IPL 2008-2019 Records

Introduction

We will use dataset obtained from Kaggle. It contains dataset named “delivery” and “matches”.

For suggestions and inquiries: sulovekoirala@gmail.com

Let’s head to the analysis.

Loading the packages

library(dplyr)
library(ggplot2)
library(ggthemes)
library(kableExtra)

Import

You can find the dataset at: https://www.kaggle.com/nowke9/ipldata

library(readr)
ball <- read_csv("R:/Datasets/IPL/deliveries.csv")
matches <- read_csv("R:/Datasets/IPL/matches.csv")

Head of the dataset

head(ball) %>%
    kable(digits = 2, format = "html", row.names = TRUE) %>%
    kable_styling(full_width = T,
                  font_size = 15) %>%
    scroll_box(width = "800px")

	match_id	inning	batting_team	bowling_team	over	ball	batsman	non_striker	bowler	wide_runs	batsman_runs	extra_runs	total_runs	player_dismissed	dismissal_kind	fielder
1	1	1	Sunrisers Hyderabad	Royal Challengers Bangalore	1	1	DA Warner	S Dhawan	TS Mills	0	0	0	0	NA	NA	NA
2	1	1	Sunrisers Hyderabad	Royal Challengers Bangalore	1	2	DA Warner	S Dhawan	TS Mills	0	0	0	0	NA	NA	NA
3	1	1	Sunrisers Hyderabad	Royal Challengers Bangalore	1	3	DA Warner	S Dhawan	TS Mills	0	4	0	4	NA	NA	NA
4	1	1	Sunrisers Hyderabad	Royal Challengers Bangalore	1	4	DA Warner	S Dhawan	TS Mills	0	0	0	0	NA	NA	NA
5	1	1	Sunrisers Hyderabad	Royal Challengers Bangalore	1	5	DA Warner	S Dhawan	TS Mills	2	0	2	2	NA	NA	NA
6	1	1	Sunrisers Hyderabad	Royal Challengers Bangalore	1	6	S Dhawan	DA Warner	TS Mills	0	0	0	0	NA	NA	NA

head(matches) %>%
   kable(digits = 2, format = "html", row.names = TRUE) %>%
    kable_styling(full_width = T,
                  font_size = 15) %>%
    scroll_box(width = "800px")

	id	season	city	date	team1	team2	toss_winner	toss_decision	result	winner	win_by_runs	win_by_wickets	player_of_match	venue	umpire1	umpire2	umpire3
1	1	2017	Hyderabad	2017-04-05	Sunrisers Hyderabad	Royal Challengers Bangalore	Royal Challengers Bangalore	field	normal	Sunrisers Hyderabad	35	0	Yuvraj Singh	Rajiv Gandhi International Stadium, Uppal	AY Dandekar	NJ Llong	NA
2	2	2017	Pune	2017-04-06	Mumbai Indians	Rising Pune Supergiant	Rising Pune Supergiant	field	normal	Rising Pune Supergiant	0	7	SPD Smith	Maharashtra Cricket Association Stadium	A Nand Kishore	S Ravi	NA
3	3	2017	Rajkot	2017-04-07	Gujarat Lions	Kolkata Knight Riders	Kolkata Knight Riders	field	normal	Kolkata Knight Riders	0	10	CA Lynn	Saurashtra Cricket Association Stadium	Nitin Menon	CK Nandan	NA
4	4	2017	Indore	2017-04-08	Rising Pune Supergiant	Kings XI Punjab	Kings XI Punjab	field	normal	Kings XI Punjab	0	6	GJ Maxwell	Holkar Cricket Stadium	AK Chaudhary	C Shamshuddin	NA
5	5	2017	Bangalore	2017-04-08	Royal Challengers Bangalore	Delhi Daredevils	Royal Challengers Bangalore	bat	normal	Royal Challengers Bangalore	15	0	KM Jadhav	M Chinnaswamy Stadium	NA	NA	NA
6	6	2017	Hyderabad	2017-04-09	Gujarat Lions	Sunrisers Hyderabad	Sunrisers Hyderabad	field	normal	Sunrisers Hyderabad	0	9	Rashid Khan	Rajiv Gandhi International Stadium, Uppal	A Deshmukh	NJ Llong	NA

