Using clustering on the Indian Premier League 2018
Deepika D
2024-10-24
Name : DEEPIKA D
University : ALLIANCE UNIVERSITY
Title : “A Deep Dive into IPL 2018: Unveiling the Best Batting and Bowling Statistics”
Dataset : ‘indian-premier-league-2018-batting-and-bowling-data’
Model : ” The dataset mentioned above is utilized for K-clustering in this notebook.”
library(tidyverse) # Manipulation and Visualization## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorslibrary(factoextra) # Visualizing cluster results## Welcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBalibrary(cluster) # For Clustering
library(dplyr) # data manipulation and transformation
library(ggplot2)
library(caret)## Loading required package: lattice
##
## Attaching package: 'caret'
##
## The following object is masked from 'package:purrr':
##
## liftlibrary(corrplot)## corrplot 0.94 loaded# Read the dataset
dataset <- read.csv("C:/Users/deepika.d/Downloads/total_data_na.csv")
head(dataset)## PLAYER Mat.x Inns.x NO Runs.x HS Avg.x BF SR.x X100 X50 X4s X6s
## 1 Aaron Finch 10 9 1 134 46 16.75 100 134.00 0 0 6 8
## 2 AB de Villiers 12 11 2 480 90 53.33 275 174.54 0 6 39 30
## 3 Abhishek Sharma 3 3 2 63 46 63 33 190.90 0 0 3 5
## 4 Ajinkya Rahane 15 14 1 370 65 28.46 313 118.21 0 1 39 5
## 5 Alex Hales 6 6 0 148 45 24.66 118 125.42 0 0 13 6
## 6 Ambati Rayudu 16 16 2 602 100 43 402 149.75 1 3 53 34
## Mat.y Inns.y Ov Runs.y Wkts BBI Avg.y Econ SR.y X4w X5w y
## 1 0 0 0 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0 0 0 0View(dataset)names(dataset)## [1] "PLAYER" "Mat.x" "Inns.x" "NO" "Runs.x" "HS" "Avg.x" "BF"
## [9] "SR.x" "X100" "X50" "X4s" "X6s" "Mat.y" "Inns.y" "Ov"
## [17] "Runs.y" "Wkts" "BBI" "Avg.y" "Econ" "SR.y" "X4w" "X5w"
## [25] "y"dim(dataset)## [1] 143 25str(dataset)## 'data.frame': 143 obs. of 25 variables:
## $ PLAYER: chr "Aaron Finch" "AB de Villiers" "Abhishek Sharma" "Ajinkya Rahane" ...
## $ Mat.x : int 10 12 3 15 6 16 16 14 9 9 ...
## $ Inns.x: int 9 11 3 14 6 16 14 8 8 6 ...
## $ NO : int 1 2 2 1 0 2 3 2 2 2 ...
## $ Runs.x: int 134 480 63 370 148 602 316 32 80 96 ...
## $ HS : int 46 90 46 65 45 100 88 14 19 37 ...
## $ Avg.x : chr "16.75" "53.33" "63" "28.46" ...
## $ BF : int 100 275 33 313 118 402 171 38 69 58 ...
## $ SR.x : num 134 175 191 118 125 ...
## $ X100 : int 0 0 0 0 0 1 0 0 0 0 ...
## $ X50 : int 0 6 0 1 0 3 1 0 0 0 ...
## $ X4s : int 6 39 3 39 13 53 17 2 3 5 ...
## $ X6s : int 8 30 5 5 6 34 31 1 4 8 ...
## $ Mat.y : int 0 0 0 0 0 0 16 14 9 9 ...
## $ Inns.y: int 0 0 0 0 0 0 15 14 8 7 ...
## $ Ov : num 0 0 0 0 0 0 37.5 56 26 17 ...
## $ Runs.y: int 0 0 0 0 0 0 355 448 218 168 ...
## $ Wkts : int 0 0 0 0 0 0 13 24 3 2 ...
## $ BBI : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Avg.y : chr "0" "0" "0" "0" ...
## $ Econ : num 0 0 0 0 0 0 9.38 8 8.38 9.88 ...
## $ SR.y : chr "0" "0" "0" "0" ...
## $ X4w : int 0 0 0 0 0 0 0 3 0 0 ...
## $ X5w : int 0 0 0 0 0 0 0 0 0 0 ...
## $ y : int 0 0 0 0 0 0 0 0 0 0 ...summary(dataset)## PLAYER Mat.x Inns.x NO
## Length:143 Min. : 0.000 Min. : 0.000 Min. :0.000
## Class :character 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.:0.000
## Mode :character Median : 7.000 Median : 5.000 Median :1.000
## Mean : 7.287 Mean : 6.014 Mean :1.252
## 3rd Qu.:13.000 3rd Qu.:11.000 3rd Qu.:2.000
## Max. :17.000 Max. :17.000 Max. :9.000
## Runs.x HS Avg.x BF
## Min. : 0.0 Min. : 0.00 Length:143 Min. : 0.00
## 1st Qu.: 0.0 1st Qu.: 0.00 Class :character 1st Qu.: 0.00
## Median : 52.0 Median : 27.00 Mode :character Median : 41.00
## Mean :132.3 Mean : 33.15 Mean : 95.03
## 3rd Qu.:202.0 3rd Qu.: 53.50 3rd Qu.:152.50
## Max. :735.0 Max. :128.00 Max. :516.00
## SR.x X100 X50 X4s
## Min. : 0.00 Min. :0.00000 Min. :0.0000 Min. : 0.00
## 1st Qu.: 0.00 1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.: 0.00
## Median :117.02 Median :0.00000 Median :0.0000 Median : 3.00
## Mean : 93.12 Mean :0.03497 Mean :0.7063 Mean :11.46
## 3rd Qu.:140.59 3rd Qu.:0.00000 3rd Qu.:1.0000 3rd Qu.:17.00
## Max. :300.00 Max. :2.00000 Max. :8.0000 Max. :68.00
## X6s Mat.y Inns.y Ov
## Min. : 0.00 Min. : 0.000 Min. : 0.000 Min. : 0.00
## 1st Qu.: 0.00 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.00
## Median : 2.00 Median : 4.000 Median : 3.000 Median : 7.00
## Mean : 6.07 Mean : 5.608 Mean : 5.007 Mean :16.05
## 3rd Qu.: 8.00 3rd Qu.:10.000 3rd Qu.: 8.500 3rd Qu.:28.00
## Max. :37.00 Max. :17.000 Max. :17.000 Max. :68.00
## Runs.y Wkts BBI Avg.y
## Min. : 0.0 Min. : 0.000 Min. :0 Length:143
## 1st Qu.: 0.0 1st Qu.: 0.000 1st Qu.:0 Class :character
## Median : 82.0 Median : 2.000 Median :0 Mode :character
## Mean :137.2 Mean : 4.629 Mean :0
## 3rd Qu.:243.5 3rd Qu.: 7.500 3rd Qu.:0
## Max. :547.0 Max. :24.000 Max. :0
## Econ SR.y X4w X5w
## Min. : 0.000 Length:143 Min. :0.00000 Min. :0.000000
## 1st Qu.: 0.000 Class :character 1st Qu.:0.00000 1st Qu.:0.000000
## Median : 7.860 Mode :character Median :0.00000 Median :0.000000
## Mean : 6.158 Mean :0.05594 Mean :0.006993
## 3rd Qu.: 9.500 3rd Qu.:0.00000 3rd Qu.:0.000000
## Max. :16.950 Max. :3.00000 Max. :1.000000
## y
## Min. :0
## 1st Qu.:0
## Median :0
## Mean :0
## 3rd Qu.:0
## Max. :0sum(is.na(dataset)) # Returns the total count of NA values## [1] 0# Check for missing values in each column
colSums(is.na(dataset))## PLAYER Mat.x Inns.x NO Runs.x HS Avg.x BF SR.x X100 X50
## 0 0 0 0 0 0 0 0 0 0 0
## X4s X6s Mat.y Inns.y Ov Runs.y Wkts BBI Avg.y Econ SR.y
## 0 0 0 0 0 0 0 0 0 0 0
## X4w X5w y
## 0 0 0# Check if any missing values exist
any(is.na(dataset))## [1] FALSEConverting non-numeric to numeric
## AS Avg.x , Avg.y , SR.x are numeric but stored as chr so we can convert into numeric
#We have to clean non-numeric data before converting it into numeric
# Inspect problematic values
unique(dataset$Avg.x[is.na(as.numeric(dataset$Avg.x))])## Warning in unique(dataset$Avg.x[is.na(as.numeric(dataset$Avg.x))]): NAs
## introduced by coercion## [1] "-"# Remove non-numeric characters
dataset$Avg.x <- gsub("[^0-9.]", "", dataset$Avg.x)
dataset$Avg.x## [1] "16.75" "53.33" "63" "28.46" "24.66" "43" "28.72" "5.33" "13.33"
## [10] "24" "16.33" "6.5" "21.16" "25" "40.88" "32.73" "46" "8.5"
## [19] "26.2" "12.6" "5.66" "16.42" "13" "37" "16.66" "21.75" "49.8"
## [28] "35.25" "29.38" "32.4" "17" "14.08" "9.66" "28.88" "60" "19"
## [37] "22.91" "30" "12.25" "3" "54.8" "36" "52.5" "25.08" ""
## [46] "19" "14" "22.8" "54.91" "20" "13.75" "25.2" "25.81" "15.66"
## [55] "24.75" "12" "10.5" "" "19.25" "9" "12.5" "75.83" "23.38"
## [64] "30.6" "6.75" "27.22" "25.12" "16.66" "25.11" "11.8" "12.75" "17.8"
## [73] "7.25" "52.61" "21.93" "23.83" "13.5" "31.5" "10.2" "21.72" "39.64"
## [82] "" "38.23" "4.33" "17.33" "16.66" "37.36" "33.83" "8.8" "22.31"
## [91] "37.08" "36.57" "26" "7.66" "53" "48.18" "21.66" "15.25" "28.88"
## [100] "10.83" "0" "0" "0" "0" "0" "0" "0" "0"
## [109] "0" "0" "0" "0" "0" "0" "0" "0" "0"
## [118] "0" "0" "0" "0" "0" "0" "0" "0" "0"
## [127] "0" "0" "0" "0" "0" "0" "0" "0" "0"
## [136] "0" "0" "0" "0" "0" "0" "0" "0"# Trim spaces
dataset$Avg.x <- trimws(dataset$Avg.x)
dataset$Avg.x## [1] "16.75" "53.33" "63" "28.46" "24.66" "43" "28.72" "5.33" "13.33"
## [10] "24" "16.33" "6.5" "21.16" "25" "40.88" "32.73" "46" "8.5"
## [19] "26.2" "12.6" "5.66" "16.42" "13" "37" "16.66" "21.75" "49.8"
## [28] "35.25" "29.38" "32.4" "17" "14.08" "9.66" "28.88" "60" "19"
## [37] "22.91" "30" "12.25" "3" "54.8" "36" "52.5" "25.08" ""
## [46] "19" "14" "22.8" "54.91" "20" "13.75" "25.2" "25.81" "15.66"
## [55] "24.75" "12" "10.5" "" "19.25" "9" "12.5" "75.83" "23.38"
## [64] "30.6" "6.75" "27.22" "25.12" "16.66" "25.11" "11.8" "12.75" "17.8"
## [73] "7.25" "52.61" "21.93" "23.83" "13.5" "31.5" "10.2" "21.72" "39.64"
## [82] "" "38.23" "4.33" "17.33" "16.66" "37.36" "33.83" "8.8" "22.31"
## [91] "37.08" "36.57" "26" "7.66" "53" "48.18" "21.66" "15.25" "28.88"
## [100] "10.83" "0" "0" "0" "0" "0" "0" "0" "0"
## [109] "0" "0" "0" "0" "0" "0" "0" "0" "0"
## [118] "0" "0" "0" "0" "0" "0" "0" "0" "0"
## [127] "0" "0" "0" "0" "0" "0" "0" "0" "0"
## [136] "0" "0" "0" "0" "0" "0" "0" "0"# Remove non-numeric characters
dataset$Avg.y <- gsub("[^0-9.]", "", dataset$Avg.y)
dataset$Avg.y## [1] "0" "0" "0" "0" "0" "0" "27.3" "18.66" "72.66"
## [10] "84" "37.87" "39.33" "0" "18.8" "0" "0" "47.66" "23.75"
## [19] "64.5" "0" "38.33" "19" "25.25" "0" "27.8" "" "0"
## [28] "38.07" "0" "0" "0" "26.4" "38.57" "21.16" "23.85" "0"
## [37] "0" "0" "44.18" "21.66" "0" "" "0" "0" "0"
## [46] "0" "28.36" "23.66" "0" "" "0" "0" "0" ""
## [55] "40" "0" "24.53" "108" "32.33" "47" "33.36" "0" "11"
## [64] "0" "29.42" "0" "0" "28.83" "0" "21.8" "41" "27.54"
## [73] "0" "0" "0" "0" "0" "0" "0" "32.57" "41.83"
## [82] "26.93" "0" "54" "0" "21.45" "0" "0" "" "27.47"
## [91] "0" "0" "52.2" "19.66" "58" "0" "48" "0" "14"
## [100] "" "" "22" "20.27" "" "42" "51" "57.25" "22.8"
## [109] "23.44" "26" "" "47.5" "47" "31.33" "27" "21.88" "44.5"
## [118] "" "22.25" "" "25" "24.58" "53.5" "56.25" "14.18" ""
## [127] "23.71" "48" "46" "20.64" "17.66" "32.85" "49" "" "26"
## [136] "16.4" "27.75" "53.66" "26.04" "25.88" "20.9" "32.5" "30.25"# Trim spaces
dataset$Avg.y <- trimws(dataset$Avg.y)
dataset$Avg.y## [1] "0" "0" "0" "0" "0" "0" "27.3" "18.66" "72.66"
## [10] "84" "37.87" "39.33" "0" "18.8" "0" "0" "47.66" "23.75"
## [19] "64.5" "0" "38.33" "19" "25.25" "0" "27.8" "" "0"
## [28] "38.07" "0" "0" "0" "26.4" "38.57" "21.16" "23.85" "0"
## [37] "0" "0" "44.18" "21.66" "0" "" "0" "0" "0"
## [46] "0" "28.36" "23.66" "0" "" "0" "0" "0" ""
## [55] "40" "0" "24.53" "108" "32.33" "47" "33.36" "0" "11"
## [64] "0" "29.42" "0" "0" "28.83" "0" "21.8" "41" "27.54"
## [73] "0" "0" "0" "0" "0" "0" "0" "32.57" "41.83"
## [82] "26.93" "0" "54" "0" "21.45" "0" "0" "" "27.47"
## [91] "0" "0" "52.2" "19.66" "58" "0" "48" "0" "14"
## [100] "" "" "22" "20.27" "" "42" "51" "57.25" "22.8"
## [109] "23.44" "26" "" "47.5" "47" "31.33" "27" "21.88" "44.5"
## [118] "" "22.25" "" "25" "24.58" "53.5" "56.25" "14.18" ""
## [127] "23.71" "48" "46" "20.64" "17.66" "32.85" "49" "" "26"
## [136] "16.4" "27.75" "53.66" "26.04" "25.88" "20.9" "32.5" "30.25"# Remove non-numeric characters
dataset$SR.y <- gsub("[^0-9.]", "", dataset$SR.y)
dataset$SR.y## [1] "0" "0" "0" "0" "0" "0" "17.46" "14" "52"
## [10] "51" "27.75" "30.77" "0" "12.2" "0" "0" "28" "13.75"
## [19] "45" "0" "17.33" "18" "17.75" "0" "22.9" "" "0"
## [28] "22.92" "0" "0" "0" "19.2" "27.28" "14.22" "15" "0"
## [37] "0" "0" "27.45" "15.53" "0" "" "0" "0" "0"
## [46] "0" "21.81" "20.08" "0" "" "0" "0" "0" ""
## [55] "22" "0" "17.6" "63" "26.33" "30" "22.36" "0" "9.25"
## [64] "0" "21" "0" "0" "22" "0" "19.42" "30.4" "22.36"
## [73] "0" "0" "0" "0" "0" "0" "0" "24.42" "28"
## [82] "17.5" "0" "33.6" "0" "16.9" "0" "0" "" "21.52"
## [91] "0" "0" "34.8" "10.16" "30" "0" "30" "0" "12"
## [100] "" "" "18.5" "14.27" "" "24" "28.5" "33" "12.2"
## [109] "14" "19.2" "" "30" "29.75" "20.66" "27.6" "19.05" "21"
## [118] "" "14.25" "" "12" "18.11" "32.5" "37.5" "14.18" ""
## [127] "17.14" "27.66" "25.42" "17.71" "14" "23.57" "24" "" "16.8"
## [136] "14.4" "22" "30" "18.85" "17.55" "15.95" "11.5" "25"# Trim spaces
dataset$SR.y <- trimws(dataset$SR.y)
dataset$SR.y## [1] "0" "0" "0" "0" "0" "0" "17.46" "14" "52"
## [10] "51" "27.75" "30.77" "0" "12.2" "0" "0" "28" "13.75"
## [19] "45" "0" "17.33" "18" "17.75" "0" "22.9" "" "0"
## [28] "22.92" "0" "0" "0" "19.2" "27.28" "14.22" "15" "0"
## [37] "0" "0" "27.45" "15.53" "0" "" "0" "0" "0"
## [46] "0" "21.81" "20.08" "0" "" "0" "0" "0" ""
## [55] "22" "0" "17.6" "63" "26.33" "30" "22.36" "0" "9.25"
## [64] "0" "21" "0" "0" "22" "0" "19.42" "30.4" "22.36"
## [73] "0" "0" "0" "0" "0" "0" "0" "24.42" "28"
## [82] "17.5" "0" "33.6" "0" "16.9" "0" "0" "" "21.52"
## [91] "0" "0" "34.8" "10.16" "30" "0" "30" "0" "12"
## [100] "" "" "18.5" "14.27" "" "24" "28.5" "33" "12.2"
## [109] "14" "19.2" "" "30" "29.75" "20.66" "27.6" "19.05" "21"
## [118] "" "14.25" "" "12" "18.11" "32.5" "37.5" "14.18" ""
## [127] "17.14" "27.66" "25.42" "17.71" "14" "23.57" "24" "" "16.8"
## [136] "14.4" "22" "30" "18.85" "17.55" "15.95" "11.5" "25"dataset <- dataset %>%
mutate(Avg.x = as.numeric(Avg.x),
Avg.y = as.numeric(Avg.y),
SR.y = as.numeric(SR.y))
dataset## PLAYER Mat.x Inns.x NO Runs.x HS Avg.x BF SR.x X100 X50
## 1 Aaron Finch 10 9 1 134 46 16.75 100 134.00 0 0
## 2 AB de Villiers 12 11 2 480 90 53.33 275 174.54 0 6
## 3 Abhishek Sharma 3 3 2 63 46 63.00 33 190.90 0 0
## 4 Ajinkya Rahane 15 14 1 370 65 28.46 313 118.21 0 1
## 5 Alex Hales 6 6 0 148 45 24.66 118 125.42 0 0
## 6 Ambati Rayudu 16 16 2 602 100 43.00 402 149.75 1 3
## 7 Andre Russell 16 14 3 316 88 28.72 171 184.79 0 1
## 8 Andrew Tye 14 8 2 32 14 5.33 38 84.21 0 0
## 9 Axar Patel 9 8 2 80 19 13.33 69 115.94 0 0
## 10 Ben Cutting 9 6 2 96 37 24.00 58 165.51 0 0
## 11 Ben Stokes 13 13 1 196 45 16.33 161 121.73 0 0
## 12 Bhuvneshwar Kumar 12 4 2 13 7 6.50 16 81.25 0 0
## 13 Brendon McCullum 6 6 0 127 43 21.16 88 144.31 0 0
## 14 Carlos Brathwaite 4 4 1 75 43 25.00 48 156.25 0 0
## 15 Chris Gayle 11 11 2 368 104 40.88 252 146.03 1 3
## 16 Chris Lynn 16 16 1 491 74 32.73 377 130.23 0 3
## 17 Chris Morris 4 4 3 46 27 46.00 26 176.92 0 0
## 18 Chris Woakes 5 4 2 17 11 8.50 19 89.47 0 0
## 19 Colin de Grandhomme 9 8 3 131 40 26.20 84 155.95 0 0
## 20 Colin Munro 5 5 0 63 33 12.60 41 153.65 0 0
## 21 Corey Anderson 3 3 0 17 15 5.66 22 77.27 0 0
## 22 D'Arcy Short 7 7 0 115 44 16.42 99 116.16 0 0
## 23 Dan Christian 4 3 1 26 13 13.00 33 78.78 0 0
## 24 David Miller 3 3 1 74 26 37.00 64 115.62 0 0
## 25 Deepak Chahar 12 4 1 50 39 16.66 29 172.41 0 0
## 26 Deepak Hooda 9 8 4 87 32 21.75 81 107.40 0 0
## 27 Dinesh Karthik 16 16 6 498 52 49.80 337 147.77 0 2
## 28 Dwayne Bravo 16 10 6 141 68 35.25 91 154.94 0 1
## 29 Evin Lewis 13 13 0 382 65 29.38 276 138.40 0 2
## 30 Faf du Plessis 6 6 1 162 67 32.40 129 125.58 0 1
## 31 Gautam Gambhir 6 5 0 85 55 17.00 88 96.59 0 1
## 32 Glenn Maxwell 12 12 0 169 47 14.08 120 140.83 0 0
## 33 Harbhajan Singh 13 3 0 29 19 9.66 36 80.55 0 0
## 34 Hardik Pandya 13 13 4 260 50 28.88 195 133.33 0 1
## 35 Harshal Patel 5 2 1 60 36 60.00 33 181.81 0 0
## 36 Heinrich Klaasen 4 4 1 57 32 19.00 47 121.27 0 0
## 37 Ishan Kishan 14 12 0 275 62 22.91 184 149.45 0 2
## 38 Jason Roy 5 5 1 120 91 30.00 94 127.65 0 1
## 39 Jaydev U0dkat 15 7 3 49 26 12.25 38 128.94 0 0
## 40 Jofra Archer 10 8 3 15 8 3.00 21 71.42 0 0
## 41 Jos Buttler 13 13 3 548 95 54.80 353 155.24 0 5
## 42 JP Duminy 6 4 3 36 23 36.00 40 90.00 0 0
## 43 Kane Williamson 17 17 3 735 84 52.50 516 142.44 0 8
## 44 Karun 0ir 13 12 0 301 54 25.08 221 136.19 0 2
## 45 Kedar Jadhav 1 1 1 24 24 NA 22 109.09 0 0
## 46 Kieron Pollard 9 8 1 133 50 19.00 100 133.00 0 1
## 47 Krish0ppa Gowtham 15 13 4 126 33 14.00 64 196.87 0 0
## 48 Kru0l Pandya 14 13 3 228 41 22.80 157 145.22 0 0
## 49 Lokesh Rahul 14 14 2 659 95 54.91 416 158.41 0 6
## 50 Mahipal Lomror 2 2 1 20 11 20.00 21 95.23 0 0
## 51 Ma0n Vohra 4 4 0 55 45 13.75 47 117.02 0 0
## 52 Mandeep Singh 14 13 3 252 47 25.20 186 135.48 0 0
## 53 Manish Pandey 15 13 2 284 62 25.81 246 115.44 0 3
## 54 Manoj Tiwary 5 4 1 47 35 15.66 44 106.81 0 0
## 55 Marcus Stoinis 7 7 3 99 29 24.75 76 130.26 0 0
## 56 Mayank Agarwal 11 11 1 120 30 12.00 94 127.65 0 0
## 57 Mayank Markande 14 6 4 21 7 10.50 24 87.50 0 0
## 58 Mitchell Johnson 6 2 2 16 12 NA 11 145.45 0 0
## 59 Moeen Ali 5 4 0 77 65 19.25 46 167.39 0 1
## 60 Mohammad 0bi 2 2 0 18 14 9.00 12 150.00 0 0
## 61 Mohammed Siraj 11 4 2 25 14 12.50 22 113.63 0 0
## 62 MS Dhoni 16 15 9 455 79 75.83 302 150.66 0 3
## 63 Nitish Ra0 15 15 2 304 59 23.38 232 131.03 0 1
## 64 Parthiv Patel 6 6 1 153 53 30.60 109 140.36 0 1
## 65 Piyush Chawla 15 7 3 27 12 6.75 34 79.41 0 0
## 66 Prithvi Shaw 9 9 0 245 65 27.22 160 153.12 0 2
## 67 Quinton de Kock 8 8 0 201 53 25.12 162 124.07 0 1
## 68 Rahul Tewatia 8 5 2 50 24 16.66 43 116.27 0 0
## 69 Rahul Tripathi 12 12 3 226 80 25.11 167 135.32 0 1
## 70 Rashid Khan 17 7 2 59 34 11.80 31 190.32 0 0
## 71 Ravichandran Ashwin 14 9 1 102 45 12.75 71 143.66 0 0
## 72 Ravindra Jadeja 16 10 5 89 27 17.80 74 120.27 0 0
## 73 Rinku Singh 4 4 0 29 16 7.25 31 93.54 0 0
## 74 Rishabh Pant 14 14 1 684 128 52.61 394 173.60 1 5
## 75 Robin Uthappa 16 16 0 351 54 21.93 265 132.45 0 1
## 76 Rohit Sharma 14 14 2 286 94 23.83 215 133.02 0 2
## 77 Sam Billings 10 8 0 108 56 13.50 78 138.46 0 1
## 78 Sanju Samson 15 15 1 441 92 31.50 320 137.81 0 3
## 79 Sarfaraz Khan 7 6 1 51 22 10.20 41 124.39 0 0
## 80 Shakib Al Hasan 17 13 2 239 35 21.72 197 121.31 0 0
## 81 Shane Watson 15 15 1 555 117 39.64 359 154.59 2 2
## 82 Shardul Thakur 13 1 1 15 15 NA 5 300.00 0 0
## 83 Shikhar Dhawan 16 16 3 497 92 38.23 363 136.91 0 4
## 84 Shivam Mavi 9 4 1 13 7 4.33 15 86.66 0 0
## 85 Shreevats Goswami 6 3 0 52 35 17.33 40 130.00 0 0
## 86 Shreyas Gopal 11 4 1 50 24 16.66 45 111.11 0 0
## 87 Shreyas Iyer 14 14 3 411 93 37.36 310 132.58 0 4
## 88 Shubman Gill 13 11 5 203 57 33.83 139 146.04 