batters <- read.csv("C:/Data science/IPL2025Batters.csv")
bowlers <- read.csv("C:/Data science/IPL2025Bowlers.csv")
final_data <- merge(batters, bowlers, by = "Player.Name")
final_data
## Player.Name Team.x Runs Matches Inn No HS AVG.x BF SR.x
## 1 Aiden Markram LSG 445 13 13 0 66 34.23 299 148.82
## 2 Akash Deep LSG 6 6 2 1 6* 6.00 2 300.00
## 3 Akash Madhwal RR 4 4 1 1 4* - 9 44.44
## 4 Andre Russell KKR 167 13 10 1 57* 18.56 102 163.72
## 5 Anshul Kamboj CSK 14 8 5 3 5* 7.00 12 116.66
## 6 Arshdeep Singh PBKS 2 17 2 1 1* 2.00 6 33.33
## 7 Avesh Khan LSG 21 13 4 3 19* 21.00 12 175.00
## 8 Axar Patel DC 263 12 11 1 43 26.30 167 157.48
## 9 Ayush Badoni LSG 329 14 11 1 74 32.90 222 148.19
## 10 Azmatullah Omarzai PBKS 57 9 5 1 21* 14.25 41 139.02
## 11 Bhuvneshwar Kumar RCB 14 14 6 3 8 4.67 25 56.00
## 12 Corbin Bosch MI 47 2 2 0 27 23.50 32 146.87
## 13 Deepak Chahar MI 37 14 4 3 28* 37.00 25 148.00
## 14 Digvesh Singh LSG 1 13 2 1 1 1.00 3 33.33
## 15 Dushmantha Chameera DC 10 6 3 3 8* - 14 71.42
## 16 Gerald Coetzee GT 17 4 2 0 12 8.50 11 154.54
## 17 Glenn Maxwell PBKS 48 7 6 0 30 8.00 49 97.95
## 18 Hardik Pandya MI 224 15 12 3 48* 24.89 137 163.50
## 19 Harpreet Brar PBKS 11 8 2 1 7* 11.00 13 84.61
## 20 Harshal Patel SRH 21 13 4 2 12* 10.50 26 80.76
## 21 Harshit Rana KKR 57 13 7 3 34 14.25 53 107.54
## 22 Jofra Archer RR 63 12 7 1 30 10.50 50 126.00
## 23 Kagiso Rabada GT 9 4 2 1 7* 9.00 8 112.50
## 24 Kamindu Mendis SRH 92 5 5 2 32* 30.67 69 133.33
## 25 Karn Sharma MI 1 6 1 1 1* - 1 100.00
## 26 Khaleel Ahmed CSK 1 14 2 2 1* - 2 50.00
## 27 Krunal Pandya RCB 109 15 7 1 73* 18.17 86 126.74
## 28 Kuldeep Yadav DC 18 14 5 2 7 6.00 16 112.50
## 29 Kumar Kartikeya Singh RR 3 4 2 0 2 1.50 5 60.00
## 30 Kwena Maphaka RR 8 2 1 1 8* - 2 400.00
## 31 Liam Livingstone RCB 112 10 8 1 54 16.00 84 133.33
## 32 Lockie Ferguson PBKS 4 4 1 1 4* - 1 400.00
## 33 Maheesh Theekshana RR 10 11 4 2 5 5.00 27 37.03
## 34 Marco Jansen PBKS 75 14 8 4 34* 18.75 63 119.04
## 35 Marcus Stoinis PBKS 160 13 11 5 44* 26.67 86 186.04
## 36 Mitchell Santner MI 40 13 8 6 18* 20.00 33 121.21
## 37 Mitchell Starc DC 6 11 6 4 2* 3.00 10 60.00
## 38 Moeen Ali KKR 5 6 2 0 5 2.50 14 35.71
## 39 Mohammad Shami SRH 10 9 4 3 6* 10.00 10 100.00
## 40 Mohammed Siraj GT 3 15 1 1 3* - 4 75.00
## 41 Mohd Arshad Khan GT 23 9 5 3 20 11.50 19 121.05
## 42 Mohit Sharma DC 1 8 2 1 1* 1.00 3 33.33
## 43 Nitish Kumar Reddy SRH 182 13 11 3 32 22.75 153 118.95
## 44 Noor Ahmad CSK 7 14 6 2 2* 1.75 17 41.17
## 45 Pat Cummins SRH 97 14 9 5 22* 24.25 58 167.24
## 46 Prince Yadav LSG 5 6 2 2 4* - 10 50.00
## 47 Rashid Khan GT 40 15 8 3 12 8.00 28 142.85
## 48 Ravi Bishnoi LSG 13 11 3 1 13 6.50 16 81.25
## 49 Ravichandran Ashwin CSK 33 9 4 0 13 8.25 30 110.00
## 50 Ravindra Jadeja CSK 301 14 14 5 77* 33.44 222 135.58
## 51 Riyan Parag RR 393 14 14 2 95 32.75 236 166.52
## 52 Romario Shepherd RCB 70 8 3 1 53* 35.00 24 291.66
## 53 Sai Kishore GT 5 15 3 0 3 1.67 10 50.00
## 54 Sam Curran CSK 114 5 5 0 88 22.80 84 135.71
## 55 Sandeep Sharma RR 6 10 1 1 6* - 5 120.00
## 56 Shahbaz Ahmed LSG 9 3 1 0 9 9.00 8 112.50
## 57 Shahrukh Khan GT 179 15 11 5 57 29.83 100 179.00
## 58 Shardul Thakur LSG 18 10 5 2 6 6.00 12 150.00
## 59 Simarjeet Singh SRH 3 4 3 1 3* 1.50 7 42.85
## 60 Spencer Johnson KKR 2 4 2 2 1* - 4 50.00
## 61 Sunil Narine KKR 246 12 12 1 44 22.36 144 170.83
## 62 Trent Boult MI 2 16 2 1 1* 2.00 3 66.66
## 63 Tushar Deshpande RR 7 10 4 3 3 7.00 8 87.50
## 64 V Satyanarayana Penmetsa MI 1 2 1 1 1* - 1 100.00
## 65 Varun Chakaravarthy KKR 1 13 1 1 1* - 1 100.00
## 66 Vipraj Nigam DC 142 14 8 1 39 20.29 79 179.74
## 67 Wanindu Hasaranga RR 9 11 5 0 4 1.80 15 60.00
## 68 Washington Sundar GT 133 6 5 0 49 26.60 80 166.25
## 69 Will Jacks MI 233 13 11 1 53 23.30 172 135.46
## 70 Xavier Bartlett PBKS 11 4 1 0 11 11.00 15 73.33
## 71 Yash Dayal RCB 4 15 3 1 3 2.00 8 50.00
## X100s X50s X4s X6s Team.y WKT MAT INN OVR RUNS BBI AVG.y ECO SR.y X4W
## 1 0 5 38 22 LSG 4 13 5 11.0 102 30/2 25.50 9.27 16.50 0
## 2 0 0 0 1 LSG 3 6 6 19.0 229 55/2 76.33 12.05 38.00 0
## 3 0 0 0 0 RR 4 4 4 15.0 166 29/3 41.50 11.06 22.50 0
## 4 0 1 16 14 KKR 8 13 9 18.1 217 21/2 27.12 11.94 13.62 0
## 5 0 0 2 0 CSK 8 8 8 21.3 172 13/3 21.50 8.00 16.12 0
## 6 0 0 0 0 PBKS 21 17 16 58.2 518 16/3 24.66 8.88 16.66 0
## 7 0 0 3 1 LSG 13 13 13 47.2 487 37/3 37.46 10.28 21.84 0
## 8 0 0 23 15 DC 5 12 11 34.0 288 19/2 57.60 8.47 40.80 0
## 9 0 2 27 14 LSG 2 14 2 1.4 13 2-Apr 6.50 7.80 5.00 0
## 10 0 0 6 3 PBKS 8 9 8 27.0 279 33/2 34.87 10.33 20.25 0
## 11 0 0 1 0 RCB 17 14 14 52.0 483 33/3 28.41 9.28 18.35 0
## 12 0 0 3 3 MI 1 2 2 7.0 55 26/1 55.00 7.85 42.00 0
## 13 0 0 3 2 MI 11 14 14 41.0 376 2-Dec 34.18 9.17 22.36 0
## 14 0 0 0 0 LSG 14 13 13 52.0 429 30/2 