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SunTec.AI is a top data annotation company empowering businesses with high-quality training datasets for diverse AI/ML project needs.
Have you ever thought about how sports analysts predict match outcomes? They use techniques from players’ past games to make their predictions....
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Have you ever thought about how sports analysts predict match outcomes? They use techniques from players’ past games to make their predictions. Sometimes, the analysts also determine what kind of shot a player is going to serve in upcoming matches. These predictions are possible due to the availability of image and video annotation.
According to a Dimension Market Research report, the data annotation market size is expected to grow at a CAGR of 34.4% by 2033. This reflects the increasing importance of data annotation in various domains, including healthcare and even sports. Here, data annotation plays an important role in training AI models for conducting sports analytics.
The analysis process involves using data science and statistical techniques to gain meaningful insights regarding the performance of athletes and sports organizations. To generate better analytics results, leveraging AI can be a suitable approach.
Along with real-time analysis, AI aids in proper rule enforcement to ensure a fair game by providing reliable insights regarding false penalties. As a player, you can achieve a good physique by opting for AI-driven customised training programs. AI can also monitor sleep patterns and fatigue levels to determine the possibility of any injuries during the game or beforehand.
Let’s learn about image and video annotation in sports analysis and how a well-annotated sports dataset offers informative insights.
Over the years, the usage of data annotation has increased tremendously in sports. While training AI models to analyse sports data, annotation becomes an important process. It facilitates the labelling of data to identify entities such as equipment, players, and other match officials. Such usage of data annotation in sports provides the following benefits:
Better Decision-making: Video and image annotation services facilitate motion tracking in sports. With the help of these annotation solutions, you can monitor players’ actions to make well-informed on-field gaming decisions.
Improving Player Performances: Data annotation assists athletes in enhancing their performances. Using the visuals provided by annotation software, players can observe positive and negative aspects of their playing techniques.
Personalising Content: By annotating game footage, broadcasters can stream detailed and exclusive content so that viewers and sports analysts can gain precise players’ performance data.
Depending on the data type, there are different types of annotations, such as text, image, or video. Some techniques to annotate these data types are:
The keypoint annotation technique aids in labelling some specific points or features of objects within images or videos. You can label multiple points and use them to train AI models in such a way that they can form complete structures by connecting the labelled data points. This enables the models to identify the orientation, position, or movements of objects.
You can leverage keypoint annotation for motion tracking in sports such as football, basketball, or badminton.
Bounding box is a technique of creating a rectangular outline around objects while detecting the positions and dimensions of an object. Every box is allotted a class label to simplify object tracking for AI models. While annotating sports data, bounding box annotation helps in labelling of gaming equipment, players, and referees.
3D annotation involves labelling and classification of objects in three-dimensional space. It helps in understanding the depth and volume of an entity in addition to its position and orientation. 3D annotation is especially used when an image or video consists of point clouds. These are sets of data points in 3D coordinate systems and represent the geometry of an object.
Bounding box, LiDAR, and photogrammetry are some of the common approaches for 3D annotation. In sports, you can utilise 3D annotation to detect the trajectory of a ball in games such as cricket, football, or basketball.
It is a special type of data annotation technique that involves labeling pixels within images or video footage. Semantic segmentation is used during sports analysis for annotating images of field boundaries in a game or special zones such as penalty areas in football.
As a coach, using image and video annotation platforms for game analysis helps monitor players’ performance, including their positions and movements. Based on this information, you can refine the game strategy to increase the probability of winning.
For example, in cricket, video annotation services help identify which batsmen perform well against a specific bowling style. Based on this, reshuffling the order in which the players are sent for batting becomes easy.
Performance analysis using video annotation services helps in creating separate groups of high-performing and low-performing players. You can include the best-playing candidates on the team for subsequent matches and send underperforming players for training.
This decreases the pressure on players who are not performing well and provides them time to enhance their playing skills.
Data annotation techniques help in labelling patterns in player movements that can cause injury. By detecting these activities, players can cautiously avoid such movements to prevent injury.
Inconsistencies in data annotation guidelines can result in discrepancies in labelling data. This leads to the creation of a flawed training dataset. AI models trained on such datasets give inaccurate outcomes.
Image and video annotation for sports analysis involves labelling large volumes of data. This process is challenging and time-consuming. Further, as the amount of data increases, annotation becomes complex due to a lack of clarity or similarity in data records.
Any sport usually involves fast and spontaneous actions, such as throwing or running, which are difficult to annotate. In addition, every player has a different biomechanical profile, due to which their actions can differ from each other. This makes it challenging to use uniform annotation techniques for labelling the activities of all the players in a game.
There should be clear guidelines to annotate datasets. To do this, first thoroughly understand the types, formats, and attributes of data records. To frame proper annotation guidelines, you can take assistance from AI. Alternatively, it is more suitable to adopt a hybrid approach in which AI can perform initial labelling, which can then be rectified by a team of reviewers.
Prepare a robust data annotation strategy specifically for the sport that you want to analyse. Even after completion of labelling, regularly monitor the efficiency of the annotation process and make adjustments if required.
Invest in the right data annotation tools, and hire an expert team to save the cost of rework. This improves the annotation process and accuracy of analytical insights.
Thus, by facilitating the labelling of players’ positions and orientations of players, coaches, and game equipment, annotation techniques have transformed the world of sports. The AI models trained on annotated data are helping sports experts convert raw information into actionable insights. In the future, you will witness further enhancements in the use of AI for the sports domain.
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SunTec.AI is a top data annotation company empowering businesses with high-quality training datasets for diverse AI/ML project needs.
