Sports and Media
Change the game by anticipating behavior and outcomes
« Ingedata brought great project management skills to our project. I was a bit worried about our tight deadlines, but the quality of the project architecture secured the annotation workflows and ensured swift deployment and delivery. We could use Ingedata's annotations to retrain our AI models and hit our model accuracy targets. »
Hugo Bordigoni CEO @SkillcornerSport Technology: data meets sport
The Sport Tech revolution uses the most advanced technologies to improve performance or provide an increasingly immersive experience to fans at the game or at home, whether with better analysis of player and team performance or by slow-motion replays in augmented reality. The goal of Sport Tech innovations is to enhance the sports experience for everyone.
This 4.0 version of the sports industry is therefore characterized by a strong digitalization of athletes to enable them to achieve their best performance. It is a revolutionary concept that makes a difference by introducing innovation.
Our offer
BOUNDING BOXES
LANDMARK POSITIONING
CLASSIFICATION
Bounding Boxes
Bounding Boxes is one of the most common segmentation techniques, as it outlines the shape of an object by defining its X and Y coordinates. They are fast to draw but might include some background that will lower the accuracy with which your model is detected.
Landmark positioning
Small objects are identified and counted using keypoints. This technique is efficient for small objects that appear in large quantity in the data.
Classification
Classification tags images with single or multiple labels, based on a list of classes that was previously consolidated with experts in the relevant field to ensure that your model fits its market.
BOUNDING BOXES
Bounding Boxes
Bounding Boxes is one of the most common segmentation techniques, as it outlines the shape of an object by defining its X and Y coordinates. They are fast to draw but might include some background that will lower the accuracy with which your model is detected.
LANDMARK POSITIONING
Landmark positioning
Small objects are identified and counted using keypoints. This technique is efficient for small objects that appear in large quantity in the data.
CLASSIFICATION
Classification
Classification tags images with single or multiple labels, based on a list of classes that was previously consolidated with experts in the relevant field to ensure that your model fits its market.
They trust us
We offer high-end services with our unique data production methodology and, more importantly, our wonderful team of 500. As a people company, Ingedata thrives on bringing talents from developing countries on the international AI scene.
Our expertise
AI applied to sports helps with sports performance analysis and injury prevention
Sports fans, bettors and those who simply enjoy a good soccer or basketball game will welcome the use of artificial intelligence in their lives.
The initial goal was to teach AI about different types of athletes, now the plan is to apply that knowledge to individual sports.
By annotating thousands of images of soccer players in their various games, Ingedata helps apply machine learning to training to provide coaches with information they never had before.
These findings focus specifically on the individual athlete and provide coaches with feedback that allows them to fine-tune their strategies.
Client stories
Our Case Studies
Learn more about our approach and explore our case studies
The path to success
Why trust us?
With more than 100 projects in the field of ML model training, we are recognized for our remarkable know-how in production management and our guarantee of quality.
Learn More
Externalizing your data can represent a significant risk in terms of loss, deterioration, or theft. At Ingedata, your projects are designed and carried out in-house, from our secure production centers.
Ingedata's annotators have degrees ranging from bachelor's to engineer's or doctorate in your field. All our teams work from our production centers and are trained in the specific requirements of preparing data for machine learning.
Accelerate the optimization of your algorithm by using data prepared specifically for you. We collect, enrich, and categorize your data to build your own datasets.
We fit seamlessly into your current production mode, take over any coordination and adapt our team to your specific constraints in terms of data volume and quality.