30.64 8.25 22.28 0
## 15 0 0 0 0 DC 4 6 5 15.0 171 24/1 42.75 11.40 22.50 0
## 16 0 0 2 1 GT 2 4 4 12.0 131 1-Oct 65.50 10.91 36.00 0
## 17 0 0 5 1 PBKS 4 7 6 13.0 110 1-May 27.50 8.46 19.50 0
## 18 0 0 18 12 MI 14 15 14 35.0 342 36/5 24.42 9.77 15.00 0
## 19 0 0 0 1 PBKS 10 8 7 22.0 190 22/3 19.00 8.63 13.20 0
## 20 0 0 0 0 SRH 16 13 13 43.5 430 28/4 26.87 9.80 16.43 2
## 21 0 0 5 3 KKR 15 13 13 44.0 448 25/3 29.86 10.18 17.60 0
## 22 0 0 3 4 RR 11 12 12 45.3 431 25/3 39.18 9.47 24.81 0
## 23 0 0 0 1 GT 2 4 4 14.0 162 41/1 81.00 11.57 42.00 0
## 24 0 0 7 2 SRH 2 5 4 7.0 60 1-Apr 30.00 8.57 21.00 0
## 25 0 0 0 0 MI 7 6 5 15.0 128 23/3 18.28 8.53 12.85 0
## 26 0 0 0 0 CSK 15 14 14 46.4 447 29/3 29.80 9.57 18.66 0
## 27 0 1 9 4 RCB 17 15 15 46.0 379 45/4 22.29 8.23 16.23 1
## 28 0 0 2 0 DC 15 14 13 51.0 361 22/3 24.06 7.07 20.40 0
## 29 0 0 0 0 RR 2 4 4 8.0 78 21/1 39.00 9.75 24.00 0
## 30 0 0 2 0 RR 1 2 2 5.0 54 32/1 54.00 10.80 30.00 0
## 31 0 1 4 9 RCB 2 10 5 9.0 76 28/2 38.00 8.44 27.00 0
## 32 0 0 1 0 PBKS 5 4 4 11.2 104 37/2 20.80 9.17 13.60 0
## 33 0 0 0 0 RR 11 11 11 42.0 410 26/2 37.27 9.76 22.90 0
## 34 0 0 3 4 PBKS 16 14 14 47.1 434 17/3 27.12 9.20 17.68 0
## 35 0 0 8 15 PBKS 1 13 9 14.1 175 14/1 175.00 12.35 85.00 0
## 36 0 0 2 3 MI 10 13 13 39.3 313 3-Nov 31.30 7.92 23.70 0
## 37 0 0 0 0 DC 14 11 10 36.0 366 35/5 26.14 10.16 15.42 0
## 38 0 0 0 0 KKR 6 6 5 16.0 136 23/2 22.66 8.50 16.00 0
## 39 0 0 1 0 SRH 6 9 9 30.0 337 28/2 56.16 11.23 30.00 0
## 40 0 0 0 0 GT 16 15 15 57.0 527 17/4 32.93 9.24 21.37 1
## 41 0 0 0 3 GT 6 9 9 21.0 217 1-Jul 36.16 10.33 21.00 0
## 42 0 0 0 0 DC 2 8 8 25.0 257 1-Oct 128.50 10.28 75.00 0
## 43 0 0 16 4 SRH 2 13 3 5.0 47 13/1 23.50 9.40 15.00 0
## 44 0 0 0 0 CSK 24 14 14 50.0 408 18/4 17.00 8.16 12.50 2
## 45 0 0 6 7 SRH 16 14 14 49.4 450 19/3 28.12 9.06 18.62 0
## 46 0 0 0 0 LSG 3 6 6 22.5 225 29/1 75.00 9.85 45.66 0
## 47 0 0 2 3 GT 9 15 15 55.0 514 25/2 57.11 9.34 36.66 0
## 48 0 0 0 2 LSG 9 11 11 37.0 401 18/2 44.55 10.83 24.66 0
## 49 0 0 3 1 CSK 7 9 9 31.0 283 41/2 40.42 9.12 26.57 0
## 50 0 2 25 10 CSK 10 14 14 37.5 324 17/2 32.40 8.56 22.70 0
## 51 0 1 27 27 RR 3 14 9 20.0 170 1-Dec 56.66 8.50 40.00 0
## 52 0 1 5 7 RCB 6 8 7 14.0 151 14/2 25.16 10.78 14.00 0
## 53 0 0 0 0 GT 19 15 15 42.3 393 30/3 20.68 9.24 13.42 0
## 54 0 1 11 4 CSK 1 5 5 12.0 133 34/1 133.00 11.08 72.00 0
## 55 0 0 0 0 RR 9 10 10 36.3 361 21/2 40.11 9.89 24.33 0
## 56 0 0 1 0 LSG 1 3 3 8.3 102 41/1 102.00 12.00 51.00 0
## 57 0 1 11 13 GT 1 15 1 1.0 13 13/1 13.00 13.00 6.00 0
## 58 0 0 3 0 LSG 13 10 10 34.0 375 34/4 28.84 11.02 15.69 1
## 59 0 0 0 0 SRH 2 4 4 10.0 141 46/2 70.50 14.10 30.00 0
## 60 0 0 0 0 KKR 1 4 4 11.2 133 42/1 133.00 11.73 68.00 0
## 61 0 0 25 19 KKR 12 12 12 45.0 351 13/3 29.25 7.80 22.50 0
## 62 0 0 0 0 MI 22 16 16 57.4 517 26/4 23.50 8.96 15.72 1
## 63 0 0 0 0 RR 9 10 10 32.0 340 44/3 37.77 10.62 21.33 0
## 64 0 0 0 0 MI 1 2 2 4.0 53 40/1 53.00 13.25 24.00 0
## 65 0 0 0 0 KKR 17 13 13 50.0 383 22/3 22.52 7.66 17.64 0
## 66 0 0 15 8 DC 11 14 13 39.0 356 18/2 32.36 9.12 21.27 0
## 67 0 0 0 0 RR 11 11 11 41.0 371 35/4 33.72 9.04 22.36 1
## 68 0 0 10 7 GT 2 6 5 10.5 111 28/1 55.50 10.24 32.50 0
## 69 0 1 22 11 MI 6 13 8 14.0 120 14/2 20.00 8.57 14.00 0
## 70 0 0 1 0 PBKS 2 4 4 10.0 96 26/1 48.00 9.60 30.00 0
## 71 0 0 0 0 RCB 13 15 15 49.0 470 18/2 36.15 9.59 22.61 0
## X5W
## 1 0
## 2 0
## 3 0
## 4 0
## 5 0
## 6 0
## 7 0
## 8 0
## 9 0
## 10 0
## 11 0
## 12 0
## 13 0
## 14 0
## 15 0
## 16 0
## 17 0
## 18 1
## 19 0
## 20 0
## 21 0
## 22 0
## 23 0
## 24 0
## 25 0
## 26 0
## 27 0
## 28 0
## 29 0
## 30 0
## 31 0
## 32 0
## 33 0
## 34 0
## 35 0
## 36 0
## 37 1
## 38 0
## 39 0
## 40 0
## 41 0
## 42 0
## 43 0
## 44 0
## 45 0
## 46 0
## 47 0
## 48 0
## 49 0
## 50 0
## 51 0
## 52 0
## 53 0
## 54 0
## 55 0
## 56 0
## 57 0
## 58 0
## 59 0
## 60 0
## 61 0
## 62 0
## 63 0
## 64 0
## 65 0
## 66 0
## 67 0
## 68 0
## 69 0
## 70 0
## 71 0
head(final_data)
## Player.Name Team.x Runs Matches Inn No HS AVG.x BF SR.x X100s X50s X4s
## 1 Aiden Markram LSG 445 13 13 0 66 34.23 299 148.82 0 5 38
## 2 Akash Deep LSG 6 6 2 1 6* 6.00 2 300.00 0 0 0
## 3 Akash Madhwal RR 4 4 1 1 4* - 9 44.44 0 0 0
## 4 Andre Russell KKR 167 13 10 1 57* 18.56 102 163.72 0 1 16
## 5 Anshul Kamboj CSK 14 8 5 3 5* 7.00 12 116.66 0 0 2
## 6 Arshdeep Singh PBKS 2 17 2 1 1* 2.00 6 33.33 0 0 0
## X6s Team.y WKT MAT INN OVR RUNS BBI AVG.y ECO SR.y X4W X5W
## 1 22 LSG 4 13 5 11.0 102 30/2 25.50 9.27 16.50 0 0
## 2 1 LSG 3 6 6 19.0 229 55/2 76.33 12.05 38.00 0 0
## 3 0 RR 4 4 4 15.0 166 29/3 41.50 11.06 22.50 0 0
## 4 14 KKR 8 13 9 18.1 217 21/2 27.12 11.94 13.62 0 0
## 5 0 CSK 8 8 8 21.3 172 13/3 21.50 8.00 16.12 0 0
## 6 0 PBKS 21 17 16 58.2 518 16/3 24.66 8.88 16.66 0 0
str(final_data)
