PHASE 1: Core Functionality Goal: Lay the foundation for processing and handling odds data. Methods for OddsCalculator: american_to_decimal(american_odds):, decimal_to_american(decimal_odds):, calculate_implied_probability(odds, odds_format="decimal"), calculate_no_vig_probabilities(implied_probs), calculate_vig(implied_probs), convert_odds(odds, from_format="american", to_format="decimal") Methods for OddsDataHandler: init, load_data(self, odds_data), get_events(self) filter_events_by_sport(self, sport_key), filter_events_by_bookmaker(self, bookmaker), sort_events_by_odds(self, team_name, descending=True), get_best_odds(self)

Odds Calculator: Converts American odds (+150, -150) to implied probabilities. Removes the bookmaker's "vig" to calculate fair probabilities. Data Handling (In-Memory): Provides methods to load, filter, and sort odds data stored in memory. Focuses on temporary analysis without persisting data to a database.

**DONE (I THINK)

PHASE 2: Database Integration Goal: Introduce database functionality for persistent data storage and retrieval. Methods: init(self, db_name), create_odds_table(self), fetch_mock_odds_data(self) save_odds_to_db(self, odds_data), get_odds_by_sport(self, sport_title: str), get_odds_by_team(self, team: str), get_latest_odds(self, limit: int = 10), delete_old_odds(self, days: int = 7) Database Setup: Creates a SQLite database (odds_db.db) and initializes the OddsTable. Save Data: Fetches mock odds data and saves it to the database for long-term storage. Interaction: Enables storing, querying, and managing odds data in the database. Lays the groundwork for scalable data handling.

**DONE(I THINK) **PLEASE CHECK MAIN FILE, NO IDEA WHAT IS GOING ON.

PHASE 3: Fetch Odds from APIs Goa: Introduce real-world odds data by integrating external APIs (e.g., OddsAPI). Methods: create_table(), add_odds(sport_key, event, bookmaker, team, odds, commence_time), fetch_odds(), fetch_odds_by_sport(sport_key), fetch_odds_by_bookmaker(bookmaker), fetch_odds_by_team(team) Fetch Sports:l Retrieves available sports from OddsAPI and stores them in the database. Fetch and Normalize Odds: Collects odds data from the API. Normalizes the data for consistency across different sportsbooks. Save Odds: Stores fetched odds data in the database, ensuring proper indexing and structure. ##This is kind of not entirely true. We do not have a save method, because our ##add_odds(...)'s, fetch_odds()'s, fetch_odds_by_sport()'s functionalities includes saving, but saving isn't the main thing ##we are trying to do. It's adding, and saving comes with it.

DONE (I THINK) REST IS NOT GOOD

Phase 4: EV and Arbitrage Detection *THIS IS WHERE I AM COMPLETELY LOST Goal: Analyze odds data for profitable betting opportunities. The three things are calculate/detect +EV, detect arbitrage, save (IDK where though) *Calculate EV and Detect Arbitrage are both located within BettingAnalyzer class. MESSED UP AS WELL *The BettingAnalyzer class file also contains dataclasses for easiness of location event attributes. MESSED UP AS WELL *Save results functionality belongs in the ResultsStorage class. Methods (only for Calcuate EV and Detect Arbitrage) from BettingAnalyzer class: fetch_grouped_odds(), Calculate EV: Compares implied probabilities to calculate the expected value of a bet. Identifies bets with positive EV (profitable opportunities). Detect Arbitrage: Finds opportunities where combined odds across sportsbooks guarantee a profit regardless of the outcome. Save Results: I AM STRUGGLING WITH THIS, DO NOT KNOW HOW TO DO Saves EV and arbitrage opportunities to the database for further analysis. Having trouble with saving results, don't know how to do.

Phase 5: Notifications Goal: Notify users of high-value betting opportunities.

Define Notification Rules:

Set thresholds for EV or discrepancies (e.g., EV > 0.1). Notification System:

Sends alerts (e.g., email) to users when conditions are met.

Phase 6: PrizePicks Integration Goal: Compare PrizePicks lines with sportsbook odds to find discrepancies.

Fetch PrizePicks Data:

Retrieves PrizePicks props using OddsAPI or other sources. Highlight Discrepancies:

Compares PrizePicks lines with fair sportsbook odds. Identifies significant differences and calculates EV. Save Discrepancies:

Stores discrepancies and EV in the database.

Phase 7: Reporting and Automation Goal: Automate the entire pipeline and provide actionable insights.

Generate Reports:

Summarize discrepancies and EV data into CSV or JSON reports. Highlight top opportunities. Integrate All Phases:

Combine data fetching, analysis, and notifications into a seamless pipeline.

Phase 8: Optimization and Scaling Goal: Improve performance and scalability.

Optimize:

Enhance database queries and introduce caching. Scale:

Transition to more robust databases (e.g., PostgreSQL). Deploy on cloud platforms to handle larger datasets.

Phase 9 (Optional): User Interface Goal: Create a user-friendly dashboard.

Build UI:

Develop a web interface to display odds, EV, and discrepancies. Mobile Integration:

Add push notifications or SMS alerts. Phase 10: Deployment Goal: Make the system fully operational and accessible.

Deploy: Host the project on a cloud platform. Set up automated updates and security features.