Football data collection is more sophisticated than ever, thanks to increasingly advanced computer vision algorithms. This data can produce valuable knowledge that ups the game's competitiveness with domain expertise.
The information enables them to identify valuable potential players and eschew potentially disastrous ones. Additionally, it aids in creating defensive techniques that make the team difficult to breach.
Why does data science study football?
Football teams should partly embrace data science to prevent underperforming players' movement. I'll give you a few instances.
Fernando Torres transferred from Liverpool to Chelsea for a 50 million euro transfer fee. In 145 games for Liverpool, Torres amassed 80 goals and 22 assists. Due to his performance, Chelsea decided to sign him because they needed a striker. But nothing went as planned. He made 171 appearances and had 35 assists and 45 goals.
Robinho is another case in point. The most expensive signing in England at the time, he transferred from Real Madrid to Manchester City for a cost of 34 million euros.
In 137 games for Real Madrid, he was able to record 34 goals and 27 assists. In 53 appearances at City, he only managed to tally 16 goals and 12 assists. As a result, he sent a loan to Santos, and then, at a reduced cost of 28 million euros, he sent it to AC Milan.
Where does the information originate?
You now understand why data science is crucial for soccer clubs. You might be curious about the source of the data.
Prior to machine learning's rapid development, some people were tasked with keeping track of players' and teams' basic statistics, such as the total number of goals, assists, possessions, and so forth. This statistic only improves fan engagement; it has no effect on the team's internal communications. Before moving on to the case studies of football analytics, check out Learnbay’s Data science course in Delhi and learn cutting-edge tools and techniques.
Some Case Studies
FIFA doesn't want to fall behind, either. Additionally, they begin gathering information about events that account for both players who are on the ball and those who are not. A football game typically contains 2500 event data points. The FIFA team can gather about 15,000 event records.
Their cutting-edge FIFA Football Language makes it feasible for this data collection process to take place. This language makes it easier for analysts to quantify complex metrics that can improve team decision-making and gameplay.
An open-source version of the FIFA Football Language will be made available. We can learn more about it later, then. We'll wait until the FIFA World Cup 2022 to see how this idea does.
While Brentford is located in England, Midtjylland is located in Denmark. Although this club was created in 1999, it won the championship 15 years after it did.
Since both clubs' owners, Matthew Benham, are the same, they have a relationship with Brentford. He creates the Moneyball strategy for the team. The team won the title three times and frequently participated in European competitions, despite some of the players' reservations about using Moneyball.
The same approach Brentford employs is the source of the team's success. To measure the performance of their players and potential players, they create metrics and KPIs. They utilize that data to enhance their set pieces as well. Due to their success with set pieces, Midtjylland is the most successful team in Europe.
I hope this article broadens your understanding of the football industry's use of data science.
There isn't a single industry where data science isn't seen. So get up and master the essential data analytics skills with the best Data analytics course in Delhi.