## 'data.frame': 71 obs. of 26 variables:
## $ Player.Name: chr "Aiden Markram" "Akash Deep" "Akash Madhwal" "Andre Russell" ...
## $ Team.x : chr "LSG" "LSG" "RR" "KKR" ...
## $ Runs : int 445 6 4 167 14 2 21 263 329 57 ...
## $ Matches : int 13 6 4 13 8 17 13 12 14 9 ...
## $ Inn : int 13 2 1 10 5 2 4 11 11 5 ...
## $ No : int 0 1 1 1 3 1 3 1 1 1 ...
## $ HS : chr "66" "6*" "4*" "57*" ...
## $ AVG.x : chr "34.23" "6.00" "-" "18.56" ...
## $ BF : int 299 2 9 102 12 6 12 167 222 41 ...
## $ SR.x : num 148.8 300 44.4 163.7 116.7 ...
## $ X100s : int 0 0 0 0 0 0 0 0 0 0 ...
## $ X50s : int 5 0 0 1 0 0 0 0 2 0 ...
## $ X4s : int 38 0 0 16 2 0 3 23 27 6 ...
## $ X6s : int 22 1 0 14 0 0 1 15 14 3 ...
## $ Team.y : chr "LSG" "LSG" "RR" "KKR" ...
## $ WKT : int 4 3 4 8 8 21 13 5 2 8 ...
## $ MAT : int 13 6 4 13 8 17 13 12 14 9 ...
## $ INN : int 5 6 4 9 8 16 13 11 2 8 ...
## $ OVR : num 11 19 15 18.1 21.3 58.2 47.2 34 1.4 27 ...
## $ RUNS : int 102 229 166 217 172 518 487 288 13 279 ...
## $ BBI : chr "30/2" "55/2" "29/3" "21/2" ...
## $ AVG.y : num 25.5 76.3 41.5 27.1 21.5 ...
## $ ECO : num 9.27 12.05 11.06 11.94 8 ...
## $ SR.y : num 16.5 38 22.5 13.6 16.1 ...
## $ X4W : int 0 0 0 0 0 0 0 0 0 0 ...
## $ X5W : int 0 0 0 0 0 0 0 0 0 0 ...
names(final_data)
## [1] "Player.Name" "Team.x" "Runs" "Matches" "Inn"
## [6] "No" "HS" "AVG.x" "BF" "SR.x"
## [11] "X100s" "X50s" "X4s" "X6s" "Team.y"
## [16] "WKT" "MAT" "INN" "OVR" "RUNS"
## [21] "BBI" "AVG.y" "ECO" "SR.y" "X4W"
## [26] "X5W"
View(final_data)
#Check duplicates after merge:
any(duplicated(final_data$Player.Name))
## [1] FALSE
#Missing values check
colSums(is.na(final_data))
## Player.Name Team.x Runs Matches Inn No
## 0 0 0 0 0 0
## HS AVG.x BF SR.x X100s X50s
## 0 0 0 0 0 0
## X4s X6s Team.y WKT MAT INN
## 0 0 0 0 0 0
## OVR RUNS BBI AVG.y ECO SR.y
## 0 0 0 0 0 0
## X4W X5W
## 0 0
#1. Which player has played the highest number of matches?
batters[which.max(batters$Matches), c("Player.Name","Matches")]
## Player.Name Matches
## 6 Shreyas Iyer 17
#Interpretation:
#Shreyas Iyer has played the most matches, which shows he was regularly part of the team.
#This means he is a consistent and important player.
#2. Best batsman and bowler performance
final_data$Batting_Performance <- final_data$Runs * final_data$SR.x
#Interpretation:
#Batting performance varies a lot, showing some players scored very high runs with good strike rates
final_data
## Player.Name Team.x Runs Matches Inn No HS AVG.x BF SR.x
## 1 Aiden Markram LSG 445 13 13 0 66 34.23 299 148.82
## 2 Akash Deep LSG 6 6 2 1 6* 6.00 2 300.00
## 3 Akash Madhwal RR 4 4 1 1 4* - 9 44.44
## 4 Andre Russell KKR 167 13 10 1 57* 18.56 102 163.72
## 5 Anshul Kamboj CSK 14 8 5 3 5* 7.00 12 116.66
## 6 Arshdeep Singh PBKS 2 17 2 1 1* 2.00 6 33.33
## 7 Avesh Khan LSG 21 13 4 3 19* 21.00 12 175.00
## 8 Axar Patel DC 263 12 11 1 43 26.30 167 157.48
## 9 Ayush Badoni LSG 329 14 11 1 74 32.90 222 148.19
## 10 Azmatullah Omarzai PBKS 57 9 5 1 21* 14.25 41 139.02
## 11 Bhuvneshwar Kumar RCB 14 14 6 3 8 4.67 25 56.00
## 12 Corbin Bosch MI 47 2 2 0 27 23.50 32 146.87
## 13 Deepak Chahar MI 37 14 4 3 28* 37.00 25 148.00
## 14 Digvesh Singh LSG 1 13 2 1 1 1.00 3 33.33
## 15 Dushmantha Chameera DC 10 6 3 3 8* - 14 71.42
## 16 Gerald Coetzee GT 17 4 2 0 12 8.50 11 154.54
## 17 Glenn Maxwell PBKS 48 7 6 0 30 8.00 49 97.95
## 18 Hardik Pandya MI 224 15 12 3 48* 24.89 137 163.50
## 19 Harpreet Brar PBKS 11 8 2 1 7* 11.00 13 84.61
## 20 Harshal Patel SRH 21 13 4 2 12* 10.50 26 80.76
## 21 Harshit Rana KKR 57 13 7 3 34 14.25 53 107.54
## 22 Jofra Archer RR 63 12 7 1 30 10.50 50 126.00
## 23 Kagiso Rabada GT 9 4 2 1 7* 9.00 8 112.50
## 24 Kamindu Mendis SRH 92 5 5 2 32* 30.67 69 133.33
## 25 Karn Sharma MI 1 6 1 1 1* - 1 100.00
## 26 Khaleel Ahmed CSK 1 14 2 2 1* - 2 50.00
## 27 Krunal Pandya RCB 109 15 7 1 73* 18.17 86 126.74
## 28 Kuldeep Yadav DC 18 14 5 2 7 6.00 16 112.50
## 29 Kumar Kartikeya Singh RR 3 4 2 0 2 1.50 5 60.00
## 30 Kwena Maphaka RR 8 2 1 1 8* - 2 400.00
## 31 Liam Livingstone RCB 112 10 8 1 54 16.00 84 133.33
## 32 Lockie Ferguson PBKS 4 4 1 1 4* - 1 400.00
## 33 Maheesh Theekshana RR 10 11 4 2 5 5.00 27 37.03
## 34 Marco Jansen PBKS 75 14 8 4 34* 18.75 63 119.04
## 35 Marcus Stoinis PBKS 160 13 11 5 44* 26.67 86 186.04
## 36 Mitchell Santner MI 40 13 8 6 18* 20.00 33 121.21
## 37 Mitchell Starc DC 6 11 6 4 2* 3.00 10 60.00
## 38 Moeen Ali KKR 5 6 2 0 5 2.50 14 35.71
## 39 Mohammad Shami SRH 10 9 4 3 6* 10.00 10 100.00
## 40 Mohammed Siraj GT 3 15 1 1 3* - 4 75.00
## 41 Mohd Arshad Khan GT 23 9 5 3 20 11.50 19 121.05
## 42 Mohit Sharma DC 1 8 2 1 1* 1.00 3 33.33
## 43 Nitish Kumar Reddy SRH 182 13 11 3 32 22.75 153 118.95
## 44 Noor Ahmad CSK 7 14 6 2 2* 1.75 17 41.17
## 45 Pat Cummins SRH 97 14 9 5 22* 24.25 58 167.24
## 46 Prince Yadav LSG 5 6 2 2 4* - 10 50.00
## 47 Rashid Khan GT 40 15 8 3 12 8.00 28 142.85
## 48 Ravi Bishnoi LSG 13 11 3 1 13 6.50 16 81.25
## 49 Ravichandran Ashwin CSK 33 9 4 0 13 8.25 30 110.00
## 50 Ravindra Jadeja CSK 301 14 14 5 77* 33.44 222 135.58
## 51 Riyan Parag RR 393 14 14 2 95 32.75 236 166.52
## 52 Romario