Individual Records

Most number of runs

ball %>% 
    group_by(batsman) %>% 
    summarise(Runs = sum(batsman_runs)) %>% 
    arrange(-Runs) %>% 
    head(15) %>% 
    kable(digits = 2, format = "html", row.names = TRUE) %>%
    kable_styling(bootstrap_options = c("striped", "hover"),
                  full_width = T,
                  font_size = 15) %>%
    scroll_box(height = "300px")

## `summarise()` ungrouping output (override with `.groups` argument)

	batsman	Runs
1	V Kohli	5434
2	SK Raina	5415
3	RG Sharma	4914
4	DA Warner	4741
5	S Dhawan	4632
6	CH Gayle	4560
7	MS Dhoni	4477
8	RV Uthappa	4446
9	AB de Villiers	4428
10	G Gambhir	4223
11	AM Rahane	3850
12	KD Karthik	3688
13	SR Watson	3614
14	AT Rayudu	3326
15	YK Pathan	3241

Most wickets

ball %>%
    filter(dismissal_kind != "run out") %>%
    group_by(bowler) %>% 
    summarise(wickets = sum(table(dismissal_kind))) %>%
    arrange(-wickets) %>% 
  head(15)%>%
kable(digits = 2, format = "html", row.names = TRUE, col.names = c ("bowler", "wickets")) %>%
    kable_styling(bootstrap_options = c("striped", "hover"),
                  full_width = T,
                  font_size = 15)%>%
    scroll_box(height = "300px")

## `summarise()` ungrouping output (override with `.groups` argument)

	bowler	wickets
1	SL Malinga	170
2	A Mishra	156
3	Harbhajan Singh	150
4	PP Chawla	149
5	DJ Bravo	147
6	B Kumar	133
7	R Ashwin	125
8	SP Narine	122
9	UT Yadav	119
10	RA Jadeja	108
11	A Nehra	106
12	R Vinay Kumar	105
13	Z Khan	103
14	YS Chahal	100
15	DW Steyn	96

Highest score by a batsman in a match

top_scores = ball %>% 
    group_by(batsman, match_id) %>% 
    summarise(runs = sum(batsman_runs)) %>% 
    arrange(-runs) %>%
    select(batsman, runs) %>% 
    head(10) %>%
    ungroup() %>%
    mutate(Rank = as.factor(1:10))

## `summarise()` regrouping output by 'batsman' (override with `.groups` argument)

ggplot(top_scores)+
    aes(reorder(Rank, runs), runs)+
    geom_bar(stat = "identity", width = 0.03, alpha = 0.3)+
    geom_point(size=7, color="pink", alpha=0.4, shape=20, stroke=5)+
    scale_x_discrete(labels = top_scores[order(top_scores$runs),]$batsman) +
    theme_classic()+
    geom_text(aes(Rank, runs, label = runs))+
    labs(x = "", y = "Runs", title = "Highest runs scored by a player in a match", subtitle = "")+
    coord_flip()+
    theme(axis.text = element_text(size = 12, face = "bold"), title  =  element_text(size = 16))

Bowlers with 5 or more wickets

ball %>%
    filter(dismissal_kind != "run out") %>%
    group_by(bowler, match_id) %>% 
    summarise(wickets = sum(table(dismissal_kind))) %>%
    arrange(-wickets) %>% 
    filter(wickets >= 5) %>% 
    select(bowler, wickets) %>%
    table() %>% 
    kable(digits = 2, format = "html", row.names = TRUE, col.names = c("5 Wickets", "6 Wickets")) %>%
    kable_styling(bootstrap_options = c("striped", "hover"),
                  full_width = T,
                  font_size = 15) %>%
    scroll_box(height = "300px")

## `summarise()` regrouping output by 'bowler' (override with `.groups` argument)

	5 Wickets	6 Wickets
A Joseph	0	1
A Kumble	1	0
A Mishra	1	0
A Zampa	0	1
AD Mascarenhas	1	0
AJ Tye	1	0
AS Rajpoot	1	0
B Kumar	1	0
Harbhajan Singh	1	0
I Sharma	1	0
JD Unadkat	2	0
JP Faulkner	2	0
L Balaji	1	0
MM Patel	1	0
RA Jadeja	1	0
SL Malinga	1	0
Sohail Tanvir	0	1
SP Narine	1	0

