Articificial intelligence (AI) is being applied in various fields and sports is not far behind. There are many applications of AI and Machine Leaning (ML) in sports and more are being worked out. Let us look at a few such application below.
Talent scouting: Machine Learning is being used extensively in scouting out young talent. Data is collected in a few key areas and then processed through an algorithm which accounts for physical factors such as height, weight and age. This is widely acceptable in a sport like football where smaller clubs rely on developing players and then selling them off to bigger teams for a profit. Data Science Techniques will enable clubs to demand a higher fee for their best prospects and also allow the club to make changes to their training regime with help of the metrics collected.
For an extremely competitive and cut-throat sport like football a good scouting system will always be necessary.
Refereeing: It has almost become the norm in most sports worldwide to use technology for officiating games. It has significantly cut down on human error while making game-deciding calls. Even though it has had it’s fair share of controversy – replacing humans with machines and technology, errors have been reduced and has brought a positive impact to most sports. It has been used to its fullest in cricket – mainly with the LBWs, where the path of the ball is simulated to check whether it hits the stumps or not.
Recovery & Safety: Athletes recovering from their injuries is always a huge challenge and is difficult to determine when they can get back to their sports. But with the help of AI along with the data of previous injured athletes, doctors and physicians have been able to ensure a safer, smoother and speedier recovery for most athletes. The instances of a player being on the field while still not completely healed and further aggravating his injury has reduced. Most sports pour millions into ensuring the best healthcare for its athletes and it makes sense – you would want them to be at the peak of their careers for the longest time possible.
Better Training & Strategy : Athletes and their trainers now spend time pouring over data after training. They look at improving in the Key Result Areas (KRAs). It is imperative in sports to keep looking at the results and comparing it to your opponent and then try and improve them. AI can be trained to detect weaknesses as well. Say for example, a team concedes more goals to headers than other sort of goals, then the other team would deploy wingbacks who can cross the ball to the strikers. Image and video analytics are used widely by AI and analytics team. By looking at and analyzing how the opponent performs, the team finds out where to improve and how to deploy themselves the best they can.
While there are many ways AI and ML are being used in improving sports, the important question is – Can AI & Machine Learning eliminate the need for coaches all together? Not in the near future! With that being said, coaches who do not adapt to the technology being used risk being left behind.