Introduction

This is an analysis of player performances in the cricket tournament Women’s Premier League played in India in March 2023. This data analysis is submitted as part of the Data Visualization Capstone Project offered by JHU.

First we will import the data from cricsheet and take a quick look at the data available. The data is available at https://cricsheet.org/matches/ and has been downloaded to the project folder. We can see that the data has 4999 rows and 22 columns. The data type is also mentioned.

data <- import(here("all_matches.csv"))

glimpse(data)

Rows: 4,999
Columns: 22
$ match_id               <int> 1358929, 1358929, 1358929, 1358929, 1…
$ season                 <chr> "2022/23", "2022/23", "2022/23", "202…
$ start_date             <IDate> 2023-03-04, 2023-03-04, 2023-03-04,…
$ venue                  <chr> "Dr DY Patil Sports Academy, Mumbai",…
$ innings                <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ ball                   <dbl> 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 1.1, 1.…
$ batting_team           <chr> "Mumbai Indians", "Mumbai Indians", "…
$ bowling_team           <chr> "Gujarat Giants", "Gujarat Giants", "…
$ striker                <chr> "YH Bhatia", "YH Bhatia", "YH Bhatia"…
$ non_striker            <chr> "HK Matthews", "HK Matthews", "HK Mat…
$ bowler                 <chr> "A Gardner", "A Gardner", "A Gardner"…
$ runs_off_bat           <int> 0, 0, 0, 0, 1, 1, 6, 4, 0, 1, 0, 0, 0…
$ extras                 <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0…
$ wides                  <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ noballs                <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ byes                   <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ legbyes                <int> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ penalty                <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ wicket_type            <chr> "", "", "", "", "", "", "", "", "", "…
$ player_dismissed       <chr> "", "", "", "", "", "", "", "", "", "…
$ other_wicket_type      <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…
$ other_player_dismissed <lgl> NA, NA, NA, NA, NA, NA, NA, NA, NA, N…

Batting Analysis

For the first section we will look at Batting Analysis of teams. We will look at 1. The most runs in the tournament 2. The highest strike rates in the tournament (with a minimum of 16 balls faced)

##  Data Prep Batting
batting_data <- data %>% 
  select(batting_team, match_id, striker, runs_off_bat) %>% 
  group_by(striker) %>% 
  summarize(total_runs =  sum(runs_off_bat), 
            total_balls = n(),
            no_of_innings = n_distinct(match_id)
            ) %>% 
  mutate(strike_rate = (total_runs / total_balls)*100)

Viz 1. A gt table (because even tables need to look good)

most_runs_gt <- batting_data %>% 
  arrange(desc(total_runs)) %>% 
  head(.,5) %>% 
  mutate(team = c("Delhi Capitals", "Mumbai Indians", "UP Warriors", "Mumbai Indians", "Mumbai Indians")) %>% 
  mutate(logo = c("https://logowik.com/content/uploads/images/delhi-capitals3041.jpg" , "https://1000logos.net/wp-content/uploads/2022/08/Mumbai-Indians-Logo.png","https://upload.wikimedia.org/wikipedia/en/thumb/a/a2/UP_Warriorz_Logo.webp/301px-UP_Warriorz_Logo.webp.png",
    "https://1000logos.net/wp-content/uploads/2022/08/Mumbai-Indians-Logo.png","https://1000logos.net/wp-content/uploads/2022/08/Mumbai-Indians-Logo.png")) %>% 
  select(striker, logo, total_runs,total_balls,no_of_innings,  strike_rate  ) %>% 
  gt() %>% 
  gt_img_rows(columns = logo, height = 20)

most_runs_gt_table <- most_runs_gt %>% 
  tab_header(title = "Top Scorers of WPL 2023"
                          ) %>%
  fmt_number(columns = vars(strike_rate), decimals = 2) %>% 
  cols_label(
    striker = "Batter",
    total_runs = "Runs",
    total_balls = "Balls",
    no_of_innings = "Innings",
    strike_rate = "SR",
    logo = ""
  ) %>%  
  gt_theme_538()

most_runs_gt_table

Batter	Runs	Balls	Innings	SR
Top Scorers of WPL 2023
MM Lanning	345	260	9	132.69
NR Sciver	332	239	10	138.91
TM McGrath	302	196	8	154.08
H Kaur	281	214	9	131.31
HK Matthews	271	221	10	122.62