Shepherd RCB 70 8 3 1 53* 35.00 24 291.66
## 53 Sai Kishore GT 5 15 3 0 3 1.67 10 50.00
## 54 Sam Curran CSK 114 5 5 0 88 22.80 84 135.71
## 55 Sandeep Sharma RR 6 10 1 1 6* - 5 120.00
## 56 Shahbaz Ahmed LSG 9 3 1 0 9 9.00 8 112.50
## 57 Shahrukh Khan GT 179 15 11 5 57 29.83 100 179.00
## 58 Shardul Thakur LSG 18 10 5 2 6 6.00 12 150.00
## 59 Simarjeet Singh SRH 3 4 3 1 3* 1.50 7 42.85
## 60 Spencer Johnson KKR 2 4 2 2 1* - 4 50.00
## 61 Sunil Narine KKR 246 12 12 1 44 22.36 144 170.83
## 62 Trent Boult MI 2 16 2 1 1* 2.00 3 66.66
## 63 Tushar Deshpande RR 7 10 4 3 3 7.00 8 87.50
## 64 V Satyanarayana Penmetsa MI 1 2 1 1 1* - 1 100.00
## 65 Varun Chakaravarthy KKR 1 13 1 1 1* - 1 100.00
## 66 Vipraj Nigam DC 142 14 8 1 39 20.29 79 179.74
## 67 Wanindu Hasaranga RR 9 11 5 0 4 1.80 15 60.00
## 68 Washington Sundar GT 133 6 5 0 49 26.60 80 166.25
## 69 Will Jacks MI 233 13 11 1 53 23.30 172 135.46
## 70 Xavier Bartlett PBKS 11 4 1 0 11 11.00 15 73.33
## 71 Yash Dayal RCB 4 15 3 1 3 2.00 8 50.00
## X100s X50s X4s X6s Team.y WKT MAT INN OVR RUNS BBI AVG.y ECO SR.y X4W
## 1 0 5 38 22 LSG 4 13 5 11.0 102 30/2 25.50 9.27 16.50 0
## 2 0 0 0 1 LSG 3 6 6 19.0 229 55/2 76.33 12.05 38.00 0
## 3 0 0 0 0 RR 4 4 4 15.0 166 29/3 41.50 11.06 22.50 0
## 4 0 1 16 14 KKR 8 13 9 18.1 217 21/2 27.12 11.94 13.62 0
## 5 0 0 2 0 CSK 8 8 8 21.3 172 13/3 21.50 8.00 16.12 0
## 6 0 0 0 0 PBKS 21 17 16 58.2 518 16/3 24.66 8.88 16.66 0
## 7 0 0 3 1 LSG 13 13 13 47.2 487 37/3 37.46 10.28 21.84 0
## 8 0 0 23 15 DC 5 12 11 34.0 288 19/2 57.60 8.47 40.80 0
## 9 0 2 27 14 LSG 2 14 2 1.4 13 2-Apr 6.50 7.80 5.00 0
## 10 0 0 6 3 PBKS 8 9 8 27.0 279 33/2 34.87 10.33 20.25 0
## 11 0 0 1 0 RCB 17 14 14 52.0 483 33/3 28.41 9.28 18.35 0
## 12 0 0 3 3 MI 1 2 2 7.0 55 26/1 55.00 7.85 42.00 0
## 13 0 0 3 2 MI 11 14 14 41.0 376 2-Dec 34.18 9.17 22.36 0
## 14 0 0 0 0 LSG 14 13 13 52.0 429 30/2 30.64 8.25 22.28 0
## 15 0 0 0 0 DC 4 6 5 15.0 171 24/1 42.75 11.40 22.50 0
## 16 0 0 2 1 GT 2 4 4 12.0 131 1-Oct 65.50 10.91 36.00 0
## 17 0 0 5 1 PBKS 4 7 6 13.0 110 1-May 27.50 8.46 19.50 0
## 18 0 0 18 12 MI 14 15 14 35.0 342 36/5 24.42 9.77 15.00 0
## 19 0 0 0 1 PBKS 10 8 7 22.0 190 22/3 19.00 8.63 13.20 0
## 20 0 0 0 0 SRH 16 13 13 43.5 430 28/4 26.87 9.80 16.43 2
## 21 0 0 5 3 KKR 15 13 13 44.0 448 25/3 29.86 10.18 17.60 0
## 22 0 0 3 4 RR 11 12 12 45.3 431 25/3 39.18 9.47 24.81 0
## 23 0 0 0 1 GT 2 4 4 14.0 162 41/1 81.00 11.57 42.00 0
## 24 0 0 7 2 SRH 2 5 4 7.0 60 1-Apr 30.00 8.57 21.00 0
## 25 0 0 0 0 MI 7 6 5 15.0 128 23/3 18.28 8.53 12.85 0
## 26 0 0 0 0 CSK 15 14 14 46.4 447 29/3 29.80 9.57 18.66 0
## 27 0 1 9 4 RCB 17 15 15 46.0 379 45/4 22.29 8.23 16.23 1
## 28 0 0 2 0 DC 15 14 13 51.0 361 22/3 24.06 7.07 20.40 0
## 29 0 0 0 0 RR 2 4 4 8.0 78 21/1 39.00 9.75 24.00 0
## 30 0 0 2 0 RR 1 2 2 5.0 54 32/1 54.00 10.80 30.00 0
## 31 0 1 4 9 RCB 2 10 5 9.0 76 28/2 38.00 8.44 27.00 0
## 32 0 0 1 0 PBKS 5 4 4 11.2 104 37/2 20.80 9.17 13.60 0
## 33 0 0 0 0 RR 11 11 11 42.0 410 26/2 37.27 9.76 22.90 0
## 34 0 0 3 4 PBKS 16 14 14 47.1 434 17/3 27.12 9.20 17.68 0
## 35 0 0 8 15 PBKS 1 13 9 14.1 175 14/1 175.00 12.35 85.00 0
## 36 0 0 2 3 MI 10 13 13 39.3 313 3-Nov 31.30 7.92 23.70 0
## 37 0 0 0 0 DC 14 11 10 36.0 366 35/5 26.14 10.16 15.42 0
## 38 0 0 0 0 KKR 6 6 5 16.0 136 23/2 22.66 8.50 16.00 0
## 39 0 0 1 0 SRH 6 9 9 30.0 337 28/2 56.16 11.23 30.00 0
## 40 0 0 0 0 GT 16 15 15 57.0 527 17/4 32.93 9.24 21.37 1
## 41 0 0 0 3 GT 6 9 9 21.0 217 1-Jul 36.16 10.33 21.00 0
## 42 0 0 0 0 DC 2 8 8 25.0 257 1-Oct 128.50 10.28 75.00 0
## 43 0 0 16 4 SRH 2 13 3 5.0 47 13/1 23.50 9.40 15.00 0
## 44 0 0 0 0 CSK 24 14 14 50.0 408 18/4 17.00 8.16 12.50 2
## 45 0 0 6 7 SRH 16 14 14 49.4 450 19/3 28.12 9.06 18.62 0
## 46 0 0 0 0 LSG 3 6 6 22.5 225 29/1 75.00 9.85 45.66 0
## 47 0 0 2 3 GT 9 15 15 55.0 514 25/2 57.11 9.34 36.66 0
## 48 0 0 0 2 LSG 9 11 11 37.0 401 18/2 44.55 10.83 24.66 0
## 49 0 0 3 1 CSK 7 9 9 31.0 283 41/2 40.42 9.12 26.57 0
## 50 0 2 25 10 CSK 10 14 14 37.5 324 17/2 32.40 8.56 22.70 0
## 51 0 1 27 27 RR 3 14 9 20.0 170 1-Dec 56.66 8.50 40.00 0
## 52 0 1 5 7 RCB 6 8 7 14.0 151 14/2 25.16 10.78 14.00 0
## 53 0 0 0 0 GT 19 15 15 42.3 393 30/3 20.68 9.24 13.42 0
## 54 0 1 11 4 CSK 1 5 5 12.0 133 34/1 133.00 11.08 72.00 0
## 55 0 0 0 0 RR 9 10 10 36.3 361 21/2 40.11 9.89 24.33 0
## 56 0 0 1 0 LSG 1 3 3 8.3 102 41/1 102.00 12.00 51.00 0
## 57 0 1 11 13 GT 1 15 1 1.0 13 13/1 13.00 13.00 6.00 0
## 58 0 0 3 0 LSG 13 10 10 34.0 375 34/4 28.84 11.02 15.69 1
## 59 0 0 0 0 SRH 2 4 4 10.0 141 46/2 70.50 14.10 30.00 0
## 60 0 0 0 0 KKR 1 4 4 11.2 133 42/1 133.00 11.73 68.00 0
## 61 0 0 25 19 KKR 12 12 12 45.0 351 13/3 29.25 7.80 22.50 0
## 62 0 0 0 0 MI 22 16 16 57.4 517 26/4 23.50 8.96 15.72 1
## 63 0 0 0 0 RR 9 10 10 32.0 340 44/3 37.77 10.62 21.33 0
## 64 0 0 0 0 MI 1 2 2 4.0 53 40/1 53.00 13.25 24.00 0
## 65 0 0 0 0 KKR 17 13 13 50.0 383 22/3 22.52 7.66 17.64 0
## 66 0 0 15 8 DC 11 14 13 39.0 356 18/2 32.36 9.12 21.27 0
## 67 0 0 0 0 RR 11 11 11 41.0 371 35/4 33.72 9.04 22.36 1
## 68 0 0 10 7 GT 2 6 5 10.5 111 28/1 55.50 10.24 32.50 0
## 69 0 1 22 11 MI 6 13 8 14.0 120 14/2 20.00 8.57 