How do football clubs use Big Data not to buy success, but to create it? The answer to this question is looking for Nikolai Dubinin, host of the YouTube channel Industry 4.0
Football players on the field perform a lot of technical and tactical actions: strikes, passes, interceptions, passes, tackles. If you count them, you can see that football, like everything in our life, is controlled in a mathematical way.
In modern football, a “smart” system is used, which itself takes and analyzes a huge amount of data. For example, the company Instat works with such systems, which analyze the statistics collected during the game. The company’s website says that it has the most accurate data and the largest player base around the world – the video platform covers 960 football players. They evaluate 95 parameters of team actions and 70 – individual player actions: for example, how many touches he made, how many constructive passes and how many he ran. All this, including the mistakes of the players, is monitored by a special camera in the stands, and then the program processes the statistics. It turns out deep statistics for each player. In one match, it is possible to collect up to 2,5 thousand tactical and technical actions.
However, some clubs use other statistics as well. For example, the players of the Dutch club AZ Alkmaar provide analysts with their physical, cognitive and personal data. It is important to understand the psychology of the player. For example, if a football player has problems in the family – let’s say a close relative is seriously ill – this is reflected in the game. It is important for the coach to know that this is not due to the player losing his grip, but due to temporary difficulties.
The way of collecting statistics about players could have been taken in our country or Ukraine for a long time. Valery Lobanovsky has been a coach at Dynamo Kiev since 1973. He hired the scientist Anatoly Zelentsov. He developed a training system using mathematical modeling and headed a scientific laboratory, which was called the Zelentsov Center. Lobanovsky said: “The task of controlling the game is connected with a new scientific discipline – sports cybernetics.” The players were tested for endurance, which consisted of constantly pressing a computer button. The tapping test can determine the aerobic, anaerobic and general endurance of a person.
There are at least two examples on the Web where artificial intelligence and big data helped both the player and the team:
The German “Wolfsburg” took advantage of the algorithm that Wout Wegorst picked up for the club. He was found in the lower league of the Dutch championship: with statistics, he was doing well, but with psychology – not so much. As a result, in 60 matches by March 2020, he scored 28 goals.
Dutch footballer Memphis Depay did not have much success at Manchester United. He used an algorithm that advised him to go to Olympique Lyon. Bottom line: over 100 goals scored in 40 matches.
Can AI and Big Data turn a team into a champion?
They can play a very big role in this, but the person himself becomes the champion. To analyze all the opponents, a lot of time is spent. Artificial intelligence can help you choose tactics for a specific opponent, for a specific player who needs to be closed. That is, during the analysis of the game against Cristiano Ronaldo, machine vision and big data can predict what actions he will take on the field by understanding what he has done before. Conventionally, it is best to cover a football player on the right side, because he has more marriage there than on the left. The statistics also show which corner of the goal is most often scored by individual players.
How else can big data help?
The abilities of young football players are also trying to be determined mathematically as early as possible. In other words, the coaches want to get into the mind of a football player by a mathematical method. It takes a fraction of a second for a player on the field to predict in which direction he will run in the direction of the opponent’s knee, and turn the game in a completely different direction. And even this is subject to analysis.
There are things that algorithms determine better than coaching instincts. For example, young football players may have different biological ages. For example, in AZ Alkmaar, algorithms based on tests determine the biological age, and then compare all indicators with other players, focusing on the history of the development of this biological age. This helps not to miss the true talent.
Big data is analyzed not only to prepare for a particular match or to identify young talents. One can try to predict the likelihood of injury by matching a particular player’s medical and injury history with their current fitness, training behavior, and load bearing capacity.
The information is collected using a simple gadget. It is attached to the players on the back on the shirt-fronts. What is he tracking?
pulse and other medical indicators;
the distance that the athlete ran during the training;
time of maximum jerk;
maximum thrust.
One of the first to massively develop it was the Australian company Catapult. The company’s official website contains information about more than 2,5 thousand sports teams that use only their technology.
What else to read on the topic:
- Big data for big football: how Spartak’s IT infrastructure was built
- How big data is helping athletes run faster and jump higher (ENG)
- Data analysis in sports: interaction between scientists, clubs and federations
- How big data is changing big sport
- Data analytics as a tool to attract sports fans (ENG)
Subscribe to the Trends Telegram channel and stay up to date with current trends and forecasts about the future of technology, economics, education and innovation.