Padel Video Analytics Backend

@Guptapuru304

We have successfully executed similar projects, transforming raw sports footage into detailed analytics. We understand your need for a robust backend to convert padel match footage into structured data, with adaptable calibration routines for diverse video sources. With over 8 years of experience in AI-first product development and video processing, we excel in utilizing Python, OpenCV, and YOLO for detection and tracking. Our expertise in GPU-accelerated processing on AWS ensures efficient and scalable solutions. We recently completed a project involving complex player and ball tracking using YOLO and pose estimation, delivering clean, documented code. Our approach will focus on precise detection of players and ball, rally segmentation, and shot classification, producing comprehensive JSON outputs. We ensure transparent collaboration and customer-centric delivery, always maintaining uncompromised quality. Could you elaborate on any specific challenges faced in previous attempts, if any? Q: What is your desired timeline for the prototype and final delivery? We invite you to discuss how our expertise can bring your analytics engine to life. Looking forward to your response. Best regards, Puru Gupta

@AwaisChaudhry

Hello, I’m Muhammad Awais and I can build your end-to-end Padel video analytics backend. The system will detect and track four players, the ball, and court lines, map positions to real-world coordinates, and work with phone clips or fixed cameras through calibration. It will segment rallies, flag serves/returns, and classify shots (forehand, backhand, volley, lob, smash, bandeja, víbora). It will identify winners and errors so each rally has a clear outcome. Output is a strict JSON with events, rallies, frame data, and statistics. The data will also include coordinates for future heatmaps. I have delivered sports-analytics CV projects, including a tennis shot classifier and a multi-view tracking demo with a clean data model. Approach: modular pipeline in Python with OpenCV, YOLO detection, and pose estimation; calibrate for multi-view inputs; track players/ball; derive rallies, shots, and outcomes; serialize to a robust JSON schema. GPU processing on AWS/GCP or local CUDA is supported; I’ll include a setup script and documentation. Kick-off to prototype in ~4 weeks; final delivery in ~8 weeks. How many camera sources do you plan to support (phones and fixed cams), and what are typical resolutions/framerates? Do you need real-time processing or is batch acceptable, and what latency target do you have? Is there a preferred JSON schema or field names for events, rallies, and frame data? Should heatmap data be included in the final JSON along with movement traces? Are there

@ZohaibRoy

⭐⭐⭐⭐⭐ Build an Analytics Engine for Padel Match Footage ❇️ Hi My Friend, I hope you're doing well. I've reviewed your project requirements and I see you are looking for an analytics engine to process padel match footage. You don't need to look any further; Zohaib is here to help you! My team has successfully completed 50+ similar projects in sports analytics. I will create a solution that detects players and tracks their movements while classifying shot types, all within your budget. ➡️ Why Me? I can easily do your project on building an analytics engine as I have 5 years of experience in computer vision and sports analytics. My expertise includes Python, OpenCV, YOLO detection, and data processing. I also have a strong grip on GPU-accelerated processing and pose estimation, ensuring a smooth and efficient approach to your project. ➡️ Let's have a quick chat to discuss your project in detail and let me show you samples of my previous work. I'm excited to explore how I can help turn padel video into actionable insights. ➡️ Skills & Experience: ✅ Python Programming ✅ OpenCV ✅ YOLO Detection ✅ Pose Estimation ✅ Data Processing ✅ JSON Data Handling ✅ Sports Analytics ✅ Player Tracking ✅ Event Segmentation ✅ API Development ✅ GPU Acceleration ✅ Documentation Waiting for your response! Best Regards, Zohaib

@irsolutions

With over 10 years of experience in web and mobile development, specializing in advanced technologies such as Python, OpenCV, and AI/ML, I understand the challenges you face in building the Padel Video Analytics Backend. Your project demands precise player tracking, shot identification, and outcome analysis, all of which align perfectly with my expertise. In the realm of sports analytics, I've successfully delivered projects that enhance user experience and provide actionable insights. Leveraging Python, OpenCV, and GPU acceleration, I have developed innovative solutions that meet and exceed client expectations. I propose a meticulous approach to detection, tracking, and event logic, ensuring accurate data extraction and analysis. With a commitment to clean and well-documented code, I guarantee a seamless hand-off process and timely delivery from kick-off to the final product. I invite you to reach out to discuss your project further and provide you with detailed examples of my previous work in computer vision and sports analytics. Let's collaborate to transform raw padel match footage into valuable data insights.

