Understanding QQ and KDE Plots

Understanding Recovery Time Distribution

This density plot (KDE) shows how quickly your team recovers possession after losing the ball:

• Blue curve: Shows the frequency of different recovery times - the peak shows your team's most common recovery time
• Blue histogram: The actual count of recovery instances at each time point
• Blue circles: Individual recovery instances from the match data
• Vertical lines: Show the median (blue) and average (green) recovery times

What to look for:

• Peak position: Lower values (left-side peaks) mean faster recoveries - better pressing efficiency
• Curve width: Narrow peaks mean consistent recovery approach; wide spread suggests variable recovery tactics
• Right-side tail: Long tail means some situations lead to much longer recovery times
• Multiple peaks: Could indicate different recovery strategies in different game situations
• Average vs Median gap: Large gap suggests a few problematic recovery situations affecting overall performance

Football applications:

• Compare recovery performance across different matches, periods, or opponents
• Identify if tactical changes are improving recovery times
• Spot if fatigue is affecting recovery (later periods showing longer times)
• Evaluate effectiveness of pressing strategies
• Identify if specific player rotations improve recovery performance

Understanding Recovery Time Patterns

This QQ plot helps coaches identify if recovery times follow expected patterns or show unusual trends:

• Red line: Represents "normal" distribution - what we'd expect if recovery was consistent
• Blue dots: Each dot represents actual recovery time instances
• P-value: Statistical measure of how "normal" your recovery patterns are (below 0.05 means unusual patterns)

What to look for:

• Dots following the line: Team recovers consistently across all situations
• Dots curving above line (right side): Some recovery instances taking much longer than expected
• Dots curving below line (left side): Some unusually quick recoveries
• S-shaped curve: Team has mixed recovery performance - very quick in some situations, very slow in others

Football applications:

• Identify if you have distinct "recovery profiles" within your team that need different training
• Spot if your recovery approach changes dramatically in different match situations
• Evaluate if your team's recovery times are predictable (following the line) or highly variable
• Determine if recovery performance is being affected by specific opposition tactics
• Use to decide if you need one unified pressing strategy or multiple approaches for different scenarios

Understanding Match Recovery Comparison

This plot compares ball recovery times between teams and across different match periods:

• Different colored lines: Each color represents a different team
• Line styles: Solid lines show all periods combined; dashed/dotted lines show individual periods
• Peaks: The most common recovery time for each team
• Vertical lines: Show mean recovery times for each team

What to look for:

• Left-shifted peaks: Teams with peaks further left recover possession faster
• Peak height: Taller, narrower peaks indicate more consistent recovery times
• Period differences: Compare how recovery times change across periods to spot fatigue or tactical shifts
• Curve overlap: Where curves overlap, teams have similar recovery performance
• Multiple peaks: Could indicate different recovery scenarios (e.g., high press vs. low block)

Football applications:

• Directly compare your pressing efficiency against opponents
• Identify which periods your team had recovery advantage/disadvantage
• Spot if opponent's tactics affected your recovery performance
• See if substitutions improved recovery times in later periods
• Evaluate if recovery performance correlates with match outcome
• Determine when to increase/decrease pressing intensity based on recovery trends

Understanding Match Events Plot

This timeline visualization shows recovery times alongside key match events, helping coaches connect tactical moments with recovery performance:

• Recovery time lines: The colored lines show recovery times of each team throughout the match
• Y-axis: Shows time taken to recover possession (in seconds) - lower values indicate faster recovery
• X-axis: Match timeline in minutes
• Event icons: Shows goals ⚽, injuries 🤕, substitutions 🔄, tactical shifts 🔄, and cards 🟨/🟥
• Hover tooltips: Reveal detailed information about specific recovery instances or match events

What to look for:

• Recovery time patterns: Identify periods of effective and poor recovery performance
• Event impact: See how events like goals, substitutions or tactical shifts affect recovery times
• Pre/post injury: Compare recovery performance before and after player injuries
• Formation influence: Notice how formation changes impact recovery efficiency
• Period-to-period changes: Track how fatigue affects recovery across match periods

Football applications:

• Identify which in-game events create recovery vulnerabilities
• Assess if substitutions improve or disrupt recovery patterns
• Determine optimal times for tactical adjustments based on recovery trends
• Analyze which formations deliver most consistent recovery performance
• Spot connections between recovery breakdown and conceded goals
• Understand how specific player injuries impact team-wide recovery efficiency

The detailed match events table below the plot provides additional context about each event, and clicking on formation details reveals full team positioning information.