14.00 0
## 70 0 0 1 0 PBKS 2 4 4 10.0 96 26/1 48.00 9.60 30.00 0
## 71 0 0 0 0 RCB 13 15 15 49.0 470 18/2 36.15 9.59 22.61 0
## X5W Batting_Performance
## 1 0 66224.90
## 2 0 1800.00
## 3 0 177.76
## 4 0 27341.24
## 5 0 1633.24
## 6 0 66.66
## 7 0 3675.00
## 8 0 41417.24
## 9 0 48754.51
## 10 0 7924.14
## 11 0 784.00
## 12 0 6902.89
## 13 0 5476.00
## 14 0 33.33
## 15 0 714.20
## 16 0 2627.18
## 17 0 4701.60
## 18 1 36624.00
## 19 0 930.71
## 20 0 1695.96
## 21 0 6129.78
## 22 0 7938.00
## 23 0 1012.50
## 24 0 12266.36
## 25 0 100.00
## 26 0 50.00
## 27 0 13814.66
## 28 0 2025.00
## 29 0 180.00
## 30 0 3200.00
## 31 0 14932.96
## 32 0 1600.00
## 33 0 370.30
## 34 0 8928.00
## 35 0 29766.40
## 36 0 4848.40
## 37 1 360.00
## 38 0 178.55
## 39 0 1000.00
## 40 0 225.00
## 41 0 2784.15
## 42 0 33.33
## 43 0 21648.90
## 44 0 288.19
## 45 0 16222.28
## 46 0 250.00
## 47 0 5714.00
## 48 0 1056.25
## 49 0 3630.00
## 50 0 40809.58
## 51 0 65442.36
## 52 0 20416.20
## 53 0 250.00
## 54 0 15470.94
## 55 0 720.00
## 56 0 1012.50
## 57 0 32041.00
## 58 0 2700.00
## 59 0 128.55
## 60 0 100.00
## 61 0 42024.18
## 62 0 133.32
## 63 0 612.50
## 64 0 100.00
## 65 0 100.00
## 66 0 25523.08
## 67 0 540.00
## 68 0 22111.25
## 69 0 31562.18
## 70 0 806.63
## 71 0 200.00
final_data$Bowling_Performance <- final_data$WKT / final_data$ECO
#Interpretation:
#Bowling performance shows that only a few players have strong impact, while most have moderate or low effectiveness.
final_data
## Player.Name Team.x Runs Matches Inn No HS AVG.x BF SR.x
## 1 Aiden Markram LSG 445 13 13 0 66 34.23 299 148.82
## 2 Akash Deep LSG 6 6 2 1 6* 6.00 2 300.00
## 3 Akash Madhwal RR 4 4 1 1 4* - 9 44.44
## 4 Andre Russell KKR 167 13 10 1 57* 18.56 102 163.72
## 5 Anshul Kamboj CSK 14 8 5 3 5* 7.00 12 116.66
## 6 Arshdeep Singh PBKS 2 17 2 1 1* 2.00 6 33.33
## 7 Avesh Khan LSG 21 13 4 3 19* 21.00 12 175.00
## 8 Axar Patel DC 263 12 11 1 43 26.30 167 157.48
## 9 Ayush Badoni LSG 329 14 11 1 74 32.90 222 148.19
## 10 Azmatullah Omarzai PBKS 57 9 5 1 21* 14.25 41 139.02
## 11 Bhuvneshwar Kumar RCB 14 14 6 3 8 4.67 25 56.00
## 12 Corbin Bosch MI 47 2 2 0 27 23.50 32 146.87
## 13 Deepak Chahar MI 37 14 4 3 28* 37.00 25 148.00
## 14 Digvesh Singh LSG 1 13 2 1 1 1.00 3 33.33
## 15 Dushmantha Chameera DC 10 6 3 3 8* - 14 71.42
## 16 Gerald Coetzee GT 17 4 2 0 12 8.50 11 154.54
## 17 Glenn Maxwell PBKS 48 7 6 0 30 8.00 49 97.95
## 18 Hardik Pandya MI 224 15 12 3 48* 24.89 137 163.50
## 19 Harpreet Brar PBKS 11 8 2 1 7* 11.00 13 84.61
## 20 Harshal Patel SRH 21 13 4 2 12* 10.50 26 80.76
## 21 Harshit Rana KKR 57 13 7 3 34 14.25 53 107.54
## 22 Jofra Archer RR 63 12 7 1 30 10.50 50 126.00
## 23 Kagiso Rabada GT 9 4 2 1 7* 9.00 8 112.50
## 24 Kamindu Mendis SRH 92 5 5 2 32* 30.67 69 133.33
## 25 Karn Sharma MI 1 6 1 1 1* - 1 100.00
## 26 Khaleel Ahmed CSK 1 14 2 2 1* - 2 50.00
## 27 Krunal Pandya RCB 109 15 7 1 73* 18.17 86 126.74
## 28 Kuldeep Yadav DC 18 14 5 2 7 6.00 16 112.50
## 29 Kumar Kartikeya Singh RR 3 4 2 0 2 1.50 5 60.00
## 30 Kwena Maphaka RR 8 2 1 1 8* - 2 400.00
## 31 Liam Livingstone RCB 112 10 8 1 54 16.00 84 133.33
## 32 Lockie Ferguson PBKS 4 4 1 1 4* - 1 400.00
## 33 Maheesh Theekshana RR 10 11 4 2 5 5.00 27 37.03
## 34 Marco Jansen PBKS 75 14 8 4 34* 18.75 63 119.04
## 35 Marcus Stoinis PBKS 160 13 11 5 44* 26.67 86 186.04
## 36 Mitchell Santner MI 40 13 8 6 18* 20.00 33 121.21
## 37 Mitchell Starc DC 6 11 6 4 2* 3.00 10 60.00
## 38 Moeen Ali KKR 5 6 2 0 5 2.50 14 35.71
## 39 Mohammad Shami SRH 10 9 4 3 6* 10.00 10 100.00
## 40 Mohammed Siraj GT 3 15 1 1 3* - 4 75.00
## 41 Mohd Arshad Khan GT 23 9 5 3 20 11.50 19 121.05
## 42 Mohit Sharma DC 1 8 2 1 1* 1.00 3 33.33
## 43 Nitish Kumar Reddy SRH 182 13 11 3 32 22.75 153 118.95
## 44 Noor Ahmad CSK 7 14 6 2 2* 1.75 17 41.17
## 45 Pat Cummins SRH 97 14 9 5 22* 24.25 58 167.24
## 46 Prince Yadav LSG 5 6 2 2 4* - 10 50.00
## 47 Rashid Khan GT 40 15 8 3 12 8.00 28 142.85
## 48 Ravi Bishnoi LSG 13 11 3 1 13 6.50 16 81.25
## 49 Ravichandran Ashwin CSK 33 9 4 0 13 8.25 30 110.00
## 50 Ravindra Jadeja CSK 301 14 14 5 77* 33.44 222 135.58
## 51 Riyan Parag RR 393 14 14 2 95 32.75 236 166.52
## 52 Romario Shepherd RCB 70 8 3 1 53* 35.00 24 291.66
## 53 Sai Kishore GT 5 15 3 0 3 1.67 10 50.00
## 54 Sam Curran CSK 114 5 5 0 88 22.80 84 135.71
## 55 Sandeep Sharma RR 6 10 1 1 6* - 5 120.00
## 56 Shahbaz Ahmed LSG 9 3 1 0 9 9.00 8 112.50
## 57 Shahrukh Khan GT 179 15 11 5 57 29.83 100 179.00
## 58 Shardul Thakur LSG 18 10 5 2 6 6.00 12 150.00
## 59 Simarjeet Singh SRH 3 4 3 1 3* 1.50 7 42.85
## 60 Spencer Johnson KKR 2 4 2 2 1* - 4 50.00
## 61 Sunil Narine KKR 246 12 12 1 44 22.36 144 170.83
## 62 Trent Boult MI 2 16 2 1 1* 2.00 3 66.66
## 63 Tushar Deshpande RR 7 10 4 3 3 7.00 8 87.50
## 64 V Satyanarayana Penmetsa MI 1 2 1 1 1* - 1 100.00
## 65 Varun Chakaravarthy KKR 1 13 1 1 1* - 1 100.00
## 66 Vipraj Nigam DC 142 14 8 1 39 20.29 79 179.74
## 67 Wanindu Hasaranga RR 9 11 5 0 4 1.80 15 60.00
## 68 Washington Sundar GT 133 6 5 0 49 26.60 80 166.25
## 69 Will Jacks MI 233 13 11 1 53 23.30 172 135.46
## 70 Xavier Bartlett PBKS 11 4 1 