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.
https://www.machineroad.com/
Location: Auckland, New Zealand
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
Below was the scope of work to develop a Cricket Training app:
- User should be able to download the app from Play and Google store if the device meets the specific requirement of camera and Video processing.
- User can then calibrate and start taking video when doing bowling and the app guides on the right placement and setup so as to get the most accurate video for processing and calculating the speed.
- AI and ML based video processing to give accurate results for the speed and if it there are issues like objects etc detected on the video it then informs the user that speed could not be calculated.
Implementation
Technology and Architecture
Technology
- The Mobile app was deployed with the below technological component
- Backend Code: .NET Core, C#, Node.js
- Mobile App code: Native Android, Native iOS
- Database: SQL Server, MongoDB
- Cloud: AWS
Integrations
- Single Sign-on using Auth0
- Sendgrid for sending email notifications
- Single Sign-on using Auth0
Security
- Data Encryption
- Multi-Factor Authentication for Admin, Teacher, and Students
- All API endpoints are tokenized
Backup and Recovery
Cloud systems and components used in the attendance management system are secure and 99.99% SLA.
HA/DR mechanism is implemented to create service replicas.
Scalability
Application is designed to scale up to 10x the average load from the first 6 months,
with auto-scaling cloud resources.
Cost Optimization
Alerts and notifications are configured to monitor budget usage. The environment is actively managed
to optimize costs.
Code Management & Deployment
Code for the app is handed over through Microsoft AppCenter.
CI/CD is implemented to automatically build and deploy code changes.
Features of AI ML Based Mobile App for Cricket Training
- Users can create bowling videos, store data, and add it to their player profile on the Machineroad app.
- The app records bowling speed, line, length, and trajectory, saving images and videos for each session.
- Detailed analytics reports allow weekly and monthly progress comparison, including benchmarking with other users and professional athletes.
- Monthly subscription includes comparison charts and leaderboard submission for speed and videos.
- AI/ML-based video processing analyzes recordings and delivers speed accuracy comparable to a speed gun.
- Social media integration enables users to share training results, badges, and streaks.
- Gamification and leaderboard features motivate users with customizable performance targets.
Challenges – AI ML Based Mobile App
- Achieving accurate results with a single camera compared to hawk-eye systems using multiple cameras was challenging.
- Performance depended on background noise, camera position, and device quality.
- App restricted usage on devices without 240FPS or slow-motion support; supported device list was provided.
- Processing videos across varying environments, lighting conditions, and pitches was complex.
- ML models required training across multiple scenarios, but adapting to new locations and pitches remained difficult.
- Accurate camera alignment and orientation were essential for reliable results.
- Help screens and video tutorials were implemented to guide users for optimal usage.
Support
As part of the project implementation, we provided 1 month of extended support, including major and minor bug fixes.
Additional long-term support was also provided for select issues over the years.
Next Phase – AI ML Based Mobile App
We are currently planning the next phase of development and are in the POC stage.
- Video post-processing will be performed directly on the mobile device to deliver faster results to users.
- New features will be implemented and released as part of the ongoing support agreement.