@corpmember29

HELLO!! I HAVE READ YOUR REQUIREMENTS CAREFULLY AND UNDERSTOOD THE PROJECT SCOPE VERY WELL AND WILL START WORKING ACCORDINGLY IN STAGES. I am a computer vision and backend engineer with 10+ years of experience in video analytics, sports tracking, and AI-driven event detection using Python, OpenCV, YOLO-based models, pose estimation, and GPU acceleration (AWS/GCP/CUDA). I have previously delivered analytics systems for racket sports, football training, and broadcast-style player/ball tracking, converting raw video into structured, queryable datasets. My approach will include adaptive court calibration for phone and fixed cameras, YOLO-based detection for players/ball/court lines, multi-object tracking with ID persistence, pose-assisted shot classification, and deterministic rally/serve/shot outcome logic. All outputs will be delivered as clean, well-structured JSON containing frame-level positions, rally segmentation, shot types, outcomes, movement traces, and statistics—ready for downstream heatmaps and dashboards. Estimated timeline: 2–3 weeks to first working prototype, 5–6 weeks for full v1 delivery with documentation and setup scripts. Code will be clean, modular, and fully documented for easy extension. I WILL PROVIDE 2 YEARS OF FREE ONGOING SUPPORT, COMPLETE SOURCE CODE OWNERSHIP, AND WILL WORK USING AGILE METHODOLOGY, ASSISTING FROM INITIAL SETUP TO FINAL DEPLOYMENT. I eagerly await your positive response. Thanks

@kamran2012

Hello there, I can develop a robust analytics engine for your padel match footage using Python with OpenCV and YOLO-based detection, ensuring seamless integration with both phone-recorded clips and fixed-camera streams. My solution will accurately detect and track players, the ball, and court lines, translating these into real-world coordinates. It will segment rallies, classify shot types, and determine rally outcomes with precision. The system will output comprehensive JSON data, detailing player positions, shot types, rally durations, and outcomes. The codebase will be clean, well-documented, and include a setup script, with processing accelerated on AWS or GCP. Questions: • Do you have a preference for AWS or GCP, or would a local CUDA setup be more suitable for your needs? • Are there any specific metrics or additional features you foresee needing in future iterations? I have experience with similar computer-vision projects and can offer an outline and timeline upon request. Looking forward to transforming your padel footage into valuable insights. Thanks and best regards, Kamran

@Julian29

Hello, HAVE HANDS-ON EXPERIENCE WITH SUCH PROJECT I have 11+ years of proven experience in computer vision and sports analytics, and I confidently understand the requirement. The goal is to build a robust, scalable backend that converts raw padel footage into structured, queryable JSON data for actionable insights. -->> Player and ball detection with YOLO + pose estimation, adaptable to phone and fixed cameras -->> Rally segmentation, shot classification (forehand, backhand, volley, lob, smash, bandeja, víbora) -->> Outcome detection (winner, forced/unforced errors) and frame-level tracking in real-world coordinates -->> JSON output with positions, movements, rally details, and statistics for downstream visualization My approach ensures GPU-accelerated, modular Python pipelines with OpenCV and cloud deployment (AWS/GCP), clean code, and clear documentation for reproducibility and future expansion. I have a few technical clarifying questions regarding your preferred input formats and the expected frame rates to scope the processing pipeline accurately. Thanks & regards, Julian

@Mahihassi

Having worked extensively in the field of embedded systems and IoT development, I’m confident my skill-set aligns well with your project's requirements. My valuable expertise in Computer Vision combined with Python, Linux, and circuit design knowledge will be immensely helpful in building your Padel Video Analytics Backend. Additionally, my meticulous approach to problem-solving and proven ability to deliver scalable software solutions will be instrumental in providing a clean, well-documented codebase that perfectly meets your needs. To give you a glimpse of my abilities in this domain, I've previously developed complex algorithms for object detection, tracking and recognition using technologies like OpenCV and YOLO. A standout example includes designing real-time video analytics systems for various sports scenarios. As for timeliness, I’ve consistently delivered high quality work with short turnaround times. I envisage providing you with a functional prototype within the first month and delivering the entire project within two months.