Viz 2. A bar chart with players on the x axis and runs scored on the y axis

## Visualization for most runs

most_runs_viz <- batting_data %>% 
  arrange(desc(total_runs)) %>% 
  head(.,10) %>% 
  ggplot(aes(reorder(striker, total_runs), y = total_runs))+
  geom_col()+
  coord_flip()+
  theme_economist()+
  theme(
    plot.background = element_blank(),
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    panel.border = element_blank(),
    axis.title = element_blank(),
    axis.ticks = element_blank()
  )+
  ggtitle("Most Runs in WPL 2023")


most_runs_viz

Viz 3. A gt table of players with highest strike rate

# Strike Rate

strike_rate_gt <- batting_data %>% 
  arrange(desc(strike_rate)) %>% 
  head(.,5) %>% 
  mutate(team = c("Delhi Capitals", "Gujarat Giants", "RCB", "UP Warriorz", "Delhi Capitals")) %>% 
  mutate(logo = c("https://logowik.com/content/uploads/images/delhi-capitals3041.jpg" , "https://telugu.mykhel.com/img/1200x60x675/2023/02/gujaratgiants-1677427143.jpg","https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcRhQeM6pkJFIuz04WddU0ZSoxziK4Z2MQqclMSdeE-J&s",
                  "https://upload.wikimedia.org/wikipedia/en/thumb/a/a2/UP_Warriorz_Logo.webp/301px-UP_Warriorz_Logo.webp.png","https://logowik.com/content/uploads/images/delhi-capitals3041.jpg")) %>% 
  select(striker, logo, total_runs,total_balls,no_of_innings,  strike_rate  ) %>% 
  gt() %>% 
  gt_img_rows(columns = logo, height = 20)


strike_rate_gt_table <- strike_rate_gt %>% 
  tab_header(title = "Highest SR of WPL 2023"
  ) %>%
  fmt_number(columns = vars(strike_rate), decimals = 2) %>% 
  cols_label(
    striker = "Batter",
    total_runs = "Runs",
    total_balls = "Balls",
    no_of_innings = "Innings",
    strike_rate = "SR",
    logo = ""
  ) %>%  
  gt_theme_538()


strike_rate_gt_table

Batter	Runs	Balls	Innings	SR
Highest SR of WPL 2023
Shafali Verma	252	144	9	175.00
SIR Dunkley	121	70	6	172.86
SFM Devine	266	156	8	170.51
GM Harris	230	144	5	159.72
RP Yadav	38	24	3	158.33

Viz 4. A bar chart with players with the highest strike rate with strike rate on the y axis

## Strike Rate Viz

sr_viz <- batting_data %>% 
  arrange(desc(strike_rate)) %>% 
  head(.,10) %>% 
  ggplot(aes(reorder(striker, strike_rate), y = strike_rate))+
  geom_col()+
  coord_flip()+
  theme_economist()+
  theme(
    plot.background = element_blank(),
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    panel.border = element_blank(),
    axis.title = element_blank(),
    axis.ticks = element_blank()
  )+
  ggtitle("Highest Strike Rate in WPL 2023")

sr_viz

Viz 5. A Scatter Plot showing Runs Scored vs Strike Rate

runs_vs_sr <- batting_data %>% 
  filter(total_balls >= 50) %>% 
  ggplot(aes(x= total_runs, y = strike_rate)) +
  geom_point() + geom_text_repel(aes(label = striker), size = 2.5)+ 
  gghighlight(max(total_runs) > 250 & max(strike_rate) > 140)+
  theme_economist()+ 
  theme( plot.background = element_blank(), 
         panel.grid.major = element_blank(), 
         panel.grid.minor = element_blank(), 
         panel.border = element_blank(), 
         #axis.title = element_blank(), 
         axis.ticks = element_blank() )+ 
  xlab("Runs Scored")+ 
  ylab("Strike Rate")+
  ggtitle("Runs Scored vs Strike Rate in WPL 2023")


runs_vs_sr

Bowling Analysis

For this we will look at

the highest wicket takers in the tournament
The most economical bowlers in the tournament.

bowling_data <- data %>% 
  mutate(runs_conceded = rowSums(select(., runs_off_bat, wides, noballs), na.rm =TRUE)) %>% 
  
  mutate(bowler_wicket = 
           case_when(
             wicket_type = NA ~ "No", 
             wicket_type == "bowled" ~ "Yes",
             wicket_type == "caught" ~ "Yes",
             wicket_type == "caught and bowled" ~ "Yes", 
             wicket_type == "lbw" ~ "Yes", 
             wicket_type == "retired hurt" ~ "No", 
             wicket_type == "run out" ~ "No" , 
             wicket_type == "stumped" ~ "Yes"
           )) %>% 
  group_by(bowler) %>% 
  summarize(
    wickets = sum(bowler_wicket == "Yes", na.rm = TRUE),
    total_runs_conceded =  sum(runs_conceded), 
            total_balls_bowled = n(),
            no_of_innings = n_distinct(match_id),
            economy = ((total_runs_conceded / total_balls_bowled) * 6))