0 1
## 89 Stuart Binny 7 5 0 44 22 8.80 39 112.82 0 0
## 90 Sunil 0rine 16 16 0 357 75 22.31 188 189.89 0 2
## 91 Suresh Rai0 15 15 3 445 75 37.08 336 132.44 0 4
## 92 Suryakumar Yadav 14 14 0 512 72 36.57 384 133.33 0 4
## 93 Tim Southee 8 4 2 52 36 26.00 46 113.04 0 0
## 94 Tom Curran 5 4 1 23 18 7.66 28 82.14 0 0
## 95 Vijay Shankar 13 11 7 212 54 53.00 148 143.24 0 1
## 96 Virat Kohli 14 14 3 530 92 48.18 381 139.10 0 4
## 97 Washington Sundar 7 6 3 65 35 21.66 38 171.05 0 0
## 98 Wriddhiman Saha 11 10 2 122 35 15.25 102 119.60 0 0
## 99 Yusuf Pathan 15 13 4 260 45 28.88 200 130.00 0 0
## 100 Yuvraj Singh 8 6 0 65 20 10.83 73 89.04 0 0
## 101 Akila Da0njaya 0 0 0 0 0 0.00 0 0.00 0 0
## 102 Amit Mishra 0 0 0 0 0 0.00 0 0.00 0 0
## 103 Ankit Rajpoot 0 0 0 0 0 0.00 0 0.00 0 0
## 104 Ankit Sharma 0 0 0 0 0 0.00 0 0.00 0 0
## 105 Anureet Singh 0 0 0 0 0 0.00 0 0.00 0 0
## 106 Avesh Khan 0 0 0 0 0 0.00 0 0.00 0 0
## 107 Barinder Sran 0 0 0 0 0 0.00 0 0.00 0 0
## 108 Basil Thampi 0 0 0 0 0 0.00 0 0.00 0 0
## 109 Ben Laughlin 0 0 0 0 0 0.00 0 0.00 0 0
## 110 Billy Stanlake 0 0 0 0 0 0.00 0 0.00 0 0
## 111 Chris Jordan 0 0 0 0 0 0.00 0 0.00 0 0
## 112 David Willey 0 0 0 0 0 0.00 0 0.00 0 0
## 113 Dhawal Kulkarni 0 0 0 0 0 0.00 0 0.00 0 0
## 114 Imran Tahir 0 0 0 0 0 0.00 0 0.00 0 0
## 115 Ish Sodhi 0 0 0 0 0 0.00 0 0.00 0 0
## 116 Jasprit Bumrah 0 0 0 0 0 0.00 0 0.00 0 0
## 117 Javon Searles 0 0 0 0 0 0.00 0 0.00 0 0
## 118 Junior Dala 0 0 0 0 0 0.00 0 0.00 0 0
## 119 Karn Sharma 0 0 0 0 0 0.00 0 0.00 0 0
## 120 Khaleel Ahmed 0 0 0 0 0 0.00 0 0.00 0 0
## 121 KM Asif 0 0 0 0 0 0.00 0 0.00 0 0
## 122 Kuldeep Yadav 0 0 0 0 0 0.00 0 0.00 0 0
## 123 Kulwant Khejroliya 0 0 0 0 0 0.00 0 0.00 0 0
## 124 Liam Plunkett 0 0 0 0 0 0.00 0 0.00 0 0
## 125 Lungi Ngidi 0 0 0 0 0 0.00 0 0.00 0 0
## 126 Mark Wood 0 0 0 0 0 0.00 0 0.00 0 0
## 127 Mitchell McCle0ghan 0 0 0 0 0 0.00 0 0.00 0 0
## 128 Mohammed Shami 0 0 0 0 0 0.00 0 0.00 0 0
## 129 Mohit Sharma 0 0 0 0 0 0.00 0 0.00 0 0
## 130 Mujeeb Ur Rahman 0 0 0 0 0 0.00 0 0.00 0 0
## 131 Murugan Ashwin 0 0 0 0 0 0.00 0 0.00 0 0
## 132 Mustafizur Rahman 0 0 0 0 0 0.00 0 0.00 0 0
## 133 Pawan Negi 0 0 0 0 0 0.00 0 0.00 0 0
## 134 Pradeep Sangwan 0 0 0 0 0 0.00 0 0.00 0 0
## 135 Prasidh Krish0 0 0 0 0 0 0.00 0 0.00 0 0
## 136 Sandeep Lamichhane 0 0 0 0 0 0.00 0 0.00 0 0
## 137 Sandeep Sharma 0 0 0 0 0 0.00 0 0.00 0 0
## 138 Shahbaz 0deem 0 0 0 0 0 0.00 0 0.00 0 0
## 139 Siddarth Kaul 0 0 0 0 0 0.00 0 0.00 0 0
## 140 Trent Boult 0 0 0 0 0 0.00 0 0.00 0 0
## 141 Umesh Yadav 0 0 0 0 0 0.00 0 0.00 0 0
## 142 Vi0y Kumar 0 0 0 0 0 0.00 0 0.00 0 0
## 143 Yuzvendra Chahal 0 0 0 0 0 0.00 0 0.00 0 0
## X4s X6s Mat.y Inns.y Ov Runs.y Wkts BBI Avg.y Econ SR.y X4w X5w y
## 1 6 8 0 0 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 2 39 30 0 0 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 3 3 5 0 0 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 4 39 5 0 0 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 5 13 6 0 0 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 6 53 34 0 0 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 7 17 31 16 15 37.5 355 13 0 27.30 9.38 17.46 0 0 0
## 8 2 1 14 14 56.0 448 24 0 18.66 8.00 14.00 3 0 0
## 9 3 4 9 8 26.0 218 3 0 72.66 8.38 52.00 0 0 0
## 10 5 8 9 7 17.0 168 2 0 84.00 9.88 51.00 0 0 0
## 11 13 6 13 12 37.0 303 8 0 37.87 8.18 27.75 0 0 0
## 12 1 0 12 12 46.1 354 9 0 39.33 7.66 30.77 0 0 0
## 13 16 6 0 0 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 14 1 8 4 4 10.1 94 5 0 18.80 9.24 12.20 0 0 0
## 15 30 27 0 0 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 16 56 18 0 0 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 17 3 2 4 4 14.0 143 3 0 47.66 10.21 28.00 0 0 0
## 18 1 1 5 5 18.2 190 8 0 23.75 10.36 13.75 0 0 0
## 19 4 10 9 7 15.0 129 2 0 64.50 8.60 45.00 0 0 0
## 20 7 4 0 0 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 21 0 1 3 3 8.4 115 3 0 38.33 13.26 17.33 0 0 0
## 22 11 5 7 2 3.0 19 1 0 19.00 6.33 18.00 0 0 0
## 23 0 1 4 4 11.5 101 4 0 25.25 8.53 17.75 0 0 0
## 24 3 2 0 0 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 25 1 4 12 12 38.1 278 10 0 27.80 7.28 22.90 0 0 0
## 26 2 3 9 2 3.0 24 0 0 NA 8.00 NA 0 0 0
## 27 49 16 0 0 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 28 8 10 16 16 53.3 533 14 0 38.07 9.96 22.92 0 0 0
## 29 32 24 0 0 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 30 17 6 0 0 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 31 8 1 0 0 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 32 14 9 12 10 16.0 132 5 0 26.40 8.25 19.20 0 0 0
## 33 3 1 13 12 31.5 270 7 0 38.57 8.48 27.28 0 0 0
## 34 20 11 13 13 42.4 381 18 0 21.16 8.92 14.22 0 0 0
## 35 1 6 5 5 17.3 167 7 0 23.85 9.54 15.00 0 0 0
## 36 5 1 0 0 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 37 22 17 0 0 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 38 9 7 0 0 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 39 6 1 15 15 50.2 486 11 0 44.18 9.65 27.45 0 0 0
## 40 2 0 10 10 38.5 325 15 0 21.66 8.36 15.53 0 0 0
## 41 52 21 0 0 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 42 3 1 6 2 3.0 36 0 0 NA 12.00 NA 0 0 0
## 43 64 28 0 0 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 44 23 13 0 0 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 45 1 2 0 0 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 46 10 7 0 0 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 47 9 9 15 15 40.0 312 11 0 28.36 7.80 21.81 0 0 0
## 48 22 10 14 13 40.1 284 12 0 23.66 7.07 20.08 0 0 0
## 49 66 32 0 0 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 50 1 0 2 2 3.0 27 0 0 NA 9.00 NA 0 0 0
## 51 2 4 0 0 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 52 16 11 0 0 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 53 22 5 0 0 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 54 4 1 5 1 1.0 10 0 0 NA 10.00 NA 0 0 0
## 55 6 4 7 6 11.0 120 3 0 40.00 10.90 22.00 0 0 0
## 56 9 5 0 0 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 57 2 0 14 14 44.0 368 15 0 24.53 8.36 17.60 1 0 0
## 58 2 0 6 6 21.0 216 2 0 108.00 10.28 63.00 0 0 0
## 59 4 6 5 5 13.1 97 3 0 32.33 7.36 26.33 0 0 0
## 60 3 0 2 2 5.0 47 1 0 47.00 9.40 30.00 0 0 0
## 61 2 1 11 11 41.0 367 11 0 33.36 8.95 22.36 0 0 0
## 62 24 30 0 0 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 63 26 14 15 5 6.1 44 4 0 11.00 7.13 9.25 0 0 0
## 64 20 4 0 0 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 65 1 1 15 15 49.0 412 14 0 29.42 8.40 21.00 0 0 0
## 66 27 10 0 0 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 67 20 8 0 0 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 68 5 1 8 8 22.0 173 6 0 28.83 7.86 22.00 0 0 0
## 69 18 8 0 0 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 70 3 6 17 17 68.0 458 21 0 21.80 6.73 19.42 0 0 0
## 71 7 5 14 14 50.4 410 10 0 41.00 8.09 30.40 0 0 0
## 72 3 4 16 14 41.0 303 11 0 27.54 7.39 22.36 0 0 0
## 73 4 0 0 0 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 74 68 37 0 0 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 75 30 21 0 0 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 76 25 12 0 0 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 77 8 5 0 0 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 78 30 19 0 0 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 79 7 1 0 0 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 80 26 5 17 17 57.0 456 14 0 32.57 8.00 24.42 0 0 0
## 81 44 35 15 11 28.0 251 6 0 41.83 8.96 28.00 0 0 0
## 82 3 0 13 13 46.4 431 16 0 26.93 9.23 17.50 0 0 0
## 83 59 14 0 0 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 84 1 0 9 9 28.0 270 5 0 54.00 9.64 33.60 0 0 0
## 85 6 1 0 0 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 86 5 0 11 10 31.0 236 11 0 21.45 7.61 16.90 1 0 0
## 87 29 21 0 0 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 88 22 5 0 0 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 89 2 2 7 1 2.0 33 0 0 NA 16.50 NA 0 0 0
## 90 40 23 16 16 61.0 467 17 0 27.47 7.65 21.52 0 0 0
## 91 46 12 0 0 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 92 61 16 0 0 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 93 5 1 8 8 29.0 261 5 0 52.20 9.00 34.80 0 0 0
## 94 3 0 5 5 10.1 118 6 0 19.66 11.60 10.16 0 0 0
## 95 11 11 13 4 5.0 58 1 0 58.00 11.60 30.00 0 0 0
## 96 52 18 0 0 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 97 5 4 7 7 20.0 192 4 0 48.00 9.60 30.00 0 0 0
## 98 17 1 0 0 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 99 22 11 15 1 2.0 14 1 0 14.00 7.00 12.00 0 0 0
## 100 6 2 8 2 2.0 23 0 0 NA 11.50 NA 0 0 0
## 101 0 0 1 1 4.0 47 0 0 NA 11.75 NA 0 0 0
## 102 0 0 10 10 37.0 264 12 0 22.00 7.13 18.50 0 0 0
## 103 0 0 8 8 26.1 223 11 0 20.27 8.52 14.27 0 1 0
## 104 0 0 1 1 1.0 11 0 0 NA 11.00 NA 0 0 0
## 105 0 0 3 3 4.0 42 1 0 42.00 10.50 24.00 0 0 0
## 106 0 0 6 6 19.0 204 4 0 51.00 10.73 28.50 0 0 0
## 107 0 0 6 6 22.0 229 4 0 57.25 10.40 33.00 0 0 0
## 108 0 0 4 4 10.1 114 5 0 22.80 11.21 12.20 0 0 0
## 109 0 0 7 7 21.0 211 9 0 23.44 10.04 14.00 0 0 0
## 110 0 0 4 4 16.0 130 5 0 26.00 8.12 19.20 0 0 0
## 111 0 0 1 1 4.0 31 0 0 NA 7.75 NA 0 0 0
## 112 0 0 3 3 10.0 95 2 0 47.50 9.50 30.00 0 0 0
## 113 0 0 8 8 19.5 188 4 0 47.00 9.47 29.75 0 0 0
## 114 0 0 6 6 20.4 188 6 0 31.33 9.09 20.66 0 0 0
## 115 0 0 6 6 23.0 135 5 0 27.00 5.86 27.60 0 0 0
## 116 0 0 14 14 54.0 372 17 0 21.88 6.88 19.05 0 0 0
## 117 0 0 4 3 7.0 89 2 0 44.50 12.71 21.00 0 0 0
## 118 0 0 1 1 3.0 34 0 0 NA 11.33 NA 0 0 0
## 119 0 0 6 5 9.3 89 4 0 22.25 9.36 14.25 0 0 0
## 120 0 0 1 1 3.0 38 0 0 NA 12.66 NA 0 0 0
## 121 0 0 2 2 6.0 75 3 0 25.00 12.50 12.00 0 0 0
## 122 0 0 16 16 51.2 418 17 0 24.58 8.14 18.11 1 0 0
## 123 0 0 3 3 10.5 107 2 0 53.50 9.87 32.50 0 0 0
## 124 0 0 7 7 25.0 225 4 0 56.25 9.00 37.50 0 0 0
## 125 0 0 7 7 26.0 156 11 0 14.18 6.00 14.18 1 0 0
## 126 0 0 1 1 4.0 49 0 0 NA 12.25 NA 0 0 0
## 127 0 0 11 11 40.0 332 14 0 23.71 8.30 17.14 0 0 0
## 128 0 0 4 4 13.5 144 3 0 48.00 10.40 27.66 0 0 0
## 129 0 0 9 9 29.4 322 7 0 46.00 10.85 25.42 0 0 0
## 130 0 0 11 11 41.2 289 14 0 20.64 6.99 17.71 0 0 0
## 131 0 0 2 2 7.0 53 3 0 17.66 7.57 14.00 0 0 0
## 132 0 0 7 7 27.3 230 7 0 32.85 8.36 23.57 0 0 0
## 133 0 0 2 2 4.0 49 1 0 49.00 12.25 24.00 0 0 0
## 134 0 0 1 1 2.0 19 0 0 NA 9.50 NA 0 0 0
## 135 0 0 7 7 28.0 260 10 0 26.00 9.28 16.80 1 0 0
## 136 0 0 3 3 12.0 82 5 0 16.40 6.83 14.40 0 0 0
## 137 0 0 12 12 44.0 333 12 0 27.75 7.56 22.00 0 0 0
## 138 0 0 6 6 15.0 161 3 0 53.66 10.73 30.00 0 0 0
## 139 0 0 17 17 66.0 547 21 0 26.04 8.28 18.85 0 0 0
## 140 0 0 14 14 52.4 466 18 0 25.88 8.84 17.55 0 0 0
## 141 0 0 14 14 53.1 418 20 0 20.90 7.86 15.95 0 0 0
## 142 0 0 2 2 3.5 65 2 0 32.50 16.95 11.50 0 0 0
## 143 0 0 14 14 50.0 363 12 0 30.25 7.26 25.00 0 0 0str(dataset)## 'data.frame': 143 obs. of 25 variables:
## $ PLAYER: chr "Aaron Finch" "AB de Villiers" "Abhishek Sharma" "Ajinkya Rahane" ...
## $ Mat.x : int 10 12 3 15 6 16 16 14 9 9 ...
## $ Inns.x: int 9 11 3 14 6 16 14 8 8 6 ...
## $ NO : int 1 2 2 1 0 2 3 2 2 2 ...
## $ Runs.x: int 134 480 63 370 148 602 316 32 80 96 ...
## $ HS : int 46 90 46 65 45 100 88 14 19 37 ...
## $ Avg.x : num 16.8 53.3 63 28.5 24.7 ...
## $ BF : int 100 275 33 313 118 402 171 38 69 58 ...
## $ SR.x : num 134 175 191 118 125 ...
## $ X100 : int 0 0 0 0 0 1 0 0 0 0 ...
## $ X50 : int 0 6 0 1 0 3 1 0 0 0 ...
## $ X4s : int 6 39 3 39 13 53 17 2 3 5 ...
## $ X6s : int 8 30 5 5 6 34 31 1 4 8 ...
## $ Mat.y : int 0 0 0 0 0 0 16 14 9 9 ...
## $ Inns.y: int 0 0 0 0 0 0 15 14 8 7 ...
## $ Ov : num 0 0 0 0 0 0 37.5 56 26 17 ...
## $ Runs.y: int 0 0 0 0 0 0 355 448 218 168 ...
## $ Wkts : int 0 0 0 0 0 0 13 24 3 2 ...
## $ BBI : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Avg.y : num 0 0 0 0 0 ...
## $ Econ : num 0 0 0 0 0 0 9.38 8 8.38 9.88 ...
## $ SR.y : num 0 0 0 0 0 ...
## $ X4w : int 0 0 0 0 0 0 0 3 0 0 ...
## $ X5w : int 0 0 0 0 0 0 0 0 0 0 ...
## $ y : int 0 0 0 0 0 0 0 0 0 0 ...Finding Missing Values
# Check for missing values in each column
missing_per_column <- colSums(is.na(dataset))
print(missing_per_column)## PLAYER Mat.x Inns.x NO Runs.x HS Avg.x BF SR.x X100 X50
## 0 0 0 0 0 0 3 0 0 0 0
## X4s X6s Mat.y Inns.y Ov Runs.y Wkts BBI Avg.y Econ SR.y
## 0 0 0 0 0 0 0 0 13 0 13
## X4w X5w y
## 0 0 0Filling Missing Values
#Impute Missing Values with Mean or Median
#A more common approach is to fill the missing values with the mean or median of the respective column.
# Impute with Mean:
dataset_imputed <- dataset %>%
mutate(
Avg.x = ifelse(is.na(Avg.x), mean(Avg.x, na.rm = TRUE), Avg.x),
Avg.y = ifelse(is.na(Avg.y), mean(Avg.y, na.rm = TRUE), Avg.y),
SR.y = ifelse(is.na(SR.y), mean(SR.y, na.rm = TRUE), SR.y)
)
dataset_imputed## PLAYER Mat.x Inns.x NO Runs.x HS Avg.x BF SR.x X100 X50
## 1 Aaron Finch 10 9 1 134 46 16.75000 100 134.00 0 0
## 2 AB de Villiers 12 11 2 480 90 53.33000 275 174.54 0 6
## 3 Abhishek Sharma 3 3 2 63 46 63.00000 33 190.90 0 0
## 4 Ajinkya Rahane 15 14 1 370 65 28.46000 313 118.21 0 1
## 5 Alex Hales 6 6 0 148 45 24.66000 118 125.42 0 0
## 6 Ambati Rayudu 16 16 2 602 100 43.00000 402 149.75 1 3
## 7 Andre Russell 16 14 3 316 88 28.72000 171 184.79 0 1
## 8 Andrew Tye 14 8 2 32 14 5.33000 38 84.21 0 0
## 9 Axar Patel 9 8 2 80 19 13.33000 69 115.94 0 0
## 10 Ben Cutting 9 6 2 96 37 24.00000 58 165.51 0 0
## 11 Ben Stokes 13 13 1 196 45 16.33000 161 121.73 0 0
## 12 Bhuvneshwar Kumar 12 4 2 13 7 6.50000 16 81.25 0 0
## 13 Brendon McCullum 6 6 0 127 43 21.16000 88 144.31 0 0
## 14 Carlos Brathwaite 4 4 1 75 43 25.00000 48 156.25 0 0
## 15 Chris Gayle 11 11 2 368 104 40.88000 252 146.03 1 3
## 16 Chris Lynn 16 16 1 491 74 32.73000 377 130.23 0 3
## 17 Chris Morris 4 4 3 46 27 46.00000 26 176.92 0 0
## 18 Chris Woakes 5 4 2 17 11 8.50000 19 89.47 0 0
## 19 Colin de Grandhomme 9 8 3 131 40 26.20000 84 155.95 0 0
## 20 Colin Munro 5 5 0 63 33 12.60000 41 153.65 0 0
## 21 Corey Anderson 3 3 0 17 15 5.66000 22 77.27 0 0
## 22 D'Arcy Short 7 7 0 115 44 16.42000 99 116.16 0 0
## 23 Dan Christian 4 3 1 26 13 13.00000 33 78.78 0 0
## 24 David Miller 3 3 1 74 26 37.00000 64 115.62 0 0
## 25 Deepak Chahar 12 4 1 50 39 16.66000 29 172.41 0 0
## 26 Deepak Hooda 9 8 4 87 32 21.75000 81 107.40 0 0
## 27 Dinesh Karthik 16 16 6 498 52 49.80000 337 147.77 0 2
## 28 Dwayne Bravo 16 10 6 141 68 35.25000 91 154.94 0 1
## 29 Evin Lewis 13 13 0 382 65 29.38000 276 138.40 0 2
## 30 Faf du Plessis 6 6 1 162 67 32.40000 129 125.58 0 1
## 31 Gautam Gambhir 6 5 0 85 55 17.00000 88 96.59 0 1
## 32 Glenn Maxwell 12 12 0 169 47 14.08000 120 140.83 0 0
## 33 Harbhajan Singh 13 3 0 29 19 9.66000 36 80.55 0 0
## 34 Hardik Pandya 13 13 4 260 50 28.88000 195 133.33 0 1
## 35 Harshal Patel 5 2 1 60 36 60.00000 33 181.81 0 0
## 36 Heinrich Klaasen 4 4 1 57 32 19.00000 47 121.27 0 0
## 37 Ishan Kishan 14 12 0 275 62 22.91000 184 149.45 0 2
## 38 Jason Roy 5 5 1 120 91 30.00000 94 127.65 0 1
## 39 Jaydev U0dkat 15 7 3 49 26 12.25000 38 128.94 0 0
## 40 Jofra Archer 10 8 3 15 8 3.00000 21 71.42 0 0
## 41 Jos Buttler 13 13 3 548 95 54.80000 353 155.24 0 5
## 42 JP Duminy 6 4 3 36 23 36.00000 40 90.00 0 0
## 43 Kane Williamson 17 17 3 735 84 52.50000 516 142.44 0 8
## 44 Karun 0ir 13 12 0 301 54 25.08000 221 136.19 0 2
## 45 Kedar Jadhav 1 1 1 24 24 17.40893 22 109.09 0 0
## 46 Kieron Pollard 9 8 1 133 50 19.00000 100 133.00 0 1
## 47 Krish0ppa Gowtham 15 13 4 126 33 14.00000 64 196.87 0 0
## 48 Kru0l Pandya 14 13 3 228 41 22.80000 157 145.22 0 0
## 49 Lokesh Rahul 14 14 2 659 95 54.91000 416 158.41 0 6
## 50 Mahipal Lomror 2 2 1 20 11 20.00000 21 95.23 0 0
## 51 Ma0n Vohra 4 4 0 55 45 13.75000 47 117.02 0 0
## 52 Mandeep Singh 14 13 3 252 47 25.20000 186 135.48 0 0
## 53 Manish Pandey 15 13 2 284 62 25.81000 246 115.44 0 3
## 54 Manoj Tiwary 5 4 1 47 35 15.66000 44 106.81 0 0
## 55 Marcus Stoinis 7 7 3 99 29 24.75000 76 130.26 0 0
## 56 Mayank Agarwal 11 11 1 120 30 12.00000 94 127.65 0 0
## 57 Mayank Markande 14 6 4 21 7 10.50000 24 87.50 0 0
## 58 Mitchell Johnson 6 2 2 16 12 17.40893 11 145.45 0 0
## 59 Moeen Ali 5 4 0 77 65 19.25000 46 167.39 0 1
## 60 Mohammad 0bi 2 2 0 18 14 9.00000 12 150.00 0 0
## 61 Mohammed Siraj 11 4 2 25 14 12.50000 22 113.63 0 0
## 62 MS Dhoni 16 15 9 455 79 75.83000 302 150.66 0 3
## 63 Nitish Ra0 15 15 2 304 59 23.38000 232 131.03 0 1
## 64 Parthiv Patel 6 6 1 153 53 30.60000 109 140.36 0 1
## 65 Piyush Chawla 15 7 3 27 12 6.75000 34 79.41 0 0
## 66 Prithvi Shaw 9 9 0 245 65 27.22000 160 153.12 0 2
## 67 Quinton de Kock 8 8 0 201 53 25.12000 162 124.07 0 1
## 68 Rahul Tewatia 8 5 2 50 24 16.66000 43 116.27 0 0
## 69 Rahul Tripathi 12 12 3 226 80 25.11000 167 135.32 0 1
## 70 Rashid Khan 17 7 2 59 34 11.80000 31 190.32 0 0
## 71 Ravichandran Ashwin 14 9 1 102 45 12.75000 71 143.66 0 0
## 72 Ravindra Jadeja 16 10 5 89 27 17.80000 74 120.27 0 0
## 73 Rinku Singh 4 4 0 29 16 7.25000 31 93.54 0 0
## 74 Rishabh Pant 14 14 1 684 128 52.61000 394 173.60 1 5
## 75 Robin Uthappa 16 16 0 351 54 21.93000 265 132.45 0 1
## 76 Rohit Sharma 14 14 2 286 94 23.83000 215 