Most dismissed batsman by a bowler

ball %>%
    filter(dismissal_kind != "run out") %>%
    group_by(bowler, batsman) %>% 
    summarise(total_dismissed = sum(table(dismissal_kind))) %>%
    arrange(-total_dismissed) %>% 
    head(10) %>%
    kable(digits = 2, format = "html", row.names = TRUE) %>%
    kable_styling(bootstrap_options = c("striped", "hover"),
                  full_width = T,
                  font_size = 15) %>%
    scroll_box(height = "300px")

## `summarise()` regrouping output by 'bowler' (override with `.groups` argument)

	bowler	batsman	total_dismissed
1	Z Khan	MS Dhoni	7
2	A Mishra	RG Sharma	6
3	A Nehra	V Kohli	6
4	B Kumar	AM Rahane	6
5	B Kumar	PA Patel	6
6	MM Sharma	AT Rayudu	6
7	PP Chawla	AT Rayudu	6
8	PP Ojha	MS Dhoni	6
9	R Ashwin	RV Uthappa	6
10	R Vinay Kumar	RG Sharma	6

Batsman with most Runs against a single bowler

ball %>% 
    group_by(batsman, bowler) %>% 
    summarise(runs = sum(table(batsman_runs)))%>%
    arrange(-runs) %>% 
  head(10) %>%
    kable(digits = 2, format = "html", row.names = TRUE) %>%
    kable_styling(bootstrap_options = c("striped", "hover"),
                  full_width = T,
                  font_size = 15) %>%
    scroll_box(height = "300px")

## `summarise()` regrouping output by 'batsman' (override with `.groups` argument)

	batsman	bowler	runs
1	SK Raina	Harbhajan Singh	125
2	S Dhawan	Harbhajan Singh	123
3	V Kohli	R Ashwin	119
4	RG Sharma	PP Chawla	113
5	RG Sharma	SP Narine	111
6	CH Gayle	Harbhajan Singh	103
7	V Kohli	A Mishra	103
8	SK Raina	PP Chawla	102
9	MS Dhoni	PP Ojha	100
10	V Kohli	RA Jadeja	99

Batsman with most dismissal by its kind

ball %>% 
    group_by(player_dismissed, dismissal_kind) %>% 
    summarise(total = sum(table(dismissal_kind)))%>%
    arrange(-total) %>% 
    na.omit() %>%
    group_by(dismissal_kind) %>%
    slice(which.max(total)) %>% 
    kable(digits = 2, format = "html", row.names = TRUE) %>%
    kable_styling(bootstrap_options = c("striped", "hover"),
                  full_width = T,
                  font_size = 15) %>%
    scroll_box(height = "300px")

## `summarise()` regrouping output by 'player_dismissed' (override with `.groups` argument)

	player_dismissed	dismissal_kind	total
1	SR Watson	bowled	32
2	SK Raina	caught	112
3	SK Raina	caught and bowled	7
4	DA Warner	hit wicket	1
5	BB McCullum	lbw	16
6	A Mishra	obstructing the field	1
7	Harbhajan Singh	retired hurt	2
8	G Gambhir	run out	16
9	SK Raina	stumped	8

Bowlers with most dismissal by its kind

ball %>% 
    group_by(bowler, dismissal_kind) %>% 
    summarise(total = sum(table(dismissal_kind)))%>%
    arrange(-total) %>% 
    na.omit() %>%
    group_by(dismissal_kind) %>%
    slice(which.max(total)) %>% 
    kable(digits = 2, format = "html", row.names = TRUE) %>%
    kable_styling(bootstrap_options = c("striped", "hover"),
                  full_width = T,
                  font_size = 15) %>%
    scroll_box(height = "300px")