0 11 11.00 15 73.33
## 71 Yash Dayal RCB 4 15 3 1 3 2.00 8 50.00
## X100s X50s X4s X6s Team.y WKT MAT INN OVR RUNS BBI AVG.y ECO SR.y X4W
## 1 0 5 38 22 LSG 4 13 5 11.0 102 30/2 25.50 9.27 16.50 0
## 2 0 0 0 1 LSG 3 6 6 19.0 229 55/2 76.33 12.05 38.00 0
## 3 0 0 0 0 RR 4 4 4 15.0 166 29/3 41.50 11.06 22.50 0
## 4 0 1 16 14 KKR 8 13 9 18.1 217 21/2 27.12 11.94 13.62 0
## 5 0 0 2 0 CSK 8 8 8 21.3 172 13/3 21.50 8.00 16.12 0
## 6 0 0 0 0 PBKS 21 17 16 58.2 518 16/3 24.66 8.88 16.66 0
## 7 0 0 3 1 LSG 13 13 13 47.2 487 37/3 37.46 10.28 21.84 0
## 8 0 0 23 15 DC 5 12 11 34.0 288 19/2 57.60 8.47 40.80 0
## 9 0 2 27 14 LSG 2 14 2 1.4 13 2-Apr 6.50 7.80 5.00 0
## 10 0 0 6 3 PBKS 8 9 8 27.0 279 33/2 34.87 10.33 20.25 0
## 11 0 0 1 0 RCB 17 14 14 52.0 483 33/3 28.41 9.28 18.35 0
## 12 0 0 3 3 MI 1 2 2 7.0 55 26/1 55.00 7.85 42.00 0
## 13 0 0 3 2 MI 11 14 14 41.0 376 2-Dec 34.18 9.17 22.36 0
## 14 0 0 0 0 LSG 14 13 13 52.0 429 30/2 30.64 8.25 22.28 0
## 15 0 0 0 0 DC 4 6 5 15.0 171 24/1 42.75 11.40 22.50 0
## 16 0 0 2 1 GT 2 4 4 12.0 131 1-Oct 65.50 10.91 36.00 0
## 17 0 0 5 1 PBKS 4 7 6 13.0 110 1-May 27.50 8.46 19.50 0
## 18 0 0 18 12 MI 14 15 14 35.0 342 36/5 24.42 9.77 15.00 0
## 19 0 0 0 1 PBKS 10 8 7 22.0 190 22/3 19.00 8.63 13.20 0
## 20 0 0 0 0 SRH 16 13 13 43.5 430 28/4 26.87 9.80 16.43 2
## 21 0 0 5 3 KKR 15 13 13 44.0 448 25/3 29.86 10.18 17.60 0
## 22 0 0 3 4 RR 11 12 12 45.3 431 25/3 39.18 9.47 24.81 0
## 23 0 0 0 1 GT 2 4 4 14.0 162 41/1 81.00 11.57 42.00 0
## 24 0 0 7 2 SRH 2 5 4 7.0 60 1-Apr 30.00 8.57 21.00 0
## 25 0 0 0 0 MI 7 6 5 15.0 128 23/3 18.28 8.53 12.85 0
## 26 0 0 0 0 CSK 15 14 14 46.4 447 29/3 29.80 9.57 18.66 0
## 27 0 1 9 4 RCB 17 15 15 46.0 379 45/4 22.29 8.23 16.23 1
## 28 0 0 2 0 DC 15 14 13 51.0 361 22/3 24.06 7.07 20.40 0
## 29 0 0 0 0 RR 2 4 4 8.0 78 21/1 39.00 9.75 24.00 0
## 30 0 0 2 0 RR 1 2 2 5.0 54 32/1 54.00 10.80 30.00 0
## 31 0 1 4 9 RCB 2 10 5 9.0 76 28/2 38.00 8.44 27.00 0
## 32 0 0 1 0 PBKS 5 4 4 11.2 104 37/2 20.80 9.17 13.60 0
## 33 0 0 0 0 RR 11 11 11 42.0 410 26/2 37.27 9.76 22.90 0
## 34 0 0 3 4 PBKS 16 14 14 47.1 434 17/3 27.12 9.20 17.68 0
## 35 0 0 8 15 PBKS 1 13 9 14.1 175 14/1 175.00 12.35 85.00 0
## 36 0 0 2 3 MI 10 13 13 39.3 313 3-Nov 31.30 7.92 23.70 0
## 37 0 0 0 0 DC 14 11 10 36.0 366 35/5 26.14 10.16 15.42 0
## 38 0 0 0 0 KKR 6 6 5 16.0 136 23/2 22.66 8.50 16.00 0
## 39 0 0 1 0 SRH 6 9 9 30.0 337 28/2 56.16 11.23 30.00 0
## 40 0 0 0 0 GT 16 15 15 57.0 527 17/4 32.93 9.24 21.37 1
## 41 0 0 0 3 GT 6 9 9 21.0 217 1-Jul 36.16 10.33 21.00 0
## 42 0 0 0 0 DC 2 8 8 25.0 257 1-Oct 128.50 10.28 75.00 0
## 43 0 0 16 4 SRH 2 13 3 5.0 47 13/1 23.50 9.40 15.00 0
## 44 0 0 0 0 CSK 24 14 14 50.0 408 18/4 17.00 8.16 12.50 2
## 45 0 0 6 7 SRH 16 14 14 49.4 450 19/3 28.12 9.06 18.62 0
## 46 0 0 0 0 LSG 3 6 6 22.5 225 29/1 75.00 9.85 45.66 0
## 47 0 0 2 3 GT 9 15 15 55.0 514 25/2 57.11 9.34 36.66 0
## 48 0 0 0 2 LSG 9 11 11 37.0 401 18/2 44.55 10.83 24.66 0
## 49 0 0 3 1 CSK 7 9 9 31.0 283 41/2 40.42 9.12 26.57 0
## 50 0 2 25 10 CSK 10 14 14 37.5 324 17/2 32.40 8.56 22.70 0
## 51 0 1 27 27 RR 3 14 9 20.0 170 1-Dec 56.66 8.50 40.00 0
## 52 0 1 5 7 RCB 6 8 7 14.0 151 14/2 25.16 10.78 14.00 0
## 53 0 0 0 0 GT 19 15 15 42.3 393 30/3 20.68 9.24 13.42 0
## 54 0 1 11 4 CSK 1 5 5 12.0 133 34/1 133.00 11.08 72.00 0
## 55 0 0 0 0 RR 9 10 10 36.3 361 21/2 40.11 9.89 24.33 0
## 56 0 0 1 0 LSG 1 3 3 8.3 102 41/1 102.00 12.00 51.00 0
## 57 0 1 11 13 GT 1 15 1 1.0 13 13/1 13.00 13.00 6.00 0
## 58 0 0 3 0 LSG 13 10 10 34.0 375 34/4 28.84 11.02 15.69 1
## 59 0 0 0 0 SRH 2 4 4 10.0 141 46/2 70.50 14.10 30.00 0
## 60 0 0 0 0 KKR 1 4 4 11.2 133 42/1 133.00 11.73 68.00 0
## 61 0 0 25 19 KKR 12 12 12 45.0 351 13/3 29.25 7.80 22.50 0
## 62 0 0 0 0 MI 22 16 16 57.4 517 26/4 23.50 8.96 15.72 1
## 63 0 0 0 0 RR 9 10 10 32.0 340 44/3 37.77 10.62 21.33 0
## 64 0 0 0 0 MI 1 2 2 4.0 53 40/1 53.00 13.25 24.00 0
## 65 0 0 0 0 KKR 17 13 13 50.0 383 22/3 22.52 7.66 17.64 0
## 66 0 0 15 8 DC 11 14 13 39.0 356 18/2 32.36 9.12 21.27 0
## 67 0 0 0 0 RR 11 11 11 41.0 371 35/4 33.72 9.04 22.36 1
## 68 0 0 10 7 GT 2 6 5 10.5 111 28/1 55.50 10.24 32.50 0
## 69 0 1 22 11 MI 6 13 8 14.0 120 14/2 20.00 8.57 14.00 0
## 70 0 0 1 0 PBKS 2 4 4 10.0 96 26/1 48.00 9.60 30.00 0
## 71 0 0 0 0 RCB 13 15 15 49.0 470 18/2 36.15 9.59 22.61 0
## X5W Batting_Performance Bowling_Performance
## 1 0 66224.90 0.43149946
## 2 0 1800.00 0.24896266
## 3 0 177.76 0.36166365
## 4 0 27341.24 0.67001675
## 5 0 1633.24 1.00000000
## 6 0 66.66 2.36486486
## 7 0 3675.00 1.26459144
## 8 0 41417.24 0.59031877
## 9 0 48754.51 0.25641026
## 10 0 7924.14 0.77444337
## 11 0 784.00 1.83189655
## 12 0 6902.89 0.12738854
## 13 0 5476.00 1.19956379
## 14 0 33.33 1.69696970
## 15 0 714.20 0.35087719
## 16 0 2627.18 0.18331806
## 17 0 4701.60 0.47281324
## 18 1 36624.00 1.43295803
## 19 0 930.71 1.15874855
## 20 0 1695.96 1.63265306
## 21 0 6129.78 1.47347741
## 22 0 7938.00 1.16156283
## 23 0 1012.50 0.17286085
## 24 0 12266.36 0.23337223
## 25 0 100.00 0.82063306
## 26 0 50.00 1.56739812
## 27 0 13814.66 2.06561361
## 28 0 2025.00 2.12164074
## 29 0 180.00 0.20512821
## 30 