133.02 0 2
## 77 Sam Billings 10 8 0 108 56 13.50000 78 138.46 0 1
## 78 Sanju Samson 15 15 1 441 92 31.50000 320 137.81 0 3
## 79 Sarfaraz Khan 7 6 1 51 22 10.20000 41 124.39 0 0
## 80 Shakib Al Hasan 17 13 2 239 35 21.72000 197 121.31 0 0
## 81 Shane Watson 15 15 1 555 117 39.64000 359 154.59 2 2
## 82 Shardul Thakur 13 1 1 15 15 17.40893 5 300.00 0 0
## 83 Shikhar Dhawan 16 16 3 497 92 38.23000 363 136.91 0 4
## 84 Shivam Mavi 9 4 1 13 7 4.33000 15 86.66 0 0
## 85 Shreevats Goswami 6 3 0 52 35 17.33000 40 130.00 0 0
## 86 Shreyas Gopal 11 4 1 50 24 16.66000 45 111.11 0 0
## 87 Shreyas Iyer 14 14 3 411 93 37.36000 310 132.58 0 4
## 88 Shubman Gill 13 11 5 203 57 33.83000 139 146.04 0 1
## 89 Stuart Binny 7 5 0 44 22 8.80000 39 112.82 0 0
## 90 Sunil 0rine 16 16 0 357 75 22.31000 188 189.89 0 2
## 91 Suresh Rai0 15 15 3 445 75 37.08000 336 132.44 0 4
## 92 Suryakumar Yadav 14 14 0 512 72 36.57000 384 133.33 0 4
## 93 Tim Southee 8 4 2 52 36 26.00000 46 113.04 0 0
## 94 Tom Curran 5 4 1 23 18 7.66000 28 82.14 0 0
## 95 Vijay Shankar 13 11 7 212 54 53.00000 148 143.24 0 1
## 96 Virat Kohli 14 14 3 530 92 48.18000 381 139.10 0 4
## 97 Washington Sundar 7 6 3 65 35 21.66000 38 171.05 0 0
## 98 Wriddhiman Saha 11 10 2 122 35 15.25000 102 119.60 0 0
## 99 Yusuf Pathan 15 13 4 260 45 28.88000 200 130.00 0 0
## 100 Yuvraj Singh 8 6 0 65 20 10.83000 73 89.04 0 0
## 101 Akila Da0njaya 0 0 0 0 0 0.00000 0 0.00 0 0
## 102 Amit Mishra 0 0 0 0 0 0.00000 0 0.00 0 0
## 103 Ankit Rajpoot 0 0 0 0 0 0.00000 0 0.00 0 0
## 104 Ankit Sharma 0 0 0 0 0 0.00000 0 0.00 0 0
## 105 Anureet Singh 0 0 0 0 0 0.00000 0 0.00 0 0
## 106 Avesh Khan 0 0 0 0 0 0.00000 0 0.00 0 0
## 107 Barinder Sran 0 0 0 0 0 0.00000 0 0.00 0 0
## 108 Basil Thampi 0 0 0 0 0 0.00000 0 0.00 0 0
## 109 Ben Laughlin 0 0 0 0 0 0.00000 0 0.00 0 0
## 110 Billy Stanlake 0 0 0 0 0 0.00000 0 0.00 0 0
## 111 Chris Jordan 0 0 0 0 0 0.00000 0 0.00 0 0
## 112 David Willey 0 0 0 0 0 0.00000 0 0.00 0 0
## 113 Dhawal Kulkarni 0 0 0 0 0 0.00000 0 0.00 0 0
## 114 Imran Tahir 0 0 0 0 0 0.00000 0 0.00 0 0
## 115 Ish Sodhi 0 0 0 0 0 0.00000 0 0.00 0 0
## 116 Jasprit Bumrah 0 0 0 0 0 0.00000 0 0.00 0 0
## 117 Javon Searles 0 0 0 0 0 0.00000 0 0.00 0 0
## 118 Junior Dala 0 0 0 0 0 0.00000 0 0.00 0 0
## 119 Karn Sharma 0 0 0 0 0 0.00000 0 0.00 0 0
## 120 Khaleel Ahmed 0 0 0 0 0 0.00000 0 0.00 0 0
## 121 KM Asif 0 0 0 0 0 0.00000 0 0.00 0 0
## 122 Kuldeep Yadav 0 0 0 0 0 0.00000 0 0.00 0 0
## 123 Kulwant Khejroliya 0 0 0 0 0 0.00000 0 0.00 0 0
## 124 Liam Plunkett 0 0 0 0 0 0.00000 0 0.00 0 0
## 125 Lungi Ngidi 0 0 0 0 0 0.00000 0 0.00 0 0
## 126 Mark Wood 0 0 0 0 0 0.00000 0 0.00 0 0
## 127 Mitchell McCle0ghan 0 0 0 0 0 0.00000 0 0.00 0 0
## 128 Mohammed Shami 0 0 0 0 0 0.00000 0 0.00 0 0
## 129 Mohit Sharma 0 0 0 0 0 0.00000 0 0.00 0 0
## 130 Mujeeb Ur Rahman 0 0 0 0 0 0.00000 0 0.00 0 0
## 131 Murugan Ashwin 0 0 0 0 0 0.00000 0 0.00 0 0
## 132 Mustafizur Rahman 0 0 0 0 0 0.00000 0 0.00 0 0
## 133 Pawan Negi 0 0 0 0 0 0.00000 0 0.00 0 0
## 134 Pradeep Sangwan 0 0 0 0 0 0.00000 0 0.00 0 0
## 135 Prasidh Krish0 0 0 0 0 0 0.00000 0 0.00 0 0
## 136 Sandeep Lamichhane 0 0 0 0 0 0.00000 0 0.00 0 0
## 137 Sandeep Sharma 0 0 0 0 0 0.00000 0 0.00 0 0
## 138 Shahbaz 0deem 0 0 0 0 0 0.00000 0 0.00 0 0
## 139 Siddarth Kaul 0 0 0 0 0 0.00000 0 0.00 0 0
## 140 Trent Boult 0 0 0 0 0 0.00000 0 0.00 0 0
## 141 Umesh Yadav 0 0 0 0 0 0.00000 0 0.00 0 0
## 142 Vi0y Kumar 0 0 0 0 0 0.00000 0 0.00 0 0
## 143 Yuzvendra Chahal 0 0 0 0 0 0.00000 0 0.00 0 0
## X4s X6s Mat.y Inns.y Ov Runs.y Wkts BBI Avg.y Econ SR.y X4w X5w
## 1 6 8 0 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0
## 2 39 30 0 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0
## 3 3 5 0 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0
## 4 39 5 0 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0
## 5 13 6 0 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0
## 6 53 34 0 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0
## 7 17 31 16 15 37.5 355 13 0 27.30000 9.38 17.46000 0 0
## 8 2 1 14 14 56.0 448 24 0 18.66000 8.00 14.00000 3 0
## 9 3 4 9 8 26.0 218 3 0 72.66000 8.38 52.00000 0 0
## 10 5 8 9 7 17.0 168 2 0 84.00000 9.88 51.00000 0 0
## 11 13 6 13 12 37.0 303 8 0 37.87000 8.18 27.75000 0 0
## 12 1 0 12 12 46.1 354 9 0 39.33000 7.66 30.77000 0 0
## 13 16 6 0 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0
## 14 1 8 4 4 10.1 94 5 0 18.80000 9.24 12.20000 0 0
## 15 30 27 0 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0
## 16 56 18 0 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0
## 17 3 2 4 4 14.0 143 3 0 47.66000 10.21 28.00000 0 0
## 18 1 1 5 5 18.2 190 8 0 23.75000 10.36 13.75000 0 0
## 19 4 10 9 7 15.0 129 2 0 64.50000 8.60 45.00000 0 0
## 20 7 4 0 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0
## 21 0 1 3 3 8.4 115 3 0 38.33000 13.26 17.33000 0 0
## 22 11 5 7 2 3.0 19 1 0 19.00000 6.33 18.00000 0 0
## 23 0 1 4 4 11.5 101 4 0 25.25000 8.53 17.75000 0 0
## 24 3 2 0 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0
## 25 1 4 12 12 38.1 278 10 0 27.80000 7.28 22.90000 0 0
## 26 2 3 9 2 3.0 24 0 0 21.75931 8.00 14.44492 0 0
## 27 49 16 0 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0
## 28 8 10 16 16 53.3 533 14 0 38.07000 9.96 22.92000 0 0
## 29 32 24 0 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0
## 30 17 6 0 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0
## 31 8 1 0 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0
## 32 14 9 12 10 16.0 132 5 0 26.40000 8.25 19.20000 0 0
## 33 3 1 13 12 31.5 270 7 0 38.57000 8.48 27.28000 0 0
## 34 20 11 13 13 42.4 381 18 0 21.16000 8.92 14.22000 0 0
## 35 1 6 5 5 17.3 167 7 0 23.85000 9.54 15.00000 0 0
## 36 5 1 0 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0
## 37 22 17 0 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0
## 38 9 7 0 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0
## 39 6 1 15 15 50.2 486 11 0 44.18000 9.65 27.45000 0 0
## 40 2 0 10 10 38.5 325 15 0 21.66000 8.36 15.53000 0 0
## 41 52 21 0 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0
## 42 3 1 6 2 3.0 36 0 0 21.75931 12.00 14.44492 0 0
## 43 64 28 0 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0
## 44 23 13 0 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0
## 45 1 2 0 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0
## 46 10 7 0 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0
## 47 9 9 15 15 40.0 312 11 0 28.36000 7.80 21.81000 0 0
## 48 22 10 14 13 40.1 284 12 0 23.66000 7.07 20.08000 0 0
## 49 66 32 0 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0
## 50 1 0 2 2 3.0 27 0 0 21.75931 9.00 14.44492 0 0
## 51 2 4 0 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0
## 52 16 11 0 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0
## 53 22 5 0 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0
## 54 4 1 5 1 1.0 10 0 0 21.75931 10.00 14.44492 0 0
## 55 6 4 7 6 11.0 120 3 0 40.00000 10.90 22.00000 0 0
## 56 9 5 0 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0
## 57 2 0 14 14 44.0 368 15 0 24.53000 8.36 17.60000 1 0
## 58 2 0 6 6 21.0 216 2 0 108.00000 10.28 63.00000 0 0
## 59 4 6 5 5 13.1 97 3 0 32.33000 7.36 26.33000 0 0
## 60 3 0 2 2 5.0 47 1 0 47.00000 9.40 30.00000 0 0
## 61 2 1 11 11 41.0 367 11 0 33.36000 8.95 22.36000 0 0
## 62 24 30 0 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0
## 63 26 14 15 5 6.1 44 4 0 11.00000 7.13 9.25000 0 0
## 64 20 4 0 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0
## 65 1 1 15 15 49.0 412 14 0 29.42000 8.40 21.00000 0 0
## 66 27 10 0 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0
## 67 20 8 0 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0
## 68 5 1 8 8 22.0 173 6 0 28.83000 7.86 22.00000 0 0
## 69 18 8 0 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0
## 70 3 6 17 17 68.0 458 21 0 21.80000 6.73 19.42000 0 0
## 71 7 5 14 14 50.4 410 10 0 41.00000 8.09 30.40000 0 0
## 72 3 4 16 14 41.0 303 11 0 27.54000 7.39 22.36000 0 0
## 73 4 0 0 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0
## 74 68 37 0 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0
## 75 30 21 0 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0
## 76 25 12 0 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0
## 77 8 5 0 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0
## 78 30 19 0 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0
## 79 7 1 0 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0
## 80 26 5 17 17 57.0 456 14 0 32.57000 8.00 24.42000 0 0
## 81 44 35 15 11 28.0 251 6 0 41.83000 8.96 28.00000 0 0
## 82 3 0 13 13 46.4 431 16 0 26.93000 9.23 17.50000 0 0
## 83 59 14 0 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0
## 84 1 0 9 9 28.0 270 5 0 54.00000 9.64 33.60000 0 0
## 85 6 1 0 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0
## 86 5 0 11 10 31.0 236 11 0 21.45000 7.61 16.90000 1 0
## 87 29 21 0 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0
## 88 22 5 0 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0
## 89 2 2 7 1 2.0 33 0 0 21.75931 16.50 14.44492 0 0
## 90 40 23 16 16 61.0 467 17 0 27.47000 7.65 21.52000 0 0
## 91 46 12 0 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0
## 92 61 16 0 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0
## 93 5 1 8 8 29.0 261 5 0 52.20000 9.00 34.80000 0 0
## 94 3 0 5 5 10.1 118 6 0 19.66000 11.60 10.16000 0 0
## 95 11 11 13 4 5.0 58 1 0 58.00000 11.60 30.00000 0 0
## 96 52 18 0 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0
## 97 5 4 7 7 20.0 192 4 0 48.00000 9.60 30.00000 0 0
## 98 17 1 0 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0
## 99 22 11 15 1 2.0 14 1 0 14.00000 7.00 12.00000 0 0
## 100 6 2 8 2 2.0 23 0 0 21.75931 11.50 14.44492 0 0
## 101 0 0 1 1 4.0 47 0 0 21.75931 11.75 14.44492 0 0
## 102 0 0 10 10 37.0 264 12 0 22.00000 7.13 18.50000 0 0
## 103 0 0 8 8 26.1 223 11 0 20.27000 8.52 14.27000 0 1
## 104 0 0 1 1 1.0 11 0 0 21.75931 11.00 14.44492 0 0
## 105 0 0 3 3 4.0 42 1 0 42.00000 10.50 24.00000 0 0
## 106 0 0 6 6 19.0 204 4 0 51.00000 10.73 28.50000 0 0
## 107 0 0 6 6 22.0 229 4 0 57.25000 10.40 33.00000 0 0
## 108 0 0 4 4 10.1 114 5 0 22.80000 11.21 12.20000 0 0
## 109 0 0 7 7 21.0 211 9 0 23.44000 10.04 14.00000 0 0
## 110 0 0 4 4 16.0 130 5 0 26.00000 8.12 19.20000 0 0
## 111 0 0 1 1 4.0 31 0 0 21.75931 7.75 14.44492 0 0
## 112 0 0 3 3 10.0 95 2 0 47.50000 9.50 30.00000 0 0
## 113 0 0 8 8 19.5 188 4 0 47.00000 9.47 29.75000 0 0
## 114 0 0 6 6 20.4 188 6 0 31.33000 9.09 20.66000 0 0
## 115 0 0 6 6 23.0 135 5 0 27.00000 5.86 27.60000 0 0
## 116 0 0 14 14 54.0 372 17 0 21.88000 6.88 19.05000 0 0
## 117 0 0 4 3 7.0 89 2 0 44.50000 12.71 21.00000 0 0
## 118 0 0 1 1 3.0 34 0 0 21.75931 11.33 14.44492 0 0
## 119 0 0 6 5 9.3 89 4 0 22.25000 9.36 14.25000 0 0
## 120 0 0 1 1 3.0 38 0 0 21.75931 12.66 14.44492 0 0
## 121 0 0 2 2 6.0 75 3 0 25.00000 12.50 12.00000 0 0
## 122 0 0 16 16 51.2 418 17 0 24.58000 8.14 18.11000 1 0
## 123 0 0 3 3 10.5 107 2 0 53.50000 9.87 32.50000 0 0
## 124 0 0 7 7 25.0 225 4 0 56.25000 9.00 37.50000 0 0
## 125 0 0 7 7 26.0 156 11 0 14.18000 6.00 14.18000 1 0
## 126 0 0 1 1 4.0 49 0 0 21.75931 12.25 14.44492 0 0
## 127 0 0 11 11 40.0 332 14 0 23.71000 8.30 17.14000 0 0
## 128 0 0 4 4 13.5 144 3 0 48.00000 10.40 27.66000 0 0
## 129 0 0 9 9 29.4 322 7 0 46.00000 10.85 25.42000 0 0
## 130 0 0 11 11 41.2 289 14 0 20.64000 6.99 17.71000 0 0
## 131 0 0 2 2 7.0 53 3 0 17.66000 7.57 14.00000 0 0
## 132 0 0 7 7 27.3 230 7 0 32.85000 8.36 23.57000 0 0
## 133 0 0 2 2 4.0 49 1 0 49.00000 12.25 24.00000 0 0
## 134 0 0 1 1 2.0 19 0 0 21.75931 9.50 14.44492 0 0
## 135 0 0 7 7 28.0 260 10 0 26.00000 9.28 16.80000 1 0
## 136 0 0 3 3 12.0 82 5 0 16.40000 6.83 14.40000 0 0
## 137 0 0 12 12 44.0 333 12 0 27.75000 7.56 22.00000 0 0
## 138 0 0 6 6 15.0 161 3 0 53.66000 10.73 30.00000 0 0
## 139 0 0 17 17 66.0 547 21 0 26.04000 8.28 18.85000 0 0
## 140 0 0 14 14 52.4 466 18 0 25.88000 8.84 17.55000 0 0
## 141 0 0 14 14 53.1 418 20 0 20.90000 7.86 15.95000 0 0
## 142 0 0 2 2 3.5 65 2 0 32.50000 16.95 11.50000 0 0
## 143 0 0 14 14 50.0 363 12 0 30.25000 7.26 25.00000 0 0
## y
## 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 0
## 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 0
## 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
## 72 0
## 73 0
## 74 0
## 75 0
## 76 0
## 77 0
## 78 0
## 79 0
## 80 0
## 81 0
## 82 0
## 83 0
## 84 0
## 85 0
## 86 0
## 87 0
## 88 0
## 89 0
## 90 0
## 91 0
## 92 0
## 93 0
## 94 0
## 95 0
## 96 0
## 97 0
## 98 0
## 99 0
## 100 0
## 101 0
## 102 0
## 103 0
## 104 0
## 105 0
## 106 0
## 107 0
## 108 0
## 109 0
## 110 0
## 111 0
## 112 0
## 113 0
## 114 0
## 115 0
## 116 0
## 117 0
## 118 0
## 119 0
## 120 0
## 121 0
## 122 0
## 123 0
## 124 0
## 125 0
## 126 0
## 127 0
## 128 0
## 129 0
## 130 0
## 131 0
## 132 0
## 133 0
## 134 0
## 135 0
## 136 0
## 137 0
## 138 0
## 139 0
## 140 0
## 141 0
## 142 0
## 143 0# Check for missing values in each column
missing_per_column <- colSums(is.na(dataset_imputed))
print(missing_per_column)## PLAYER Mat.x Inns.x NO Runs.x HS Avg.x BF SR.x X100 X50
## 0 0 0 0 0 0 0 0 0 0 0
## X4s X6s Mat.y Inns.y Ov Runs.y Wkts BBI Avg.y Econ SR.y
## 0 0 0 0 0 0 0 0 0 0 0
## X4w X5w y
## 0 0 0total_missing <- sum(is.na(dataset_imputed))
print(paste("Total missing values after imputation:", total_missing))## [1] "Total missing values after imputation: 0"str(dataset_imputed)## 'data.frame': 143 obs. of 25 variables:
## $ PLAYER: chr "Aaron Finch" "AB de Villiers" "Abhishek Sharma" "Ajinkya Rahane" ...
## $ Mat.x : int 10 12 3 15 6 16 16 14 9 9 ...
## $ Inns.x: int 9 11 3 14 6 16 14 8 8 6 ...
## $ NO : int 1 2 2 1 0 2 3 2 2 2 ...
## $ Runs.x: int 134 480 63 370 148 602 316 32 80 96 ...
## $ HS : int 46 90 46 65 45 100 88 14 19 37 ...
## $ Avg.x : num 16.8 53.3 63 28.5 24.7 ...
## $ BF : int 100 275 33 313 118 402 171 38 69 58 ...
## $ SR.x : num 134 175 191 118 125 ...
## $ X100 : int 0 0 0 0 0 1 0 0 0 0 ...
## $ X50 : int 0 6 0 1 0 3 1 0 0 0 ...
## $ X4s : int 6 39 3 39 13 53 17 2 3 5 ...
## $ X6s : int 8 30 5 5 6 34 31 1 4 8 ...
## $ Mat.y : int 0 0 0 0 0 0 16 14 9 9 ...
## $ Inns.y: int 0 0 0 0 0 0 15 14 8 7 ...
## $ Ov : num 0 0 0 0 0 0 37.5 56 26 17 ...
## $ Runs.y: int 0 0 0 0 0 0 355 448 218 168 ...
## $ Wkts : int 0 0 0 0 0 0 13 24 3 2 ...
## $ BBI : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Avg.y : num 0 0 0 0 0 ...
## $ Econ : num 0 0 0 0 0 0 9.38 8 8.38 9.88 ...
## $ SR.y : num 0 0 0 0 0 ...
## $ X4w : int 0 0 0 0 0 0 0 3 0 0 ...
## $ X5w : int 0 0 0 0 0 0 0 0 0 0 ...
## $ y : int 0 0 0 0 0 0 0 0 0 0 ...summary(dataset_imputed)## PLAYER Mat.x Inns.x NO
## Length:143 Min. : 0.000 Min. : 0.000 Min. :0.000
## Class :character 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.:0.000
## Mode :character Median : 7.000 Median : 5.000 Median :1.000
## Mean : 7.287 Mean : 6.014 Mean :1.252
## 3rd Qu.:13.000 3rd Qu.:11.000 3rd Qu.:2.000
## Max. :17.000 Max. :17.000 Max. :9.000
## Runs.x HS Avg.x BF
## Min. : 0.0 Min. : 0.00 Min. : 0.00 Min. : 0.00
## 1st Qu.: 0.0 1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.: 0.00
## Median : 52.0 Median : 27.00 Median :15.66 Median : 41.00
## Mean :132.3 Mean : 33.15 Mean :17.41 Mean : 95.03
## 3rd Qu.:202.0 3rd Qu.: 53.50 3rd Qu.:25.91 3rd Qu.:152.50
## Max. :735.0 Max. :128.00 Max. :75.83 Max. :516.00
## SR.x X100 X50 X4s
## Min. : 0.00 Min. :0.00000 Min. :0.0000 Min. : 0.00
## 1st Qu.: 0.00 1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.: 0.00
## Median :117.02 Median :0.00000 Median :0.0000 Median : 3.00
## Mean : 93.12 Mean :0.03497 Mean :0.7063 Mean :11.46
## 3rd Qu.:140.59 3rd Qu.:0.00000 3rd Qu.:1.0000 3rd Qu.:17.00
## Max. :300.00 Max. :2.00000 Max. :8.0000 Max. :68.00
## X6s Mat.y Inns.y Ov
## Min. : 0.00 Min. : 0.000 Min. : 0.000 Min. : 0.00
## 1st Qu.: 0.00 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.00
## Median : 2.00 Median : 4.000 Median : 3.000 Median : 7.00
## Mean : 6.07 Mean : 5.608 Mean : 5.007 Mean :16.05
## 3rd Qu.: 8.00 3rd Qu.:10.000 3rd Qu.: 8.500 3rd Qu.:28.00
## Max. :37.00 Max. :17.000 Max. :17.000 Max. :68.00
## Runs.y Wkts BBI Avg.y Econ
## Min. : 0.0 Min. : 0.000 Min. :0 Min. : 0.00 Min. : 0.000
## 1st Qu.: 0.0 1st Qu.: 0.000 1st Qu.:0 1st Qu.: 0.00 1st Qu.: 0.000
## Median : 82.0 Median : 2.000 Median :0 Median : 21.76 Median : 7.860
## Mean :137.2 Mean : 4.629 Mean :0 Mean : 21.76 Mean : 6.158
## 3rd Qu.:243.5 3rd Qu.: 7.500 3rd Qu.:0 3rd Qu.: 31.83 3rd Qu.: 9.500
## Max. :547.0 Max. :24.000 Max. :0 Max. :108.00 Max. :16.950
## SR.y X4w X5w y
## Min. : 0.00 Min. :0.00000 Min. :0.000000 Min. :0
## 1st Qu.: 0.00 1st Qu.:0.00000 1st Qu.:0.000000 1st Qu.:0
## Median :14.44 Median :0.00000 Median :0.000000 Median :0
## Mean :14.44 Mean :0.05594 Mean :0.006993 Mean :0
## 3rd Qu.:22.18 3rd Qu.:0.00000 3rd Qu.:0.000000 3rd Qu.:0
## Max. :63.00 Max. :3.00000 Max. :1.000000 Max. :0dim(dataset_imputed)## [1] 143 25str(dataset_imputed)## 'data.frame': 143 obs. of 25 variables:
## $ PLAYER: chr "Aaron Finch" "AB de Villiers" "Abhishek Sharma" "Ajinkya Rahane" ...
## $ Mat.x : int 10 12 3 15 6 16 16 14 9 9 ...
## $ Inns.x: int 9 11 3 14 6 16 14 8 8 6 ...
## $ NO : int 1 2 2 1 0 2 3 2 2 2 ...
## $ Runs.x: int 134 480 63 370 148 602 316 32 80 96 ...
## $ HS : int 46 90 46 65 45 100 88 14 19 37 ...
## $ Avg.x : num 16.8 53.3 63 28.5 24.7 ...
## $ BF : int 100 275 33 313 118 402 171 38 69 58 ...
## $ SR.x : num 134 175 191 118 125 ...
## $ X100 : int 0 0 0 0 0 1 0 0 0 0 ...
## $ X50 : int 0 6 0 1 0 3 1 0 0 0 ...
## $ X4s : int 6 39 3 39 13 53 17 2 3 5 ...
## $ X6s : int 8 30 5 5 6 34 31 1 4 8 ...
## $ Mat.y : int 0 0 0 0 0 0 16 14 9 9 ...
## $ Inns.y: int 0 0 0 0 0 0 15 14 8 7 ...
## $ Ov : num 0 0 0 0 0 0 37.5 56 26 17 ...
## $ Runs.y: int 0 0 0 0 0 0 355 448 218 168 ...
## $ Wkts : int 0 0 0 0 0 0 13 24 3 2 ...
## $ BBI : int 0 0 0 0 0 0 0 0 0 0 ...
## $ Avg.y : num 0 0 0 0 0 ...
## $ Econ : num 0 0 0 0 0 0 9.38 8 8.38 9.88 ...
## $ SR.y : num 0 0 0 0 0 ...
## $ X4w : int 0 0 0 0 0 0 0 3 0 0 ...
## $ X5w : int 0 0 0 0 0 0 0 0 0 0 ...
## $ y : int 0 0 0 0 0 0 0 0 0 0 ...summary(dataset_imputed)## PLAYER Mat.x Inns.x NO
## Length:143 Min. : 0.000 Min. : 0.000 Min. :0.000
## Class :character 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.:0.000
## Mode :character Median : 7.000 Median : 5.000 Median :1.000
## Mean : 7.287 Mean : 6.014 Mean :1.252
## 3rd Qu.:13.000 3rd Qu.:11.000 3rd Qu.:2.000
## Max. :17.000 Max. :17.000 Max. :9.000
## Runs.x HS Avg.x BF
## Min. : 0.0 Min. : 0.00 Min. : 0.00 Min. : 0.00
## 1st Qu.: 0.0 1st Qu.: 0.00 1st Qu.: 0.00 1st Qu.: 0.00
## Median : 52.0 Median : 27.00 Median :15.66 Median : 41.00
## Mean :132.3 Mean : 33.15 Mean :17.41 Mean : 95.03
## 3rd Qu.:202.0 3rd Qu.: 53.50 3rd Qu.:25.91 3rd Qu.:152.50
## Max. :735.0 Max. :128.00 Max. :75.83 Max. :516.00
## SR.x X100 X50 X4s
## Min. : 0.00 Min. :0.00000 Min. :0.0000 Min. : 0.00
## 1st Qu.: 0.00 1st Qu.:0.00000 1st Qu.:0.0000 1st Qu.: 0.00
## Median :117.02 Median :0.00000 Median :0.0000 Median : 3.00
## Mean : 93.12 Mean :0.03497 Mean :0.7063 Mean :11.46
## 3rd Qu.:140.59 3rd Qu.:0.00000 3rd Qu.:1.0000 3rd Qu.:17.00
## Max. :300.00 Max. :2.00000 Max. :8.0000 Max. :68.00
## X6s Mat.y Inns.y Ov
## Min. : 0.00 Min. : 0.000 Min. : 0.000 Min. : 0.00
## 1st Qu.: 0.00 1st Qu.: 0.000 1st Qu.: 0.000 1st Qu.: 0.00
## Median : 2.00 Median : 4.000 Median : 3.000 Median : 7.00
## Mean : 6.07 Mean : 5.608 Mean : 5.007 Mean :16.05
## 3rd Qu.: 8.00 3rd Qu.:10.000 3rd Qu.: 8.500 3rd Qu.:28.00
## Max. :37.00 Max. :17.000 Max. :17.000 Max. :68.00
## Runs.y Wkts BBI Avg.y Econ
## Min. : 0.0 Min. : 0.000 Min. :0 Min. : 0.00 Min. : 0.000
## 1st Qu.: 0.0 1st Qu.: 0.000 1st Qu.:0 1st Qu.: 0.00 1st Qu.: 0.000
## Median : 82.0 Median : 2.000 Median :0 Median : 21.76 Median : 7.860
## Mean :137.2 Mean : 4.629 Mean :0 Mean : 21.76 Mean : 6.158
## 3rd Qu.:243.5 3rd Qu.: 7.500 3rd Qu.:0 3rd Qu.: 31.83 3rd Qu.: 9.500
## Max. :547.0 Max. :24.000 Max. :0 Max. :108.00 Max. :16.950
## SR.y X4w X5w y
## Min. : 0.00 Min. :0.00000 Min. :0.000000 Min. :0
## 1st Qu.: 0.00 1st Qu.:0.00000 1st Qu.:0.000000 1st Qu.:0
## Median :14.44 Median :0.00000 Median :0.000000 Median :0
## Mean :14.44 Mean :0.05594 Mean :0.006993 Mean :0
## 3rd Qu.:22.18 3rd Qu.:0.00000 3rd Qu.:0.000000 3rd Qu.:0
## Max. :63.00 Max. :3.00000 Max. :1.000000 Max. :0dim(dataset_imputed)## [1] 143 25Trying Some Plots
ggplot(data=dataset_imputed,mapping=aes (x=Mat.x , y=Inns.x))+ geom_point()
#Scatter Plot: Runs vs Strike Rate (Batting Performance)
# Scatter plot for Runs vs Strike Rate
ggplot(dataset_imputed, aes(x = SR.x, y = Runs.x, color = PLAYER)) +
geom_point(size = 3) +
labs(title = "Runs vs Strike Rate (Batting Performance)",
x = "Strike Rate (SR.x)",
y = "Runs Scored (Runs.x)") +
theme_minimal()
#Bar Plot: Number of Matches Played by Each Player
# Bar plot for Matches Played
ggplot(dataset_imputed, aes(x = reorder(PLAYER, Mat.x), y = Mat.x)) +
geom_bar(stat = "identity", fill = "skyblue") +
labs(title = "Number of Matches Played by Each Player",
x = "Player",
y = "Matches Played (Mat.x)") +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
numeric_data <- dataset_imputed[, sapply(dataset_imputed, is.numeric)]
numeric_data## Mat.x Inns.x NO Runs.x HS Avg.x BF SR.x X100 X50 X4s X6s Mat.y
## 1 10 9 1 134 46 16.75000 100 134.00 0 0 6 8 0
## 2 12 11 2 480 90 53.33000 275 174.54 0 6 39 30 0
## 3 3 3 2 63 46 63.00000 33 190.90 0 0 3 5 0
## 4 15 14 1 370 65 28.46000 313 118.21 0 1 39 5 0
## 5 6 6 0 148 45 24.66000 118 125.42 0 0 13 6 0
## 6 16 16 2 602 100 43.00000 402 149.75 1 3 53 34 0
## 7 16 14 3 316 88 28.72000 171 184.79 0 1 17 31 16
## 8 14 8 2 32 14 5.33000 38 84.21 0 0 2 1 14
## 9 9 8 2 80 19 13.33000 69 115.94 0 0 3 4 9
## 10 9 6 2 96 37 24.00000 58 165.51 0 0 5 8 9
## 11 13 13 1 196 45 16.33000 161 121.73 0 0 13 6 13
## 12 12 4 2 13 7 6.50000 16 81.25 0 0 1 0 12
## 13 6 6 0 127 43 21.16000 88 144.31 0 0 16 6 0
## 14 4 4 1 75 43 25.00000 48 156.25 0 0 1 8 4
## 15 11 11 2 368 104 40.88000 252 146.03 1 3 30 27 0
## 16 16 16 1 491 74 32.73000 377 130.23 0 3 56 18 0
## 17 4 4 3 46 27 46.00000 26 176.92 0 0 3 2 4
## 18 5 4 2 17 11 8.50000 19 89.47 0 0 1 1 5
## 19 9 8 3 131 40 26.20000 84 155.95 0 0 4 10 9
## 20 5 5 0 63 33 12.60000 41 153.65 0 0 7 4 0
## 21 3 3 0 17 15 5.66000 22 77.27 0 0 0 1 3
## 22 7 7 0 115 44 16.42000 99 116.16 0 0 11 5 7
## 23 4 3 1 26 13 13.00000 33 78.78 0 0 0 1 4
## 24 3 3 1 74 26 37.00000 64 115.62 0 0 3 2 0
## 25 12 4 1 50 39 16.66000 29 172.41 0 0 1 4 12
## 26 9 8 4 87 32 21.75000 81 107.40 0 0 2 3 9
## 27 16 16 6 498 52 49.80000 337 147.77 0 2 49 16 0
## 28 16 10 6 141 68 35.25000 91 154.94 0 1 8 10 16
## 29 13 13 0 382 65 29.38000 276 138.40 0 2 32 24 0
## 30 6 6 1 162 67 32.40000 129 125.58 0 1 17 6 0
## 31 6 5 0 85 55 17.00000 88 96.59 0 1 8 1 0
## 32 12 12 0 169 47 14.08000 120 140.83 0 0 14 9 12
## 33 13 3 0 29 19 9.66000 36 80.55 0 0 3 1 13
## 34 13 13 4 260 50 28.88000 195 133.33 0 1 20 11 13
## 35 5 2 1 60 36 60.00000 33 181.81 0 0 1 6 5
## 36 4 4 1 57 32 19.00000 47 121.27 0 0 5 1 0
## 37 14 12 0 275 62 22.91000 184 149.45 0 2 22 17 0
## 38 5 5 1 120 91 30.00000 94 127.65 0 1 9 7 0
## 39 15 7 3 49 26 12.25000 38 128.94 0 0 6 1 15
## 40 10 8 3 15 8 3.00000 21 71.42 0 0 2 0 10
## 41 13 13 3 548 95 54.80000 353 155.24 0 5 52 21 0
## 42 6 4 3 36 23 36.00000 40 90.00 0 0 3 1 6
## 43 17 17 3 735 84 52.50000 516 142.44 0 8 64 28 0
## 44 13 12 0 301 54 25.08000 221 136.19 0 2 23 13 0
## 45 1 1 1 24 24 17.40893 22 109.09 0 0 1 2 0
## 46 9 8 1 133 50 19.00000 100 133.00 0 1 10 7 0
## 47 15 13 4 126 33 14.00000 64 196.87 0 0 9 9 15
## 48 14 13 3 228 41 22.80000 157 145.22 0 0 22 10 14
## 49 14 14 2 659 95 54.91000 416 158.41 0 6 66 32 0
## 50 2 2 1 20 11 20.00000 21 95.23 0 0 1 0 2
## 51 4 4 0 55 45 13.75000 47 117.02 0 0 2 4 0
## 52 14 13 3 252 47 25.20000 186 135.48 0 0 16 11 0
## 53 15 13 2 284 62 25.81000 246 115.44 0 3 22 5 0
## 54 5 4 1 47 35 15.66000 44 106.81 0 0 4 1 5
## 55 7 7 3 99 29 24.75000 76 130.26 0 0 6 4 7
## 56 11 11 1 120 30 12.00000 94 127.65 0 0 9 5 0
## 57 14 6 4 21 7 10.50000 24 87.50 0 0 2 0 14
## 58 6 2 2 16 12 17.40893 11 145.45 0 0 2 0 6
## 59 5 4 0 77 65 19.25000 46 167.39 0 1 4 6 5
## 60 2 2 0 18 14 9.00000 12 150.00 0 0 3 0 2
## 61 11 4 2 25 14 12.50000 22 113.63 0 0 2 1 11
## 62 16 15 9 455 79 75.83000 302 150.66 0 3 24 30 0
## 63 15 15 2 304 59 23.38000 232 131.03 0 1 26 14 15
## 64 6 6 1 153 53 30.60000 109 140.36 0 1 20 4 0
## 65 15 7 3 27 12 6.75000 34 79.41 0 0 1 1 15
## 66 9 9 0 245 65 27.22000 160 153.12 0 2 27 10 0
## 67 8 8 0 201 53 25.12000 162 124.07 0 1 20 8 0
## 68 8 5 2 50 24 16.66000 43 116.27 0 0 5 1 8
## 69 12 12 3 226 80 25.11000 167 135.32 0 1 18 8 0
## 70 17 7 2 59 34 11.80000 31 190.32 0 0 3 6 17
## 71 14 9 1 102 45 12.75000 71 143.66 0 0 7 5 14
## 72 16 10 5 89 27 17.80000 74 120.27 0 0 3 4 16
## 73 4 4 0 29 16 7.25000 31 93.54 0 0 4 0 0
## 74 14 14 1 684 128 52.61000 394 173.60 1 5 68 37 0
## 75 16 16 0 351 54 21.93000 265 132.45 0 1 30 21 0
## 76 14 14 2 286 94 23.83000 215 133.02 0 2 25 12 0
## 77 10 8 0 108 56 13.50000 78 138.46 0 1 8 5 0
## 78 15 15 1 441 92 31.50000 320 137.81 0 3 30 19 0
## 79 7 6 1 51 22 10.20000 41 124.39 0 0 7 1 0
## 80 17 13 2 239 35 21.72000 197 121.31 0 0 26 5 17
## 81 15 15 1 555 117 39.64000 359 154.59 2 2 44 35 15
## 82 13 1 1 15 15 17.40893 5 300.00 0 0 3 0 13
## 83 16 16 3 497 92 38.23000 363 136.91 0 4 59 14 0
## 84 9 4 1 13 7 4.33000 15 86.66 0 0 1 0 9
## 85 6 3 0 52 35 17.33000 40 130.00 0 0 6 1 0
## 86 11 4 1 50 24 16.66000 45 111.11 0 0 5 0 11
## 87 14 14 3 411 93 37.36000 310 132.58 0 4 29 21 0
## 88 13 11 5 203 57 33.83000 139 146.04 0 1 22 5 0
## 89 7 5 0 44 22 8.80000 39 112.82 0 0 2 2 7
## 90 16 16 0 357 75 22.31000 188 189.89 0 2 40 23 16
## 91 15 15 3 445 75 37.08000 336 132.44 0 4 46 12 0
## 92 14 14 0 512 72 36.57000 384 133.33 0 4 61 16 0
## 93 8 4 2 52 36 26.00000 46 113.04 0 0 5 1 8
## 94 5 4 1 23 18 7.66000 28 82.14 0 0 3 0 5
## 95 13 11 7 212 54 53.00000 148 143.24 0 1 11 11 13
## 96 14 14 3 530 92 48.18000 381 139.10 0 4 52 18 0
## 97 7 6 3 65 35 21.66000 38 171.05 0 0 5 4 7
## 98 11 10 2 122 35 15.25000 102 119.60 0 0 17 1 0
## 99 15 13 4 260 45 28.88000 200 130.00 0 0 22 11 15
## 100 8 6 0 65 20 10.83000 73 89.04 0 0 6 2 8
## 101 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 1
## 102 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 10
## 103 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 8
## 104 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 1
## 105 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 3
## 106 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 6
## 107 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 6
## 108 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 4
## 109 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 7
## 110 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 4
## 111 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 1
## 112 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 3
## 113 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 8
## 114 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 6
## 115 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 6
## 116 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 14
## 117 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 4
## 118 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 1
## 119 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 6
## 120 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 1
## 121 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 2
## 122 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 16
## 123 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 3
## 124 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 7
## 125 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 7
## 126 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 1
## 127 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 11
## 128 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 4
## 129 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 9
## 130 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 11
## 131 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 2
## 132 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 7
## 133 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 2
## 134 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 1
## 135 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 7
## 136 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 3
## 137 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 12
## 138 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 6
## 139 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 17
## 140 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 14
## 141 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 14
## 142 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 2
## 143 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 14
## Inns.y Ov Runs.y Wkts BBI Avg.y Econ SR.y X4w X5w y
## 1 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 2 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 3 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 4 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 5 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 6 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 7 15 37.5 355 13 0 27.30000 9.38 17.46000 0 0 0
## 8 14 56.0 448 24 0 18.66000 8.00 14.00000 3 0 0
## 9 8 26.0 218 3 0 72.66000 8.38 52.00000 0 0 0
## 10 7 17.0 168 2 0 84.00000 9.88 51.00000 0 0 0
## 11 12 37.0 303 8 0 37.87000 8.18 27.75000 0 0 0
## 12 12 46.1 354 9 0 39.33000 7.66 30.77000 0 0 0
## 13 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 14 4 10.1 94 5 0 18.80000 9.24 12.20000 0 0 0
## 15 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 16 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 17 4 14.0 143 3 0 47.66000 10.21 28.00000 0 0 0
## 18 5 18.2 190 8 0 23.75000 10.36 13.75000 0 0 0
## 19 7 15.0 129 2 0 64.50000 8.60 45.00000 0 0 0
## 20 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 21 3 8.4 115 3 0 38.33000 13.26 17.33000 0 0 0
## 22 2 3.0 19 1 0 19.00000 6.33 18.00000 0 0 0
## 23 4 11.5 101 4 0 25.25000 8.53 17.75000 0 0 0
## 24 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 25 12 38.1 278 10 0 27.80000 7.28 22.90000 0 0 0
## 26 2 3.0 24 0 0 21.75931 8.00 14.44492 0 0 0
## 27 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 28 16 53.3 533 14 0 38.07000 9.96 22.92000 0 0 0
## 29 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 30 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 31 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 32 10 16.0 132 5 0 26.40000 8.25 19.20000 0 0 0
## 33 12 31.5 270 7 0 38.57000 8.48 27.28000 0 0 0
## 34 13 42.4 381 18 0 21.16000 8.92 14.22000 0 0 0
## 35 5 17.3 167 7 0 23.85000 9.54 15.00000 0 0 0
## 36 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 37 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 38 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 39 15 50.2 486 11 0 44.18000 9.65 27.45000 0 0 0
## 40 10 38.5 325 15 0 21.66000 8.36 15.53000 0 0 0
## 41 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 42 2 3.0 36 0 0 21.75931 12.00 14.44492 0 0 0
## 43 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 44 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 45 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 46 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 47 15 40.0 312 11 0 28.36000 7.80 21.81000 0 0 0
## 48 13 40.1 284 12 0 23.66000 7.07 20.08000 0 0 0
## 49 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 50 2 3.0 27 0 0 21.75931 9.00 14.44492 0 0 0
## 51 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 52 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 53 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 54 1 1.0 10 0 0 21.75931 10.00 14.44492 0 0 0
## 55 6 11.0 120 3 0 40.00000 10.90 22.00000 0 0 0
## 56 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 57 14 44.0 368 15 0 24.53000 8.36 17.60000 1 0 0
## 58 6 21.0 216 2 0 108.00000 10.28 63.00000 0 0 0
## 59 5 13.1 97 3 0 32.33000 7.36 26.33000 0 0 0
## 60 2 5.0 47 1 0 47.00000 9.40 30.00000 0 0 0
## 61 11 41.0 367 11 0 33.36000 8.95 22.36000 0 0 0
## 62 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 63 5 6.1 44 4 0 11.00000 7.13 9.25000 0 0 0
## 64 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 65 15 49.0 412 14 0 29.42000 8.40 21.00000 0 0 0
## 66 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 67 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 68 8 22.0 173 6 0 28.83000 7.86 22.00000 0 0 0
## 69 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 70 17 68.0 458 21 0 21.80000 6.73 19.42000 0 0 0
## 71 14 50.4 410 10 0 41.00000 8.09 30.40000 0 0 0
## 72 14 41.0 303 11 0 27.54000 7.39 22.36000 0 0 0
## 73 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 74 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 75 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 76 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 77 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 78 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 79 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 80 17 57.0 456 14 0 32.57000 8.00 24.42000 0 0 0
## 81 11 28.0 251 6 0 41.83000 8.96 28.00000 0 0 0
## 82 13 46.4 431 16 0 26.93000 9.23 17.50000 0 0 0
## 83 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 84 9 28.0 270 5 0 54.00000 9.64 33.60000 0 0 0
## 85 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 86 10 31.0 236 11 0 21.45000 7.61 16.90000 1 0 0
## 87 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 88 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 89 1 2.0 33 0 0 21.75931 16.50 14.44492 0 0 0
## 90 16 61.0 467 17 0 27.47000 7.65 21.52000 0 0 0
## 91 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 92 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 93 8 29.0 261 5 0 52.20000 9.00 34.80000 0 0 0
## 94 5 10.1 118 6 0 19.66000 11.60 10.16000 0 0 0
## 95 4 5.0 58 1 0 58.00000 11.60 30.00000 0 0 0
## 96 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 97 7 20.0 192 4 0 48.00000 9.60 30.00000 0 0 0
## 98 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 99 1 2.0 14 1 0 14.00000 7.00 12.00000 0 0 0
## 100 2 2.0 23 0 0 21.75931 11.50 14.44492 0 0 0
## 101 1 4.0 47 0 0 21.75931 11.75 14.44492 0 0 0
## 102 10 37.0 264 12 0 22.00000 7.13 18.50000 0 0 0
## 103 8 26.1 223 11 0 20.27000 8.52 14.27000 0 1 0
## 104 1 1.0 11 0 0 21.75931 11.00 14.44492 0 0 0
## 105 3 4.0 42 1 0 42.00000 10.50 24.00000 0 0 0
## 106 6 19.0 204 4 0 51.00000 10.73 28.50000 0 0 0
## 107 6 22.0 229 4 0 57.25000 10.40 33.00000 0 0 0
## 108 4 10.1 114 5 0 22.80000 11.21 12.20000 0 0 0
## 109 7 21.0 211 9 0 23.44000 10.04 14.00000 0 0 0
## 110 4 16.0 130 5 0 26.00000 8.12 19.20000 0 0 0
## 111 1 4.0 31 0 0 21.75931 7.75 14.44492 0 0 0
## 112 3 10.0 95 2 0 47.50000 9.50 30.00000 0 0 0
## 113 8 19.5 188 4 0 47.00000 9.47 29.75000 0 0 0
## 114 6 20.4 188 6 0 31.33000 9.09 20.66000 0 0 0
## 115 6 23.0 135 5 0 27.00000 5.86 27.60000 0 0 0
## 116 14 54.0 372 17 0 21.88000 6.88 19.05000 0 0 0
## 117 3 7.0 89 2 0 44.50000 12.71 21.00000 0 0 0
## 118 1 3.0 34 0 0 21.75931 11.33 14.44492 0 0 0
## 119 5 9.3 89 4 0 22.25000 9.36 14.25000 0 0 0
## 120 1 3.0 38 0 0 21.75931 12.66 14.44492 0 0 0
## 121 2 6.0 75 3 0 25.00000 12.50 12.00000 0 0 0
## 122 16 51.2 418 17 0 24.58000 8.14 18.11000 1 0 0
## 123 3 10.5 107 2 0 53.50000 9.87 32.50000 0 0 0
## 124 7 25.0 225 4 0 56.25000 9.00 37.50000 0 0 0
## 125 7 26.0 156 11 0 14.18000 6.00 14.18000 1 0 0
## 126 1 4.0 49 0 0 21.75931 12.25 14.44492 0 0 0
## 127 11 40.0 332 14 0 23.71000 8.30 17.14000 0 0 0
## 128 4 13.5 144 3 0 48.00000 10.40 27.66000 0 0 0
## 129 9 29.4 322 7 0 46.00000 10.85 25.42000 0 0 0
## 130 11 41.2 289 14 0 20.64000 6.99 17.71000 0 0 0
## 131 2 7.0 53 3 0 17.66000 7.57 14.00000 0 0 0
## 132 7 27.3 230 7 0 32.85000 8.36 23.57000 0 0 0
## 133 2 4.0 49 1 0 49.00000 12.25 24.00000 0 0 0
## 134 1 2.0 19 0 0 21.75931 9.50 14.44492 0 0 0
## 135 7 28.0 260 10 0 26.00000 9.28 16.80000 1 0 0
## 136 3 12.0 82 5 0 16.40000 6.83 14.40000 0 0 0
## 137 12 44.0 333 12 0 27.75000 7.56 22.00000 0 0 0
## 138 6 15.0 161 3 0 53.66000 10.73 30.00000 0 0 0
## 139 17 66.0 547 21 0 26.04000 8.28 18.85000 0 0 0
## 140 14 52.4 466 18 0 25.88000 8.84 17.55000 0 0 0
## 141 14 53.1 418 20 0 20.90000 7.86 15.95000 0 0 0
## 142 2 3.5 65 2 0 32.50000 16.95 11.50000 0 0 0
## 143 14 50.0 363 12 0 30.25000 7.26 25.00000 0 0 0# Step 1: Identify columns with zero standard deviation
constant_columns <- sapply(numeric_data, function(x) sd(x, na.rm = TRUE) == 0)
constant_columns## Mat.x Inns.x NO Runs.x HS Avg.x BF SR.x X100 X50 X4s
## FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## X6s Mat.y Inns.y Ov Runs.y Wkts BBI Avg.y Econ SR.y X4w
## FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
## X5w y
## FALSE TRUE# Step 2: Remove constant columns (if any)
numeric_data_cleaned <- numeric_data[, !constant_columns]
# Step 3: Recalculate the correlation matrix
correlation_matrix <- cor(numeric_data_cleaned, use = "complete.obs")
print(correlation_matrix)## Mat.x Inns.x NO Runs.x HS Avg.x