@vinod00k

Hello ,I reviewed your project Padel Video Analytics Backend requirements and I’m confident that my technical expertise aligns well with what you’re looking for. I specialize in designing and developing scalable applications with a focus on performance, clean architecture, and long-term maintainability. My Technical Expertise Includes: ?AI & Automation — NLP, predictive analytics, chatbot development, workflow automation ?Python Development — FastAPI / Flask / Django, API development, backend architecture ?MERN Stack — MongoDB, Express, React, Node.js for full-stack web applications ?Mobile Development — React Native & Flutter for high-performance cross-platform apps ?Cloud & Integrations — Firebase, AWS, REST APIs, micro-services architecture I believe in clear communication, timely delivery, and building solutions that are not just functional — but future-ready, scalable, and optimised for real-world use. Whether the requirement is a small module or a full-scale system, I’m confident that I can deliver with precision, quality, and reliability. I would be glad to discuss your technical scope further and collaborate with you to bring your project to life. Looking forward to connecting with you. Best regards,
Vinod Kumar

@djsmoothy

Hi there. I understand you’re building a full video-to-data analytics engine for padel, not just demos, and you need reliable detection, tracking, and event logic that works across messy real-world footage. I’ve worked on sports-computer-vision pipelines using YOLO, pose estimation, and multi-object tracking to turn raw video into structured, queryable match data with clear outcomes. I can deliver a clean Python-based system that outputs well-structured JSON covering rallies, shots, positions, and stats, with GPU-ready performance and solid documentation for handoff. — Tayo

@vorasiddh4it

Hello Dubasic, I’m a computer-vision engineer with hands-on experience building sports-analytics pipelines that convert raw match footage into structured, queryable event data. I’ve delivered similar systems using YOLO, OpenCV, pose estimation, multi-object tracking, and real-world coordinate mapping, and I can share demo code and sample outputs before we finalize the deal. ? How I’ll Build Your Padel Analytics Engine 1️⃣ Detection, Tracking & Court Calibration YOLOv8/YOLOv9 for players + ball detection Court-line detection + homography calibration (adaptive for phone & fixed cameras) ByteTrack / DeepSORT for stable multi-object tracking Real-world coordinate mapping (meters) 2️⃣ Event Segmentation & Shot Classification Rally segmentation via ball trajectory + speed changes Serve & return detection using court zones Shot classification (forehand, backhand, volley, lob, smash, bandeja, víbora) using: Pose estimation (MediaPipe / OpenPose) Temporal features (swing angle, velocity vectors) 3️⃣ Outcome Detection & Statistics Winner vs forced/unforced error logic Rally duration & outcome tagging Frame-level positions for heatmap generation 4️⃣ Structured Output Clean JSON schema: Frame positions (players + ball) Rally & shot events Match-level statistics GPU-accelerated pipeline (CUDA / AWS / GCP) ? Deliverables ✔ Setup & run scripts (local or cloud GPU) ✔ JSON outputs ready for analytics & visualisation ? Relevant Projects • Player Tracking & Event Detection System

@techsty2014

Hello there, I am excited to assist in transforming raw padel match footage into structured, queryable data. Leveraging Python, OpenCV, and YOLO-based detection, I'll ensure accurate player, ball, and court line tracking with real-world coordinate translation. The system will segment rallies, classify shot types, and identify outcomes, delivering comprehensive JSON with event details and statistics. My approach includes adaptive calibration for varying camera inputs and GPU-accelerated processing on AWS/GCP. You'll receive a well-documented codebase and setup script for seamless implementation. Questions: • Do you have a preference for AWS or GCP for GPU processing? • Is there a specific JSON structure you envision for the output? I have experience with computer vision projects in sports analytics and can share examples upon request. Anticipated timeline: 6 weeks for a prototype, followed by 4 weeks to finalize delivery. Looking forward to turning your vision into a reality. Thanks and best regards, Faizan

@annes01

Projects like this excite me because they push me and keep the work interesting. Your need for an end-to-end analytics engine that transforms raw padel match footage into structured, queryable data aligns perfectly with my expertise in delivering polished and automated solutions that enhance player performance insights. I have successfully completed several computer-vision projects, including a real-time sports analytics tool that utilized YOLO for object detection and OpenCV for tracking. For your project, I would implement a systematic approach: start with robust object detection, followed by precise tracking using pose estimation, and finally integrate event logic to classify shots and outcomes. I estimate a timeline of 4 weeks to a working prototype and 2 additional weeks for final delivery. Happy to outline how I would turn this plan into a working solution. Chat soon, Anne S

