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How AI Empowers Sports Games? — Part2

Data Annotation service fuels AI sports

Artificial Intelligence Has Become the “GoldenEye” of Human Judges

During the 2018 World Cup in Russia, FIFA introduced the video assistant referee system (VaR) for the first time, which has attracted great attention. The scene is changing rapidly, it is inevitable that there would be disputes only based on the judgment of naked eyes. The VaR is composed of 35 professional cameras scattered in all angles of the court. When there is a dispute, The “golden eye” will help the referee make the right decision through video playback and automatic recognition technology.

According to reports, in the 2021 Tokyo Olympic Games, gymnastics and other sports may also usher in artificial intelligence referees. Gymnastics, which aims at scoring skills and difficulties, puts forward higher requirements. For instance, it is required to recognize and understand the subtle differences of gymnasts’ movements, and to learn the evaluation criteria of technical movements for scoring.

With the continuous development of artificial intelligence technology, we believe that in the future, sports competition will be more fair and standardized.

AI Brings New Experience For Fans

In addition to bringing revolution to sports events, AI will also change the way sports events spread. As early as in the 2015 La Liga League, as a member of enthusiastic fans, an AI chatbot chatted with fans and friends in the live broadcast room. Based on the user behavior data, we can quickly carry out personalized chats with fans, give comfort when losing the game, and share the joy when winning. At the same time, AI ”host“ also learned a lot of football knowledge and terminology, initiated the event topic to discuss with the fans, which greatly improved the fans’ pleasure and interactivity, and also increased the fans’ stickiness of the video platform as well.

Moreover, AI can also automatically select the right slot to show to the audience according to their preference, and provide the corresponding subtitles according to the location and language. It is a piece of cake for AI to quickly generate wonderful moments of athletes by recognizing human faces, shirt numbers, audience voices, and movement tracks, etc.

In addition, we can also create customized sports peripheral for fans. At present, for example, Xiaobing has combined Tebu and Ali rhinoceros to design and produce a series of personalized T-shirts, so that consumers can have their own exclusive design.

Why the High-Quality Training Data is so Important to AI Machine Learning?

Data, Algorithms, and Processing are Three indispensable Elements of AI.

Data is the starting point.

From the perspective of the research direction of artificial intelligence technology, whether in the field of traditional machine learning or deep learning, supervised learning based on training data is still a major model training method. Especially in the field of deep learning, more labeled data is needed to improve the effectiveness of the model.

What we need to be clear is for AI companies and the entire industry, data annotation is an important part of the realization of artificial intelligence. If the data used in artificial intelligence training is not sufficiently diverse and unbiased, problems such as artificial “AI bias” may arise.

Therefore, the measures to provide high-quality AI data for different scenarios and different needs have gradually become the consensus of artificial intelligence solutions.

ByteBridge, a human-powered and ML-powered data labeling tooling platform

ByteBridge is a data labeling SAAS platform with robust tools and real-time workflow management. It provides high-quality training data for the machine learning industry.


  • ML-assisted capacity can help reduce human errors by automatically pre-labeling
  • The real-time QA and QC are integrated into the labeling workflow as the consensus mechanism is introduced to ensure accuracy.
  • Consensus — Assign the same task to several workers, and the correct answer is the one that comes back from the majority output.
  • All results are thoroughly assessed and verified by a human workforce and machine

In this way, ByteBridge can affirm the data acceptance and accuracy rate is over 98%.

Communication Cost Saving

On ByteBridge’s SaaS dashboard, developers can start the labeling projects by using the labeling instruction template and get the results back instantly.
From online setting labeling briefing to expert support alongside, the instruction communication is not that hard anymore.

ByteBridge Labeling Instruction Template

Here is the beginner operational guideline:

ByteBridge Data Labeling Platform Beginner Operational Guideline

Flexibility: More Engaged in the 2D Images Labeling Loop

  • On ByteBridg’s dashboard, developers can set labeling rules directly, check the ongoing process simultaneously on a pay-per-task model with a clear estimated time and price.
  • Real-time management and monitoring of project
  • As a fully managed platform, it provides API for data transfer. The platform also allows users to get involved in the QC process.
ByteBridge, a Human-powered and ML-powered Data Labeling Tooling Platform

These labeling tools are available: Image Classification, 2D Boxing, Polygon, Cuboid.

We can provide personalized annotation tools and services according to customer requirements.


A collaboration of the human-work force and AI algorithms ensure a 50% lower price compared to the conventional market.


If you need data labeling and collection services, please have a look at, the clear pricing is available.

Please feel free to contact us:





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A data labeling platform with robust tools for real-time workflow management, providing high-quality training data with efficiency. —

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