Artificial Intelligence is an upcoming technology that is currently being implemented in many industries. Also in the sport industry, AI is being implemented in many more places than before. To get a good insight in where AI would be interesting to implement, we need to analyse the entire sporting industry first. The sport industry can be divided into two sections: media & fan experience and management & operations.
The media & fan experience include things such as news & content, eSports and sport betting. You can divide this section even further into post-game analysis and talent ID & selection
- Post-game analysis retrieves its data from wearables and game equipment and AI would be interesting to implement in analysis & feedback, recovery and injury management.
- In the talent ID & selection section, sensory data is used from peripheral devices and AI could be used in talent identification & selection.
Sponsoring, merchandise, venues, events & ticketing are all parts of the management & operations section of the sports industry. This section can also be divided further into in-game activity and pre-game preparation.
- In-game activity gets its data from other systems and when implementing AI in this region, you have to think about umpiring and specialist coaching.
- Machine & deep-learning is used to retrieve data for the pre-game preparation section. AI can be implemented in things such as training & coaching, nutrition, injury management, strategic and tactical game planning and team selection.
As described above, the sporting industry is a very interesting industry with several different aspects to consider. There are many different stakeholders in the industry and all off them could potentially benefit from more use of AI. There are still a lot of things that need to be explored further but the use of AI in sports is very promising.
References
Rizzoli, A. (2022, October 3). 7 Game-Changing AI Applications in the Sports Industry. V7. Retrieved October 11, 2022, from https://www.v7labs.com/blog/ai-in-sports
Very interesting article and good theoretical overview. Maybe I could add to this by providing a real case of AI in sports. I’m a big fan of ice hockey and already a few years back we saw the introduction of the “smart puck” in the Finnish hockey league. Basically, the puck has a sensor inside, the players have a sensor in their jerseys, and there are locators outside the rink. The locators track the players and the puck in real time and collect all sorts of game play data. For example, it knows when a player is on the bench vs. when the player is in the rink, it tracks which player has the puck, the speed of shots etc. Also the collected data is used in multiple ways. As a spectator whether you are at the arena or watching on tv, you can see statistics from the jumbotron, tv, or your smartphone. Betting companies can buy the data to make their odd setting better. And, of course, coaches can analyze the game a lot better. So overall, we see lots of value added from the collected data that probably justifies the cost of development. Here’s an interesting link (https://bitwise.fi/referenssit/wisehockey/), scroll down to see a picture of how the whole system setup looks like.