# krishna752006/ipl-prediction-system architecture > Architecture diagram of the GitHub repository krishna752006/ipl-prediction-system, generated by GitDiagram from its file tree, README and a sample of source files (last updated 2026-09-26). It is an AI-made overview: check details against the source. - Interactive diagram: https://gitdiagram.com/krishna752006/ipl-prediction-system - Repository: https://github.com/krishna752006/ipl-prediction-system ## Overview IPL Project is a full-stack IPL analysis and match-prediction application. Users register or log in, then access protected prediction, schedule, points-table, history, statistics, and team-analysis views. The backend’s documented ball-by-ball simulator builds match context and features, blends model predictions with heuristics, and returns innings results; authentication and prediction history use MongoDB. Separately, the repository includes an ML inference service and an offline data-preparation pipeline. Frontend page capabilities are documented by routes and filenames, but their detailed data wiring was not sampled. Main entry points: [`frontend/src/App.tsx`](https://github.com/krishna752006/ipl-prediction-system/blob/main/frontend/src/App.tsx) (Web routes), [`backend/services/match_engine.py`](https://github.com/krishna752006/ipl-prediction-system/blob/main/backend/services/match_engine.py) (Match simulator), [`frontend/src/api/authApi.ts`](https://github.com/krishna752006/ipl-prediction-system/blob/main/frontend/src/api/authApi.ts) (API client), [`data-service/run_pipeline.py`](https://github.com/krishna752006/ipl-prediction-system/blob/main/data-service/run_pipeline.py) (Data pipeline) ## Diagram (Mermaid) ```mermaid flowchart TD subgraph group_web["Web application"] node_webapp["Web routes
[App.tsx]"] node_authpages["Login and register
[Login.tsx]"] node_predpage["Predictions
[Predictions.tsx]"] node_analysispages["Analysis views
[Statistics.tsx]"] node_schedule["Schedule and table
[Schedule.tsx]"] node_historypage["Prediction history"] node_authstore["Auth state
[authStore.ts]"] node_authapi["API client
[authApi.ts]"] end subgraph group_api["Application backend"] node_backend["FastAPI application
[main.py]"] node_predictapi["Prediction endpoint
[predict.py]"] node_historyapi["History endpoint"] node_simulator["Match simulator
[match_engine.py]"] node_features["Ball features
[feature_builder.py]"] node_embeddings["Match embeddings
[embeddings.py]"] node_form["Historical form
[historical_form.py]"] node_bowler["Bowler selection
[bowler_selector.py]"] node_runner["Model runner
[model_runner.py]"] node_db[("MongoDB
[database.py]")] end subgraph group_ml["Model inference"] node_mlroutes["ML prediction route
[routes.py]"] node_inference["Prediction inference
[inference.py]"] node_loader["Model loading
[model_loader.py]"] node_bundle["Model bundle
[model_bundle.py]"] end subgraph group_data["Data preparation"] node_pipeline["Data pipeline
[run_pipeline.py]"] node_cleanmatches["Clean matches
[clean_matches.py]"] node_cleandeliveries["Clean deliveries"] node_offlinestats["Offline statistics"] node_buildfeatures["Build features
[build_features.py]"] end node_user(("User")) node_user -->|"uses"| node_webapp node_webapp -->|"routes to"| node_authpages node_webapp -->|"routes to"| node_predpage node_webapp -->|"routes to"| node_analysispages node_webapp -->|"routes to"| node_schedule node_webapp -->|"routes to"| node_historypage node_authpages -->|"sends requests"| node_authapi node_authapi -->|"reads token"| node_authstore node_predpage -->|"uses client"| node_authapi node_historypage -->|"uses client"| node_authapi node_authapi -->|"HTTP requests"| node_backend node_backend -->|"mounts router"| node_predictapi node_backend -->|"mounts router"| node_historyapi node_backend -->|"reads and writes users"| node_db node_predictapi -->|"runs simulation"| node_simulator node_historyapi -->|"reads and writes history"| node_db node_simulator -->|"builds features"| node_features node_simulator -->|"builds context"| node_embeddings node_simulator -->|"gets player form"| node_form node_simulator -->|"selects bowler"| node_bowler node_simulator -->|"predicts balls"| node_runner node_mlroutes -->|"runs prediction"| node_inference node_inference -->|"loads model"| node_loader node_loader -->|"loads bundle"| node_bundle node_pipeline -->|"cleans matches"| node_cleanmatches node_pipeline -->|"cleans deliveries"| node_cleandeliveries node_pipeline -->|"generates stats"| node_offlinestats node_pipeline -->|"builds features"| node_buildfeatures click node_webapp "https://github.com/krishna752006/ipl-prediction-system/blob/main/frontend/src/App.tsx" click node_authpages "https://github.com/krishna752006/ipl-prediction-system/blob/main/frontend/src/pages/Login.tsx" click node_predpage "https://github.com/krishna752006/ipl-prediction-system/blob/main/frontend/src/pages/Predictions.tsx" click node_analysispages "https://github.com/krishna752006/ipl-prediction-system/blob/main/frontend/src/pages/Statistics.tsx" click node_schedule "https://github.com/krishna752006/ipl-prediction-system/blob/main/frontend/src/pages/Schedule.tsx" click node_historypage "https://github.com/krishna752006/ipl-prediction-system/blob/main/frontend/src/pages/PredictionHistory.tsx" click node_authstore "https://github.com/krishna752006/ipl-prediction-system/blob/main/frontend/src/store/authStore.ts" click node_authapi "https://github.com/krishna752006/ipl-prediction-system/blob/main/frontend/src/api/authApi.ts" click node_backend "https://github.com/krishna752006/ipl-prediction-system/blob/main/backend/main.py" click node_predictapi "https://github.com/krishna752006/ipl-prediction-system/blob/main/backend/routers/predict.py" click node_historyapi "https://github.com/krishna752006/ipl-prediction-system/blob/main/backend/routers/prediction_history.py" click node_simulator "https://github.com/krishna752006/ipl-prediction-system/blob/main/backend/services/match_engine.py" click node_features "https://github.com/krishna752006/ipl-prediction-system/blob/main/backend/services/feature_builder.py" click node_embeddings "https://github.com/krishna752006/ipl-prediction-system/blob/main/backend/services/embeddings.py" click node_form "https://github.com/krishna752006/ipl-prediction-system/blob/main/backend/services/historical_form.py" click node_bowler "https://github.com/krishna752006/ipl-prediction-system/blob/main/backend/services/bowler_selector.py" click node_runner "https://github.com/krishna752006/ipl-prediction-system/blob/main/backend/services/model_runner.py" click node_db "https://github.com/krishna752006/ipl-prediction-system/blob/main/backend/database.py" click node_mlroutes "https://github.com/krishna752006/ipl-prediction-system/blob/main/ml-service/app/routes.py" click node_inference "https://github.com/krishna752006/ipl-prediction-system/blob/main/ml-service/app/inference.py" click node_loader "https://github.com/krishna752006/ipl-prediction-system/blob/main/ml-service/core/model_loader.py" click node_bundle "https://github.com/krishna752006/ipl-prediction-system/blob/main/ml-service/core/model_bundle.py" click node_pipeline "https://github.com/krishna752006/ipl-prediction-system/blob/main/data-service/run_pipeline.py" click node_cleanmatches "https://github.com/krishna752006/ipl-prediction-system/blob/main/data-service/pipeline/clean_matches.py" click node_cleandeliveries "https://github.com/krishna752006/ipl-prediction-system/blob/main/data-service/pipeline/clean_deliveries.py" click node_offlinestats "https://github.com/krishna752006/ipl-prediction-system/blob/main/data-service/pipeline/generate_offline_stats.py" click node_buildfeatures "https://github.com/krishna752006/ipl-prediction-system/blob/main/data-service/pipeline/build_features.py" classDef toneNeutral fill:#f8fafc,stroke:#334155,stroke-width:1.5px,color:#0f172a classDef toneBlue fill:#dbeafe,stroke:#2563eb,stroke-width:1.5px,color:#172554 classDef toneAmber fill:#fef3c7,stroke:#d97706,stroke-width:1.5px,color:#78350f classDef toneMint fill:#dcfce7,stroke:#16a34a,stroke-width:1.5px,color:#14532d classDef toneRose fill:#ffe4e6,stroke:#e11d48,stroke-width:1.5px,color:#881337 classDef toneIndigo fill:#e0e7ff,stroke:#4f46e5,stroke-width:1.5px,color:#312e81 classDef toneTeal fill:#ccfbf1,stroke:#0f766e,stroke-width:1.5px,color:#134e4a class node_webapp,node_authpages,node_predpage,node_analysispages,node_schedule,node_historypage,node_authstore,node_authapi,node_user toneBlue class node_backend,node_predictapi,node_historyapi,node_simulator,node_features,node_embeddings,node_form,node_bowler,node_runner,node_db toneAmber class node_mlroutes,node_inference,node_loader,node_bundle toneMint class node_pipeline,node_cleanmatches,node_cleandeliveries,node_offlinestats,node_buildfeatures toneRose ``` ## Components ### Web application - [Web routes](https://github.com/krishna752006/ipl-prediction-system/blob/main/frontend/src/App.tsx): `frontend/src/App.tsx` - [Login and register](https://github.com/krishna752006/ipl-prediction-system/blob/main/frontend/src/pages/Login.tsx): `frontend/src/pages/Login.tsx` - [Predictions](https://github.com/krishna752006/ipl-prediction-system/blob/main/frontend/src/pages/Predictions.tsx): `frontend/src/pages/Predictions.tsx` - [Analysis views](https://github.com/krishna752006/ipl-prediction-system/blob/main/frontend/src/pages/Statistics.tsx): `frontend/src/pages/Statistics.tsx` - [Schedule and table](https://github.com/krishna752006/ipl-prediction-system/blob/main/frontend/src/pages/Schedule.tsx): `frontend/src/pages/Schedule.tsx` - [Prediction