## Mat.x 1.000000000 0.91647267 0.62134708 0.72313599 0.74458299 0.6465189
## Inns.x 0.916472671 1.00000000 0.56239764 0.87236880 0.85402152 0.7187387
## NO 0.621347077 0.56239764 1.00000000 0.39672270 0.40931881 0.6205010
## Runs.x 0.723135993 0.87236880 0.39672270 1.00000000 0.88415213 0.7778843
## HS 0.744582992 0.85402152 0.40931881 0.88415213 1.00000000 0.8282415
## Avg.x 0.646518863 0.71873866 0.62050103 0.77788425 0.82824145 1.0000000
## BF 0.739627648 0.88824952 0.39764305 0.99038509 0.87403252 0.7642742
## SR.x 0.777216813 0.71386963 0.49580267 0.55550183 0.73919729 0.7534433
## X100 0.182690012 0.23908702 0.01461432 0.38481366 0.40213426 0.2462435
## X50 0.523430867 0.65771693 0.27935388 0.89333698 0.76231036 0.6757533
## X4s 0.673887809 0.82322184 0.30514100 0.97227224 0.83047431 0.7116448
## X6s 0.661067538 0.79583412 0.36985807 0.92117641 0.86660717 0.7479033
## Mat.y 0.116252928 -0.09084334 0.16095880 -0.27762874 -0.28073531 -0.2739305
## Inns.y 0.053495244 -0.16569559 0.07842845 -0.31731010 -0.32745299 -0.3360648
## Ov 0.023220048 -0.19637329 0.04007402 -0.32264664 -0.34485415 -0.3512185
## Runs.y 0.004211701 -0.21966520 0.05059525 -0.34297605 -0.36196045 -0.3562644
## Wkts -0.003294748 -0.19378534 0.02511810 -0.30492278 -0.33788216 -0.3444058
## Avg.y -0.290656553 -0.42376374 -0.03100552 -0.47398571 -0.49637737 -0.3854666
## Econ -0.444838506 -0.56779734 -0.14800576 -0.60613558 -0.65986240 -0.5499628
## SR.y -0.260778382 -0.41183463 -0.03671464 -0.48258824 -0.50190039 -0.4084577
## X4w 0.032654389 -0.05849876 0.01379557 -0.11584569 -0.13706656 -0.1304875
## X5w -0.100965418 -0.09209937 -0.06470032 -0.06351398 -0.08733237 -0.0873079
## BF SR.x X100 X50 X4s X6s
## Mat.x 0.73962765 0.77721681 0.18269001 0.52343087 0.67388781 0.66106754
## Inns.x 0.88824952 0.71386963 0.23908702 0.65771693 0.82322184 0.79583412
## NO 0.39764305 0.49580267 0.01461432 0.27935388 0.30514100 0.36985807
## Runs.x 0.99038509 0.55550183 0.38481366 0.89333698 0.97227224 0.92117641
## HS 0.87403252 0.73919729 0.40213426 0.76231036 0.83047431 0.86660717
## Avg.x 0.76427423 0.75344327 0.24624354 0.67575327 0.71164477 0.74790325
## BF 1.00000000 0.54634389 0.34471464 0.87388121 0.96664754 0.87689612
## SR.x 0.54634389 1.00000000 0.14958855 0.38365853 0.50360556 0.56173223
## X100 0.34471464 0.14958855 1.00000000 0.25444125 0.34934874 0.50279799
## X50 0.87388121 0.38365853 0.25444125 1.00000000 0.88171284 0.80529000
## X4s 0.96664754 0.50360556 0.34934874 0.88171284 1.00000000 0.84333872
## X6s 0.87689612 0.56173223 0.50279799 0.80529000 0.84333872 1.00000000
## Mat.y -0.29508014 -0.06161870 0.01114789 -0.36764583 -0.30185951 -0.18098363
## Inns.y -0.34351652 -0.11151407 -0.01809655 -0.35547414 -0.32578655 -0.22031338
## Ov -0.34802532 -0.14418371 -0.04136825 -0.33220588 -0.31803136 -0.24149853
## Runs.y -0.36916779 -0.15287755 -0.03849561 -0.34680719 -0.34048297 -0.25575979
## Wkts -0.32801275 -0.16478013 -0.06058746 -0.30334700 -0.29897875 -0.22714886
## Avg.y -0.49734889 -0.26901412 -0.03998219 -0.45069404 -0.48695079 -0.37842035
## Econ -0.62700594 -0.47267670 -0.08835257 -0.55547298 -0.60827710 -0.49913613
## SR.y -0.50601610 -0.26007484 -0.04078624 -0.47190586 -0.49550685 -0.38376811
## X4w -0.10938878 -0.09963142 -0.02908427 -0.08896170 -0.10737494 -0.11809802
## X5w -0.06652927 -0.11669076 -0.01343077 -0.04108145 -0.05777584 -0.05812737
## Mat.y Inns.y Ov Runs.y Wkts
## Mat.x 0.11625293 0.05349524 0.02322005 0.004211701 -0.003294748
## Inns.x -0.09084334 -0.16569559 -0.19637329 -0.219665197 -0.193785339
## NO 0.16095880 0.07842845 0.04007402 0.050595249 0.025118101
## Runs.x -0.27762874 -0.31731010 -0.32264664 -0.342976054 -0.304922779
## HS -0.28073531 -0.32745299 -0.34485415 -0.361960452 -0.337882156
## Avg.x -0.27393047 -0.33606475 -0.35121845 -0.356264410 -0.344405806
## BF -0.29508014 -0.34351652 -0.34802532 -0.369167786 -0.328012750
## SR.x -0.06161870 -0.11151407 -0.14418371 -0.152877554 -0.164780129
## X100 0.01114789 -0.01809655 -0.04136825 -0.038495608 -0.060587456
## X50 -0.36764583 -0.35547414 -0.33220588 -0.346807194 -0.303347003
## X4s -0.30185951 -0.32578655 -0.31803136 -0.340482967 -0.298978748
## X6s -0.18098363 -0.22031338 -0.24149853 -0.255759790 -0.227148855
## Mat.y 1.00000000 0.93801331 0.89034650 0.885475911 0.837922297
## Inns.y 0.93801331 1.00000000 0.97758803 0.973544310 0.922488677
## Ov 0.89034650 0.97758803 1.00000000 0.986239983 0.952272962
## Runs.y 0.88547591 0.97354431 0.98623998 1.000000000 0.933494451
## Wkts 0.83792230 0.92248868 0.95227296 0.933494451 1.000000000
## Avg.y 0.51536804 0.50481244 0.44528137 0.508150972 0.285297580
## Econ 0.54583370 0.49514387 0.43331336 0.493844926 0.387963108
## SR.y 0.60697419 0.59741377 0.54450311 0.582972691 0.376147420
## X4w 0.21063482 0.23672501 0.26624961 0.249838965 0.381733474
## X5w 0.03569779 0.04678759 0.04498080 0.047004847 0.090780698
## Avg.y Econ SR.y X4w X5w
## Mat.x -0.290656553 -0.44483851 -0.260778382 0.032654389 -0.100965418
## Inns.x -0.423763739 -0.56779734 -0.411834633 -0.058498759 -0.092099369
## NO -0.031005519 -0.14800576 -0.036714635 0.013795574 -0.064700319
## Runs.x -0.473985708 -0.60613558 -0.482588243 -0.115845692 -0.063513975
## HS -0.496377367 -0.65986240 -0.501900392 -0.137066564 -0.087332372
## Avg.x -0.385466611 -0.54996278 -0.408457665 -0.130487521 -0.087307895
## BF -0.497348895 -0.62700594 -0.506016100 -0.109388778 -0.066529266
## SR.x -0.269014116 -0.47267670 -0.260074840 -0.099631416 -0.116690758
## X100 -0.039982192 -0.08835257 -0.040786239 -0.029084272 -0.013430770
## X50 -0.450694038 -0.55547298 -0.471905860 -0.088961696 -0.041081452
## X4s -0.486950793 -0.60827710 -0.495506845 -0.107374943 -0.057775844
## X6s -0.378420345 -0.49913613 -0.383768115 -0.118098022 -0.058127366
## Mat.y 0.515368041 0.54583370 0.606974193 0.210634820 0.035697791
## Inns.y 0.504812444 0.49514387 0.597413771 0.236725010 0.046787586
## Ov 0.445281374 0.43331336 0.544503110 0.266249613 0.044980796
## Runs.y 0.508150972 0.49384493 0.582972691 0.249838965 0.047004847
## Wkts 0.285297580 0.38796311 0.376147420 0.381733474 0.090780698
## Avg.y 1.000000000 0.76763892 0.975624597 -0.008301566 -0.006210496
## Econ 0.767638917 1.00000000 0.743471685 0.068797784 0.042499892
## SR.y 0.975624597 0.74347168 1.000000000 0.017894305 -0.001152835
## X4w -0.008301566 0.06879778 0.017894305 1.000000000 -0.015249968
## X5w -0.006210496 0.04249989 -0.001152835 -0.015249968 1.000000000# Box plot for Batting Average (Avg.x) by Player
ggplot(dataset_imputed, aes(x = PLAYER, y = Avg.x)) +
geom_boxplot(fill = "lightgreen") +
labs(title = "Batting Average by Player",
x = "Player",
y = "Batting Average (Avg.x)") +
theme(axis.text.x = element_text(angle = 90, hjust = 1))
# Data distribution: Histogram for runs, strike rate, sixes
ggplot(dataset_imputed, aes(x = Runs.x)) +
geom_histogram(binwidth = 50, fill = "blue", color = "black") +
labs(title = "Distribution of Runs", x = "Runs", y = "Count")
# Calculate the correlation matrix
correlation_matrix <- cor(dataset_imputed[, c('Runs.x', 'SR.x', 'X4s', 'X6s')])
# Print the correlation matrix
print(correlation_matrix)## Runs.x SR.x X4s X6s
## Runs.x 1.0000000 0.5555018 0.9722722 0.9211764
## SR.x 0.5555018 1.0000000 0.5036056 0.5617322
## X4s 0.9722722 0.5036056 1.0000000 0.8433387
## X6s 0.9211764 0.5617322 0.8433387 1.0000000Selecting Numeric Data For Clustering
# Select numeric columns for clustering
numeric_data <- dataset_imputed %>%
select(where(is.numeric)) # Automatically selects all numeric columns
# View the numeric data
head(numeric_data)## Mat.x Inns.x NO Runs.x HS Avg.x BF SR.x X100 X50 X4s X6s Mat.y Inns.y Ov
## 1 10 9 1 134 46 16.75 100 134.00 0 0 6 8 0 0 0
## 2 12 11 2 480 90 53.33 275 174.54 0 6 39 30 0 0 0
## 3 3 3 2 63 46 63.00 33 190.90 0 0 3 5 0 0 0
## 4 15 14 1 370 65 28.46 313 118.21 0 1 39 5 0 0 0
## 5 6 6 0 148 45 24.66 118 125.42 0 0 13 6 0 0 0
## 6 16 16 2 602 100 43.00 402 149.75 1 3 53 34 0 0 0
## Runs.y Wkts BBI Avg.y Econ SR.y X4w X5w y
## 1 0 0 0 0 0 0 0 0 0
## 2 0 0 0 0 0 0 0 0 0
## 3 0 0 0 0 0 0 0 0 0
## 4 0 0 0 0 0 0 0 0 0
## 5 0 0 0 0 0 0 0 0 0
## 6 0 0 0 0 0 0 0 0 0# Check for NA values
na_counts <- colSums(is.na(numeric_data))
print(na_counts)## Mat.x Inns.x NO Runs.x HS Avg.x BF SR.x X100 X50 X4s
## 0 0 0 0 0 0 0 0 0 0 0
## X6s Mat.y Inns.y Ov Runs.y Wkts BBI Avg.y Econ SR.y X4w
## 0 0 0 0 0 0 0 0 0 0 0
## X5w y
## 0 0# Optionally, remove rows with NA values
clean_data <- numeric_data[complete.cases(numeric_data), ]
clean_data## Mat.x Inns.x NO Runs.x HS Avg.x BF SR.x X100 X50 X4s X6s Mat.y
## 1 10 9 1 134 46 16.75000 100 134.00 0 0 6 8 0
## 2 12 11 2 480 90 53.33000 275 174.54 0 6 39 30 0
## 3 3 3 2 63 46 63.00000 33 190.90 0 0 3 5 0
## 4 15 14 1 370 65 28.46000 313 118.21 0 1 39 5 0
## 5 6 6 0 148 45 24.66000 118 125.42 0 0 13 6 0
## 6 16 16 2 602 100 43.00000 402 149.75 1 3 53 34 0
## 7 16 14 3 316 88 28.72000 171 184.79 0 1 17 31 16
## 8 14 8 2 32 14 5.33000 38 84.21 0 0 2 1 14
## 9 9 8 2 80 19 13.33000 69 115.94 0 0 3 4 9
## 10 9 6 2 96 37 24.00000 58 165.51 0 0 5 8 9
## 11 13 13 1 196 45 16.33000 161 121.73 0 0 13 6 13
## 12 12 4 2 13 7 6.50000 16 81.25 0 0 1 0 12
## 13 6 6 0 127 43 21.16000 88 144.31 0 0 16 6 0
## 14 4 4 1 75 43 25.00000 48 156.25 0 0 1 8 4
## 15 11 11 2 368 104 40.88000 252 146.03 1 3 30 27 0
## 16 16 16 1 491 74 32.73000 377 130.23 0 3 56 18 0
## 17 4 4 3 46 27 46.00000 26 176.92 0 0 3 2 4
## 18 5 4 2 17 11 8.50000 19 89.47 0 0 1 1 5
## 19 9 8 3 131 40 26.20000 84 155.95 0 0 4 10 9
## 20 5 5 0 63 33 12.60000 41 153.65 0 0 7 4 0
## 21 3 3 0 17 15 5.66000 22 77.27 0 0 0 1 3
## 22 7 7 0 115 44 16.42000 99 116.16 0 0 11 5 7
## 23 4 3 1 26 13 13.00000 33 78.78 0 0 0 1 4
## 24 3 3 1 74 26 37.00000 64 115.62 0 0 3 2 0
## 25 12 4 1 50 39 16.66000 29 172.41 0 0 1 4 12
## 26 9 8 4 87 32 21.75000 81 107.40 0 0 2 3 9
## 27 16 16 6 498 52 49.80000 337 147.77 0 2 49 16 0
## 28 16 10 6 141 68 35.25000 91 154.94 0 1 8 10 16
## 29 13 13 0 382 65 29.38000 276 138.40 0 2 32 24 0
## 30 6 6 1 162 67 32.40000 129 125.58 0 1 17 6 0
## 31 6 5 0 85 55 17.00000 88 96.59 0 1 8 1 0
## 32 12 12 0 169 47 14.08000 120 140.83 0 0 14 9 12
## 33 13 3 0 29 19 9.66000 36 80.55 0 0 3 1 13
## 34 13 13 4 260 50 28.88000 195 133.33 0 1 20 11 13
## 35 5 2 1 60 36 60.00000 33 181.81 0 0 1 6 5
## 36 4 4 1 57 32 19.00000 47 121.27 0 0 5 1 0
## 37 14 12 0 275 62 22.91000 184 149.45 0 2 22 17 0
## 38 5 5 1 120 91 30.00000 94 127.65 0 1 9 7 0
## 39 15 7 3 49 26 12.25000 38 128.94 0 0 6 1 15
## 40 10 8 3 15 8 3.00000 21 71.42 0 0 2 0 10
## 41 13 13 3 548 95 54.80000 353 155.24 0 5 52 21 0
## 42 6 4 3 36 23 36.00000 40 90.00 0 0 3 1 6
## 43 17 17 3 735 84 52.50000 516 142.44 0 8 64 28 0
## 44 13 12 0 301 54 25.08000 221 136.19 0 2 23 13 0
## 45 1 1 1 24 24 17.40893 22 109.09 0 0 1 2 0
## 46 9 8 1 133 50 19.00000 100 133.00 0 1 10 7 0
## 47 15 13 4 126 33 14.00000 64 196.87 0 0 9 9 15
## 48 14 13 3 228 41 22.80000 157 145.22 0 0 22 10 14
## 49 14 14 2 659 95 54.91000 416 158.41 0 6 66 32 0
## 50 2 2 1 20 11 20.00000 21 95.23 0 0 1 0 2
## 51 4 4 0 55 45 13.75000 47 117.02 0 0 2 4 0
## 52 14 13 3 252 47 25.20000 186 135.48 0 0 16 11 0
## 53 15 13 2 284 62 25.81000 246 115.44 0 3 22 5 0
## 54 5 4 1 47 35 15.66000 44 106.81 0 0 4 1 5
## 55 7 7 3 99 29 24.75000 76 130.26 0 0 6 4 7
## 56 11 11 1 120 30 12.00000 94 127.65 0 0 9 5 0
## 57 14 6 4 21 7 10.50000 24 87.50 0 0 2 0 14
## 58 6 2 2 16 12 17.40893 11 145.45 0 0 2 0 6
## 59 5 4 0 77 65 19.25000 46 167.39 0 1 4 6 5
## 60 2 2 0 18 14 9.00000 12 150.00 0 0 3 0 2
## 61 11 4 2 25 14 12.50000 22 113.63 0 0 2 1 11
## 62 16 15 9 455 79 75.83000 302 150.66 0 3 24 30 0
## 63 15 15 2 304 59 23.38000 232 131.03 0 1 26 14 15
## 64 6 6 1 153 53 30.60000 109 140.36 0 1 20 4 0
## 65 15 7 3 27 12 6.75000 34 79.41 0 0 1 1 15
## 66 9 9 0 245 65 27.22000 160 153.12 0 2 27 10 0
## 67 8 8 0 201 53 25.12000 162 124.07 0 1 20 8 0
## 68 8 5 2 50 24 16.66000 43 116.27 0 0 5 1 8
## 69 12 12 3 226 80 25.11000 167 135.32 0 1 18 8 0
## 70 17 7 2 59 34 11.80000 31 190.32 0 0 3 6 17
## 71 14 9 1 102 45 12.75000 71 143.66 0 0 7 5 14
## 72 16 10 5 89 27 17.80000 74 120.27 0 0 3 4 16
## 73 4 4 0 29 16 7.25000 31 93.54 0 0 4 0 0
## 74 14 14 1 684 128 52.61000 394 173.60 1 5 68 37 0
## 75 16 16 0 351 54 21.93000 265 132.45 0 1 30 21 0
## 76 14 14 2 286 94 23.83000 215 133.02 0 2 25 12 0
## 77 10 8 0 108 56 13.50000 78 138.46 0 1 8 5 0
## 78 15 15 1 441 92 31.50000 320 137.81 0 3 30 19 0
## 79 7 6 1 51 22 10.20000 41 124.39 0 0 7 1 0
## 80 17 13 2 239 35 21.72000 197 121.31 0 0 26 5 17
## 81 15 15 1 555 117 39.64000 359 154.59 2 2 44 35 15
## 82 13 1 1 15 15 17.40893 5 300.00 0 0 3 0 13
## 83 16 16 3 497 92 38.23000 363 136.91 0 4 59 14 0
## 84 9 4 1 13 7 4.33000 15 86.66 0 0 1 0 9
## 85 6 3 0 52 35 17.33000 40 130.00 0 0 6 1 0
## 86 11 4 1 50 24 16.66000 45 111.11 0 0 5 0 11
## 87 14 14 3 411 93 37.36000 310 132.58 0 4 29 21 0
## 88 13 11 5 203 57 33.83000 139 146.04 0 1 22 5 0
## 89 7 5 0 44 22 8.80000 39 112.82 0 0 2 2 7
## 90 16 16 0 357 75 22.31000 188 189.89 0 2 40 23 16
## 91 15 15 3 445 75 37.08000 336 132.44 0 4 46 12 0
## 92 14 14 0 512 72 36.57000 384 133.33 0 4 61 16 0
## 93 8 4 2 52 36 26.00000 46 113.04 0 0 5 1 8
## 94 5 4 1 23 18 7.66000 28 82.14 0 0 3 0 5
## 95 13 11 7 212 54 53.00000 148 143.24 0 1 11 11 13
## 96 14 14 3 530 92 48.18000 381 139.10 0 4 52 18 0
## 97 7 6 3 65 35 21.66000 38 171.05 0 0 5 4 7
## 98 11 10 2 122 35 15.25000 102 119.60 0 0 17 1 0
## 99 15 13 4 260 45 28.88000 200 130.00 0 0 22 11 15
## 100 8 6 0 65 20 10.83000 73 89.04 0 0 6 2 8
## 101 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 1
## 102 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 10
## 103 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 8
## 104 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 1
## 105 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 3
## 106 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 6
## 107 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 6
## 108 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 4
## 109 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 7
## 110 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 4
## 111 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 1
## 112 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 3
## 113 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 8
## 114 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 6
## 115 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 6
## 116 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 14
## 117 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 4
## 118 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 1
## 119 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 6
## 120 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 1
## 121 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 2
## 122 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 16
## 123 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 3
## 124 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 7
## 125 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 7
## 126 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 1
## 127 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 11
## 128 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 4
## 129 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 9
## 130 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 11
## 131 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 2
## 132 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 7
## 133 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 2
## 134 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 1
## 135 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 7
## 136 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 3
## 137 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 12
## 138 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 6
## 139 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 17
## 140 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 14
## 141 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 14
## 142 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 2
## 143 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 14
## Inns.y Ov Runs.y Wkts BBI Avg.y Econ SR.y X4w X5w y
## 1 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 2 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 3 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 4 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 5 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 6 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 7 15 37.5 355 13 0 27.30000 9.38 17.46000 0 0 0
## 8 14 56.0 448 24 0 18.66000 8.00 14.00000 3 0 0
## 9 8 26.0 218 3 0 72.66000 8.38 52.00000 0 0 0
## 10 7 17.0 168 2 0 84.00000 9.88 51.00000 0 0 0
## 11 12 37.0 303 8 0 37.87000 8.18 27.75000 0 0 0
## 12 12 46.1 354 9 0 39.33000 7.66 30.77000 0 0 0
## 13 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 14 4 10.1 94 5 0 18.80000 9.24 12.20000 0 0 0
## 15 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 16 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 17 4 14.0 143 3 0 47.66000 10.21 28.00000 0 0 0
## 18 5 18.2 190 8 0 23.75000 10.36 13.75000 0 0 0
## 19 7 15.0 129 2 0 64.50000 8.60 45.00000 0 0 0
## 20 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 21 3 8.4 115 3 0 38.33000 13.26 17.33000 0 0 0
## 22 2 3.0 19 1 0 19.00000 6.33 18.00000 0 0 0
## 23 4 11.5 101 4 0 25.25000 8.53 17.75000 0 0 0
## 24 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 25 12 38.1 278 10 0 27.80000 7.28 22.90000 0 0 0
## 26 2 3.0 24 0 0 21.75931 8.00 14.44492 0 0 0
## 27 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 28 16 53.3 533 14 0 38.07000 9.96 22.92000 0 0 0
## 29 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 30 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 31 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 32 10 16.0 132 5 0 26.40000 8.25 19.20000 0 0 0
## 33 12 31.5 270 7 0 38.57000 8.48 27.28000 0 0 0
## 34 13 42.4 381 18 0 21.16000 8.92 14.22000 0 0 0
## 35 5 17.3 167 7 0 23.85000 9.54 15.00000 0 0 0
## 36 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 37 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 38 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 39 15 50.2 486 11 0 44.18000 9.65 27.45000 0 0 0
## 40 10 38.5 325 15 0 21.66000 8.36 15.53000 0 0 0
## 41 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 42 2 3.0 36 0 0 21.75931 12.00 14.44492 0 0 0
## 43 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 44 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 45 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 46 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 47 15 40.0 312 11 0 28.36000 7.80 21.81000 0 0 0
## 48 13 40.1 284 12 0 23.66000 7.07 20.08000 0 0 0
## 49 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 50 2 3.0 27 0 0 21.75931 9.00 14.44492 0 0 0
## 51 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 52 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 53 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 54 1 1.0 10 0 0 21.75931 10.00 14.44492 0 0 0
## 55 6 11.0 120 3 0 40.00000 10.90 22.00000 0 0 0
## 56 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 57 14 44.0 368 15 0 24.53000 8.36 17.60000 1 0 0
## 58 6 21.0 216 2 0 108.00000 10.28 63.00000 0 0 0
## 59 5 13.1 97 3 0 32.33000 7.36 26.33000 0 0 0
## 60 2 5.0 47 1 0 47.00000 9.40 30.00000 0 0 0
## 61 11 41.0 367 11 0 33.36000 8.95 22.36000 0 0 0
## 62 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 63 5 6.1 44 4 0 11.00000 7.13 9.25000 0 0 0
## 64 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 65 15 49.0 412 14 0 29.42000 8.40 21.00000 0 0 0
## 66 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 67 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 68 8 22.0 173 6 0 28.83000 7.86 22.00000 0 0 0
## 69 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 70 17 68.0 458 21 0 21.80000 6.73 19.42000 0 0 0
## 71 14 50.4 410 10 0 41.00000 8.09 30.40000 0 0 0
## 72 14 41.0 303 11 0 27.54000 7.39 22.36000 0 0 0
## 73 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 74 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 75 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 76 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 77 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 78 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 79 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 80 17 57.0 456 14 0 32.57000 8.00 24.42000 0 0 0
## 81 11 28.0 251 6 0 41.83000 8.96 28.00000 0 0 0
## 82 13 46.4 431 16 0 26.93000 9.23 17.50000 0 0 0
## 83 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 84 9 28.0 270 5 0 54.00000 9.64 33.60000 0 0 0
## 85 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 86 10 31.0 236 11 0 21.45000 7.61 16.90000 1 0 0
## 87 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 88 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 89 1 2.0 33 0 0 21.75931 16.50 14.44492 0 0 0
## 90 16 61.0 467 17 0 27.47000 7.65 21.52000 0 0 0
## 91 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 92 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 93 8 29.0 261 5 0 52.20000 9.00 34.80000 0 0 0
## 94 5 10.1 118 6 0 19.66000 11.60 10.16000 0 0 0
## 95 4 5.0 58 1 0 58.00000 11.60 30.00000 0 0 0
## 96 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 97 7 20.0 192 4 0 48.00000 9.60 30.00000 0 0 0
## 98 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 99 1 2.0 14 1 0 14.00000 7.00 12.00000 0 0 0
## 100 2 2.0 23 0 0 21.75931 11.50 14.44492 0 0 0
## 101 1 4.0 47 0 0 21.75931 11.75 14.44492 0 0 0
## 102 10 37.0 264 12 0 22.00000 7.13 18.50000 0 0 0
## 103 8 26.1 223 11 0 20.27000 8.52 14.27000 0 1 0
## 104 1 1.0 11 0 0 21.75931 11.00 14.44492 0 0 0
## 105 3 4.0 42 1 0 42.00000 10.50 24.00000 0 0 0
## 106 6 19.0 204 4 0 51.00000 10.73 28.50000 0 0 0
## 107 6 22.0 229 4 0 57.25000 10.40 33.00000 0 0 0
## 108 4 10.1 114 5 0 22.80000 11.21 12.20000 0 0 0
## 109 7 21.0 211 9 0 23.44000 10.04 14.00000 0 0 0
## 110 4 16.0 130 5 0 26.00000 8.12 19.20000 0 0 0
## 111 1 4.0 31 0 0 21.75931 7.75 14.44492 0 0 0
## 112 3 10.0 95 2 0 47.50000 9.50 30.00000 0 0 0
## 113 8 19.5 188 4 0 47.00000 9.47 29.75000 0 0 0
## 114 6 20.4 188 6 0 31.33000 9.09 20.66000 0 0 0
## 115 6 23.0 135 5 0 27.00000 5.86 27.60000 0 0 0
## 116 14 54.0 372 17 0 21.88000 6.88 19.05000 0 0 0
## 117 3 7.0 89 2 0 44.50000 12.71 21.00000 0 0 0
## 118 1 3.0 34 0 0 21.75931 11.33 14.44492 0 0 0
## 119 5 9.3 89 4 0 22.25000 9.36 14.25000 0 0 0
## 120 1 3.0 38 0 0 21.75931 12.66 14.44492 0 0 0
## 121 2 6.0 75 3 0 25.00000 12.50 12.00000 0 0 0
## 122 16 51.2 418 17 0 24.58000 8.14 18.11000 1 0 0
## 123 3 10.5 107 2 0 53.50000 9.87 32.50000 0 0 0
## 124 7 25.0 225 4 0 56.25000 9.00 37.50000 0 0 0
## 125 7 26.0 156 11 0 14.18000 6.00 14.18000 1 0 0
## 126 1 4.0 49 0 0 21.75931 12.25 14.44492 0 0 0
## 127 11 40.0 332 14 0 23.71000 8.30 17.14000 0 0 0
## 128 4 13.5 144 3 0 48.00000 10.40 27.66000 0 0 0
## 129 9 29.4 322 7 0 46.00000 10.85 25.42000 0 0 0
## 130 11 41.2 289 14 0 20.64000 6.99 17.71000 0 0 0
## 131 2 7.0 53 3 0 17.66000 7.57 14.00000 0 0 0
## 132 7 27.3 230 7 0 32.85000 8.36 23.57000 0 0 0
## 133 2 4.0 49 1 0 49.00000 12.25 24.00000 0 0 0
## 134 1 2.0 19 0 0 21.75931 9.50 14.44492 0 0 0
## 135 7 28.0 260 10 0 26.00000 9.28 16.80000 1 0 0
## 136 3 12.0 82 5 0 16.40000 6.83 14.40000 0 0 0
## 137 12 44.0 333 12 0 27.75000 7.56 22.00000 0 0 0
## 138 6 15.0 161 3 0 53.66000 10.73 30.00000 0 0 0
## 139 17 66.0 547 21 0 26.04000 8.28 18.85000 0 0 0
## 140 14 52.4 466 18 0 25.88000 8.84 17.55000 0 0 0
## 141 14 53.1 418 20 0 20.90000 7.86 15.95000 0 0 0
## 142 2 3.5 65 2 0 32.50000 16.95 11.50000 0 0 0
## 143 14 50.0 363 12 0 30.25000 7.26 25.00000 0 0 0sum(is.na(clean_data))## [1] 0# First, select only the numeric columns, since k-means only works with numeric data
clean_data <- dataset_imputed %>%
select(where(is.numeric))
clean_data## Mat.x Inns.x NO Runs.x HS Avg.x BF SR.x X100 X50 X4s X6s Mat.y