@mughiraaa

Hey, i will use OpenCV with YOLO for detecting and tracking the four players, the ball, and the court lines, while ensuring that calibration routines adapt to varying viewpoints and resolutions from phone-recorded clips and fixed-camera streams. I’ll use pose estimation techniques to enhance player tracking and accurately determine shot types, including forehand, backhand, volley, lob, smash, bandeja, and víbora. Each rally will be segmented, with serves and returns identified, and the logic will classify winners, errors, and outcomes for every rally. The system will then generate detailed JSON outputs, containing player positions, shot types, rally durations, and match statistics for future analysis. Let's have a detailed discussion, as it will help me give you a complete plan, including a timeline and estimated budget. I will share my portfolio in chat I look forward to hear from you. Thanks Best Regards, Mughira

@DigitalLogics

Hi Dubasic, Building a full analytics engine for padel with both phone and fixed-camera footage is right in my wheelhouse. I’ve delivered sports analytics and computer vision projects using Python, OpenCV, YOLO, and pose estimation including player tracking, ball detection, and event classification. For your engine, I’d start with multi-view calibration and YOLO-based detection for players and ball, then apply pose estimation for shot classification. Rally segmentation and outcome determination would combine temporal tracking with event logic. I’d aim for a working prototype in approximately 4–5 weeks and full delivery in ~8 weeks. Would you like me to include automated serve/return detection in v1 or focus on rally-level stats first? Also, do you prefer AWS/GCP or a local CUDA setup? Looking forward to your thoughts!

@sekai1116

✅ Nice to meet you here ✅ Hi, I’m Jiayin, a Python computer-vision engineer with experience building sports video analytics pipelines end to end. I can deliver a production-ready backend that detects and tracks players, ball, and court lines, converts footage into real-world coordinates, segments rallies, classifies core padel shots, and determines winners/errors. The system will output clean, well-structured JSON covering frame-level positions, rallies, events, and match statistics—ready for heatmaps or further analysis. I’ll use Python, OpenCV, YOLO-based detection, pose estimation, and GPU acceleration (AWS/GCP or local CUDA), with a clean, well-documented codebase and setup scripts. Estimated delivery: prototype in 2–3 weeks, full v1 in 6–8 weeks. Happy to share relevant CV/sports-analytics examples and walk through the technical approach using your sample footage. Best regards, Jiayin

@revival786

Greetings! I’m a top-rated freelancer with 15+ years of experience and a portfolio of 700+ satisfied clients. I specialize in delivering high-quality, professional padel video analytics backend developing services tailored to your unique needs. Please feel free to message me to discuss your project and review my portfolio. I’d love to help bring your ideas to life! Looking forward to collaborating with you! Best regards, Revival

@TechWagger2026

As a veteran Web and Mobile App Developer with over 10 years' experience primarily working with Python-based platforms, I bring a host of relevant skills to your Padel Video Analytics Backend project. I have a successful track record in delivering complex computer vision and sports analytics projects. Just like yours, these projects required real-time detection, tracking, and event classification while ensuring clean data output. To tackle your specific requirements, I'd propose employing the tried-and-tested OpenCV library, utilizing YOLO-based detection for player and ball tracking and pose estimation for enhanced tracking. For event annotation, we can implement deep learning algorithms capable of classifying shot types and identifying rally outcomes accurately. Considering your technology preferences, we can leverage GPU-accelerated processing on AWS or GCP facilitating faster video analysis. Given the significant workload involved in your project, I understand that timely delivery is paramount. With this in mind, my estimated timeline would be 4 weeks for the first working prototype and an additional 2 weeks for final delivery after incorporating your feedback. Partnering with me won't simply provide you value-for-money but also a client-centric approach driven by my passion for continued project excellence. Let's collaborate to transform padel videos into actionable insights!

@devbaloni1983

Hello , Hope you are doing great! I am a Python Developer with strong experience in building scalable, high-performing applications, automation scripts, APIs, data processing systems, and backend development. I can help you deliver a clean, optimized, and efficient Python solution for your project. Why Choose Me 1. Strong command over Python, Django, Flask, FastAPI 2. Expertise in API development & integration 3. Experience with automation, scripting, bots, and AI/ML tasks 4. Database experience: MySQL, PostgreSQL, MongoDB 5. Clean, optimized, well-commented code 6. End-to-end development: design -build - test - deploy What I Can Build 1. REST APIs (FastAPI / Flask / Django) 2. Automation scripts & bots 3. Web applications 4. Data scraping, cleaning, ETL pipelines 5. Machine Learning / AI models 6. Python-based tools and utilities 7. Integration with third-party APIs 8. Bug fixing and performance optimization Availability I can start immediately and share updates daily. Let’s Discuss Share your exact requirements or any reference, and I’ll provide: 1. Timeline 2. Cost estimate 3. Technical plan Looking forward to working with you!