Analytics Workflows

# Analytics Workflows ## Python Example: Weekly Game Prep Workflow ```python import pandas as pd from datetime import datetime, timedelta class GamePrepWorkflow: def __init__(self, game_date): self.game_date = game_date self.tasks = [] def create_weekly_schedule(self): """Generate analytics tasks for game week""" days_before = 6 tasks = [ {'day': 6, 'task': 'Opponent film breakdown', 'hours': 8}, {'day': 5, 'task': 'Tendency analysis', 'hours': 6}, {'day': 4, 'task': 'Personnel matchups', 'hours': 5}, {'day': 3, 'task': 'Situational analysis', 'hours': 4}, {'day': 2, 'task': 'Game plan support', 'hours': 6}, {'day': 1, 'task': 'Final reports & presentation', 'hours': 4}, {'day': 0, 'task': 'In-game analytics support', 'hours': 3} ] schedule = pd.DataFrame(tasks) schedule['date'] = [self.game_date - timedelta(days=d) for d in schedule['day']] return schedule # Example usage game_date = datetime(2024, 9, 15) workflow = GamePrepWorkflow(game_date) print(workflow.create_weekly_schedule()) ``` ## R Example: In-Game Analytics Process ```r # Real-time decision support workflow in_game_workflow <- function(game_state) { # Simulate in-game analytics pipeline steps <- data.frame( step = 1:5, process = c("Data Collection", "Situation Classification", "Recommendation Generation", "Coach Communication", "Outcome Tracking"), target_time_seconds = c(5, 10, 15, 5, 10) ) steps$cumulative_time <- cumsum(steps$target_time_seconds) return(steps) } workflow <- in_game_workflow("4th down decision") print(workflow) ```