## `summarise()` regrouping output by 'bowler' (override with `.groups` argument)

	bowler	dismissal_kind	total
1	SL Malinga	bowled	63
2	DJ Bravo	caught	113
3	Harbhajan Singh	caught and bowled	10
4	S Sreesanth	hit wicket	2
5	PP Chawla	lbw	16
6	K Ahmed	obstructing the field	1
7	BB Sran	retired hurt	1
8	R Vinay Kumar	run out	22
9	A Mishra	stumped	26

Most Maiden Overs in IPL 2008-19

maidens <- as.data.frame(ball %>% 
                            group_by(bowler, match_id, over) %>%
                            summarise(runs = sum(total_runs)) %>% 
                            filter(runs == 0) %>%
                            ungroup() %>% 
                            select(bowler) %>% 
                            table()) %>%
    arrange(-Freq)

## `summarise()` regrouping output by 'bowler', 'match_id' (override with `.groups` argument)

colnames(maidens) = c ("Bowler", "No")
maidens %>%
    filter(No > 5) %>%
    ggplot() +
    aes(reorder(Bowler, No), No) + 
    geom_bar(stat = "identity", width = 0.54) +
    geom_text(aes(Bowler, No, label = No), hjust = -0.1)+
    theme_minimal() + 
    coord_flip()+ 
    theme(axis.text.x =  element_blank())+
    labs(x = "", y = "", title = "Most Maiden Overs in IPL (2008-19)")

Highest run scorer of a team

ball %>% 
    group_by(batting_team, batsman) %>% 
    summarise(runs = sum(batsman_runs)) %>% 
    arrange(-runs) %>%
    group_by(batting_team) %>%
    slice(which.max(runs))%>%
kable(digits = 2, format = "html", row.names = TRUE, col.names = c ("team", "batsman", "runs")) %>%
    kable_styling(bootstrap_options = c("striped", "hover"),
                  full_width = T,
                  font_size = 15)

## `summarise()` regrouping output by 'batting_team' (override with `.groups` argument)

	team	batsman	runs
1	Chennai Super Kings	SK Raina	4574
2	Deccan Chargers	AC Gilchrist	1220
3	Delhi Capitals	S Dhawan	542
4	Delhi Daredevils	V Sehwag	2174
5	Gujarat Lions	SK Raina	841
6	Kings XI Punjab	SE Marsh	2489
7	Kochi Tuskers Kerala	BB McCullum	357
8	Kolkata Knight Riders	G Gambhir	3035
9	Mumbai Indians	RG Sharma	3744
10	Pune Warriors	RV Uthappa	1103
11	Rajasthan Royals	AM Rahane	2840
12	Rising Pune Supergiant	SPD Smith	472
13	Rising Pune Supergiants	AM Rahane	480
14	Royal Challengers Bangalore	V Kohli	5434
15	Sunrisers Hyderabad	DA Warner	3306

Highest Wicket Taker of a team

ball %>%
    filter(dismissal_kind != "run out") %>%
    group_by(bowling_team, bowler) %>% 
    summarise(wickets = sum(table(dismissal_kind))) %>%
    arrange(-wickets) %>% 
    group_by(bowling_team) %>% 
    slice(which.max(wickets))%>%
kable(digits = 2, format = "html", row.names = TRUE, col.names = c ("team", "bowler", "wickets")) %>%
    kable_styling(bootstrap_options = c("striped", "hover"),
                  full_width = T,
                  font_size = 15) %>%
    scroll_box(height = "300px")

## `summarise()` regrouping output by 'bowling_team' (override with `.groups` argument)

	team	bowler	wickets
1	Chennai Super Kings	DJ Bravo	104
2	Deccan Chargers	PP Ojha	60
3	Delhi Capitals	K Rabada	25
4	Delhi Daredevils	A Mishra	86
5	Gujarat Lions	DS Kulkarni	21
6	Kings XI Punjab	PP Chawla	84
7	Kochi Tuskers Kerala	RP Singh	13
8	Kolkata Knight Riders	SP Narine	122
9	Mumbai Indians	SL Malinga	170
10	Pune Warriors	R Sharma	34
11	Rajasthan Royals	SK Trivedi	65
12	Rising Pune Supergiant	JD Unadkat	24
13	Rising Pune Supergiants	A Zampa	12
14	Royal Challengers Bangalore	YS Chahal	100
15	Sunrisers Hyderabad	B Kumar	109