history](https://github.com/krishna752006/ipl-prediction-system/blob/main/frontend/src/pages/PredictionHistory.tsx): `frontend/src/pages/PredictionHistory.tsx` - [Auth state](https://github.com/krishna752006/ipl-prediction-system/blob/main/frontend/src/store/authStore.ts): `frontend/src/store/authStore.ts` - [API client](https://github.com/krishna752006/ipl-prediction-system/blob/main/frontend/src/api/authApi.ts): `frontend/src/api/authApi.ts` ### Application backend - [FastAPI application](https://github.com/krishna752006/ipl-prediction-system/blob/main/backend/main.py): `backend/main.py` - [Prediction endpoint](https://github.com/krishna752006/ipl-prediction-system/blob/main/backend/routers/predict.py): `backend/routers/predict.py` - [History endpoint](https://github.com/krishna752006/ipl-prediction-system/blob/main/backend/routers/prediction_history.py): `backend/routers/prediction_history.py` - [Match simulator](https://github.com/krishna752006/ipl-prediction-system/blob/main/backend/services/match_engine.py): `backend/services/match_engine.py` - [Ball features](https://github.com/krishna752006/ipl-prediction-system/blob/main/backend/services/feature_builder.py): `backend/services/feature_builder.py` - [Match embeddings](https://github.com/krishna752006/ipl-prediction-system/blob/main/backend/services/embeddings.py): `backend/services/embeddings.py` - [Historical form](https://github.com/krishna752006/ipl-prediction-system/blob/main/backend/services/historical_form.py): `backend/services/historical_form.py` - [Bowler selection](https://github.com/krishna752006/ipl-prediction-system/blob/main/backend/services/bowler_selector.py): `backend/services/bowler_selector.py` - [Model runner](https://github.com/krishna752006/ipl-prediction-system/blob/main/backend/services/model_runner.py): `backend/services/model_runner.py` - [MongoDB](https://github.com/krishna752006/ipl-prediction-system/blob/main/backend/database.py): `backend/database.py` ### Model inference - [ML prediction route](https://github.com/krishna752006/ipl-prediction-system/blob/main/ml-service/app/routes.py): `ml-service/app/routes.py` - [Prediction inference](https://github.com/krishna752006/ipl-prediction-system/blob/main/ml-service/app/inference.py): `ml-service/app/inference.py` - [Model loading](https://github.com/krishna752006/ipl-prediction-system/blob/main/ml-service/core/model_loader.py): `ml-service/core/model_loader.py` - [Model bundle](https://github.com/krishna752006/ipl-prediction-system/blob/main/ml-service/core/model_bundle.py): `ml-service/core/model_bundle.py` ### Data preparation - [Data pipeline](https://github.com/krishna752006/ipl-prediction-system/blob/main/data-service/run_pipeline.py): `data-service/run_pipeline.py` - [Clean matches](https://github.com/krishna752006/ipl-prediction-system/blob/main/data-service/pipeline/clean_matches.py): `data-service/pipeline/clean_matches.py` - [Clean deliveries](https://github.com/krishna752006/ipl-prediction-system/blob/main/data-service/pipeline/clean_deliveries.py): `data-service/pipeline/clean_deliveries.py` - [Offline statistics](https://github.com/krishna752006/ipl-prediction-system/blob/main/data-service/pipeline/generate_offline_stats.py): `data-service/pipeline/generate_offline_stats.py` - [Build features](https://github.com/krishna752006/ipl-prediction-system/blob/main/data-service/pipeline/build_features.py): `data-service/pipeline/build_features.py` ### Other components - **User** ## Connections - User → Web routes: uses - Web routes → Login and register: routes to - Web routes → Predictions: routes to - Web routes → Analysis views: routes to - Web routes → Schedule and table: routes to - Web routes → Prediction history: routes to - Login and register → API client: sends requests - API client → Auth state: reads token - Predictions → API client: uses client - Prediction history → API client: uses client - API client → FastAPI application: HTTP requests - FastAPI application → Prediction endpoint: mounts router - FastAPI application → History endpoint: mounts router - FastAPI application → MongoDB: reads and writes users - Prediction endpoint → Match simulator: runs simulation - History endpoint → MongoDB: reads and writes history - Match simulator → Ball features: builds features - Match simulator → Match embeddings: builds context - Match simulator → Historical form: gets player form - Match simulator → Bowler selection: selects bowler - Match simulator → Model runner: predicts balls - ML prediction route → Prediction inference: runs prediction - Prediction inference → Model loading: loads model - Model loading → Model bundle: loads bundle - Data pipeline → Clean matches: cleans matches - Data pipeline → Clean deliveries: cleans deliveries - Data pipeline → Offline statistics: generates stats - Data pipeline → Build features: builds features