0 3200.00 0.09259259
## 31 0 14932.96 0.23696682
## 32 0 1600.00 0.54525627
## 33 0 370.30 1.12704918
## 34 0 8928.00 1.73913043
## 35 0 29766.40 0.08097166
## 36 0 4848.40 1.26262626
## 37 1 360.00 1.37795276
## 38 0 178.55 0.70588235
## 39 0 1000.00 0.53428317
## 40 0 225.00 1.73160173
## 41 0 2784.15 0.58083253
## 42 0 33.33 0.19455253
## 43 0 21648.90 0.21276596
## 44 0 288.19 2.94117647
## 45 0 16222.28 1.76600442
## 46 0 250.00 0.30456853
## 47 0 5714.00 0.96359743
## 48 0 1056.25 0.83102493
## 49 0 3630.00 0.76754386
## 50 0 40809.58 1.16822430
## 51 0 65442.36 0.35294118
## 52 0 20416.20 0.55658627
## 53 0 250.00 2.05627706
## 54 0 15470.94 0.09025271
## 55 0 720.00 0.91001011
## 56 0 1012.50 0.08333333
## 57 0 32041.00 0.07692308
## 58 0 2700.00 1.17967332
## 59 0 128.55 0.14184397
## 60 0 100.00 0.08525149
## 61 0 42024.18 1.53846154
## 62 0 133.32 2.45535714
## 63 0 612.50 0.84745763
## 64 0 100.00 0.07547170
## 65 0 100.00 2.21932115
## 66 0 25523.08 1.20614035
## 67 0 540.00 1.21681416
## 68 0 22111.25 0.19531250
## 69 0 31562.18 0.70011669
## 70 0 806.63 0.20833333
## 71 0 200.00 1.35557873
#3. Which bowler has best economy rate?
bowlers[which.min(bowlers$ECO), c("Player.Name","ECO")]
## Player.Name ECO
## 89 Nuwan Thushara 6.5
#Interpretation:
#Nuwan Thushara has the lowest economy rate, which means he gives fewer runs while bowling.
#This shows he is a very effective and economical bowler.
#4. Best overall player (bat + bowl)
final_data$Overall <- final_data$Batting_Performance + final_data$Bowling_Performance
final_data[which.max(final_data$Overall), ]
## Player.Name Team.x Runs Matches Inn No HS AVG.x BF SR.x X100s X50s X4s
## 1 Aiden Markram LSG 445 13 13 0 66 34.23 299 148.82 0 5 38
## X6s Team.y WKT MAT INN OVR RUNS BBI AVG.y ECO SR.y X4W X5W
## 1 22 LSG 4 13 5 11 102 30/2 25.5 9.27 16.5 0 0
## Batting_Performance Bowling_Performance Overall
## 1 66224.9 0.4314995 66225.33
#Interpretation:
#Aiden Markram has the highest overall performance, showing strong contribution in both batting and bowling.
#This indicates he is the best all-round player in the dataset.
#5. Top 5 all-rounders
head(final_data[order(-final_data$Overall), c("Player.Name","Overall")],5)
## Player.Name Overall
## 1 Aiden Markram 66225.33
## 51 Riyan Parag 65442.71
## 9 Ayush Badoni 48754.77
## 61 Sunil Narine 42025.72
## 8 Axar Patel 41417.83
#Interpretation:
#Aiden Markram, Riyan Parag, and others are the top all-rounders with the highest overall performance scores.
#This shows they contribute strongly in both batting and bowling compared to other players.
#6. Player with highest batting performance
final_data[which.max(final_data$Batting_Performance), ]
## Player.Name Team.x Runs Matches Inn No HS AVG.x BF SR.x X100s X50s X4s
## 1 Aiden Markram LSG 445 13 13 0 66 34.23 299 148.82 0 5 38
## X6s Team.y WKT MAT INN OVR RUNS BBI AVG.y ECO SR.y X4W X5W
## 1 22 LSG 4 13 5 11 102 30/2 25.5 9.27 16.5 0 0
## Batting_Performance Bowling_Performance Overall
## 1 66224.9 0.4314995 66225.33
#Interpretation:
#Aiden Markram has the highest batting performance, showing he scored runs with a very good strike rate.
#This means he is one of the most impactful batsmen in the dataset.
#7. Player with best bowling performance
final_data[which.max(final_data$Bowling_Performance), ]
## Player.Name Team.x Runs Matches Inn No HS AVG.x BF SR.x X100s X50s X4s X6s
## 44 Noor Ahmad CSK 7 14 6 2 2* 1.75 17 41.17 0 0 0 0
## Team.y WKT MAT INN OVR RUNS BBI AVG.y ECO SR.y X4W X5W Batting_Performance
## 44 CSK 24 14 14 50 408 18/4 17 8.16 12.5 2 0 288.19
## Bowling_Performance Overall
## 44 2.941176 291.1312
#Interpretation:
#Noor Ahmad has the best bowling performance, taking more wickets with a good economy rate.
#This shows he is a very effective and impactful bowler.
#8. Correlation between Runs and Wickets
cor(final_data$Runs, final_data$WKT)
## [1] -0.2022638
#Interpretation:
#The correlation between runs and wickets is negative, which means players who score more runs usually take fewer wickets.
#This shows batting and bowling performance are not strongly related.
#9. Players with high SR (>150
final_data[final_data$SR.x > 150, c("Player.Name","SR.x")]
## Player.Name SR.x
## 2 Akash Deep 300.00
## 4 Andre Russell 163.72
## 7 Avesh Khan 175.00
## 8 Axar Patel 157.48
## 16 Gerald Coetzee 154.54
## 18 Hardik Pandya 163.50
## 30 Kwena Maphaka 400.00
## 32 Lockie Ferguson 400.00
## 35 Marcus Stoinis 186.04
## 45 Pat Cummins 167.24
## 51 Riyan Parag 166.52
## 52 Romario Shepherd 291.66
## 57 Shahrukh Khan 179.00
## 61 Sunil Narine 170.83
## 66 Vipraj Nigam 179.74
## 68 Washington Sundar 166.25
#Interpretation:
#These players have a high strike rate, which means they score runs very quickly.
#This shows they are aggressive batsmen who can increase the run rate.
#10. Team-wise Total Runs
team_avg <- aggregate(Runs ~ Team, data = batters, mean)
barplot(team_avg$Runs,
names.arg = team_avg$Team,
main = "Average Runs by Team",
xlab = "Teams",
ylab = "Average Runs")
#Interpretation:
#Some teams like PBKS and GT have higher average runs, showing stronger batting performance.
