Line Matching Strategy

Line matching strategy involves deploying specific player combinations against opponent lines to gain tactical advantages. ## Matchup Analysis **Python Analysis:** ```python import pandas as pd import numpy as np # Line matchup data matchups = pd.DataFrame({ 'our_line': ['Line 1', 'Line 1', 'Line 2', 'Line 2'], 'opponent_line': ['Opp Line 1', 'Opp Line 2', 'Opp Line 1', 'Opp Line 2'], 'toi': [8.5, 6.2, 7.1, 9.3], # time on ice 'goals_for': [2, 1, 0, 1], 'goals_against': [1, 0, 2, 1], 'shot_diff': [5, 3, -4, 2] }) # Calculate line effectiveness matchups['goal_diff'] = matchups['goals_for'] - matchups['goals_against'] matchups['gf_per_60'] = (matchups['goals_for'] / matchups['toi']) * 60 matchups['ga_per_60'] = (matchups['goals_against'] / matchups['toi']) * 60 print("Line Matchup Performance:") print(matchups[['our_line', 'opponent_line', 'goal_diff', 'gf_per_60', 'ga_per_60']]) ``` **R Analysis:** ```r # Home vs away line deployment library(ggplot2) deployment <- data.frame( line = rep(c("Line 1", "Line 2", "Line 3", "Line 4"), 2), situation = rep(c("Home", "Away"), each = 4), toi = c(18.5, 15.2, 13.8, 11.5, 16.2, 16.8, 14.5, 11.8), offensive_zone_start_pct = c(65, 55, 48, 42, 58, 52, 45, 40) ) # Analyze deployment flexibility deployment$usage_index <- deployment$toi * (deployment$offensive_zone_start_pct / 50) print(deployment) # Visualization ggplot(deployment, aes(x = line, y = toi, fill = situation)) + geom_bar(stat = "identity", position = "dodge") + theme_minimal() + labs(title = "Line Deployment by Situation") ``` ## Strategic Considerations - Home vs away last change advantage - Offensive vs defensive zone deployment - Rest advantage and fatigue management - Situational awareness (score, time remaining)