Players with most catches, runouts and stumping

ball %>%
    group_by(dismissal_kind, fielder) %>% 
    summarise(total = sum(table(dismissal_kind))) %>%
    arrange(-total) %>% 
    group_by(dismissal_kind) %>% 
    slice(which.max(total)) %>%
    na.omit() %>%
    kable(digits = 2, format = "html", row.names = TRUE) %>%
    kable_styling(bootstrap_options = c("striped", "hover"),
                  full_width = T,
                  font_size = 15)

## `summarise()` regrouping output by 'dismissal_kind' (override with `.groups` argument)

	dismissal_kind	fielder	total
1	caught	KD Karthik	109
2	run out	MS Dhoni	23
3	stumped	MS Dhoni	38

Most number of Man of the Match Awards

matches %>% 
    select(player_of_match) %>%
    table() %>%
    data.frame() %>%
    arrange(-Freq) %>%
    head(10) %>%
    ggplot()+
    aes(reorder(., Freq), Freq)+
    geom_bar(stat = "identity", width = 0.03, alpha = 0.3)+
    geom_point(size=7, color="pink", alpha=0.4, shape=20, stroke=5)+
    geom_text(aes(., Freq, label = Freq)) + 
    theme_classic() + 
    coord_flip()+
    labs(x = "", y = "", title = "Most number of MoM awards")

Highest total individual runs against a team (aggregate)

ball %>% 
    group_by(bowling_team, batsman) %>% 
    summarise(total_runs = sum(batsman_runs))%>%
    arrange(-total_runs) %>% 
    na.omit() %>%
    group_by(bowling_team) %>%
    slice(which.max(total_runs)) %>%
  kable(digits = 2, format = "html", row.names = TRUE) %>%
    kable_styling(bootstrap_options = c("striped", "hover"),
                  full_width = T,
                  font_size = 15) %>%
    scroll_box(height = "300px")

## `summarise()` regrouping output by 'bowling_team' (override with `.groups` argument)

	bowling_team	batsman	total_runs
1	Chennai Super Kings	V Kohli	749
2	Deccan Chargers	R Dravid	339
3	Delhi Capitals	AD Russell	118
4	Delhi Daredevils	V Kohli	763
5	Gujarat Lions	DA Warner	336
6	Kings XI Punjab	DA Warner	833
7	Kochi Tuskers Kerala	SR Tendulkar	100
8	Kolkata Knight Riders	DA Warner	835
9	Mumbai Indians	SK Raina	824
10	Pune Warriors	CH Gayle	383
11	Rajasthan Royals	SK Raina	620
12	Rising Pune Supergiant	PA Patel	108
13	Rising Pune Supergiants	V Kohli	188
14	Royal Challengers Bangalore	MS Dhoni	808
15	Sunrisers Hyderabad	SR Watson	531

Highest individual wickets against a team (aggregate)

ball %>%
    filter(dismissal_kind != "run out") %>%
    group_by(batting_team, bowler) %>% 
    summarise(wickets = sum(table(dismissal_kind))) %>%
    arrange(-wickets) %>% 
    group_by(batting_team) %>%
    slice(which.max(wickets)) %>%
  kable(digits = 2, format = "html", row.names = TRUE) %>%
    kable_styling(bootstrap_options = c("striped", "hover"),
                  full_width = T,
                  font_size = 15) %>%
    scroll_box(height = "300px")

## `summarise()` regrouping output by 'batting_team' (override with `.groups` argument)

	batting_team	bowler	wickets
1	Chennai Super Kings	SL Malinga	31
2	Deccan Chargers	SL Malinga	19
3	Delhi Capitals	B Kumar	6
4	Delhi Daredevils	Harbhajan Singh	21
5	Gujarat Lions	B Kumar	12
6	Kings XI Punjab	UT Yadav	29
7	Kochi Tuskers Kerala	I Sharma	5
8	Kolkata Knight Riders	B Kumar	27
9	Mumbai Indians	DJ Bravo	28
10	Pune Warriors	SL Malinga	12
11	Rajasthan Royals	A Mishra	30
12	Rising Pune Supergiant	AJ Tye	5
13	Rising Pune Supergiants	SR Watson	5
14	Royal Challengers Bangalore	A Nehra	23
15	Sunrisers Hyderabad	DJ Bravo	17