## 1 10 9 1 134 46 16.75000 100 134.00 0 0 6 8 0
## 2 12 11 2 480 90 53.33000 275 174.54 0 6 39 30 0
## 3 3 3 2 63 46 63.00000 33 190.90 0 0 3 5 0
## 4 15 14 1 370 65 28.46000 313 118.21 0 1 39 5 0
## 5 6 6 0 148 45 24.66000 118 125.42 0 0 13 6 0
## 6 16 16 2 602 100 43.00000 402 149.75 1 3 53 34 0
## 7 16 14 3 316 88 28.72000 171 184.79 0 1 17 31 16
## 8 14 8 2 32 14 5.33000 38 84.21 0 0 2 1 14
## 9 9 8 2 80 19 13.33000 69 115.94 0 0 3 4 9
## 10 9 6 2 96 37 24.00000 58 165.51 0 0 5 8 9
## 11 13 13 1 196 45 16.33000 161 121.73 0 0 13 6 13
## 12 12 4 2 13 7 6.50000 16 81.25 0 0 1 0 12
## 13 6 6 0 127 43 21.16000 88 144.31 0 0 16 6 0
## 14 4 4 1 75 43 25.00000 48 156.25 0 0 1 8 4
## 15 11 11 2 368 104 40.88000 252 146.03 1 3 30 27 0
## 16 16 16 1 491 74 32.73000 377 130.23 0 3 56 18 0
## 17 4 4 3 46 27 46.00000 26 176.92 0 0 3 2 4
## 18 5 4 2 17 11 8.50000 19 89.47 0 0 1 1 5
## 19 9 8 3 131 40 26.20000 84 155.95 0 0 4 10 9
## 20 5 5 0 63 33 12.60000 41 153.65 0 0 7 4 0
## 21 3 3 0 17 15 5.66000 22 77.27 0 0 0 1 3
## 22 7 7 0 115 44 16.42000 99 116.16 0 0 11 5 7
## 23 4 3 1 26 13 13.00000 33 78.78 0 0 0 1 4
## 24 3 3 1 74 26 37.00000 64 115.62 0 0 3 2 0
## 25 12 4 1 50 39 16.66000 29 172.41 0 0 1 4 12
## 26 9 8 4 87 32 21.75000 81 107.40 0 0 2 3 9
## 27 16 16 6 498 52 49.80000 337 147.77 0 2 49 16 0
## 28 16 10 6 141 68 35.25000 91 154.94 0 1 8 10 16
## 29 13 13 0 382 65 29.38000 276 138.40 0 2 32 24 0
## 30 6 6 1 162 67 32.40000 129 125.58 0 1 17 6 0
## 31 6 5 0 85 55 17.00000 88 96.59 0 1 8 1 0
## 32 12 12 0 169 47 14.08000 120 140.83 0 0 14 9 12
## 33 13 3 0 29 19 9.66000 36 80.55 0 0 3 1 13
## 34 13 13 4 260 50 28.88000 195 133.33 0 1 20 11 13
## 35 5 2 1 60 36 60.00000 33 181.81 0 0 1 6 5
## 36 4 4 1 57 32 19.00000 47 121.27 0 0 5 1 0
## 37 14 12 0 275 62 22.91000 184 149.45 0 2 22 17 0
## 38 5 5 1 120 91 30.00000 94 127.65 0 1 9 7 0
## 39 15 7 3 49 26 12.25000 38 128.94 0 0 6 1 15
## 40 10 8 3 15 8 3.00000 21 71.42 0 0 2 0 10
## 41 13 13 3 548 95 54.80000 353 155.24 0 5 52 21 0
## 42 6 4 3 36 23 36.00000 40 90.00 0 0 3 1 6
## 43 17 17 3 735 84 52.50000 516 142.44 0 8 64 28 0
## 44 13 12 0 301 54 25.08000 221 136.19 0 2 23 13 0
## 45 1 1 1 24 24 17.40893 22 109.09 0 0 1 2 0
## 46 9 8 1 133 50 19.00000 100 133.00 0 1 10 7 0
## 47 15 13 4 126 33 14.00000 64 196.87 0 0 9 9 15
## 48 14 13 3 228 41 22.80000 157 145.22 0 0 22 10 14
## 49 14 14 2 659 95 54.91000 416 158.41 0 6 66 32 0
## 50 2 2 1 20 11 20.00000 21 95.23 0 0 1 0 2
## 51 4 4 0 55 45 13.75000 47 117.02 0 0 2 4 0
## 52 14 13 3 252 47 25.20000 186 135.48 0 0 16 11 0
## 53 15 13 2 284 62 25.81000 246 115.44 0 3 22 5 0
## 54 5 4 1 47 35 15.66000 44 106.81 0 0 4 1 5
## 55 7 7 3 99 29 24.75000 76 130.26 0 0 6 4 7
## 56 11 11 1 120 30 12.00000 94 127.65 0 0 9 5 0
## 57 14 6 4 21 7 10.50000 24 87.50 0 0 2 0 14
## 58 6 2 2 16 12 17.40893 11 145.45 0 0 2 0 6
## 59 5 4 0 77 65 19.25000 46 167.39 0 1 4 6 5
## 60 2 2 0 18 14 9.00000 12 150.00 0 0 3 0 2
## 61 11 4 2 25 14 12.50000 22 113.63 0 0 2 1 11
## 62 16 15 9 455 79 75.83000 302 150.66 0 3 24 30 0
## 63 15 15 2 304 59 23.38000 232 131.03 0 1 26 14 15
## 64 6 6 1 153 53 30.60000 109 140.36 0 1 20 4 0
## 65 15 7 3 27 12 6.75000 34 79.41 0 0 1 1 15
## 66 9 9 0 245 65 27.22000 160 153.12 0 2 27 10 0
## 67 8 8 0 201 53 25.12000 162 124.07 0 1 20 8 0
## 68 8 5 2 50 24 16.66000 43 116.27 0 0 5 1 8
## 69 12 12 3 226 80 25.11000 167 135.32 0 1 18 8 0
## 70 17 7 2 59 34 11.80000 31 190.32 0 0 3 6 17
## 71 14 9 1 102 45 12.75000 71 143.66 0 0 7 5 14
## 72 16 10 5 89 27 17.80000 74 120.27 0 0 3 4 16
## 73 4 4 0 29 16 7.25000 31 93.54 0 0 4 0 0
## 74 14 14 1 684 128 52.61000 394 173.60 1 5 68 37 0
## 75 16 16 0 351 54 21.93000 265 132.45 0 1 30 21 0
## 76 14 14 2 286 94 23.83000 215 133.02 0 2 25 12 0
## 77 10 8 0 108 56 13.50000 78 138.46 0 1 8 5 0
## 78 15 15 1 441 92 31.50000 320 137.81 0 3 30 19 0
## 79 7 6 1 51 22 10.20000 41 124.39 0 0 7 1 0
## 80 17 13 2 239 35 21.72000 197 121.31 0 0 26 5 17
## 81 15 15 1 555 117 39.64000 359 154.59 2 2 44 35 15
## 82 13 1 1 15 15 17.40893 5 300.00 0 0 3 0 13
## 83 16 16 3 497 92 38.23000 363 136.91 0 4 59 14 0
## 84 9 4 1 13 7 4.33000 15 86.66 0 0 1 0 9
## 85 6 3 0 52 35 17.33000 40 130.00 0 0 6 1 0
## 86 11 4 1 50 24 16.66000 45 111.11 0 0 5 0 11
## 87 14 14 3 411 93 37.36000 310 132.58 0 4 29 21 0
## 88 13 11 5 203 57 33.83000 139 146.04 0 1 22 5 0
## 89 7 5 0 44 22 8.80000 39 112.82 0 0 2 2 7
## 90 16 16 0 357 75 22.31000 188 189.89 0 2 40 23 16
## 91 15 15 3 445 75 37.08000 336 132.44 0 4 46 12 0
## 92 14 14 0 512 72 36.57000 384 133.33 0 4 61 16 0
## 93 8 4 2 52 36 26.00000 46 113.04 0 0 5 1 8
## 94 5 4 1 23 18 7.66000 28 82.14 0 0 3 0 5
## 95 13 11 7 212 54 53.00000 148 143.24 0 1 11 11 13
## 96 14 14 3 530 92 48.18000 381 139.10 0 4 52 18 0
## 97 7 6 3 65 35 21.66000 38 171.05 0 0 5 4 7
## 98 11 10 2 122 35 15.25000 102 119.60 0 0 17 1 0
## 99 15 13 4 260 45 28.88000 200 130.00 0 0 22 11 15
## 100 8 6 0 65 20 10.83000 73 89.04 0 0 6 2 8
## 101 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 1
## 102 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 10
## 103 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 8
## 104 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 1
## 105 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 3
## 106 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 6
## 107 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 6
## 108 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 4
## 109 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 7
## 110 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 4
## 111 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 1
## 112 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 3
## 113 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 8
## 114 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 6
## 115 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 6
## 116 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 14
## 117 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 4
## 118 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 1
## 119 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 6
## 120 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 1
## 121 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 2
## 122 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 16
## 123 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 3
## 124 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 7
## 125 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 7
## 126 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 1
## 127 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 11
## 128 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 4
## 129 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 9
## 130 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 11
## 131 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 2
## 132 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 7
## 133 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 2
## 134 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 1
## 135 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 7
## 136 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 3
## 137 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 12
## 138 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 6
## 139 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 17
## 140 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 14
## 141 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 14
## 142 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 2
## 143 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 14
## Inns.y Ov Runs.y Wkts BBI Avg.y Econ SR.y X4w X5w y
## 1 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 2 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 3 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 4 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 5 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 6 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 7 15 37.5 355 13 0 27.30000 9.38 17.46000 0 0 0
## 8 14 56.0 448 24 0 18.66000 8.00 14.00000 3 0 0
## 9 8 26.0 218 3 0 72.66000 8.38 52.00000 0 0 0
## 10 7 17.0 168 2 0 84.00000 9.88 51.00000 0 0 0
## 11 12 37.0 303 8 0 37.87000 8.18 27.75000 0 0 0
## 12 12 46.1 354 9 0 39.33000 7.66 30.77000 0 0 0
## 13 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 14 4 10.1 94 5 0 18.80000 9.24 12.20000 0 0 0
## 15 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 16 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 17 4 14.0 143 3 0 47.66000 10.21 28.00000 0 0 0
## 18 5 18.2 190 8 0 23.75000 10.36 13.75000 0 0 0
## 19 7 15.0 129 2 0 64.50000 8.60 45.00000 0 0 0
## 20 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 21 3 8.4 115 3 0 38.33000 13.26 17.33000 0 0 0
## 22 2 3.0 19 1 0 19.00000 6.33 18.00000 0 0 0
## 23 4 11.5 101 4 0 25.25000 8.53 17.75000 0 0 0
## 24 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 25 12 38.1 278 10 0 27.80000 7.28 22.90000 0 0 0
## 26 2 3.0 24 0 0 21.75931 8.00 14.44492 0 0 0
## 27 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 28 16 53.3 533 14 0 38.07000 9.96 22.92000 0 0 0
## 29 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 30 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 31 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 32 10 16.0 132 5 0 26.40000 8.25 19.20000 0 0 0
## 33 12 31.5 270 7 0 38.57000 8.48 27.28000 0 0 0
## 34 13 42.4 381 18 0 21.16000 8.92 14.22000 0 0 0
## 35 5 17.3 167 7 0 23.85000 9.54 15.00000 0 0 0
## 36 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 37 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 38 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 39 15 50.2 486 11 0 44.18000 9.65 27.45000 0 0 0
## 40 10 38.5 325 15 0 21.66000 8.36 15.53000 0 0 0
## 41 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 42 2 3.0 36 0 0 21.75931 12.00 14.44492 0 0 0
## 43 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 44 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 45 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 46 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 47 15 40.0 312 11 0 28.36000 7.80 21.81000 0 0 0
## 48 13 40.1 284 12 0 23.66000 7.07 20.08000 0 0 0
## 49 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 50 2 3.0 27 0 0 21.75931 9.00 14.44492 0 0 0
## 51 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 52 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 53 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 54 1 1.0 10 0 0 21.75931 10.00 14.44492 0 0 0
## 55 6 11.0 120 3 0 40.00000 10.90 22.00000 0 0 0
## 56 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 57 14 44.0 368 15 0 24.53000 8.36 17.60000 1 0 0
## 58 6 21.0 216 2 0 108.00000 10.28 63.00000 0 0 0
## 59 5 13.1 97 3 0 32.33000 7.36 26.33000 0 0 0
## 60 2 5.0 47 1 0 47.00000 9.40 30.00000 0 0 0
## 61 11 41.0 367 11 0 33.36000 8.95 22.36000 0 0 0
## 62 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 63 5 6.1 44 4 0 11.00000 7.13 9.25000 0 0 0
## 64 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 65 15 49.0 412 14 0 29.42000 8.40 21.00000 0 0 0
## 66 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 67 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 68 8 22.0 173 6 0 28.83000 7.86 22.00000 0 0 0
## 69 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 70 17 68.0 458 21 0 21.80000 6.73 19.42000 0 0 0
## 71 14 50.4 410 10 0 41.00000 8.09 30.40000 0 0 0
## 72 14 41.0 303 11 0 27.54000 7.39 22.36000 0 0 0
## 73 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 74 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 75 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 76 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 77 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 78 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 79 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 80 17 57.0 456 14 0 32.57000 8.00 24.42000 0 0 0
## 81 11 28.0 251 6 0 41.83000 8.96 28.00000 0 0 0
## 82 13 46.4 431 16 0 26.93000 9.23 17.50000 0 0 0
## 83 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 84 9 28.0 270 5 0 54.00000 9.64 33.60000 0 0 0
## 85 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 86 10 31.0 236 11 0 21.45000 7.61 16.90000 1 0 0
## 87 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 88 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 89 1 2.0 33 0 0 21.75931 16.50 14.44492 0 0 0
## 90 16 61.0 467 17 0 27.47000 7.65 21.52000 0 0 0
## 91 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 92 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 93 8 29.0 261 5 0 52.20000 9.00 34.80000 0 0 0
## 94 5 10.1 118 6 0 19.66000 11.60 10.16000 0 0 0
## 95 4 5.0 58 1 0 58.00000 11.60 30.00000 0 0 0
## 96 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 97 7 20.0 192 4 0 48.00000 9.60 30.00000 0 0 0
## 98 0 0.0 0 0 0 0.00000 0.00 0.00000 0 0 0
## 99 1 2.0 14 1 0 14.00000 7.00 12.00000 0 0 0
## 100 2 2.0 23 0 0 21.75931 11.50 14.44492 0 0 0
## 101 1 4.0 47 0 0 21.75931 11.75 14.44492 0 0 0
## 102 10 37.0 264 12 0 22.00000 7.13 18.50000 0 0 0
## 103 8 26.1 223 11 0 20.27000 8.52 14.27000 0 1 0
## 104 1 1.0 11 0 0 21.75931 11.00 14.44492 0 0 0
## 105 3 4.0 42 1 0 42.00000 10.50 24.00000 0 0 0
## 106 6 19.0 204 4 0 51.00000 10.73 28.50000 0 0 0
## 107 6 22.0 229 4 0 57.25000 10.40 33.00000 0 0 0
## 108 4 10.1 114 5 0 22.80000 11.21 12.20000 0 0 0
## 109 7 21.0 211 9 0 23.44000 10.04 14.00000 0 0 0
## 110 4 16.0 130 5 0 26.00000 8.12 19.20000 0 0 0
## 111 1 4.0 31 0 0 21.75931 7.75 14.44492 0 0 0
## 112 3 10.0 95 2 0 47.50000 9.50 30.00000 0 0 0
## 113 8 19.5 188 4 0 47.00000 9.47 29.75000 0 0 0
## 114 6 20.4 188 6 0 31.33000 9.09 20.66000 0 0 0
## 115 6 23.0 135 5 0 27.00000 5.86 27.60000 0 0 0
## 116 14 54.0 372 17 0 21.88000 6.88 19.05000 0 0 0
## 117 3 7.0 89 2 0 44.50000 12.71 21.00000 0 0 0
## 118 1 3.0 34 0 0 21.75931 11.33 14.44492 0 0 0
## 119 5 9.3 89 4 0 22.25000 9.36 14.25000 0 0 0
## 120 1 3.0 38 0 0 21.75931 12.66 14.44492 0 0 0
## 121 2 6.0 75 3 0 25.00000 12.50 12.00000 0 0 0
## 122 16 51.2 418 17 0 24.58000 8.14 18.11000 1 0 0
## 123 3 10.5 107 2 0 53.50000 9.87 32.50000 0 0 0
## 124 7 25.0 225 4 0 56.25000 9.00 37.50000 0 0 0
## 125 7 26.0 156 11 0 14.18000 6.00 14.18000 1 0 0
## 126 1 4.0 49 0 0 21.75931 12.25 14.44492 0 0 0
## 127 11 40.0 332 14 0 23.71000 8.30 17.14000 0 0 0
## 128 4 13.5 144 3 0 48.00000 10.40 27.66000 0 0 0
## 129 9 29.4 322 7 0 46.00000 10.85 25.42000 0 0 0
## 130 11 41.2 289 14 0 20.64000 6.99 17.71000 0 0 0
## 131 2 7.0 53 3 0 17.66000 7.57 14.00000 0 0 0
## 132 7 27.3 230 7 0 32.85000 8.36 23.57000 0 0 0
## 133 2 4.0 49 1 0 49.00000 12.25 24.00000 0 0 0
## 134 1 2.0 19 0 0 21.75931 9.50 14.44492 0 0 0
## 135 7 28.0 260 10 0 26.00000 9.28 16.80000 1 0 0
## 136 3 12.0 82 5 0 16.40000 6.83 14.40000 0 0 0
## 137 12 44.0 333 12 0 27.75000 7.56 22.00000 0 0 0
## 138 6 15.0 161 3 0 53.66000 10.73 30.00000 0 0 0
## 139 17 66.0 547 21 0 26.04000 8.28 18.85000 0 0 0
## 140 14 52.4 466 18 0 25.88000 8.84 17.55000 0 0 0
## 141 14 53.1 418 20 0 20.90000 7.86 15.95000 0 0 0
## 142 2 3.5 65 2 0 32.50000 16.95 11.50000 0 0 0
## 143 14 50.0 363 12 0 30.25000 7.26 25.00000 0 0 0Check Constant Columns
# Check for constant columns
# A constant column has zero variance, meaning all values are the same
constant_columns <- sapply(clean_data, function(x) var(x) == 0)
constant_columns## Mat.x Inns.x NO Runs.x HS Avg.x BF SR.x X100 X50 X4s
## FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
## X6s Mat.y Inns.y Ov Runs.y Wkts BBI Avg.y Econ SR.y X4w
## FALSE FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
## X5w y
## FALSE TRUE# Print the names of constant columns
cat("Constant columns detected:\n")## Constant columns detected:print(names(clean_data)[constant_columns])## [1] "BBI" "y"# Extract the PLAYER column for later use
player_names <- clean_data$PLAYER
player_names## NULL# Select only the numeric columns (excluding the PLAYER column)
numeric_data <- dataset %>%
select(where(is.numeric))
numeric_data## Mat.x Inns.x NO Runs.x HS Avg.x BF SR.x X100 X50 X4s X6s Mat.y Inns.y
## 1 10 9 1 134 46 16.75 100 134.00 0 0 6 8 0 0
## 2 12 11 2 480 90 53.33 275 174.54 0 6 39 30 0 0
## 3 3 3 2 63 46 63.00 33 190.90 0 0 3 5 0 0
## 4 15 14 1 370 65 28.46 313 118.21 0 1 39 5 0 0
## 5 6 6 0 148 45 24.66 118 125.42 0 0 13 6 0 0
## 6 16 16 2 602 100 43.00 402 149.75 1 3 53 34 0 0
## 7 16 14 3 316 88 28.72 171 184.79 0 1 17 31 16 15
## 8 14 8 2 32 14 5.33 38 84.21 0 0 2 1 14 14
## 9 9 8 2 80 19 13.33 69 115.94 0 0 3 4 9 8
## 10 9 6 2 96 37 24.00 58 165.51 0 0 5 8 9 7
## 11 13 13 1 196 45 16.33 161 121.73 0 0 13 6 13 12
## 12 12 4 2 13 7 6.50 16 81.25 0 0 1 0 12 12
## 13 6 6 0 127 43 21.16 88 144.31 0 0 16 6 0 0
## 14 4 4 1 75 43 25.00 48 156.25 0 0 1 8 4 4
## 15 11 11 2 368 104 40.88 252 146.03 1 3 30 27 0 0
## 16 16 16 1 491 74 32.73 377 130.23 0 3 56 18 0 0
## 17 4 4 3 46 27 46.00 26 176.92 0 0 3 2 4 4
## 18 5 4 2 17 11 8.50 19 89.47 0 0 1 1 5 5
## 19 9 8 3 131 40 26.20 84 155.95 0 0 4 10 9 7
## 20 5 5 0 63 33 12.60 41 153.65 0 0 7 4 0 0
## 21 3 3 0 17 15 5.66 22 77.27 0 0 0 1 3 3
## 22 7 7 0 115 44 16.42 99 116.16 0 0 11 5 7 2
## 23 4 3 1 26 13 13.00 33 78.78 0 0 0 1 4 4
## 24 3 3 1 74 26 37.00 64 115.62 0 0 3 2 0 0
## 25 12 4 1 50 39 16.66 29 172.41 0 0 1 4 12 12
## 26 9 8 4 87 32 21.75 81 107.40 0 0 2 3 9 2
## 27 16 16 6 498 52 49.80 337 147.77 0 2 49 16 0 0
## 28 16 10 6 141 68 35.25 91 154.94 0 1 8 10 16 16
## 29 13 13 0 382 65 29.38 276 138.40 0 2 32 24 0 0
## 30 6 6 1 162 67 32.40 129 125.58 0 1 17 6 0 0
## 31 6 5 0 85 55 17.00 88 96.59 0 1 8 1 0 0
## 32 12 12 0 169 47 14.08 120 140.83 0 0 14 9 12 10
## 33 13 3 0 29 19 9.66 36 80.55 0 0 3 1 13 12
## 34 13 13 4 260 50 28.88 195 133.33 0 1 20 11 13 13
## 35 5 2 1 60 36 60.00 33 181.81 0 0 1 6 5 5
## 36 4 4 1 57 32 19.00 47 121.27 0 0 5 1 0 0
## 37 14 12 0 275 62 22.91 184 149.45 0 2 22 17 0 0
## 38 5 5 1 120 91 30.00 94 127.65 0 1 9 7 0 0
## 39 15 7 3 49 26 12.25 38 128.94 0 0 6 1 15 15
## 40 10 8 3 15 8 3.00 21 71.42 0 0 2 0 10 10
## 41 13 13 3 548 95 54.80 353 155.24 0 5 52 21 0 0
## 42 6 4 3 36 23 36.00 40 90.00 0 0 3 1 6 2
## 43 17 17 3 735 84 52.50 516 142.44 0 8 64 28 0 0
## 44 13 12 0 301 54 25.08 221 136.19 0 2 23 13 0 0
## 45 1 1 1 24 24 NA 22 109.09 0 0 1 2 0 0
## 46 9 8 1 133 50 19.00 100 133.00 0 1 10 7 0 0
## 47 15 13 4 126 33 14.00 64 196.87 0 0 9 9 15 15
## 48 14 13 3 228 41 22.80 157 145.22 0 0 22 10 14 13
## 49 14 14 2 659 95 54.91 416 158.41 0 6 66 32 0 0
## 50 2 2 1 20 11 20.00 21 95.23 0 0 1 0 2 2
## 51 4 4 0 55 45 13.75 47 117.02 0 0 2 4 0 0
## 52 14 13 3 252 47 25.20 186 135.48 0 0 16 11 0 0
## 53 15 13 2 284 62 25.81 246 115.44 0 3 22 5 0 0
## 54 5 4 1 47 35 15.66 44 106.81 0 0 4 1 5 1
## 55 7 7 3 99 29 24.75 76 130.26 0 0 6 4 7 6
## 56 11 11 1 120 30 12.00 94 127.65 0 0 9 5 0 0
## 57 14 6 4 21 7 10.50 24 87.50 0 0 2 0 14 14
## 58 6 2 2 16 12 NA 11 145.45 0 0 2 0 6 6
## 59 5 4 0 77 65 19.25 46 167.39 0 1 4 6 5 5
## 60 2 2 0 18 14 9.00 12 150.00 0 0 3 0 2 2
## 61 11 4 2 25 14 12.50 22 113.63 0 0 2 1 11 11
## 62 16 15 9 455 79 75.83 302 150.66 0 3 24 30 0 0
## 63 15 15 2 304 59 23.38 232 131.03 0 1 26 14 15 5
## 64 6 6 1 153 53 30.60 109 140.36 0 1 20 4 0 0
## 65 15 7 3 27 12 6.75 34 79.41 0 0 1 1 15 15
## 66 9 9 0 245 65 27.22 160 153.12 0 2 27 10 0 0
## 67 8 8 0 201 53 25.12 162 124.07 0 1 20 8 0 0
## 68 8 5 2 50 24 16.66 43 116.27 0 0 5 1 8 8
## 69 12 12 3 226 80 25.11 167 135.32 0 1 18 8 0 0
## 70 17 7 2 59 34 11.80 31 190.32 0 0 3 6 17 17
## 71 14 9 1 102 45 12.75 71 143.66 0 0 7 5 14 14
## 72 16 10 5 89 27 17.80 74 120.27 0 0 3 4 16 14
## 73 4 4 0 29 16 7.25 31 93.54 0 0 4 0 0 0
## 74 14 14 1 684 128 52.61 394 173.60 1 5 68 37 0 0
## 75 16 16 0 351 54 21.93 265 132.45 0 1 30 21 0 0
## 76 14 14 2 286 94 23.83 215 133.02 0 2 25 12 0 0
## 77 10 8 0 108 56 13.50 78 138.46 0 1 8 5 0 0
## 78 15 15 1 441 92 31.50 320 137.81 0 3 30 19 0 0
## 79 7 6 1 51 22 10.20 41 124.39 0 0 7 1 0 0
## 80 17 13 2 239 35 21.72 197 121.31 0 0 26 5 17 17
## 81 15 15 1 555 117 39.64 359 154.59 2 2 44 35 15 11
## 82 13 1 1 15 15 NA 5 300.00 0 0 3 0 13 13
## 83 16 16 3 497 92 38.23 363 136.91 0 4 59 14 0 0
## 84 9 4 1 13 7 4.33 15 86.66 0 0 1 0 9 9
## 85 6 3 0 52 35 17.33 40 130.00 0 0 6 1 0 0
## 86 11 4 1 50 24 16.66 45 111.11 0 0 5 0 11 10
## 87 14 14 3 411 93 37.36 310 132.58 0 4 29 21 0 0
## 88 13 11 5 203 57 33.83 139 146.04 0 1 22 5 0 0
## 89 7 5 0 44 22 8.80 39 112.82 0 0 2 2 7 1
## 90 16 16 0 357 75 22.31 188 189.89 0 2 40 23 16 16
## 91 15 15 3 445 75 37.08 336 132.44 0 4 46 12 0 0
## 92 14 14 0 512 72 36.57 384 133.33 0 4 61 16 0 0
## 93 8 4 2 52 36 26.00 46 113.04 0 0 5 1 8 8
## 94 5 4 1 23 18 7.66 28 82.14 0 0 3 0 5 5
## 95 13 11 7 212 54 53.00 148 143.24 0 1 11 11 13 4
## 96 14 14 3 530 92 48.18 381 139.10 0 4 52 18 0 0
## 97 7 6 3 65 35 21.66 38 171.05 0 0 5 4 7 7
## 98 11 10 2 122 35 15.25 102 119.60 0 0 17 1 0 0
## 99 15 13 4 260 45 28.88 200 130.00 0 0 22 11 15 1
## 100 8 6 0 65 20 10.83 73 89.04 0 0 6 2 8 2
## 101 0 0 0 0 0 0.00 0 0.00 0 0 0 0 1 1
## 102 0 0 0 0 0 0.00 0 0.00 0 0 0 0 10 10
## 103 0 0 0 0 0 0.00 0 0.00 0 0 0 0 8 8
## 104 0 0 0 0 0 0.00 0 0.00 0 0 0 0 1 1
## 105 0 0 0 0 0 0.00 0 0.00 0 0 0 0 3 3
## 106 0 0 0 0 0 0.00 0 0.00 0 0 0 0 6 6
## 107 0 0 0 0 0 0.00 0 0.00 0 0 0 0 6 6
## 108 0 0 0 0 0 0.00 0 0.00 0 0 0 0 4 4
## 109 0 0 0 0 0 0.00 0 0.00 0 0 0 0 7 7
## 110 0 0 0 0 0 0.00 0 0.00 0 0 0 0 4 4
## 111 0 0 0 0 0 0.00 0 0.00 0 0 0 0 1 1
## 112 0 0 0 0 0 0.00 0 0.00 0 0 0 0 3 3
## 113 0 0 0 0 0 0.00 0 0.00 0 0 0 0 8 8
## 114 0 0 0 0 0 0.00 0 0.00 0 0 0 0 6 6
## 115 0 0 0 0 0 0.00 0 0.00 0 0 0 0 6 6
## 116 0 0 0 0 0 0.00 0 0.00 0 0 0 0 14 14
## 117 0 0 0 0 0 0.00 0 0.00 0 0 0 0 4 3
## 118 0 0 0 0 0 0.00 0 0.00 0 0 0 0 1 1
## 119 0 0 0 0 0 0.00 0 0.00 0 0 0 0 6 5
## 120 0 0 0 0 0 0.00 0 0.00 0 0 0 0 1 1
## 121 0 0 0 0 0 0.00 0 0.00 0 0 0 0 2 2
## 122 0 0 0 0 0 0.00 0 0.00 0 0 0 0 16 16
## 123 0 0 0 0 0 0.00 0 0.00 0 0 0 0 3 3
## 124 0 0 0 0 0 0.00 0 0.00 0 0 0 0 7 7
## 125 0 0 0 0 0 0.00 0 0.00 0 0 0 0 7 7
## 126 0 0 0 0 0 0.00 0 0.00 0 0 0 0 1 1
## 127 0 0 0 0 0 0.00 0 0.00 0 0 0 0 11 11
## 128 0 0 0 0 0 0.00 0 0.00 0 0 0 0 4 4
## 129 0 0 0 0 0 0.00 0 0.00 0 0 0 0 9 9
## 130 0 0 0 0 0 0.00 0 0.00 0 0 0 0 11 11
## 131 0 0 0 0 0 0.00 0 0.00 0 0 0 0 2 2
## 132 0 0 0 0 0 0.00 0 0.00 0 0 0 0 7 7
## 133 0 0 0 0 0 0.00 0 0.00 0 0 0 0 2 2
## 134 0 0 0 0 0 0.00 0 0.00 0 0 0 0 1 1
## 135 0 0 0 0 0 0.00 0 0.00 0 0 0 0 7 7
## 136 0 0 0 0 0 0.00 0 0.00 0 0 0 0 3 3
## 137 0 0 0 0 0 0.00 0 0.00 0 0 0 0 12 12
## 138 0 0 0 0 0 0.00 0 0.00 0 0 0 0 6 6
## 139 0 0 0 0 0 0.00 0 0.00 0 0 0 0 17 17
## 140 0 0 0 0 0 0.00 0 0.00 0 0 0 0 14 14
## 141 0 0 0 0 0 0.00 0 0.00 0 0 0 0 14 14
## 142 0 0 0 0 0 0.00 0 0.00 0 0 0 0 2 2
## 143 0 0 0 0 0 0.00 0 0.00 0 0 0 0 14 14
## Ov Runs.y Wkts BBI Avg.y Econ SR.y X4w X5w y
## 1 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 2 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 