players.

By looking at metrics that others are not, clubs can find players who may not have the name but have the attributes to succeed.

This is especially useful for smaller clubs with smaller budgets compared to bigger clubs.

However, there are limits to relying on data alone.

Football is a dynamic game, and statistics can never capture the intangible qualities that make a player great, such as leadership, resilience, or the ability to perform under pressure.

So, the best clubs balance data analytics and traditional scouting.

The human element is still crucial to evaluating a player’s mentality, adaptability and cultural fit in the team.

The Future of Data Analytics in Football Recruitment

As technology advances, the role of data analytics in football recruitment will only get bigger.

Machine learning and AI are becoming more relevant.

Clubs are developing algorithms to predict player performance based on historical data and to simulate how a player would fit into their team.

Clubs are also expanding their use of wearable technology and GPS tracking in training to gather even more data on players.