#Teams with lower bars have weaker average scoring compared to others.
#11. Players with Most Sixes
top_six <- head(batters[order(-batters$X6s), ],5)
barplot(top_six$X6s,
names.arg = top_six$Player.Name,
main = "Top Six Hitters",
xlab = "Players",
ylab = "Sixes")
#Interpretation:
#These players have hit the highest number of sixes, showing strong power-hitting ability.
#This means they are aggressive batsmen who can score quick runs.
#12. Players with Most Fours
top_four <- head(batters[order(-batters$X4s), ],5)
barplot(top_four$X4s,
names.arg = top_four$Player.Name,
main = "Top Four Hitters",
xlab = "Players",
ylab = "Fours")
#Interpretation:
#These players have hit the highest number of fours, showing strong timing and consistent boundary scoring.
#This means they rely more on placement and regular scoring rather than just power hitting.
#13. Top 5 Batsmen Runs
top_bat <- head(batters[order(-batters$Runs), ],5)
barplot(top_bat$Runs,
names.arg = top_bat$Player.Name,
col = c("navyblue","blue","red","navyblue","darkblue"),
main = "Top 5 Batsmen by Runs",
xlab = "Players",
ylab = "Runs")
#Interpretation:
#These players have scored the highest runs, showing strong and consistent batting performance.
#This means they are the top performers and key batsmen in the dataset.
#14. Top 5 Bowlers
top_bowl <- head(bowlers[order(-bowlers$WKT), ],5)
barplot(top_bowl$WKT,
names.arg = top_bowl$Player.Name,
col = "purple",
main = "Top 5 Bowlers",
xlab = "Players",
ylab = "Wickets")
#Interpretation:
#These bowlers have taken the highest number of wickets, showing strong bowling performance.
#This means they are the most effective wicket-takers in the dataset.
#15. Runs vs Balls Faced
plot(batters$BF, batters$Runs,
col = "blue",
main = "Runs vs Balls Faced",
xlab = "Balls Faced",
ylab = "Runs")
#Interpretation:
#The graph shows a clear positive relationship, meaning as balls faced increase, runs also increase.
#This indicates that players who play more balls tend to score more runs.
#16. Team-wise Run Contribution (Pie Chart)
team_runs <- aggregate(Runs ~ Team, data = batters, sum)
pie(team_runs$Runs,
labels = team_runs$Team,
main = "Team-wise Total Run Contribution")
#Interpretation:
#This pie chart shows how much each team contributes to total runs.
#Teams with larger slices have stronger overall batting performance.
#17. Wickets Distribution by Team (Pie Chart)
team_wickets <- aggregate(WKT ~ Team, data = bowlers, sum)
pie(team_wickets$WKT,
labels = team_wickets$Team,
main = "Team-wise Wickets Distribution")
#Interpretation:
#This pie chart shows how many wickets each team has taken.
#Teams with larger slices have stronger and more effective bowling performance.
#18.Pair Plot of Batting Variables
pairs(batters[, c("Runs","BF","SR","X4s","X6s")],
main = "Pair Plot of Batting Stats")
#Interpretation:
#The pair plot shows strong positive relationships, especially between Runs and Balls Faced.
#It also shows that more fours and sixes generally lead to higher runs.
#19. Pair Plot of Bowling Variables
pairs(bowlers[, c("WKT","ECO","OVR")],
main = "Pair Plot of Bowling Stats")
#Interpretation:
#The plot shows that wickets increase as overs bowled increase (positive relationship).
#It also shows that lower economy rate generally helps in taking more wickets.
#20.Boxplot of Strike Rate
boxplot(batters$SR,
main = "Strike Rate Distribution",
col = "orange")
#Interpretation:
#Most players have a strike rate around the middle range (100–150).
#Some outliers show very high strike rates, indicating highly aggressive batsmen.
#21. Simple Linear Regression (Runs vs Balls Faced)
model1 <- lm(Runs ~ BF, data = batters)
summary(model1)
##
## Call:
## lm(formula = Runs ~ BF, data = batters)
##
## Residuals:
## Min 1Q Median 3Q Max
## -65.989 -13.457 0.901 9.967 105.788
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -6.94987 2.73182 -2.544 0.0119 *
## BF 1.59237 0.01776 89.685 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 24.8 on 154 degrees of freedom
## Multiple R-squared: 0.9812, Adjusted R-squared: 0.9811
## F-statistic: 8043 on 1 and 154 DF, p-value: < 2.2e-16
#Plotting for Ques 21
plot(batters$BF, batters$Runs,
main = "Simple Linear Regression: Runs vs Balls Faced",
xlab = "Balls Faced (BF)",
ylab = "Runs",
pch = 16)
#Interpretation:
#There is a very strong positive relationship between balls faced and runs scored.
#The high R² (~0.98) shows that balls faced explains most of the variation in runs
#22. Plotting Regression Line
plot(batters$BF, batters$Runs,
main = "Regression: Runs vs Balls",
xlab = "Balls Faced",
ylab = "Runs")
abline(model1, col = "red")
#Interpretation:
#The plot shows a clear upward trend, meaning runs increase as balls faced increase.
#The regression line fits closely to the data, indicating a strong linear relationship.
#23. Multiple Regression (Runs Prediction)
model2 <- lm(Runs ~ BF + X4s + X6s, data = batters)
summary(model2)
##
## Call:
## lm(formula = Runs ~ BF + X4s + X6s, data = batters)
##
## Residuals:
## Min 1Q Median 3Q Max
## -31.606 -3.404 1.333 3.915 28.513
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -3.78202 1.08667 -3.48 0.000654 ***
## BF 0.86965 0.02786 31.21 < 2e-16 ***
## X4s 2.62334 0.15701 16.71 < 2e-16 ***
## X6s 4.27393 0.17756 24.07 < 2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 9.542 on 152 degrees of freedom
## Multiple R-squared: 0.9973, Adjusted R-squared: 0.9972
## F-statistic: 1.841e+04 on 3 and 152 DF, p-value: < 2.2e-16
#Interpretation:
#Runs are strongly influenced by balls faced, fours, and sixes, and all variables are highly significant.
#The very high R² (~0.997) shows the model predicts runs very accurately.
#24.Multiple Regression (Wickets Prediction)
model3 <- lm(WKT ~ OVR + ECO, data = bowlers)
summary(model3)
##
## Call:
## lm(formula = WKT ~ OVR + ECO, data = bowlers)
##
## Residuals:
## Min 1Q Median 3Q Max
## -8.3209 -1.6719 -0.1617 1.4807 7.9748
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 2.61686 2.00710 1.304 0.195
## OVR 0.31377 0.01649 19.029 <2e-16 ***
## ECO -0.27340 0.18129 -1.508 0.135
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.733 on 105 degrees of freedom
## Multiple R-squared: 0.815, Adjusted R-squared: 0.8115
## F-statistic: 231.3 on 2 and 105 DF, p-value: < 2.2e-16
# Plotting for Ques 24
plot(bowlers$ECO, bowlers$WKT,
main = "Regression: Wickets vs Economy",
xlab = "Economy (ECO)",
ylab = "Wickets (WKT)",
pch = 16)
#Interpretation:
#Overs bowled have a strong positive and significant impact on wickets, while economy rate is not statistically significant.
#The model explains a good portion of variation in wickets (R² ≈ 0.81).
library(ggplot2)
#25. Polynomial Regression (Runs vs Balls)
model_poly <- lm(Runs ~ poly(BF, 2), data = batters)
summary(model_poly)
##
## Call:
## lm(formula = Runs ~ poly(BF, 2), data = batters)
##
## Residuals:
## Min 1Q Median 3Q Max
## -65.697 -13.455 0.744 9.577 106.102
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 161.321 1.992 81.002 <2e-16 ***
## poly(BF, 2)1 2224.183 24.875 89.416 <2e-16 ***
## poly(BF, 2)2 6.929 24.875 0.279 0.781
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 24.87 on 153 degrees of freedom
## Multiple R-squared: 0.9812, Adjusted R-squared: 0.981
## F-statistic: 3998 on 2 and 153 DF, p-value: < 2.2e-16
#Graph for Ques 25
ggplot(batters, aes(x = BF, y = Runs)) +
geom_point() +
stat_smooth(method = "lm",
formula = y ~ poly(x, 2),
se = FALSE) +
labs(title = "Polynomial Regression (Degree 2)",
x = "Balls Faced",
y = "Runs")
#Interpretation:
#The polynomial model shows a strong relationship between balls faced and runs, but the squared term is not significant.
#This means the relationship is mostly linear, with very little curvature effect.