3 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 4 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 5 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 6 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 7 37.5 355 13 0 27.30 9.38 17.46 0 0 0
## 8 56.0 448 24 0 18.66 8.00 14.00 3 0 0
## 9 26.0 218 3 0 72.66 8.38 52.00 0 0 0
## 10 17.0 168 2 0 84.00 9.88 51.00 0 0 0
## 11 37.0 303 8 0 37.87 8.18 27.75 0 0 0
## 12 46.1 354 9 0 39.33 7.66 30.77 0 0 0
## 13 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 14 10.1 94 5 0 18.80 9.24 12.20 0 0 0
## 15 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 16 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 17 14.0 143 3 0 47.66 10.21 28.00 0 0 0
## 18 18.2 190 8 0 23.75 10.36 13.75 0 0 0
## 19 15.0 129 2 0 64.50 8.60 45.00 0 0 0
## 20 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 21 8.4 115 3 0 38.33 13.26 17.33 0 0 0
## 22 3.0 19 1 0 19.00 6.33 18.00 0 0 0
## 23 11.5 101 4 0 25.25 8.53 17.75 0 0 0
## 24 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 25 38.1 278 10 0 27.80 7.28 22.90 0 0 0
## 26 3.0 24 0 0 NA 8.00 NA 0 0 0
## 27 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 28 53.3 533 14 0 38.07 9.96 22.92 0 0 0
## 29 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 30 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 31 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 32 16.0 132 5 0 26.40 8.25 19.20 0 0 0
## 33 31.5 270 7 0 38.57 8.48 27.28 0 0 0
## 34 42.4 381 18 0 21.16 8.92 14.22 0 0 0
## 35 17.3 167 7 0 23.85 9.54 15.00 0 0 0
## 36 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 37 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 38 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 39 50.2 486 11 0 44.18 9.65 27.45 0 0 0
## 40 38.5 325 15 0 21.66 8.36 15.53 0 0 0
## 41 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 42 3.0 36 0 0 NA 12.00 NA 0 0 0
## 43 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 44 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 45 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 46 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 47 40.0 312 11 0 28.36 7.80 21.81 0 0 0
## 48 40.1 284 12 0 23.66 7.07 20.08 0 0 0
## 49 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 50 3.0 27 0 0 NA 9.00 NA 0 0 0
## 51 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 52 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 53 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 54 1.0 10 0 0 NA 10.00 NA 0 0 0
## 55 11.0 120 3 0 40.00 10.90 22.00 0 0 0
## 56 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 57 44.0 368 15 0 24.53 8.36 17.60 1 0 0
## 58 21.0 216 2 0 108.00 10.28 63.00 0 0 0
## 59 13.1 97 3 0 32.33 7.36 26.33 0 0 0
## 60 5.0 47 1 0 47.00 9.40 30.00 0 0 0
## 61 41.0 367 11 0 33.36 8.95 22.36 0 0 0
## 62 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 63 6.1 44 4 0 11.00 7.13 9.25 0 0 0
## 64 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 65 49.0 412 14 0 29.42 8.40 21.00 0 0 0
## 66 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 67 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 68 22.0 173 6 0 28.83 7.86 22.00 0 0 0
## 69 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 70 68.0 458 21 0 21.80 6.73 19.42 0 0 0
## 71 50.4 410 10 0 41.00 8.09 30.40 0 0 0
## 72 41.0 303 11 0 27.54 7.39 22.36 0 0 0
## 73 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 74 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 75 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 76 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 77 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 78 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 79 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 80 57.0 456 14 0 32.57 8.00 24.42 0 0 0
## 81 28.0 251 6 0 41.83 8.96 28.00 0 0 0
## 82 46.4 431 16 0 26.93 9.23 17.50 0 0 0
## 83 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 84 28.0 270 5 0 54.00 9.64 33.60 0 0 0
## 85 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 86 31.0 236 11 0 21.45 7.61 16.90 1 0 0
## 87 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 88 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 89 2.0 33 0 0 NA 16.50 NA 0 0 0
## 90 61.0 467 17 0 27.47 7.65 21.52 0 0 0
## 91 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 92 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 93 29.0 261 5 0 52.20 9.00 34.80 0 0 0
## 94 10.1 118 6 0 19.66 11.60 10.16 0 0 0
## 95 5.0 58 1 0 58.00 11.60 30.00 0 0 0
## 96 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 97 20.0 192 4 0 48.00 9.60 30.00 0 0 0
## 98 0.0 0 0 0 0.00 0.00 0.00 0 0 0
## 99 2.0 14 1 0 14.00 7.00 12.00 0 0 0
## 100 2.0 23 0 0 NA 11.50 NA 0 0 0
## 101 4.0 47 0 0 NA 11.75 NA 0 0 0
## 102 37.0 264 12 0 22.00 7.13 18.50 0 0 0
## 103 26.1 223 11 0 20.27 8.52 14.27 0 1 0
## 104 1.0 11 0 0 NA 11.00 NA 0 0 0
## 105 4.0 42 1 0 42.00 10.50 24.00 0 0 0
## 106 19.0 204 4 0 51.00 10.73 28.50 0 0 0
## 107 22.0 229 4 0 57.25 10.40 33.00 0 0 0
## 108 10.1 114 5 0 22.80 11.21 12.20 0 0 0
## 109 21.0 211 9 0 23.44 10.04 14.00 0 0 0
## 110 16.0 130 5 0 26.00 8.12 19.20 0 0 0
## 111 4.0 31 0 0 NA 7.75 NA 0 0 0
## 112 10.0 95 2 0 47.50 9.50 30.00 0 0 0
## 113 19.5 188 4 0 47.00 9.47 29.75 0 0 0
## 114 20.4 188 6 0 31.33 9.09 20.66 0 0 0
## 115 23.0 135 5 0 27.00 5.86 27.60 0 0 0
## 116 54.0 372 17 0 21.88 6.88 19.05 0 0 0
## 117 7.0 89 2 0 44.50 12.71 21.00 0 0 0
## 118 3.0 34 0 0 NA 11.33 NA 0 0 0
## 119 9.3 89 4 0 22.25 9.36 14.25 0 0 0
## 120 3.0 38 0 0 NA 12.66 NA 0 0 0
## 121 6.0 75 3 0 25.00 12.50 12.00 0 0 0
## 122 51.2 418 17 0 24.58 8.14 18.11 1 0 0
## 123 10.5 107 2 0 53.50 9.87 32.50 0 0 0
## 124 25.0 225 4 0 56.25 9.00 37.50 0 0 0
## 125 26.0 156 11 0 14.18 6.00 14.18 1 0 0
## 126 4.0 49 0 0 NA 12.25 NA 0 0 0
## 127 40.0 332 14 0 23.71 8.30 17.14 0 0 0
## 128 13.5 144 3 0 48.00 10.40 27.66 0 0 0
## 129 29.4 322 7 0 46.00 10.85 25.42 0 0 0
## 130 41.2 289 14 0 20.64 6.99 17.71 0 0 0
## 131 7.0 53 3 0 17.66 7.57 14.00 0 0 0
## 132 27.3 230 7 0 32.85 8.36 23.57 0 0 0
## 133 4.0 49 1 0 49.00 12.25 24.00 0 0 0
## 134 2.0 19 0 0 NA 9.50 NA 0 0 0
## 135 28.0 260 10 0 26.00 9.28 16.80 1 0 0
## 136 12.0 82 5 0 16.40 6.83 14.40 0 0 0
## 137 44.0 333 12 0 27.75 7.56 22.00 0 0 0
## 138 15.0 161 3 0 53.66 10.73 30.00 0 0 0
## 139 66.0 547 21 0 26.04 8.28 18.85 0 0 0
## 140 52.4 466 18 0 25.88 8.84 17.55 0 0 0
## 141 53.1 418 20 0 20.90 7.86 15.95 0 0 0
## 142 3.5 65 2 0 32.50 16.95 11.50 0 0 0
## 143 50.0 363 12 0 30.25 7.26 25.00 0 0 0# Remove constant columns from numeric data
numeric_data_clean <- clean_data[, !constant_columns]
numeric_data_clean## Mat.x Inns.x NO Runs.x HS Avg.x BF SR.x X100 X50 X4s X6s Mat.y
## 1 10 9 1 134 46 16.75000 100 134.00 0 0 6 8 0
## 2 12 11 2 480 90 53.33000 275 174.54 0 6 39 30 0
## 3 3 3 2 63 46 63.00000 33 190.90 0 0 3 5 0
## 4 15 14 1 370 65 28.46000 313 118.21 0 1 39 5 0
## 5 6 6 0 148 45 24.66000 118 125.42 0 0 13 6 0
## 6 16 16 2 602 100 43.00000 402 149.75 1 3 53 34 0
## 7 16 14 3 316 88 28.72000 171 184.79 0 1 17 31 16
## 8 14 8 2 32 14 5.33000 38 84.21 0 0 2 1 14
## 9 9 8 2 80 19 13.33000 69 115.94 0 0 3 4 9
## 10 9 6 2 96 37 24.00000 58 165.51 0 0 5 8 9
## 11 13 13 1 196 45 16.33000 161 121.73 0 0 13 6 13
## 12 12 4 2 13 7 6.50000 16 81.25 0 0 1 0 12
## 13 6 6 0 127 43 21.16000 88 144.31 0 0 16 6 0
## 14 4 4 1 75 43 25.00000 48 156.25 0 0 1 8 4
## 15 11 11 2 368 104 40.88000 252 146.03 1 3 30 27 0
## 16 16 16 1 491 74 32.73000 377 130.23 0 3 56 18 0
## 17 4 4 3 46 27 46.00000 26 176.92 0 0 3 2 4
## 18 5 4 2 17 11 8.50000 19 89.47 0 0 1 1 5
## 19 9 8 3 131 40 26.20000 84 155.95 0 0 4 10 9
## 20 5 5 0 63 33 12.60000 41 153.65 0 0 7 4 0
## 21 3 3 0 17 15 5.66000 22 77.27 0 0 0 1 3
## 22 7 7 0 115 44 16.42000 99 116.16 0 0 11 5 7
## 23 4 3 1 26 13 13.00000 33 78.78 0 0 0 1 4
## 24 3 3 1 74 26 37.00000 64 115.62 0 0 3 2 0
## 25 12 4 1 50 39 16.66000 29 172.41 0 0 1 4 12
## 26 9 8 4 87 32 21.75000 81 107.40 0 0 2 3 9
## 27 16 16 6 498 52 49.80000 337 147.77 0 2 49 16 0
## 28 16 10 6 141 68 35.25000 91 154.94 0 1 8 10 16
## 29 13 13 0 382 65 29.38000 276 138.40 0 2 32 24 0
## 30 6 6 1 162 67 32.40000 129 125.58 0 1 17 6 0
## 31 6 5 0 85 55 17.00000 88 96.59 0 1 8 1 0
## 32 12 12 0 169 47 14.08000 120 140.83 0 0 14 9 12
## 33 13 3 0 29 19 9.66000 36 80.55 0 0 3 1 13
## 34 13 13 4 260 50 28.88000 195 133.33 0 1 20 11 13
## 35 5 2 1 60 36 60.00000 33 181.81 0 0 1 6 5
## 36 4 4 1 57 32 19.00000 47 121.27 0 0 5 1 0
## 37 14 12 0 275 62 22.91000 184 149.45 0 2 22 17 0
## 38 5 5 1 120 91 30.00000 94 127.65 0 1 9 7 0
## 39 15 7 3 49 26 12.25000 38 128.94 0 0 6 1 15
## 40 10 8 3 15 8 3.00000 21 71.42 0 0 2 0 10
## 41 13 13 3 548 95 54.80000 353 155.24 0 5 52 21 0
## 42 6 4 3 36 23 36.00000 40 90.00 0 0 3 1 6
## 43 17 17 3 735 84 52.50000 516 142.44 0 8 64 28 0
## 44 13 12 0 301 54 25.08000 221 136.19 0 2 23 13 0
## 45 1 1 1 24 24 17.40893 22 109.09 0 0 1 2 0
## 46 9 8 1 133 50 19.00000 100 133.00 0 1 10 7 0
## 47 15 13 4 126 33 14.00000 64 196.87 0 0 9 9 15
## 48 14 13 3 228 41 22.80000 157 145.22 0 0 22 10 14
## 49 14 14 2 659 95 54.91000 416 158.41 0 6 66 32 0
## 50 2 2 1 20 11 20.00000 21 95.23 0 0 1 0 2
## 51 4 4 0 55 45 13.75000 47 117.02 0 0 2 4 0
## 52 14 13 3 252 47 25.20000 186 135.48 0 0 16 11 0
## 53 15 13 2 284 62 25.81000 246 115.44 0 3 22 5 0
## 54 5 4 1 47 35 15.66000 44 106.81 0 0 4 1 5
## 55 7 7 3 99 29 24.75000 76 130.26 0 0 6 4 7
## 56 11 11 1 120 30 12.00000 94 127.65 0 0 9 5 0
## 57 14 6 4 21 7 10.50000 24 87.50 0 0 2 0 14
## 58 6 2 2 16 12 17.40893 11 145.45 0 0 2 0 6
## 59 5 4 0 77 65 19.25000 46 167.39 0 1 4 6 5
## 60 2 2 0 18 14 9.00000 12 150.00 0 0 3 0 2
## 61 11 4 2 25 14 12.50000 22 113.63 0 0 2 1 11
## 62 16 15 9 455 79 75.83000 302 150.66 0 3 24 30 0
## 63 15 15 2 304 59 23.38000 232 131.03 0 1 26 14 15
## 64 6 6 1 153 53 30.60000 109 140.36 0 1 20 4 0
## 65 15 7 3 27 12 6.75000 34 79.41 0 0 1 1 15
## 66 9 9 0 245 65 27.22000 160 153.12 0 2 27 10 0
## 67 8 8 0 201 53 25.12000 162 124.07 0 1 20 8 0
## 68 8 5 2 50 24 16.66000 43 116.27 0 0 5 1 8
## 69 12 12 3 226 80 25.11000 167 135.32 0 1 18 8 0
## 70 17 7 2 59 34 11.80000 31 190.32 0 0 3 6 17
## 71 14 9 1 102 45 12.75000 71 143.66 0 0 7 5 14
## 72 16 10 5 89 27 17.80000 74 120.27 0 0 3 4 16
## 73 4 4 0 29 16 7.25000 31 93.54 0 0 4 0 0
## 74 14 14 1 684 128 52.61000 394 173.60 1 5 68 37 0
## 75 16 16 0 351 54 21.93000 265 132.45 0 1 30 21 0
## 76 14 14 2 286 94 23.83000 215 133.02 0 2 25 12 0
## 77 10 8 0 108 56 13.50000 78 138.46 0 1 8 5 0
## 78 15 15 1 441 92 31.50000 320 137.81 0 3 30 19 0
## 79 7 6 1 51 22 10.20000 41 124.39 0 0 7 1 0
## 80 17 13 2 239 35 21.72000 197 121.31 0 0 26 5 17
## 81 15 15 1 555 117 39.64000 359 154.59 2 2 44 35 15
## 82 13 1 1 15 15 17.40893 5 300.00 0 0 3 0 13
## 83 16 16 3 497 92 38.23000 363 136.91 0 4 59 14 0
## 84 9 4 1 13 7 4.33000 15 86.66 0 0 1 0 9
## 85 6 3 0 52 35 17.33000 40 130.00 0 0 6 1 0
## 86 11 4 1 50 24 16.66000 45 111.11 0 0 5 0 11
## 87 14 14 3 411 93 37.36000 310 132.58 0 4 29 21 0
## 88 13 11 5 203 57 33.83000 139 146.04 0 1 22 5 0
## 89 7 5 0 44 22 8.80000 39 112.82 0 0 2 2 7
## 90 16 16 0 357 75 22.31000 188 189.89 0 2 40 23 16
## 91 15 15 3 445 75 37.08000 336 132.44 0 4 46 12 0
## 92 14 14 0 512 72 36.57000 384 133.33 0 4 61 16 0
## 93 8 4 2 52 36 26.00000 46 113.04 0 0 5 1 8
## 94 5 4 1 23 18 7.66000 28 82.14 0 0 3 0 5
## 95 13 11 7 212 54 53.00000 148 143.24 0 1 11 11 13
## 96 14 14 3 530 92 48.18000 381 139.10 0 4 52 18 0
## 97 7 6 3 65 35 21.66000 38 171.05 0 0 5 4 7
## 98 11 10 2 122 35 15.25000 102 119.60 0 0 17 1 0
## 99 15 13 4 260 45 28.88000 200 130.00 0 0 22 11 15
## 100 8 6 0 65 20 10.83000 73 89.04 0 0 6 2 8
## 101 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 1
## 102 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 10
## 103 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 8
## 104 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 1
## 105 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 3
## 106 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 6
## 107 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 6
## 108 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 4
## 109 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 7
## 110 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 4
## 111 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 1
## 112 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 3
## 113 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 8
## 114 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 6
## 115 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 6
## 116 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 14
## 117 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 4
## 118 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 1
## 119 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 6
## 120 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 1
## 121 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 2
## 122 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 16
## 123 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 3
## 124 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 7
## 125 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 7
## 126 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 1
## 127 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 11
## 128 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 4
## 129 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 9
## 130 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 11
## 131 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 2
## 132 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 7
## 133 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 2
## 134 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 1
## 135 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 7
## 136 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 3
## 137 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 12
## 138 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 6
## 139 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 17
## 140 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 14
## 141 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 14
## 142 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 2
## 143 0 0 0 0 0 0.00000 0 0.00 0 0 0 0 14
## Inns.y Ov Runs.y Wkts Avg.y Econ SR.y X4w X5w
## 1 0 0.0 0 0 0.00000 0.00 0.00000 0 0
## 2 0 0.0 0 0 0.00000 0.00 0.00000 0 0
## 3 0 0.0 0 0 0.00000 0.00 0.00000 0 0
## 4 0 0.0 0 0 0.00000 0.00 0.00000 0 0
## 5 0 0.0 0 0 0.00000 0.00 0.00000 0 0
## 6 0 0.0 0 0 0.00000 0.00 0.00000 0 0
## 7 15 37.5 355 13 27.30000 9.38 17.46000 0 0
## 8 14 56.0 448 24 18.66000 8.00 14.00000 3 0
## 9 8 26.0 218 3 72.66000 8.38 52.00000 0 0
## 10 7 17.0 168 2 84.00000 9.88 51.00000 0 0
## 11 12 37.0 303 8 37.87000 8.18 27.75000 0 0
## 12 12 46.1 354 9 39.33000 7.66 30.77000 0 0
## 13 0 0.0 0 0 0.00000 0.00 0.00000 0 0
## 14 4 10.1 94 5 18.80000 9.24 12.20000 0 0
## 15 0 0.0 0 0 0.00000 0.00 0.00000 0 0
## 16 0 0.0 0 0 0.00000 0.00 0.00000 0 0
## 17 4 14.0 143 3 47.66000 10.21 28.00000 0 0
## 18 5 18.2 190 8 23.75000 10.36 13.75000 0 0
## 19 7 15.0 129 2 64.50000 8.60 45.00000 0 0
## 20 0 0.0 0 0 0.00000 0.00 0.00000 0 0
## 21 3 8.4 115 3 38.33000 13.26 17.33000 0 0
## 22 2 3.0 19 1 19.00000 6.33 18.00000 0 0
## 23 4 11.5 101 4 25.25000 8.53 17.75000 0 0
## 24 0 0.0 0 0 0.00000 0.00 0.00000 0 0
## 25 12 38.1 278 10 27.80000 7.28 22.90000 0 0
## 26 2 3.0 24 0 21.75931 8.00 14.44492 0 0
## 27 0 0.0 0 0 0.00000 0.00 0.00000 0 0
## 28 16 53.3 533 14 38.07000 9.96 22.92000 0 0
## 29 0 0.0 0 0 0.00000 0.00 0.00000 0 0
## 30 0 0.0 0 0 0.00000 0.00 0.00000 0 0
## 31 0 0.0 0 0 0.00000 0.00 0.00000 0 0
## 32 10 16.0 132 5 26.40000 8.25 19.20000 0 0
## 33 12 31.5 270 7 38.57000 8.48 27.28000 0 0
## 34 13 42.4 381 18 21.16000 8.92 14.22000 0 0
## 35 5 17.3 167 7 23.85000 9.54 15.00000 0 0
## 36 0 0.0 0 0 0.00000 0.00 0.00000 0 0
## 37 0 0.0 0 0 0.00000 0.00 0.00000 0 0
## 38 0 0.0 0 0 0.00000 0.00 0.00000 0 0
## 39 15 50.2 486 11 44.18000 9.65 27.45000 0 0
## 40 10 38.5 325 15 21.66000 8.36 15.53000 0 0
## 41 0 0.0 0 0 0.00000 0.00 0.00000 0 0
## 42 2 3.0 36 0 21.75931 12.00 14.44492 0 0
## 43 0 0.0 0 0 0.00000 0.00 0.00000 0 0
## 44 0 0.0 0 0 0.00000 0.00 0.00000 0 0
## 45 0 0.0 0 0 0.00000 0.00 0.00000 0 0
## 46 0 0.0 0 0 0.00000 0.00 0.00000 0 0
## 47 15 40.0 312 11 28.36000 7.80 21.81000 0 0
## 48 13 40.1 284 12 23.66000 7.07 20.08000 0 0
## 49 0 0.0 0 0 0.00000 0.00 0.00000 0 0
## 50 2 3.0 27 0 21.75931 9.00 14.44492 0 0
## 51 0 0.0 0 0 0.00000 0.00 0.00000 0 0
## 52 0 0.0 0 0 0.00000 0.00 0.00000 0 0
## 53 0 0.0 0 0 0.00000 0.00 0.00000 0 0
## 54 1 1.0 10 0 21.75931 10.00 14.44492 0 0
## 55 6 11.0 120 3 40.00000 10.90 22.00000 0 0
## 56 0 0.0 0 0 0.00000 0.00 0.00000 0 0
## 57 14 44.0 368 15 24.53000 8.36 17.60000 1 0
## 58 6 21.0 216 2 108.00000 10.28 63.00000 0 0
## 59 5 13.1 97 3 32.33000 7.36 26.33000 0 0
## 60 2 5.0 47 1 47.00000 9.40 30.00000 0 0
## 61 11 41.0 367 11 33.36000 8.95 22.36000 0 0
## 62 0 0.0 0 0 0.00000 0.00 0.00000 0 0
## 63 5 6.1 44 4 11.00000 7.13 9.25000 0 0
## 64 0 0.0 0 0 0.00000 0.00 0.00000 0 0
## 65 15 49.0 412 14 29.42000 8.40 21.00000 0 0
## 66 0 0.0 0 0 0.00000 0.00 0.00000 0 0
## 67 0 0.0 0 0 0.00000 0.00 0.00000 0 0
## 68 8 22.0 173 6 28.83000 7.86 22.00000 0 0
## 69 0 0.0 0 0 0.00000 0.00 0.00000 0 0
## 70 17 68.0 458 21 21.80000 6.73 19.42000 0 0
## 71 14 50.4 410 10 41.00000 8.09 30.40000 0 0
## 72 14 41.0 303 11 27.54000 7.39 22.36000 0 0
## 73 0 0.0 0 0 0.00000 0.00 0.00000 0 0
## 74 0 0.0 0 0 0.00000 0.00 0.00000 0 0
## 75 0 0.0 0 0 0.00000 0.00 0.00000 0 0
## 76 0 0.0 0 0 0.00000 0.00 0.00000 0 0
## 77 0 0.0 0 0 0.00000 0.00 0.00000 0 0
## 78 0 0.0 0 0 0.00000 0.00 0.00000 0 0
## 79 0 0.0 0 0 0.00000 0.00 0.00000 0 0
## 80 17 57.0 456 14 32.57000 8.00 24.42000 0 0
## 81 11 28.0 251 6 41.83000 8.96 28.00000 0 0
## 82 13 46.4 431 16 26.93000 9.23 17.50000 0 0
## 83 0 0.0 0 0 0.00000 0.00 0.00000 0 0
## 84 9 28.0 270 5 54.00000 9.64 33.60000 0 0
## 85 0 0.0 0 0 0.00000 0.00 0.00000 0 0
## 86 10 31.0 236 11 21.45000 7.61 16.90000 1 0
## 87 0 0.0 0 0 0.00000 0.00 0.00000 0 0
## 88 0 0.0 0 0 0.00000 0.00 0.00000 0 0
## 89 1 2.0 33 0 21.75931 16.50 14.44492 0 0
## 90 16 61.0 467 17 27.47000 7.65 21.52000 0 0
## 91 0 0.0 0 0 0.00000 0.00 0.00000 0 0
## 92 0 0.0 0 0 0.00000 0.00 0.00000 0 0
## 93 8 29.0 261 5 52.20000 9.00 34.80000 0 0
## 94 5 10.1 118 6 19.66000 11.60 10.16000 0 0
## 95 4 5.0 58 1 58.00000 11.60 30.00000 0 0
## 96 0 0.0 0 0 0.00000 0.00 0.00000 0 0
## 97 7 20.0 192 4 48.00000 9.60 30.00000 0 0
## 98 0 0.0 0 0 0.00000 0.00 0.00000 0 0
## 99 1 2.0 14 1 14.00000 7.00 12.00000 0 0
## 100 2 2.0 23 0 21.75931 11.50 14.44492 0 0
## 101 1 4.0 47 0 21.75931 11.75 14.44492 0 0
## 102 10 37.0 264 12 22.00000 7.13 18.50000 0 0
## 103 8 26.1 223 11 20.27000 8.52 14.27000 0 1
## 104 1 1.0 11 0 21.75931 11.00 14.44492 0 0
## 105 3 4.0 42 1 42.00000 10.50 24.00000 0 0
## 106 6 19.0 204 4 51.00000 10.73 28.50000 0 0
## 107 6 22.0 229 4 57.25000 10.40 33.00000 0 0
## 108 4 10.1 114 5 22.80000 11.21 12.20000 0 0
## 109 7 21.0 211 9 23.44000 10.04 14.00000 0 0
## 110 4 16.0 130 5 26.00000 8.12 19.20000 0 0
## 111 1 4.0 31 0 21.75931 7.75 14.44492 0 0
## 112 3 10.0 95 2 47.50000 9.50 30.00000 0 0
## 113 8 19.5 188 4 47.00000 9.47 29.75000 0 0
## 114 6 20.4 188 6 31.33000 9.09 20.66000 0 0
## 115 6 23.0 135 5 27.00000 5.86 27.60000 0 0
## 116 14 54.0 372 17 21.88000 6.88 19.05000 0 0
## 117 3 7.0 89 2 44.50000 12.71 21.00000 0 0
## 118 1 3.0 34 0 21.75931 11.33 14.44492 0 0
## 119 5 9.3 89 4 22.25000 9.36 14.25000 0 0
## 120 1 3.0 38 0 21.75931 12.66 14.44492 0 0
## 121 2 6.0 75 3 25.00000 12.50 12.00000 0 0
## 122 16 51.2 418 17 24.58000 8.14 18.11000 1 0
## 123 3 10.5 107 2 53.50000 9.87 32.50000 0 0
## 124 7 25.0 225 4 56.25000 9.00 37.50000 0 0
## 125 7 26.0 156 11 14.18000 6.00 14.18000 1 0
## 126 1 4.0 49 0 21.75931 12.25 14.44492 0 0
## 127 11 40.0 332 14 23.71000 8.30 17.14000 0 0
## 128 4 13.5 144 3 48.00000 10.40 27.66000 0 0
## 129 9 29.4 322 7 46.00000 10.85 25.42000 0 0
## 130 11 41.2 289 14 20.64000 6.99 17.71000 0 0
## 131 2 7.0 53 3 17.66000 7.57 14.00000 0 0
## 132 7 27.3 230 7 32.85000 8.36 23.57000 0 0
## 133 2 4.0 49 1 49.00000 12.25 24.00000 0 0
## 134 1 2.0 19 0 21.75931 9.50 14.44492 0 0
## 135 7 28.0 260 10 26.00000 9.28 16.80000 1 0
## 136 3 12.0 82 5 16.40000 6.83 14.40000 0 0
## 137 12 44.0 333 12 27.75000 7.56 22.00000 0 0
## 138 6 15.0 161 3 53.66000 10.73 30.00000 0 0
## 139 17 66.0 547 21 26.04000 8.28 18.85000 0 0
## 140 14 52.4 466 18 25.88000 8.84 17.55000 0 0
## 141 14 53.1 418 20 20.90000 7.86 15.95000 0 0
## 142 2 3.5 65 2 32.50000 16.95 11.50000 0 0
## 143 14 50.0 363 12 30.25000 7.26 25.00000 0 0# Print the dimensions of the cleaned dataset
cat("Dimensions of the cleaned dataset (after removing constant columns):\n")## Dimensions of the cleaned dataset (after removing constant columns):print(dim(numeric_data_clean))## [1] 143 22str(numeric_data_clean)## 'data.frame': 143 obs. of 22 variables:
## $ Mat.x : int 10 12 3 15 6 16 16 14 9 9 ...
## $ Inns.x: int 9 11 3 14 6 16 14 8 8 6 ...
## $ NO : int 1 2 2 1 0 2 3 2 2 2 ...
## $ Runs.x: int 134 480 63 370 148 602 316 32 80 96 ...
## $ HS : int 46 90 46 65 45 100 88 14 19 37 ...
## $ Avg.x : num 16.8 53.3 63 28.5 24.7 ...
## $ BF : int 100 275 33 313 118 402 171 38 69 58 ...
## $ SR.x : num 134 175 191 118 125 ...
## $ X100 : int 0 0 0 0 0 1 0 0 0 0 ...
## $ X50 : int 0 6 0 1 0 3 1 0 0 0 ...
## $ X4s : int 6 39 3 39 13 53 17 2 3 5 ...
## $ X6s : int 8 30 5 5 6 34 31 1 4 8 ...
## $ Mat.y : int 0 0 0 0 0 0 16 14 9 9 ...
## $ Inns.y: int 0 0 0 0 0 0 15 14 8 7 ...
## $ Ov : num 0 0 0 0 0 0 37.5 56 26 17 ...
## $ Runs.y: int 0 0 0 0 0 0 355 448 218 168 ...
## $ Wkts : int 0 0 0 0 0 0 13 24 3 2 ...
## $ Avg.y : num 0 0 0 0 0 ...
## $ Econ : num 0 0 0 0 0 0 9.38 8 8.38 9.88 ...
## $ SR.y : num 0 0 0 0 0 ...
## $ X4w : int 0 0 0 0 0 0 0 3 0 0 ...
## $ X5w : int 0 0 0 0 0 0 0 0 0 0 ...ML MODEL : K- Means Clustering
We’ll concentrate on numerical variables for clustering. Scaling the data to put all the variables on the same scale is crucial, especially when using algorithms like K-means.
We converted non-numeric data into numeric ones, scale the numerical values, and then apply K-means to the dataset because it contains mixed datatypes.
