AI ML-based app for cricket training For Machineroad

AI ML-based app for cricket training For Machineroad

In this project, we worked on AI ML-based mobile app for cricket training by a celebrity client Lockie Ferguson international cricket Player.

Cricket training app

Executive Summary

About Client

Machineroad was started by Mitch Ferguson and Lockie Fergsuon both on top of thier cricketing skills and with the right knowledge and tools helping others in developing the game skills is what they wanted to do in Machineroad. With the mobile application goal was to help athletes to see how fast they can bowl and the areas for thier improvement. The competition in the sports sector is cut-throat and this app helps amateur as well as professional athletes to up their games.

Project - AI ML based mobile app for cricket training

Machineroad requirement was for implementing a bespoke AI ML based mobile app that helps to improve cricket bowling skills for their users. They wanted an app that helps their users to measure their bowling speed and creates a trajectory image snippet for the end user which further helps to understand the areas of improvement. Machineroad needed detailed analytics to help the users see their activities and compare results each week and month to help keep a track on the progress made. The requirement for AI ML based mobile app for cricket training was to be launched on both iOS and Android Store.

The Founder of MachineRoad Lockie Ferguson as world class cricket champion had this vision in mind ‘We want to bridge the gap between talent and success as a sportsman. Regardless of your upbringing we want you to be able to compete on the world stage and become the best athlete you can be”

Scope & Requirement

Implementation

Technology and Architecture

Technology

Integrations

Security:

Backup and Recovery

Cloud systems and components used in the attendance management system are secure and 99.99% SLA. We have added HA/DR mechanism to create a replica of the services

Scalability

Application is designed to scale up to 10X times the average load received in the 1st 6 months of its usage and all cloud resources are configured for auto-scaling based on the load

Cost Optimization

Alerts and notifications are configured in the attendance management system to notify if the budget is being exceeded. Peritos being a cloud partner is managing the environment for the client keeping a close watch on the cost and finding ways to optimize the same

Code Management, Deployment

Code for the app is handed over to the client through Microsoft AppCenter.

CI/CD is implemented to automatically add, build and deploy any code changes 

Features of AI ML based Mobile app for cricket training

  • It allows the users to create bowling videos and after the video is recorded it lets the user store the data and add to the player profile on the Machineroad app.
  • The app records parameters like bowling speed, line, length and trajectory and saves the image and video for each session 
  • It offers in detail analytics reports to compare weekly and monthly progress and an option to compare the performance with other users some of them being professional athletes 
  • A monthly subscription that offers comparison charts of the monthly trainings and option to submit the speed and video for leaderboard. 
  • AL and ML based video processing to analyze the recorded videos which gives the speed same as compared to a speed gun. 
  • Integrated with social media and user can share training results on social media platforms including the badges and streaks they earned. 
  • We also implemented Gamification and Leaderboard functionality which motivates user to achieve higher results based on targets which can be customized for each user’s journey.

Challenges -AI ML based Mobile app

  • Achieving similar results from one camera while the same results in an actual game are received using hawk-eye technique that uses six cameras.  Achieving accurate results from the app each time was dependent on the background noise and the position of the camera and device quality.
  • We informed user if the camera quality and device was not compatible for 240FPS and slow-mo recording was not available we did not allow them to download the app. A list of supported devices was also released on the Machineroad’s site. 
  • Ability to process videos recorded in different environments under different lighting conditions and pitches
  • We did Machine learning for which we trained the model in different conditions like day and night, Outdoors and indoor etc. But it was difficult for the app to pick a new location and pitch automatically if it was different from the models on which it was trained.
  • User needs to align the pitch with the camera and orientation of the camera should also be accurate else the results gets impacted.
  • Help screens were implemented to inform user on the orientation and Video tutorials were released by the client as well so users can understand how to get the best results from the app. 
    •  

Project Completion

Duration

Deliverables

  • Published the app on Play Store and Apple Store for beta users and then open it for all users and all regions. 
  • Deployment using AWS architecture on the cloud to setup a scalable and optimized backend system. It was configured to scale upto 6 X times the inital setup if more users start to use the app and if the Computing and storage resources are consumed fast enough.  
  • Integration with native camera capabilities with advance machine learning algorithm incorporated to get accurate speed and trajectories
  • A test report along with On field demo with the client was done a few times to ensure the app works as expected. The average speed accuracy was set out to be 90% or more calculated for 20 bowls in the same location at the same time.

Support

As part of the project implementation we provided 1 month of extended support. This includes any Major / Minor bug fixes. And a further extended support for some issues where we have been supporting for a few years now.

Testimonial - AI ML based mobile app

We are awaiting a documented feedback from the client
Feedback image
Mitch Ferguson
Co-Founder machineroad

Next Phase - AI ML based Mobile app

We are looking at doing the next phase of development and are already in POC stage where

  • All the post processing of the video to be done on the mobile device itself this improves the chances for showing result faster to the end user. 
  • Implementing new features and rolling out new releases as part of our support agreement.
Cricket training app
Cricket training app

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Testimonial

Edward Yarashev
Product Manager | Tonkin + Taylor

Peritos is a team of highly skilled developers, technical experts, and delivery managers. We’ve been very impressed with their commitment. Their developers and delivery managers have conducted themselves with professionalism and diligence at all times, and the quality of the work they have performed has been excellent. Many times, they proposed better solutions, which resulted in better and faster products. Peritos is a reliable AWS Partner. You can trust and be satisfied.


(For services and support)
Richard Canny
Ultimation industries | President

“We are very happy with the results of this major IT project with Peritos Solutions. Their staff gained a good knowledge of our business and processes. They were able to work with many of our diverse team members and launch Business Central ERP on time and on budget according to our requirements. We will be using them again for future projects and additional development actions. Thank you!”


(For services and support)
Grant Peterson
Software Development Manager | Insight Software

The project was completed on time and as per the agreed budget. The communication was excellent, as was the dedication to quickly turning around the required report development. The overall experience was good, and I would definitely work with the Peritos Team again.


(For services and support)

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