Team Records

IPL Winners

matches %>%
    select(season, id, winner) %>%
    group_by(season) %>%
    slice(which.max(id)) %>%
    select(season, winner) %>% 
    kable(digits = 2, format = "html", row.names = TRUE) %>%
    kable_styling(bootstrap_options = c("striped", "hover"),
                  full_width = T,
                  font_size = 15) %>%
    scroll_box(height = "300px")

	season	winner
1	2008	Rajasthan Royals
2	2009	Deccan Chargers
3	2010	Chennai Super Kings
4	2011	Chennai Super Kings
5	2012	Kolkata Knight Riders
6	2013	Mumbai Indians
7	2014	Kolkata Knight Riders
8	2015	Mumbai Indians
9	2016	Sunrisers Hyderabad
10	2017	Mumbai Indians
11	2018	Chennai Super Kings
12	2019	Mumbai Indians

Decision at Toss

toss = as.data.frame(table(matches$toss_decision))
toss = mutate(toss, percentage = (Freq/sum(Freq))*100)
pie(toss$Freq, labels = round(toss$percentage), main = "Toss Decision (%)", col = c("red", "blue"))
legend("topright", c("bat","field"), cex = 0.8, fill = c("red", "blue"))

Teams winning by highest margin of runs

team = matches %>%
    select(winner, win_by_runs) %>%
    arrange(-win_by_runs) %>%
    head(10) %>%
    ungroup() %>% 
    mutate(Rank = as.factor(1:10))
  
ggplot(team)+
    aes(reorder(Rank, win_by_runs), win_by_runs)+
    geom_bar(stat = "identity", width = 0.03, alpha = 0.3)+
    geom_point(size=7, color="cyan", alpha=0.4, shape=20, stroke=5)+
    scale_x_discrete(labels = team[order(team$win_by_runs),]$winner) +
    theme_classic()+
    geom_text(aes(Rank, win_by_runs, label = win_by_runs))+
    labs(x = "", y = "", title = "Highest win by runs", subtitle = "")+
    coord_flip()+
    theme(axis.text = element_text(size = 12, face = "bold"), title  =  element_text(size = 16))

Teams scoring highest runs in a match

team2 = ball %>% 
    group_by(match_id, batting_team) %>%
    summarise(runs = sum(total_runs)) %>%
    arrange(-runs) %>%
    head(10) %>%
    ungroup() %>% 
    mutate(Rank = as.factor(1:10))

## `summarise()` regrouping output by 'match_id' (override with `.groups` argument)

ggplot(team2)+
    aes(reorder(Rank, runs), runs)+
    geom_bar(stat = "identity", width = 0.03, alpha = 0.3)+
    geom_point(size=7, color="cyan", alpha=0.4, shape=20, stroke=5)+
    scale_x_discrete(labels = team2[order(team2$runs),]$batting_team) +
    theme_classic()+
    geom_text(aes(Rank, runs, label = runs))+
    labs(x = "", y = "", title = "Highest runs by a team in a match", subtitle = "")+
    coord_flip()+
    theme(axis.text = element_text(size = 12, face = "bold"), title  =  element_text(size = 16))

IPL 2008-2019 Records

Dr. Sulove Koirala

8/19/2020

Introduction

Loading the packages

Import

Head of the dataset

Individual Records

Most number of runs

Most wickets

Highest score by a batsman in a match

Bowlers with 5 or more wickets

Most dismissed batsman by a bowler

Batsman with most Runs against a single bowler

Batsman with most dismissal by its kind

Bowlers with most dismissal by its kind

Most Maiden Overs in IPL 2008-19

Highest run scorer of a team

Highest Wicket Taker of a team

Players with most catches, runouts and stumping

Most number of Man of the Match Awards

Highest total individual runs against a team (aggregate)

Highest individual wickets against a team (aggregate)

Team Records

IPL Winners

Decision at Toss

Teams winning by highest margin of runs

Teams scoring highest runs in a match