Headline
STRIKEIQ – Personalized Cricket Performance Intelligence
Transforming smartphone cricket videos into personalized AI-powered performance insights.
Inspiration
Cricket performance analysis is often associated with professional teams, expensive equipment, specialized cameras, and expert coaching. This creates a gap for students, amateur players, and grassroots athletes who want meaningful feedback but cannot access such infrastructure.
We built STRIKEIQ around a simple question:
What if an ordinary smartphone video could become a personalized cricket performance analysis tool?
Our goal is to transform raw cricket footage into understandable and measurable insights that help players identify technical patterns, track improvement, and make training more data-driven.
What it does
STRIKEIQ is an AI-powered, camera-first cricket performance intelligence platform initially focused on batting analysis.
Players can record their batting sessions using a smartphone and use AI-powered computer vision to analyze their performance.
The platform is designed to:
- Analyze cricket batting videos
- Detect and analyze different batting shots
- Track player pose and movement
- Analyze bat movement and shot execution
- Extract performance-related visual features
- Generate personalized technique insights
- Help players track their progress over multiple sessions
The core workflow is:
Record → Analyze → Measure → Compare → Improve
Rather than providing generic cricket advice, STRIKEIQ focuses on the individual player and their observed performance.
How we built it
STRIKEIQ follows a modular AI pipeline:
Video Input → Player/Pose Detection → Bat Tracking → Temporal Feature Extraction → AI Analysis → Personalized Feedback
The current prototype explores multimodal AI for visual analysis, while the architecture is designed to support specialized machine-learning models for cricket performance in future iterations.
Technology Stack
- Python – AI and video-processing pipeline
- Computer Vision – cricket video and movement analysis
- Pose Estimation – player movement analysis
- Bat Tracking – bat trajectory and movement analysis
- Deep Learning – temporal performance modeling
- Multimodal AI – visual understanding and insight generation
- HTML/CSS/JavaScript – interactive frontend
- Git/GitHub – development and version control
The system is designed to evolve into specialized Batter and Bowler AI Engines, enabling cricket-specific performance intelligence.
Challenges we ran into
One of our biggest challenges was converting a normal cricket video into meaningful performance information.
Cricket movements happen quickly, and factors such as camera angle, lighting, player position, occlusion, bat visibility, and video quality can affect computer-vision analysis.
Another challenge was moving beyond simple shot recognition.
Recognizing a cricket shot is only one part of the problem. A useful AI coaching system needs to connect visual movement patterns with meaningful technique-related insights and communicate them in a way that players can understand.
We also had to balance:
Accuracy ↔ Computational Cost ↔ Accessibility ↔ Usability
Our goal was to explore a system that does not depend on expensive professional tracking hardware.
Accomplishments that we're proud of
We are proud to have developed the foundation of a camera-first cricket intelligence platform that can potentially make advanced performance analysis more accessible.
Our key accomplishments include:
- Building the initial STRIKEIQ prototype
- Designing a modular AI and computer-vision architecture
- Exploring pose and bat-motion analysis for cricket
- Creating a player-focused performance analysis approach
- Designing personalized performance insights
- Developing a roadmap toward specialized Batter and Bowler AI models
- Focusing on smartphone-based analysis instead of dedicated professional hardware
The most important idea behind STRIKEIQ is moving from:
“Analyzing a cricket video”
to:
“Understanding the player behind the video.”
What we learned
Building STRIKEIQ taught us that sports AI is not simply about applying a powerful model to a video.
The real challenge is connecting computer-vision outputs with meaningful, domain-specific cricket insights.
We learned the importance of:
- High-quality and representative training data
- Robust video preprocessing
- Temporal analysis instead of relying only on individual frames
- Designing AI systems around measurable features
- Separating experimental capabilities from validated performance claims
- Making complex AI outputs understandable to players
We also learned that accessibility must be considered from the beginning. A technically advanced system becomes less useful if its infrastructure makes it inaccessible to the players it is designed to help.
What's next for STRIKEIQ
Our next major step is developing a dedicated Batter Engine trained and evaluated specifically for cricket technique.
This will allow STRIKEIQ to progress from general multimodal video analysis toward specialized cricket performance intelligence.
Future development includes:
- Personalized Batter AI
- Specialized Bowler Engine
- Longitudinal player progress tracking
- Advanced technique metrics
- Player performance dashboards
- Coach and academy analytics
- Match-performance intelligence
- Edge/mobile inference
- Larger cricket-specific datasets
- Rigorous model evaluation
Our long-term vision is to create an intelligent cricket performance ecosystem where players can:
Record → Understand → Measure → Improve
using accessible AI technology.
STRIKEIQ aims to bring personalized cricket intelligence from professional analytics environments to the smartphone in every player's hand.
Log in or sign up for Devpost to join the conversation.