Player Analysis

Introduction

This vignette shows how to analyze individual player performance using bouncer’s skill tracking system. The package tracks four key metrics for every player, updated ball-by-ball: - Batting Scoring Index: Runs per ball vs expected - Batting Survival Rate: Probability of not getting out - Bowling Economy Index: Runs conceded vs expected - Bowling Strike Rate: Wickets per ball

Looking Up Players

library(bouncer)

# Search for a player by partial name
search_players("Bumrah")

# Get detailed player info
bumrah <- get_player("Jasprit Bumrah", format = "t20")
print(bumrah)

The get_player() function returns current skill indices and career statistics.

Analyzing a Player

For deeper analysis, use analyze_player():

# Comprehensive player analysis
kohli <- analyze_player("Virat Kohli", format = "t20")

# View batting performance
print(kohli$batting)

# View skill progression over time
print(kohli$skill_history)

Comparing Players

Compare two players head-to-head:

# Compare batting ability
comparison <- compare_players(
  player1 = "Virat Kohli",
  player2 = "Steve Smith",
  format = "test"
)
print(comparison)

# Visualize the comparison
plot_player_comparison("Virat Kohli", "Steve Smith", format = "test")

The comparison shows: - Current skill indices for both players - Head-to-head record (if they’ve faced each other) - Statistical summary of key metrics

Tracking Skill Progression

See how a player’s skills have evolved over time:

# Plot skill progression
plot_skill_progression("Jasprit Bumrah", format = "t20")

# Get raw skill history data
history <- query_player_stats(
  player_id = "J Bumrah",
  match_type = "t20"
)
head(history)

Head-to-Head Records

Query specific batter vs bowler matchups:

# Kohli vs Bumrah in T20s
h2h <- query_batter_stats(
  batter_id = "V Kohli",
  bowler_id = "J Bumrah",
  match_type = "t20"
)
print(h2h)

Understanding Skill Indices

Batting Scoring Index

The batting scoring index measures runs scored per ball relative to what’s expected given the match context (over, wickets fallen, venue, etc.).

Value	Interpretation
+0.10	Scores 0.10 extra runs per ball (excellent)
+0.05	Above average scorer
0.00	Average (performs as expected)
-0.05	Below average scorer

Batting Survival Rate

Probability of surviving each ball faced:

Value	Interpretation
0.99	Gets out ~1% of balls (very resilient)
0.97	Gets out ~3% of balls (average)
0.95	Gets out ~5% of balls (aggressive/risky)

Bowling Economy Index

Runs conceded vs expected (negative is better for bowlers):

Value	Interpretation
-0.10	Concedes 0.10 fewer runs per ball (excellent)
-0.05	Above average economy
0.00	Average
+0.05	Below average (expensive)

Bowling Strike Rate

Wickets taken per ball:

Value	Interpretation
0.06	Takes wicket ~6% of balls (strike bowler)
0.04	Average wicket-taking ability
0.02	Lower strike rate (holding bowler)

Querying Player Statistics

For detailed statistical analysis:

# Get all T20 innings for a player
innings <- query_batter_stats(
  batter_id = "V Kohli",
  match_type = "t20"
)

# Filter by event
ipl_innings <- query_batter_stats(
  batter_id = "V Kohli",
  event = "Indian Premier League"
)

# Get bowling stats
bowling <- query_bowler_stats(
  bowler_id = "J Bumrah",
  match_type = "t20"
)