The football industry has always faced a number of distinct obstacles. Scouting is one such challenge that has recently come to the fore. The football industry is a highly competitive environment in which several clubs and organizations encounter difficulties in developing effective acquisition strategies. Scouting is the primary method by which teams get information about the performance of opposing players. A Player Scout is a scout who collects information on players’ performance. Using Predictive Analytics to scout football players is becoming a common method.
For more than a decade, data analytics has been widely used in the football industry, and it plays an important part in scouting. Big Data enables teams to gain competitive advantage on and off the field by extracting insights that can raise player performance, reduce injuries due to weariness, and improve a player’s commercial efficiency. However, collecting these insights and contextualizing the generated data has always been a challenge.
Typically, the scout would visit to observe the prospective player, take notes on his performance, and then give it to the club’s manager. Today, however, the recruitment process begins with the analysis of player data. Data analysts develop a shortlist of players to select by leveraging the power of player data. Professional scouts then watch a plethora of videos highlighting the ability and talent of the shortlisted players. Finally, it’s time to see how the prospective player performs. If the scouts are impressed, they will make an offer to sign the player to the team.
Brentford and Liverpool have both embraced the ‘Quest for Rationality’ idea in their recruitment processes. Brentford’s salary budget was 60 percent less than the League average (when they were playing in the Championship). However, the 12 players they signed for 12.5 million Euros gained in value over time. Brentford got 109.2 million Euros when those 12 were moved out, representing a gross capital gain of 96.7 million Euros. Liverpool was not scared to sell top players like Suarez and Sterling since the proceeds were reinvested in discounted players or skills who suited the club’s playing style.
Machine Learning (ML) has been used in scouting to make better selections. To begin, data is labeled using a program called Keypoint Skeletons. The model is then trained when the required player data is acquired, allowing us to track and forecast the player’s moves.
This sort of prediction can help a team succeed by signing untapped players at a lower cost and then developing them for a few seasons before selling them for a profit in the future. Such strategies have been in use for a few years, ever since teams began collecting terabytes of data to gain insights.
The Rise of Analytics in Player Recruiting
Ubitrack, a football performance data startup, employs data extraction technology to assist football teams in identifying top-performing players. They collect millions of data points by placing cameras around a football field, which are then processed and used in Computer Vision and Machine Learning algorithms. Teams such as Ludogorets and CSKA1948 spend in acquiring performance data in order to track talented players. Using performance data, a coach can determine whether or not the process of increasing player potential is beneficial.
In the world of scouting, facts and statistics are far more reliable than football outcomes. Football clubs in Balkan countries such as Croatia and Bulgaria are still in their early phases and lack the financial resources of clubs in Western Europe. Teams, on the other hand, can recognize skills and mold them to their needs using modern technologies such as Performance Information.
FC Barcelona has been a pioneer in the use of Data Analytics to track players. When scouting, the most essential KPI is Expected Goals (xG), which ideally evaluates the quality of scoring chances created by players. These data points, along with other statistics, are displayed in a player radar or a spider chart. This player radar is then compared to the charts of other players in the league. Club scouts can benefit greatly from such diagrams. FC Barcelona’s data analysts attempt to understand how players react in various situations. A winger, for example, may dribble well in a counter-attacking situation, but his performance against an organized defense is also evaluated. This context-based analysis is used to determine how a player will perform in various situations while playing for the club.
Beyond Traditional Scouting in the Future
During a transfer market, objective data is essential. There could be a 25-year-old winger who scores and assists every game, establishing himself as the world’s next big player. However, the likelihood of him playing at that level on a consistent basis must be considered while finalizing a transfer. It is critical to comprehend the patterns related with peak performance.
OptaStats, a major company that collects football-related data, has an enormous depth in its reach because it includes lesser leagues throughout Europe. This detailed data is considered as a big savings because it eliminates the need to hire a slew of scouts to identify hidden talents in the lower levels. Sensors placed on players’ shin guards record data such as passes, crosses, and attempted shoots.
The 21st Club is making waves in the recruiting world. They aid in visualizing whether there is a link between a player’s action and reaction on the field and the team’s performance. For example, the in-house team evaluated their client’s roster using smart data to determine the value of each player. This led in the identification of a footballer who was valued significantly more than the other 18 players on the squad yet earned 25% less than the average player.
The rising use of data and player statistics will continue to be a part of the recruitment space for many years to come. Extremely talented scouts will be difficult to locate, making them extremely valuable to any club. To find the best player, statistics and traditional scouting will, understandably, have to work together. Scouting biases are frequent, but they must be eliminated so that great athletes can be given an opportunity to shine. This can be accomplished through evaluating data and gaining precise insights.
In sports, predictive analytics for football players has always been vital for gaining an advantage over an opponent. The use of Machine Learning and Artificial Intelligence for predictive analysis and the development of new applications has recently expanded. Because of the potential of predictive analytics, the future of recruiting football players will become more competitive and advanced, with increased accuracy.