Viz 6 : A gt table with highest wickets in the tournament

wickets_gt_object <- bowling_data %>% 
  arrange(desc(wickets)) %>% 
  head(.,5) %>% 
  mutate(team = c("Mumbai Indians", "UP Warriorz", "Mumbai Indians", "Mumbai Indians", "Mumbai Indians")) %>% 
  mutate(logo = c("https://1000logos.net/wp-content/uploads/2022/08/Mumbai-Indians-Logo.png" , "https://upload.wikimedia.org/wikipedia/en/thumb/a/a2/UP_Warriorz_Logo.webp/301px-UP_Warriorz_Logo.webp.png","https://1000logos.net/wp-content/uploads/2022/08/Mumbai-Indians-Logo.png","https://1000logos.net/wp-content/uploads/2022/08/Mumbai-Indians-Logo.png", "https://1000logos.net/wp-content/uploads/2022/08/Mumbai-Indians-Logo.png")) %>% 
  select(bowler, logo, no_of_innings, wickets, total_runs_conceded, total_balls_bowled, economy) %>% 
  gt() %>% 
  gt_img_rows(columns = logo, height = 20) 


wickets_gt_table <- wickets_gt_object %>%
  tab_header(title = "Most Wickets in WPL 2023"
  ) %>%
  fmt_number(columns = vars(economy), decimals = 2) %>% 
  cols_label(
    bowler = "Bowler",
    logo = "",
    no_of_innings = "Innings",
    wickets = "Wickets",
    total_runs_conceded = "Runs",
    total_balls_bowled = "Balls",
    economy = "Economy"
  ) %>%  
  gt_theme_538()

wickets_gt_table

Bowler	Innings	Wickets	Runs	Balls	Economy
Most Wickets in WPL 2023
HK Matthews	10	16	202	209	5.80
S Ecclestone	9	16	235	216	6.53
AC Kerr	10	15	211	196	6.46
IECM Wong	10	15	210	202	6.24
S Ishaque	10	15	244	211	6.94

Viz 7 : A Chart with highest wickets

wickets_dataviz <- bowling_data %>% 
  arrange(desc(wickets)) %>% 
  head(., 10) %>% 
  ggplot(aes(reorder(bowler, wickets), y = wickets))+
  geom_point()+
  geom_segment(aes(x = bowler, xend = bowler, y =0, yend = wickets))+
  coord_flip()+
  theme_economist()+
  theme(
    plot.background = element_blank(),
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    panel.border = element_blank(),
    axis.title = element_blank(),
    axis.ticks = element_blank()
  )+
  ggtitle("Most Wickets in WPL 2023")
  
wickets_dataviz

Viz 8 : A gt table describing the players with the best economy

econ_gt_object <- bowling_data %>%
  filter(total_balls_bowled >= 30) %>% 
  arrange(economy) %>% 
  head(., 5) %>% 
  mutate(team = c("Delhi Capitals", "Mumbai Indians", "Mumbai Indians", "Mumbai Indians", "Delhi Capitals")) %>% 
  mutate(logo = c("https://logowik.com/content/uploads/images/delhi-capitals3041.jpg" , "https://1000logos.net/wp-content/uploads/2022/08/Mumbai-Indians-Logo.png","https://1000logos.net/wp-content/uploads/2022/08/Mumbai-Indians-Logo.png","https://1000logos.net/wp-content/uploads/2022/08/Mumbai-Indians-Logo.png", "https://logowik.com/content/uploads/images/delhi-capitals3041.jpg")) %>% 
  select(bowler, logo, no_of_innings, wickets, total_runs_conceded, total_balls_bowled, economy) %>% 
  gt() %>% 
  gt_img_rows(columns = logo, height = 20)


econ_chart <- econ_gt_object %>%
  tab_header(title = "Best Economy in WPL 2023 (Min 30 Balls)"
  ) %>%
  fmt_number(columns = vars(economy), decimals = 2) %>% 
  cols_label(
    bowler = "Bowler",
    logo = "",
    no_of_innings = "Innings",
    wickets = "Wickets",
    total_runs_conceded = "Runs",
    total_balls_bowled = "Balls",
    economy = "Economy"
  ) %>%  
  gt_theme_538()


econ_chart

Bowler	Innings	Wickets	Runs	Balls	Economy
Best Economy in WPL 2023 (Min 30 Balls)
M Kapp	9	9	206	221	5.59
HK Matthews	10	16	202	209	5.80
IECM Wong	10	15	210	202	6.24
AC Kerr	10	15	211	196	6.46
S Pandey	9	10	211	196	6.46

Viz 9 : Runs Given vs Wickets Taken to identify the bowler who was most economical AND took the most wickets.

## Wickets vs Economy

wickets_vs_econ <- bowling_data %>% 
  filter(total_balls_bowled >= 75) %>% 
  ggplot(aes(x= wickets, y = economy)) +
  geom_point() + geom_text_repel(aes(label = bowler), size = 2.5)+ 
  gghighlight(max(wickets) > 12)+
  theme_economist()+ 
  theme( plot.background = element_blank(), 
         panel.grid.major = element_blank(), 
         panel.grid.minor = element_blank(), 
         panel.border = element_blank(), 
         #axis.title = element_blank(), 
         axis.ticks = element_blank() )+ 
  xlab("Wickets Taken")+ 
  ylab("Economy Rate")+
  ggtitle("Wickets Taken vs Economy Rates in WPL 2023")


wickets_vs_econ

An Analysis of the Womens Premier League 2023

Introduction

Batting Analysis

Bowling Analysis