INTL
Freelancer
전문가
외주
원격 가능
Cross-Sport Performance Analytics Platform
예산
$1,500~$12,500 INR
예상 기간
3~6개월
난이도
전문가
기술 스택
Python
SQL
QlikView
Tableau
Power BI
Plotly
Docker
AWS
Spark
REST API
GraphQL
Data Warehousing
ETL
Data Modeling
Machine Learning
FastAPI
AI 분석 요약
축구, 크리켓, F1, 테니스 네 가지 스포츠의 게임 및 선수 레벨 분석을 제공하는 데이터 기반 애플리케이션 개발 프로젝트입니다. 복수의 스포츠 데이터를 통합하고 고급 모델링을 수행하며, 대시보드와 API를 통해 결과를 제공하는 데 필요한 데이터 엔지니어링, 데이터 과학, 시각화 및 배포 역량이 요구됩니다.
프로젝트 원문 설명
I’m building a data-driven application that delivers complete game-level and player-level analytics for four sports: Football, Cricket, Formula One, and Tennis. The goal is to merge historical and live feeds into a unified warehouse, run advanced modelling on top of it, and expose the results through interactive dashboards and an API other products can tap into.
Scope of work
• Set up or connect to reliable data sources, normalise them, and design an extensible schema that copes with multiple sports and rule sets.
• Develop the ETL pipeline (Python, SQL, Spark or a comparable stack) to ingest, clean, and enrich the data in near-real-time.
• Build an analytics layer capable of producing player performance metrics, match summaries, and cross-sport comparisons. Predictive models for injuries, form slumps, or race outcomes are a plus.
• Create a lightweight front end or embed visualisations (Tableau, Power BI, Plotly, etc.) so users can explore the numbers intuitively.
• Package the solution with clear documentation and deployment scripts (Docker / AWS preferred) so I can spin up additional environments easily.
Acceptance criteria
1. A populated database containing at least one full season of data for each sport listed.
2. Reproducible pipeline that updates automatically once new matches, races, or tournaments finish.
3. Dashboard and REST/GraphQL endpoint returning key metrics with sub-second latency on a modest cloud instance.
4. Clean codebase with README, setup guide, and inline comments.
When you respond, focus on your experience delivering multi-sport or high-volume analytics systems—tech buzzwords alone won’t suffice. If you have live demos or repositories you can share after signing an NDA, let me know; for now a concise overview of your hands-on experience is all I need to shortlist you.
Scope of work
• Set up or connect to reliable data sources, normalise them, and design an extensible schema that copes with multiple sports and rule sets.
• Develop the ETL pipeline (Python, SQL, Spark or a comparable stack) to ingest, clean, and enrich the data in near-real-time.
• Build an analytics layer capable of producing player performance metrics, match summaries, and cross-sport comparisons. Predictive models for injuries, form slumps, or race outcomes are a plus.
• Create a lightweight front end or embed visualisations (Tableau, Power BI, Plotly, etc.) so users can explore the numbers intuitively.
• Package the solution with clear documentation and deployment scripts (Docker / AWS preferred) so I can spin up additional environments easily.
Acceptance criteria
1. A populated database containing at least one full season of data for each sport listed.
2. Reproducible pipeline that updates automatically once new matches, races, or tournaments finish.
3. Dashboard and REST/GraphQL endpoint returning key metrics with sub-second latency on a modest cloud instance.
4. Clean codebase with README, setup guide, and inline comments.
When you respond, focus on your experience delivering multi-sport or high-volume analytics systems—tech buzzwords alone won’t suffice. If you have live demos or repositories you can share after signing an NDA, let me know; for now a concise overview of your hands-on experience is all I need to shortlist you.
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