Match Analysis

Introduction

This vignette demonstrates how to analyze completed cricket matches using bouncer. You can examine scorecards, win probability swings, individual performances, and key moments.

Finding Matches

Query the database to find matches:

library(bouncer)

# Find recent IPL matches
ipl_matches <- query_matches(
  event = "Indian Premier League",
  match_type = "t20",
  limit = 20
)
head(ipl_matches)

# Find matches between specific teams
mi_vs_csk <- query_matches(
  team1 = "Mumbai Indians",
  team2 = "Chennai Super Kings",
  limit = 10
)

Analyzing a Match

Once you have a match ID, analyze it:

# Get comprehensive match analysis
match <- analyze_match("1234567")
print(match)

# The result includes:
# - Match metadata (teams, venue, result)
# - Innings summaries
# - Top performers
# - Key moments

Delivery-Level Data

Get ball-by-ball data for detailed analysis:

# Query all deliveries from a match
deliveries <- query_deliveries(match_id = "1234567")

# View first few balls
head(deliveries)

# Get specific innings
first_innings <- query_deliveries(
  match_id = "1234567",
  innings = 1
)

Win Probability Timeline

Visualize how win probability changed during the match:

# Plot win probability over the match
plot_win_probability("1234567", format = "t20")

The plot shows: - Win probability for each team after every delivery - Key moments where probability shifted significantly - The eventual winner’s probability converging to 100%

Score Progression

View how the score built up:

# Plot score progression
plot_score_progression("1234567")

This shows: - Runs scored over time - Wickets falling (marked on the chart) - Run rate trends

Understanding Key Moments

Key moments are deliveries that significantly impacted win probability:

# Get deliveries with large win probability changes
deliveries <- query_deliveries(match_id = "1234567")

# Find high-impact deliveries
high_impact <- deliveries[abs(deliveries$win_prob_change) > 0.05, ]
print(high_impact)

Typical high-impact moments: - Wickets of set batters - Sixes in crucial overs - Dot balls in the death overs

Comparing Teams

Before analyzing a match, understand the team matchup:

# Compare the two teams
matchup <- compare_teams("Mumbai Indians", "Chennai Super Kings", format = "t20")
print(matchup)

# See head-to-head history
# (included in matchup result)

Querying Specific Events

Filter deliveries by various criteria:

# Get all sixes in a match
sixes <- query_deliveries(
  match_id = "1234567",
  runs_batter = 6
)

# Get all wickets
wickets <- query_deliveries(
  match_id = "1234567",
  is_wicket = TRUE
)

# Get powerplay deliveries (overs 1-6)
powerplay <- query_deliveries(
  match_id = "1234567",
  over_start = 0,
  over_end = 5
)