Zone Entry Strategy

Zone entry strategy analyzes methods for successfully entering the offensive zone to maximize scoring opportunities. ## Entry Method Analysis **Python Analysis:** ```python import pandas as pd import numpy as np # Zone entry tracking data entries = pd.DataFrame({ 'method': ['Carry', 'Pass', 'Dump'], 'attempts': [45, 38, 52], 'successful': [32, 28, 41], 'shots_generated': [18, 15, 8], 'goals': [3, 2, 1] }) # Calculate success metrics entries['success_rate'] = (entries['successful'] / entries['attempts']) * 100 entries['shots_per_entry'] = entries['shots_generated'] / entries['successful'] entries['goals_per_entry'] = entries['goals'] / entries['successful'] entries['shooting_pct'] = (entries['goals'] / entries['shots_generated']) * 100 print("Zone Entry Effectiveness:") print(entries[['method', 'success_rate', 'shots_per_entry', 'goals_per_entry']]) ``` **R Analysis:** ```r # Entry location and success library(ggplot2) library(dplyr) entry_locations <- data.frame( location = c("Left Wing", "Center", "Right Wing"), carry_success = c(68, 72, 70), pass_success = c(65, 58, 67), dump_success = c(78, 75, 80), controlled_entry_pct = c(55, 60, 52) ) # Reshape for analysis entry_long <- entry_locations %>% tidyr::pivot_longer(cols = c(carry_success, pass_success, dump_success), names_to = "method", values_to = "success_rate") print(entry_long) # Calculate optimal method by location optimal <- entry_locations %>% rowwise() %>% mutate(best_method = names(.)[which.max(c(carry_success, pass_success, dump_success)) + 1]) print(optimal[, c("location", "best_method")]) ``` ## Strategic Considerations - Controlled vs uncontrolled entries - Opponent's neutral zone pressure - Speed and timing of entry - Support positioning and spacing - Entry success rate vs shot generation trade-offs