One example is FIFA’s EPTS technology which, thanks to devices inserted into the players’ inner tops, can collect all game data. You can better understand this technology in this FIFA video from minute 1:47 (it does not allow us to reproduce it on other websites…sic!) or in this PDF file .Another interesting initiative is that of the company PlayerMaker , which opts for wearables in the boots to be able to also collect data on the hit, percentage of use of each leg or control of the ball.
Microsoft is also investing in this type of solution with its nba중계 Performance Platform project with which teams such as Real Sociedad have collected data and presented the results in this new analysis platform.
Fans: Improving the experience
The clubs know that the fans are the basis of their business. Therefore, they are investing a lot of money in three areas related to fans:
The analysis of what your fans do in the stadiums to improve the experience of going to see a game
An example of this can be provided by SAP with its Venue Analytics solution . In it you can control attendance, degree of satisfaction, occupation of car parks near the stadium, etc. You can see it in the following product demo video (in English):
Improving the streaming experience for fans who follow you on TV.
Telecommunications companies have always been aware of the wealth of sports broadcasts, but now it is no longer enough to offer the event: the public wants additional experiences and the best image quality.
In this sense, they are trying to optimize this experience by providing additional statistics and data to the viewer thanks to real-time video analysis with Machine Learning tools. The following video shows an example in which the company SentioScope uses this type of technology to predict the player and track him in real time on the displayed image and thus be able to obtain statistics on the distance traveled:
Also companies like SynaMedia use Machine Learning technology to save up to 50% of the cost of bandwidth and storage when streaming video. You can learn more about this project at the following link.
Collection of fan data to carry out more personalized and effective marketing campaigns.
Clubs have long known that their relationship with fans is not limited to match days. They must create connections with them every day and the tools of Machine Learning and AI help them to do so.
An example is the collaboration of IBM (with its Watson solution), the FOX network and FIFA for the last World Cup in which they created the FIFA World Cup Highlight Machine application . With it, the follower can choose his team or player, the type of play he likes and the system provides him with a selection of the chosen plays in seconds so that he can create his own video of the best moments and share it on social networks.The fan must register and give their data, they leave you the information of their favorite player and the plays they liked the most and share it on their social networks. What more could you want?
Medical Equipment: Improving Injury Prediction
Going back to the beginning of the post, in 2018 Billy Beanie said in a conference at the Open Text Enterprise World that “The challenge today is to avoid injuries with data”.
That is why several companies have set to work to try to design Machine Learning models capable of predicting the probability of injury and what type of injury to players. That is why the prediction models are based on anticipating this type of injury caused by the workload of training sessions and official matches. You can see the full study at this link.
An example of all of this is the company Thermo Human, which uses a thermal camera to detect injuries in soccer players. In this case, they use Deep Learning technologies to recognize the parts of the body in the image and, together with the color of the thermal reading, to be able to detect injuries.