As we approach 2021, we can observe how far the world of sports has progressed. While statistics have always played an important role in sports, Artificial Intelligence (AI) has significantly impacted audience engagement, game strategy, and the way games are played today. As a result, data analytics and Artificial Intelligence are being employed extensively in sports.

The application of AI in sports has become a common sight in the last few years. And considering the positive impact brought by the precision of technology in sports, there is not an iota of doubt that it will continue to flourish in this domain.

In this article, we will discuss some of the new AI applications in sports and gaming, as well as how smart annotation techniques are helping to support these advancements. Some of the applications are:

• AI augmented coaching 

Before, during, and after the game, AI continues to have a significant impact on coaches’ strategic decisions. AI platforms measure a forward pass, a penalty kick, LBW in cricket, and various other comparable movements in many sports using wearable sensors and high-speed cameras. Coaches can use this information to prepare their players for competition better. In addition, this data-driven analysis of players, along with the quantitative and qualitative variables, helps coaches to develop better training programs for their teams.

• Player performance improvement 

AI is also being employed to improve player performance. Apps like HomeCourt combine Computer Vision and Machine Learning to evaluate basketball players’ abilities, providing them with a useful tool for improvement. Tracking these athletes’ performance indicators is reliable. It aids the players in determining the areas in which they have the greatest potential to excel and the areas that still need improvement.

• AI in sports journalism 

Artificial Intelligence can completely transform journalism by exploiting the potential of Natural Language Processing (NLP). Sports journalism is heavily influenced by automated journalism, which is about to enter the market. AI is using sports data to provide digestible information on various sporting events. For instance, software like Wordsmith can process sporting events to summarise the day’s significant events.

• Virtual reality for sports 

Virtual reality has given sports and gamification a new level, as followers may now compete digitally against one another from all over the world using virtual reality headsets. A virtual platform powered by AI technology creates a realistic experience in a virtual environment comparable to watching the game live. Also, with the emergence of 5G, such experiences will get more interactive and change the sports industry forever.

• Broadcasting and streaming 

AI has a significant impact on how spectators experience sports, in addition to altering the world of sports for coaches and athletes. AI algorithms can choose the best camera viewpoint to present on viewers’ displays, provide subtitles for live events in several languages based on the viewer’s location, and enable broadcasters to utilise monetisation opportunities through advertisements.

• AI in match predictions 

Match results can be predicted using machine learning. Where vast data is available, such as in soccer or cricket, a model result can be developed to anticipate impending clashes. One of the most practical implementations of this may be seen in the Great Learning students’ project on ‘IPL cricket match outcome prediction using AI techniques.

Power of data annotation behind AI for sports

Image and video annotation are helping to launch a range of AI systems in the world of sport and fitness. Implementing AI in sports generally demands annotation videos of game footage where we need to annotate players, field, ball, playing net, etc.

Player tracking using bounding boxes

– This involves annotating players from images or video footage using bounding boxes to process quality training data for real-time tracking modules on fields. This annotation is usually performed to analyse games like basketball, football, volleyball, etc.

Key point annotation

– Using key points and polylines, models must annotate various players’ poses used for action identification.

Semantic segmentation

– With semantic or instance segmentation services, annotation can help you segment players from game footage and drive meaningful insights for your model from it.

Annotation validation

– After performing quality annotation, you can validate the model-generated annotations. This involves validating your data and correcting anomalies arising in it accordingly. Artificial Intelligence in sports makes refereeing, analysing, highlighting, and satisfying fans easier to grasp and more efficient in the long run. Sports data analytics have become a huge part of the industry. 

https://www.tagxdata.com/402/