Power Play Optimization

Power play optimization uses statistical analysis to maximize scoring opportunities during man-advantage situations. ## Key Metrics **Python Analysis:** ```python import pandas as pd import numpy as np # Power play efficiency metrics pp_data = pd.DataFrame({ 'shots': [8, 6, 10, 7, 9], 'goals': [1, 0, 2, 1, 1], 'time': [120, 100, 140, 110, 130] # seconds }) # Calculate shooting percentage and shots per minute pp_data['shooting_pct'] = (pp_data['goals'] / pp_data['shots']) * 100 pp_data['shots_per_min'] = pp_data['shots'] / (pp_data['time'] / 60) pp_data['expected_goals'] = pp_data['shots'] * 0.15 # League avg PP shooting % print("Power Play Efficiency:") print(pp_data[['shots', 'goals', 'shooting_pct', 'shots_per_min']]) ``` **R Analysis:** ```r # Power play zone time analysis library(ggplot2) pp_data <- data.frame( formation = c("Umbrella", "Overload", "1-3-1", "Spread"), zone_time = c(85, 78, 82, 80), shot_attempts = c(12, 15, 10, 11), goals = c(2, 2, 1, 1) ) # Calculate efficiency pp_data$efficiency <- pp_data$goals / pp_data$zone_time * 100 print(pp_data) # Shot attempt rate pp_data$attempt_rate <- pp_data$shot_attempts / pp_data$zone_time ``` ## Optimization Strategies - Personnel deployment based on handedness - Formation selection vs opponent's PK structure - Entry zone timing and set-up efficiency - Shot location heat maps for optimal positioning