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### **Milestone: Custom Prediction Engine** **Description:** This milestone focuses on the creation of a **Custom Prediction Engine** that integrates three distinct levels of basketball data analysis—**Player-Level**, **Interaction-Level**, and **Team-Level**—to predict both game outcomes and individual player performance. The engine will leverage historical and contextual data to produce accurate predictions for key metrics such as final scores, points, rebounds, assists, and more. The **modeling section** of this milestone will involve: - Developing a **Player-Level Model** to analyze and predict individual player stats based on historical trends, recent performance, and game context. - Building an **Interaction-Level Model** to capture lineup synergy and performance by analyzing how player combinations on the court influence outcomes. - Designing a **Team-Level Model** to predict team performance metrics using aggregated player data, contextual features (e.g., rest days, home/away games), and opponent information. - Creating a **Fusion Model** that integrates outputs from the above levels, combining predictions into a unified system capable of providing game-level and player-specific predictions. The system will prioritize modularity, enabling independent validation of each level while facilitating seamless integration into a cohesive prediction framework. It will incorporate advanced modeling techniques, including **time-series models** (e.g., Transformers, LSTMs, or GRUs) and potentially **graph neural networks (GNNs)** for lineup interactions. The architecture will emphasize the time-sensitive nature of basketball data, allowing for updates based on recent performances while retaining historical data insights. The final deliverable is a deployable prediction engine with: - Modular functionality for testing and refining each model independently. - A RESTful API to serve predictions to the existing web app, offering real-time and historical analysis for users. - Scalability to adapt to additional data inputs or refined modeling strategies in future iterations.
Overdue by 1 year(s)•Due by January 1, 2025- Overdue by 1 year(s)•Due by October 20, 2024•2/2 issues closed