AI revolution in football: How PLAIER is helping Premier League clubs make better decisions | Football News happymamay

“Data analyzes were a big step,” says Jean Wind Sky Sports. “This is a giant leap in the development of football data.” Wendt is the co -founder of PlayerA company that helps to bring artificial intelligence technology to football in the Premier League.

They are already working with four Premier League clubs and 10 times this number worldwide. By harvesting extensive data and using artificial intelligence to understand it, they provide guidance on transfers, the cohesion of the team and whether it has come now to dismiss the coach.

WENDT’s preliminary plan and his work partner was to use artificial intelligence as a prediction tool, but they soon realized that just measuring football is the main challenge for decision -makers inside the game. “This is the same as the importance of making better decisions.”

After they built what they claim to be the largest database that was ever created in football – “a strange network of events data, injury data and salary data across more than 100 championships” – it works with everyone who analysts to help change the game.

“We look at it in three steps. One, we can tell you exactly how strong your team and your strength to achieve your goals. Two, we can tell you the expected influence of each player on your team’s goal at the end of the season.

“We offer a very clear vision so that you know exactly what situations aim to get stronger. The third part is to help clubs find the right players, and invest money in the best possible way. It is better to help make better decisions,.

You can just listen to the cough.

Then comes a X -ray man to see what is happening in their club.

“If you want to reach Europe, this is your competitors, and this is their strength. You want to outperform it? You will have to do this and this. We can clarify how strong your team is, and provide a clear picture of how to reach the next level.”

While all clubs can access the same data through PlayerHow do they use it. Some were also ready to use it as a candidate to better target scouts, while others are more careful and prefer to be as a verification of contracts.

“They create a list of the players who love them, and we create a list of players who are recommended by our system and see if there is interference. This is how the most careful clubs are working. The most advanced way is to use it really to find recommendations.”

Surprise Ken prediction

What are the lessons learned? “Defense positions are cheaper than offensive positions.” Not a surprise there, perhaps. But the consequences of obtaining this new expensive striker can be complicated. There is more than the goals they a result.

Some ideas may be shocked. When Harry Kane signed Bayern Munich, for example, Player The headlines in Germany topped when it was revealed that their models predicted that the signing of the German league record will not have a positive impact on the team.

“In the end, they had a goal difference of 54 without him and 49 with him,” says Wind. Bayern below 13 years. “Tottenham also became better, won more points, scored more goals and had a better goal in the goal. This is how we simulated that too.”

It is not slight on Kane, just a recognition that football is a group sport with many moving parts. Add something to a team and you can lose something else. “In the first season with Erling Halland, Manchester City also scored fewer goals,” Wind added.

Accounting the individual player’s effect is very useful for clubs when making decisions on employment and keeping them. “This is the most used tool.” With a click of a button, WENDT can show the possible result to replace one player with another.

“The event’s data has been present for 15 years or more now. What we want to measure is the efficiency of the systems, not how it can beg or how quickly it. How does the player contribute to the team’s success? This is what we have to work with.

“This is all that is because this is a collective sport with a lot of dependencies and relationships. What artificial intelligence does like a helicopter, it requires an overview. Then he tries to find patterns in these billions of connections. There is a lot of data, what is important?

“If we suggest a player to join Manchester City, we will simulate 38 games against each team in the Premier League 100,000 times to see how it works. You cannot hand this data manually, you will kill the computer. You need artificial intelligence.”

Continuing with Manchester City, he explains his point of view. “For example, Ederson or Stefan Ortega? The result is almost the same.” But then Rodri removes the city. “It has a tremendous effect.” This has been marked as a risk before being injured for a long time.

“A player like Rodri in the range of 7000 on our scale. There may be 200 players or so in the world at the level of 6000, so there is a risk in having a good player. Replacement is very difficult. Can you replace Roger Federer in your Davis Cup team?”

The impact of the exaggerated coach

Contrary to Wind’s view on the great influence of players on a global level, the effect of coaches is something he considers exaggerated. He did not bring him with prejudices. It was a discovery. “This was a kind of amazement for us,” he confessed.

“One of the most interesting learning from artificial intelligence is that the quality of the player is responsible for about 90 percent of sporting success and the coach’s contribution is only about 10 percent. Now, remember that 10 percent can still make the difference.

“But coaches tend to Overperform only with a maximum team team. No coach in our system has been emptied over the past ten years for a long time, with few exceptions.” Who are they? “Christian Street in Freiburg and Pep Guardiola in Manchester City.”

Guardiola’s recent struggles – without Rodri, of course – only highlights this point. It can also work in the other direction. The models also suggested, unlike popular opinion at the time, that Jurgen Klopp was not excessive in Liverpool.

Wendt displays a graphic fee containing Klopp directly on the place line that is expected to be the results of a team of this quality. “You can see Jurgen here, directly on the green line, as it was in the previous season.” This line represents performance in line with expectations.

Again, not the reputation of Klopp. Part of the reason why these players are very classified is that Klopp improved them. “Jurgen contributed to it, of course,” says Winda. “But it is a very strong group,” says Windh.

“At the present time, I think our models show that Liverpool has the strongest team in Europe, and perhaps the second most powerful group. If a new coach comes with a new motivation, and that he is able to add 2 percent, then the results of that can be amazing.”

The temptation of changing coaches will only grow, the transfer window is closed. “It is the only real thing you can change now. It is like a cold in football.” But through the ideas it provides PlayerHope is to make better decisions.

“The owners always have this problem, my team is not good enough or that the coach did not get the best performance from the team? Thomas Tuchille in Chelsea, for example. It was not necessary.”

Wendt talks about “market education” and “providing a more accurate basis for decisions” in an industry that can still be guided by emotion and a feeling of dead. One suspects that this could be just a beginning Player It intends to expand its arrival.

“We have some psychological parameters now. We can predict wounds. It’s an unusual tool and add things every few weeks but in an accurate way because there is already a lot. If we put everything in it, people will also find it difficult to work.”

Football seems more complicated. Ironically, the goal of all this is to make the decisions simpler. “We want to make it in black and white for the club. This player is either good enough to make you better or not. Simple.” Welcome to the world of artificial intelligence.