library(dplyr)
##
## Attaching package: 'dplyr'
## The following objects are masked from 'package:stats':
##
## filter, lag
## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union
library(magrittr) # Load the magrittr package
library(ggplot2)
# Load IPL deliveries dataset (assuming it has the necessary columns)
deliveries <- read.csv("deliveries.csv")
# Group by match ID and innings (assuming innings information is in the 'inning' column)
bowlers_by_innings <- deliveries %>%
group_by(match_id, inning) %>%
summarise(bowler_count = n_distinct(bowler))
## `summarise()` regrouping output by 'match_id' (override with `.groups` argument)
# Find the innings with the maximum number of bowlers
max_bowlers_innings <- bowlers_by_innings %>%
filter(bowler_count == max(bowler_count))
# Extract bowling statistics for the max bowlers innings
max_bowlers_stats <- deliveries %>%
filter(match_id == max_bowlers_innings$match_id[1] & inning == max_bowlers_innings$inning[1]) %>%
group_by(bowler) %>%
summarise(runs_conceded = sum(total_runs),
wickets_taken = sum(player_dismissed == 1),
overs = sum(over) + sum(ball) / 6, # Calculate overs based on over and ball columns
economy = runs_conceded / overs)
## `summarise()` ungrouping output (override with `.groups` argument)
# Print the results
cat("Team with maximum bowlers in an innings:", max_bowlers_innings$match_id[1], "Inn:", max_bowlers_innings$inning[1], "\n")
## Team with maximum bowlers in an innings: 1 Inn: 1
print(max_bowlers_stats)
## # A tibble: 7 x 5
## bowler runs_conceded wickets_taken overs economy
## <chr> <int> <int> <dbl> <dbl>
## 1 A Choudhary 55 0 328. 0.168
## 2 S Aravind 36 0 202. 0.178
## 3 SR Watson 41 0 250. 0.164
## 4 STR Binny 10 0 69.5 0.144
## 5 TM Head 11 0 51.5 0.214
## 6 TS Mills 32 0 256. 0.125
## 7 YS Chahal 22 0 230 0.0957