Real-Time NFL Game Outcome Prediction

Requirements Clarification & Assessment

1. Understanding the Problem:

   • The task is to develop a real-time classifier for predicting the outcomes of NFL games. This involves not only pre-game predictions but also dynamic, in-game predictions as the game progresses.
   • Clarify the scope of "real-time" prediction: Are predictions required after every play, at the end of each quarter, or at other intervals?
   • Determine the metrics for success: Is the focus on accuracy, precision, recall, or a combination of these?

2. Data Requirements:

   • Historical data: Past NFL game data, including scores, team and player statistics, weather conditions, and other contextual information.
   • Real-time data: In-game statistics such as play-by-play data, current scores, possession times, etc.

3. Technical Requirements:

   • Infrastructure for real-time data ingestion and processing.
   • Capability to deploy models in a production environment, potentially on cloud servers or edge devices.

4. Stakeholder Input:

   • Engage with stakeholders, including NFL analysts and data engineers, to understand the nuances of NFL games and the feasibility of collecting certain features in real-time.