From tracking sports data to using Artificial Intelligence in Sports in multiple ways
Use of Big Data Analytics and Artificial Intelligence in Sports is an exploding industry. Sports have been tracking data since long ago. Some, like football, have been data-based games for a long time. Others, like hockey or tennis, are coming along. But until recently, these data were recorded, analyzed and used by humans. Today, advanced digitization techniques such as Big Data Analytics and Artificial Intelligence tools are becoming mainstream.
This new trend provides new, compelling tools to all, from players and coaches to media reporters, as it allows to process and analyze the real time information in a much richer context and come up with impressive information.
The main areas to which these techniques are used are Data Gathering, Data Analysis, and Decision Making, as explained below.
However, for those that do not want to read more, I recommend watching the video on the recent performance by IBM-Watson during the U.S.Open Tennis championship these last weeks, as well as the attached IBM video to understand how Watson works on these issues.
We are already used to the Q&A game with personal assistants and the like, so the picture on the right has nothing unexpected, though it is based on a powerful AI tool. But the more complex tasks are already being automatized, allowing added insights that are changing the games.
Sports Data Gathering: The existence of multiple cameras and fast, intelligent video analysis allow new tools to gather thousands of events, from players, coaches and public, in near-real time. It is not only who moves where, but also whether an opponent player seems to have a physical problem or another is not running at their typical speed. This data gathering can work automatically and can be directed to amplify the traditional human potential on data collection.
Sports Data Analytics: Making sense of all the data that is coming in an unstructured way (the face of a player, the speech of a coach, the movement of players as a team, etc.) is difficult for humans. So Sports Data Analytic tools take care of translating these data into facts. Sports Data Analytics tools also take care of understanding what is important and what is not, what are valuable data and which information should be readily available, or just stored for further analysis after the match.
Artificial Intelligence in Sports allows not only to gather data, and to train the data gathering tools like what you can watch in the videos above (training to understand the movements or gestures of players), but to help in decision making. The AI machines, especially after the generalization of the latest advances in neural networks and deep learning, can suggest decisions in real time for coaches and players to follow.