Getting Coaching Buy-In

# Getting Coaching Buy-In ## Python Example: Presenting Insights ```python import pandas as pd import matplotlib.pyplot as plt # Example: Show coaches actionable insights play_success = pd.DataFrame({ 'formation': ['11 Personnel', '12 Personnel', '21 Personnel'], 'success_rate': [0.62, 0.58, 0.54], 'yards_per_play': [6.2, 5.8, 4.9] }) # Simple visualization coaches can understand fig, ax = plt.subplots(1, 2, figsize=(12, 4)) play_success.plot(x='formation', y='success_rate', kind='bar', ax=ax[0], legend=False) ax[0].set_title('Success Rate by Formation') play_success.plot(x='formation', y='yards_per_play', kind='bar', ax=ax[1], legend=False, color='orange') ax[1].set_title('Yards per Play by Formation') plt.tight_layout() plt.show() ``` ## R Example: Building Trust ```r # Track prediction accuracy to build credibility predictions <- data.frame( week = 1:10, predicted_outcome = c("W", "L", "W", "W", "L", "W", "L", "W", "W", "L"), actual_outcome = c("W", "L", "W", "L", "L", "W", "L", "W", "W", "W"), stringsAsFactors = FALSE ) predictions$correct <- predictions$predicted_outcome == predictions$actual_outcome accuracy <- mean(predictions$correct) cat(sprintf("Model Accuracy: %.1f%%\n", accuracy * 100)) ```