# Now scale the cleaned numeric data (standardize it to have mean=0 and standard deviation=1)
scaled_data_clean <- scale(numeric_data_clean)
scaled_data_clean## Mat.x Inns.x NO Runs.x HS
## [1,] 0.4464337 0.543008220 -0.1545170 0.009404653 0.401822989
## [2,] 0.7755060 0.906709277 0.4592588 1.981114117 1.778126881
## [3,] -0.7053192 -0.548094948 0.4592588 -0.395194688 0.401822989
## [4,] 1.2691144 1.452260861 -0.1545170 1.354270068 0.996136033
## [5,] -0.2117108 -0.002543364 -0.7682928 0.089184805 0.370543355
## [6,] 1.4336505 1.815961917 0.4592588 2.676341154 2.090923220
## [7,] 1.4336505 1.452260861 1.0730346 1.046546626 1.715567613
## [8,] 1.1045783 0.361157692 0.4592588 -0.571850738 -0.599125295
## [9,] 0.2818976 0.361157692 0.4592588 -0.298318789 -0.442727126
## [10,] 0.2818976 -0.002543364 0.4592588 -0.207141473 0.120306284
## [11,] 0.9400421 1.270410333 -0.1545170 0.362716754 0.370543355
## [12,] 0.7755060 -0.366244420 0.4592588 -0.680123801 -0.818082733
## [13,] -0.2117108 -0.002543364 -0.7682928 -0.030485423 0.307984088
## [14,] -0.5407831 -0.366244420 -0.1545170 -0.326811700 0.307984088
## [15,] 0.6109698 0.906709277 0.4592588 1.342872904 2.216041756
## [16,] 1.4336505 1.815961917 -0.1545170 2.043798522 1.277652739
## [17,] -0.5407831 -0.366244420 1.0730346 -0.492070586 -0.192490055
## [18,] -0.3762470 -0.366244420 0.4592588 -0.657329472 -0.692964197
## [19,] 0.2818976 0.361157692 1.0730346 -0.007691094 0.214145186
## [20,] -0.3762470 -0.184393892 -0.7682928 -0.395194688 -0.004812251
## [21,] -0.7053192 -0.548094948 -0.7682928 -0.657329472 -0.567845662
## [22,] -0.0471747 0.179307164 -0.7682928 -0.098868410 0.339263722
## [23,] -0.5407831 -0.548094948 -0.1545170 -0.606042232 -0.630404929
## [24,] -0.7053192 -0.548094948 -0.1545170 -0.332510283 -0.223769689
## [25,] 0.7755060 -0.366244420 -0.1545170 -0.469276257 0.182865552
## [26,] 0.2818976 0.361157692 1.6868104 -0.258428713 -0.036091885
## [27,] 1.4336505 1.815961917 2.9143619 2.083688598 0.589500793
## [28,] 1.4336505 0.724858749 2.9143619 0.049294729 1.089974935
## [29,] 0.9400421 1.270410333 -0.7682928 1.422653055 0.996136033
## [30,] -0.2117108 -0.002543364 -0.1545170 0.168964957 1.058695301
## [31,] -0.2117108 -0.184393892 -0.7682928 -0.269825878 0.683339694
## [32,] 0.7755060 1.088559805 -0.7682928 0.208855033 0.433102623
## [33,] 0.9400421 -0.548094948 -0.7682928 -0.588946485 -0.442727126
## [34,] 0.9400421 1.270410333 1.6868104 0.727426019 0.526941525
## [35,] -0.3762470 -0.729945477 -0.1545170 -0.412290434 0.089026650
## [36,] -0.5407831 -0.366244420 -0.1545170 -0.429386181 -0.036091885
## [37,] 1.1045783 1.088559805 -0.7682928 0.812904753 0.902297132
## [38,] -0.3762470 -0.184393892 -0.1545170 -0.070375498 1.809406515
## [39,] 1.2691144 0.179307164 1.0730346 -0.474974839 -0.223769689
## [40,] 0.4464337 0.361157692 1.0730346 -0.668726636 -0.786803099
## [41,] 0.9400421 1.270410333 1.0730346 2.368617712 1.934525050
## [42,] -0.2117108 -0.366244420 1.0730346 -0.549056409 -0.317608590
## [43,] 1.5981867 1.997812445 1.0730346 3.434252595 1.590449078
## [44,] 0.9400421 1.088559805 -0.7682928 0.961067892 0.652060061
## [45,] -1.0343915 -0.911796005 -0.1545170 -0.617439396 -0.286328956
## [46,] 0.2818976 0.361157692 -0.1545170 0.003706071 0.526941525
## [47,] 1.2691144 1.270410333 1.6868104 -0.036184005 -0.004812251
## [48,] 1.1045783 1.270410333 1.0730346 0.545071386 0.245424820
## [49,] 1.1045783 1.452260861 0.4592588 3.001160343 1.934525050
## [50,] -0.8698554 -0.729945477 -0.1545170 -0.640233725 -0.692964197
## [51,] -0.5407831 -0.366244420 -0.7682928 -0.440783346 0.370543355
## [52,] 1.1045783 1.270410333 1.0730346 0.681837361 0.433102623
## [53,] 1.2691144 1.270410333 0.4592588 0.864191993 0.902297132
## [54,] -0.3762470 -0.366244420 -0.1545170 -0.486372004 0.057747016
## [55,] -0.0471747 0.179307164 1.0730346 -0.190045726 -0.129930787
## [56,] 0.6109698 0.906709277 -0.1545170 -0.070375498 -0.098651153
## [57,] 1.1045783 -0.002543364 1.6868104 -0.634535143 -0.818082733
## [58,] -0.2117108 -0.729945477 0.4592588 -0.663028054 -0.661684563
## [59,] -0.3762470 -0.366244420 -0.7682928 -0.315414536 0.996136033
## [60,] -0.8698554 -0.729945477 -0.7682928 -0.651630890 -0.599125295
## [61,] 0.6109698 -0.366244420 0.4592588 -0.611740814 -0.599125295
## [62,] 1.4336505 1.634111389 4.7556893 1.838649561 1.434050908
## [63,] 1.2691144 1.634111389 0.4592588 0.978163639 0.808458230
## [64,] -0.2117108 -0.002543364 -0.1545170 0.117677716 0.620780427
## [65,] 1.2691144 0.179307164 1.0730346 -0.600343649 -0.661684563
## [66,] 0.2818976 0.543008220 -0.7682928 0.641947285 0.996136033
## [67,] 0.1173614 0.361157692 -0.7682928 0.391209665 0.620780427
## [68,] 0.1173614 -0.184393892 0.4592588 -0.469276257 -0.286328956
## [69,] 0.7755060 1.088559805 1.0730346 0.533674222 1.465330542
## [70,] 1.5981867 0.179307164 0.4592588 -0.417989017 0.026467383
## [71,] 1.1045783 0.543008220 -0.1545170 -0.172949979 0.370543355
## [72,] 1.4336505 0.724858749 2.3005862 -0.247031549 -0.192490055
## [73,] -0.5407831 -0.366244420 -0.7682928 -0.588946485 -0.536566028
## [74,] 1.1045783 1.452260861 -0.1545170 3.143624900 2.966752969
## [75,] 1.4336505 1.815961917 -0.7682928 1.245997005 0.652060061
## [76,] 1.1045783 1.452260861 0.4592588 0.875589158 1.903245417
## [77,] 0.4464337 0.361157692 -0.7682928 -0.138758486 0.714619328
## [78,] 1.2691144 1.634111389 -0.1545170 1.758869409 1.840686149
## [79,] -0.0471747 -0.002543364 -0.1545170 -0.463577675 -0.348888224
## [80,] 1.5981867 1.270410333 0.4592588 0.607755791 0.057747016
## [81,] 1.2691144 1.634111389 -0.1545170 2.408507787 2.622676996
## [82,] 0.9400421 -0.911796005 -0.1545170 -0.668726636 -0.567845662
## [83,] 1.4336505 1.815961917 1.0730346 2.077990016 1.840686149
## [84,] 0.2818976 -0.366244420 -0.1545170 -0.680123801 -0.818082733
## [85,] -0.2117108 -0.548094948 -0.7682928 -0.457879093 0.057747016
## [86,] 0.6109698 -0.366244420 -0.1545170 -0.469276257 -0.286328956
## [87,] 1.1045783 1.452260861 1.0730346 1.587911941 1.871965783
## [88,] 0.9400421 0.906709277 2.3005862 0.402606830 0.745898962
## [89,] -0.0471747 -0.184393892 -0.7682928 -0.503467751 -0.348888224
## [90,] 1.4336505 1.815961917 -0.7682928 1.280188499 1.308932372
## [91,] 1.2691144 1.634111389 1.0730346 1.781663738 1.308932372
## [92,] 1.1045783 1.452260861 -0.7682928 2.163468750 1.215093471
## [93,] 0.1173614 -0.366244420 0.4592588 -0.457879093 0.089026650
## [94,] -0.3762470 -0.366244420 -0.1545170 -0.623137978 -0.474006760
## [95,] 0.9400421 0.906709277 3.5281377 0.453894070 0.652060061
## [96,] 1.1045783 1.452260861 1.0730346 2.266043231 1.840686149
## [97,] -0.0471747 -0.002543364 1.0730346 -0.383797523 0.057747016
## [98,] 0.6109698 0.724858749 0.4592588 -0.058978334 0.057747016
## [99,] 1.2691144 1.270410333 1.6868104 0.727426019 0.370543355
## [100,] 0.1173614 -0.002543364 -0.7682928 -0.383797523 -0.411447492
## [101,] -1.1989276 -1.093646533 -0.7682928 -0.754205370 -1.037040170
## [102,] -1.1989276 -1.093646533 -0.7682928 -0.754205370 -1.037040170
## [103,] -1.1989276 -1.093646533 -0.7682928 -0.754205370 -1.037040170
## [104,] -1.1989276 -1.093646533 -0.7682928 -0.754205370 -1.037040170
## [105,] -1.1989276 -1.093646533 -0.7682928 -0.754205370 -1.037040170
## [106,] -1.1989276 -1.093646533 -0.7682928 -0.754205370 -1.037040170
## [107,] -1.1989276 -1.093646533 -0.7682928 -0.754205370 -1.037040170
## [108,] -1.1989276 -1.093646533 -0.7682928 -0.754205370 -1.037040170
## [109,] -1.1989276 -1.093646533 -0.7682928 -0.754205370 -1.037040170
## [110,] -1.1989276 -1.093646533 -0.7682928 -0.754205370 -1.037040170
## [111,] -1.1989276 -1.093646533 -0.7682928 -0.754205370 -1.037040170
## [112,] -1.1989276 -1.093646533 -0.7682928 -0.754205370 -1.037040170
## [113,] -1.1989276 -1.093646533 -0.7682928 -0.754205370 -1.037040170
## [114,] -1.1989276 -1.093646533 -0.7682928 -0.754205370 -1.037040170
## [115,] -1.1989276 -1.093646533 -0.7682928 -0.754205370 -1.037040170
## [116,] -1.1989276 -1.093646533 -0.7682928 -0.754205370 -1.037040170
## [117,] -1.1989276 -1.093646533 -0.7682928 -0.754205370 -1.037040170
## [118,] -1.1989276 -1.093646533 -0.7682928 -0.754205370 -1.037040170
## [119,] -1.1989276 -1.093646533 -0.7682928 -0.754205370 -1.037040170
## [120,] -1.1989276 -1.093646533 -0.7682928 -0.754205370 -1.037040170
## [121,] -1.1989276 -1.093646533 -0.7682928 -0.754205370 -1.037040170
## [122,] -1.1989276 -1.093646533 -0.7682928 -0.754205370 -1.037040170
## [123,] -1.1989276 -1.093646533 -0.7682928 -0.754205370 -1.037040170
## [124,] -1.1989276 -1.093646533 -0.7682928 -0.754205370 -1.037040170
## [125,] -1.1989276 -1.093646533 -0.7682928 -0.754205370 -1.037040170
## [126,] -1.1989276 -1.093646533 -0.7682928 -0.754205370 -1.037040170
## [127,] -1.1989276 -1.093646533 -0.7682928 -0.754205370 -1.037040170
## [128,] -1.1989276 -1.093646533 -0.7682928 -0.754205370 -1.037040170
## [129,] -1.1989276 -1.093646533 -0.7682928 -0.754205370 -1.037040170
## [130,] -1.1989276 -1.093646533 -0.7682928 -0.754205370 -1.037040170
## [131,] -1.1989276 -1.093646533 -0.7682928 -0.754205370 -1.037040170
## [132,] -1.1989276 -1.093646533 -0.7682928 -0.754205370 -1.037040170
## [133,] -1.1989276 -1.093646533 -0.7682928 -0.754205370 -1.037040170
## [134,] -1.1989276 -1.093646533 -0.7682928 -0.754205370 -1.037040170
## [135,] -1.1989276 -1.093646533 -0.7682928 -0.754205370 -1.037040170
## [136,] -1.1989276 -1.093646533 -0.7682928 -0.754205370 -1.037040170
## [137,] -1.1989276 -1.093646533 -0.7682928 -0.754205370 -1.037040170
## [138,] -1.1989276 -1.093646533 -0.7682928 -0.754205370 -1.037040170
## [139,] -1.1989276 -1.093646533 -0.7682928 -0.754205370 -1.037040170
## [140,] -1.1989276 -1.093646533 -0.7682928 -0.754205370 -1.037040170
## [141,] -1.1989276 -1.093646533 -0.7682928 -0.754205370 -1.037040170
## [142,] -1.1989276 -1.093646533 -0.7682928 -0.754205370 -1.037040170
## [143,] -1.1989276 -1.093646533 -0.7682928 -0.754205370 -1.037040170
## Avg.x BF SR.x X100 X50 X4s
## [1,] -0.039241007 0.04133474 0.608303659 -0.1594855 -0.4878273 -0.32691790
## [2,] 2.139198494 1.49618952 1.211552169 -0.1594855 3.6562898 1.64840294
## [3,] 2.715073562 -0.51566681 1.454994340 -0.1594855 -0.4878273 -0.50649252
## [4,] 0.658121666 1.81210085 0.373343275 -0.1594855 0.2028589 1.64840294
## [5,] 0.431821225 0.19097694 0.480630442 -0.1594855 -0.4878273 0.09208955
## [6,] 1.524018613 2.55199843 0.842668830 4.4018003 1.5842313 2.48641784
## [7,] 0.673605380 0.63159011 1.364075534 -0.1594855 0.2028589 0.33152238
## [8,] -0.719333382 -0.47409953 -0.132587888 -0.1594855 -0.4878273 -0.56635073
## [9,] -0.242911403 -0.21638240 0.339564930 -0.1594855 -0.4878273 -0.50649252
## [10,] 0.392516412 -0.30783041 1.077182804 -0.1594855 -0.4878273 -0.38677611
## [11,] -0.064253161 0.54845555 0.425722031 -0.1594855 -0.4878273 0.09208955
## [12,] -0.649656668 -0.65699556 -0.176633659 -0.1594855 -0.4878273 -0.62620894
## [13,] 0.223386609 -0.05842674 0.761719844 -0.1594855 -0.4878273 0.27166417
## [14,] 0.452069159 -0.39096497 0.939390964 -0.1594855 -0.4878273 -0.62620894
## [15,] 1.397766789 1.30498004 0.787314009 4.4018003 1.5842313 1.10967907
## [16,] 0.912411897 2.34416203 0.552204821 -0.1594855 1.5842313 2.66599247
## [17,] 1.702676855 -0.57386100 1.246967350 -0.1594855 -0.4878273 -0.50649252
## [18,] -0.530551173 -0.63205520 -0.054317361 -0.1594855 -0.4878273 -0.62620894
## [19,] 0.523532456 -0.09168056 0.934926865 -0.1594855 -0.4878273 -0.44663432
## [20,] -0.286384909 -0.44915916 0.900702110 -0.1594855 -0.4878273 -0.26705969
## [21,] -0.699680976 -0.60711483 -0.235857366 -0.1594855 -0.4878273 -0.68606715
## [22,] -0.058893413 0.03302128 0.342838602 -0.1594855 -0.4878273 -0.02762686
## [23,] -0.262563810 -0.51566681 -0.213388070 -0.1594855 -0.4878273 -0.68606715
## [24,] 1.166702129 -0.25794968 0.334803225 -0.1594855 -0.4878273 -0.50649252
## [25,] -0.044600754 -0.54892064 1.179857069 -0.1594855 -0.4878273 -0.62620894
## [26,] 0.258522730 -0.11662093 0.212486926 -0.1594855 -0.4878273 -0.56635073
## [27,] 1.928977296 2.01162379 0.813205780 -0.1594855 0.8935451 2.24698501
## [28,] 1.062484821 -0.03348637 0.919897734 -0.1594855 0.2028589 -0.20720149
## [29,] 0.712910193 1.50450298 0.673777104 -0.1594855 0.8935451 1.22939549
## [30,] 0.892759490 0.28242496 0.483011295 -0.1594855 0.2028589 0.33152238
## [31,] -0.024352820 -0.05842674 0.051630577 -0.1594855 0.2028589 -0.20720149
## [32,] -0.198246842 0.20760385 0.709936301 -0.1594855 -0.4878273 0.15194776
## [33,] -0.461469986 -0.49072644 -0.187049889 -0.1594855 -0.4878273 -0.50649252
## [34,] 0.683133819 0.83111305 0.598333839 -0.1594855 0.2028589 0.51109700
## [35,] 2.536415319 -0.51566681 1.319732156 -0.1594855 -0.4878273 -0.62620894
## [36,] 0.094752675 -0.39927843 0.418877080 -0.1594855 -0.4878273 -0.38677611
## [37,] 0.327603917 0.73966503 0.838204731 -0.1594855 0.8935451 0.63081342
## [38,] 0.749832897 -0.00854600 0.513813574 -0.1594855 0.2028589 -0.14734328
## [39,] -0.307228370 -0.47409953 0.533009198 -0.1594855 -0.4878273 -0.32691790
## [40,] -0.858091284 -0.61542828 -0.322907287 -0.1594855 -0.4878273 -0.56635073
## [41,] 2.226741033 2.14463909 0.924361832 -0.1594855 2.9656036 2.42655964
## [42,] 1.107149381 -0.45747262 -0.046430787 -0.1594855 -0.4878273 -0.50649252
## [43,] 2.089769714 3.49973240 0.733893630 -0.1594855 5.0376622 3.14485813
## [44,] 0.456833379 1.04726290 0.640891578 -0.1594855 0.8935451 0.69067162
## [45,] 0.000000000 -0.60711483 0.237634681 -0.1594855 -0.4878273 -0.62620894
## [46,] 0.094752675 0.04133474 0.593423331 -0.1594855 0.2028589 -0.08748507
## [47,] -0.203011062 -0.25794968 1.543829900 -0.1594855 -0.4878273 -0.14734328
## [48,] 0.321053115 0.51520172 0.775260943 -0.1594855 -0.4878273 0.63081342
## [49,] 2.233291835 2.66838681 0.971532473 -0.1594855 3.6562898 3.26457454
## [50,] 0.154305422 -0.61542828 0.031393330 -0.1594855 -0.4878273 -0.62620894
## [51,] -0.217899249 -0.39927843 0.355635684 -0.1594855 -0.4878273 -0.56635073
## [52,] 0.463979709 0.75629195 0.630326545 -0.1594855 -0.4878273 0.27166417
## [53,] 0.500306885 1.25509930 0.332124766 -0.1594855 1.5842313 0.63081342
## [54,] -0.104153501 -0.42421880 0.203707532 -0.1594855 -0.4878273 -0.44663432
## [55,] 0.437180973 -0.15818821 0.552651231 -0.1594855 -0.4878273 -0.32691790
## [56,] -0.322116557 -0.00854600 0.513813574 -0.1594855 -0.4878273 -0.14734328
## [57,] -0.411445678 -0.59048792 -0.083631607 -0.1594855 -0.4878273 -0.56635073
## [58,] 0.000000000 -0.69856284 0.778683418 -0.1594855 -0.4878273 -0.56635073
## [59,] 0.109640862 -0.40759188 1.105157821 -0.1594855 0.2028589 -0.44663432
## [60,] -0.500774799 -0.69024939 0.846388912 -0.1594855 -0.4878273 -0.50649252
## [61,] -0.292340183 -0.60711483 0.305191371 -0.1594855 -0.4878273 -0.56635073
## [62,] 3.479135311 1.72065283 0.856209929 -0.1594855 1.5842313 0.75052983
## [63,] 0.355593709 1.13871092 0.564109084 -0.1594855 0.2028589 0.87024625
## [64,] 0.785564545 0.11615584 0.702942547 -0.1594855 0.2028589 0.51109700
## [65,] -0.634768481 -0.50735336 -0.204013463 -0.1594855 -0.4878273 -0.62620894
## [66,] 0.584276259 0.54014209 0.892815536 -0.1594855 0.8935451 0.93010445
## [67,] 0.459215489 0.55676900 0.460541999 -0.1594855 0.2028589 0.51109700
## [68,] -0.044600754 -0.43253225 0.344475438 -0.1594855 -0.4878273 -0.38677611
## [69,] 0.458619962 0.59833628 0.627945692 -0.1594855 0.2028589 0.39138059
## [70,] -0.334027107 -0.53229372 1.446363749 -0.1594855 -0.4878273 -0.50649252
## [71,] -0.277451996 -0.19975549 0.752047630 -0.1594855 -0.4878273 -0.26705969
## [72,] 0.023289378 -0.17481512 0.403996751 -0.1594855 -0.4878273 -0.50649252
## [73,] -0.604992107 -0.53229372 0.006245576 -0.1594855 -0.4878273 -0.44663432
## [74,] 2.096320516 2.48549078 1.197564660 4.4018003 2.9656036 3.38429095
## [75,] 0.269242225 1.41305496 0.585239150 -0.1594855 0.2028589 1.10967907
## [76,] 0.382392445 0.99738217 0.593720937 -0.1594855 0.8935451 0.81038804
## [77,] -0.232787436 -0.14156130 0.674669923 -0.1594855 0.2028589 -0.20720149
## [78,] 0.839162018 1.87029504 0.664997710 -0.1594855 1.5842313 1.10967907
## [79,] -0.429311502 -0.44915916 0.465303704 -0.1594855 -0.4878273 -0.26705969
## [80,] 0.256736148 0.84773996 0.419472293 -0.1594855 -0.4878273 0.87024625
## [81,] 1.323921382 2.19451982 0.914689619 8.9630860 0.8935451 1.94769398
## [82,] 0.000000000 -0.74844358 3.078438159 -0.1594855 -0.4878273 -0.50649252
## [83,] 1.239952008 2.22777365 0.651605414 -0.1594855 2.2749174 2.84556709
## [84,] -0.778886130 -0.66530902 -0.096131083 -0.1594855 -0.4878273 -0.62620894
## [85,] -0.004700413 -0.45747262 0.548782346 -0.1594855 -0.4878273 -0.32691790
## [86,] -0.044600754 -0.41590534 0.267692944 -0.1594855 -0.4878273 -0.38677611
## [87,] 1.188141118 1.78716048 0.587173593 -0.1594855 2.2749174 1.04982087
## [88,] 0.977919919 0.36555952 0.787462812 -0.1594855 0.2028589 0.63081342
## [89,] -0.512685349 -0.46578608 0.293138305 -0.1594855 -0.4878273 -0.56635073
## [90,] 0.291872269 0.77291886 1.439965208 -0.1594855 0.8935451 1.70826115
## [91,] 1.171466348 2.00331034 0.585090347 -0.1594855 2.2749174 2.06741039
## [92,] 1.141094447 2.40235622 0.598333839 -0.1594855 2.2749174 2.96528350
## [93,] 0.511621907 -0.40759188 0.296411978 -0.1594855 -0.4878273 -0.38677611
## [94,] -0.580575481 -0.55723409 -0.163390167 -0.1594855 -0.4878273 -0.50649252
## [95,] 2.119546087 0.44038062 0.745797893 -0.1594855 0.2028589 -0.02762686
## [96,] 1.832501845 2.37741585 0.684193333 -0.1594855 2.2749174 2.42655964
## [97,] 0.253162983 -0.47409953 1.159619823 -0.1594855 -0.4878273 -0.38677611
## [98,] -0.128570128 0.05796165 0.394026931 -0.1594855 -0.4878273 0.33152238
## [99,] 0.683133819 0.87268033 0.548782346 -0.1594855 -0.4878273 0.63081342
## [100,] -0.391793272 -0.18312857 -0.060715902 -0.1594855 -0.4878273 -0.32691790
## [101,] -1.036749526 -0.79001086 -1.385660335 -0.1594855 -0.4878273 -0.68606715
## [102,] -1.036749526 -0.79001086 -1.385660335 -0.1594855 -0.4878273 -0.68606715
## [103,] -1.036749526 -0.79001086 -1.385660335 -0.1594855 -0.4878273 -0.68606715
## [104,] -1.036749526 -0.79001086 -1.385660335 -0.1594855 -0.4878273 -0.68606715
## [105,] -1.036749526 -0.79001086 -1.385660335 -0.1594855 -0.4878273 -0.68606715
## [106,] -1.036749526 -0.79001086 -1.385660335 -0.1594855 -0.4878273 -0.68606715
## [107,] -1.036749526 -0.79001086 -1.385660335 -0.1594855 -0.4878273 -0.68606715
## [108,] -1.036749526 -0.79001086 -1.385660335 -0.1594855 -0.4878273 -0.68606715
## [109,] -1.036749526 -0.79001086 -1.385660335 -0.1594855 -0.4878273 -0.68606715
## [110,] -1.036749526 -0.79001086 -1.385660335 -0.1594855 -0.4878273 -0.68606715
## [111,] -1.036749526 -0.79001086 -1.385660335 -0.1594855 -0.4878273 -0.68606715
## [112,] -1.036749526 -0.79001086 -1.385660335 -0.1594855 -0.4878273 -0.68606715
## [113,] -1.036749526 -0.79001086 -1.385660335 -0.1594855 -0.4878273 -0.68606715
## [114,] -1.036749526 -0.79001086 -1.385660335 -0.1594855 -0.4878273 -0.68606715
## [115,] -1.036749526 -0.79001086 -1.385660335 -0.1594855 -0.4878273 -0.68606715
## [116,] -1.036749526 -0.79001086 -1.385660335 -0.1594855 -0.4878273 -0.68606715
## [117,] -1.036749526 -0.79001086 -1.385660335 -0.1594855 -0.4878273 -0.68606715
## [118,] -1.036749526 -0.79001086 -1.385660335 -0.1594855 -0.4878273 -0.68606715
## [119,] -1.036749526 -0.79001086 -1.385660335 -0.1594855 -0.4878273 -0.68606715
## [120,] -1.036749526 -0.79001086 -1.385660335 -0.1594855 -0.4878273 -0.68606715
## [121,] -1.036749526 -0.79001086 -1.385660335 -0.1594855 -0.4878273 -0.68606715
## [122,] -1.036749526 -0.79001086 -1.385660335 -0.1594855 -0.4878273 -0.68606715
## [123,] -1.036749526 -0.79001086 -1.385660335 -0.1594855 -0.4878273 -0.68606715
## [124,] -1.036749526 -0.79001086 -1.385660335 -0.1594855 -0.4878273 -0.68606715
## [125,] -1.036749526 -0.79001086 -1.385660335 -0.1594855 -0.4878273 -0.68606715
## [126,] -1.036749526 -0.79001086 -1.385660335 -0.1594855 -0.4878273 -0.68606715
## [127,] -1.036749526 -0.79001086 -1.385660335 -0.1594855 -0.4878273 -0.68606715
## [128,] -1.036749526 -0.79001086 -1.385660335 -0.1594855 -0.4878273 -0.68606715
## [129,] -1.036749526 -0.79001086 -1.385660335 -0.1594855 -0.4878273 -0.68606715
## [130,] -1.036749526 -0.79001086 -1.385660335 -0.1594855 -0.4878273 -0.68606715
## [131,] -1.036749526 -0.79001086 -1.385660335 -0.1594855 -0.4878273 -0.68606715
## [132,] -1.036749526 -0.79001086 -1.385660335 -0.1594855 -0.4878273 -0.68606715
## [133,] -1.036749526 -0.79001086 -1.385660335 -0.1594855 -0.4878273 -0.68606715
## [134,] -1.036749526 -0.79001086 -1.385660335 -0.1594855 -0.4878273 -0.68606715
## [135,] -1.036749526 -0.79001086 -1.385660335 -0.1594855 -0.4878273 -0.68606715
## [136,] -1.036749526 -0.79001086 -1.385660335 -0.1594855 -0.4878273 -0.68606715
## [137,] -1.036749526 -0.79001086 -1.385660335 -0.1594855 -0.4878273 -0.68606715
## [138,] -1.036749526 -0.79001086 -1.385660335 -0.1594855 -0.4878273 -0.68606715
## [139,] -1.036749526 -0.79001086 -1.385660335 -0.1594855 -0.4878273 -0.68606715
## [140,] -1.036749526 -0.79001086 -1.385660335 -0.1594855 -0.4878273 -0.68606715
## [141,] -1.036749526 -0.79001086 -1.385660335 -0.1594855 -0.4878273 -0.68606715
## [142,] -1.036749526 -0.79001086 -1.385660335 -0.1594855 -0.4878273 -0.68606715
## [143,] -1.036749526 -0.79001086 -1.385660335 -0.1594855 -0.4878273 -0.68606715
## X6s Mat.y Inns.y Ov Runs.y
## [1,] 0.219477662 -0.99405388 -0.929437558 -0.85267681 -0.89306475
## [2,] 2.721204923 -0.99405388 -0.929437558 -0.85267681 -0.89306475
## [3,] -0.121666965 -0.99405388 -0.929437558 -0.85267681 -0.89306475
## [4,] -0.121666965 -0.99405388 -0.929437558 -0.85267681 -0.89306475
## [5,] -0.007952089 -0.99405388 -0.929437558 -0.85267681 -0.89306475
## [6,] 3.176064425 -0.99405388 -0.929437558 -0.85267681 -0.89306475
## [7,] 2.834919798 1.84185046 1.854980825 1.13986259 1.41718964
## [8,] -0.576526467 1.48736242 1.669352933 2.12284869 2.02241121
## [9,] -0.235381840 0.60114231 0.555585580 0.52881717 0.52562668
## [10,] 0.219477662 0.60114231 0.369957687 0.05060771 0.20023873
## [11,] -0.007952089 1.31011840 1.298097149 1.11329539 1.07878618
## [12,] -0.690241342 1.13287438 1.298097149 1.59681829 1.41068188
## [13,] -0.007952089 -0.99405388 -0.929437558 -0.85267681 -0.89306475
## [14,] 0.219477662 -0.28507780 -0.186925989 -0.31601954 -0.28133542
## [15,] 2.380060296 -0.99405388 -0.929437558 -0.85267681 -0.89306475
## [16,] 1.356626417 -0.99405388 -0.929437558 -0.85267681 -0.89306475
## [17,] -0.462811591 -0.28507780 -0.186925989 -0.10879544 0.03754476
## [18,] -0.576526467 -0.10783378 -0.001298097 0.11436897 0.34340943
## [19,] 0.446907413 0.60114231 0.369957687 -0.05566105 -0.05356386
## [20,] -0.235381840 -0.99405388 -0.929437558 -0.85267681 -0.89306475
## [21,] -0.576526467 -0.46232182 -0.372553882 -0.40634799 -0.14467248
## [22,] -0.121666965 0.24665427 -0.558181774 -0.69327366 -0.76941733
## [23,] -0.576526467 -0.28507780 -0.186925989 -0.24163140 -0.23578111
## [24,] -0.462811591 -0.99405388 -0.929437558 -0.85267681 -0.89306475
## [25,] -0.235381840 1.13287438 1.298097149 1.17174322 0.91609221
## [26,] -0.349096716 0.60114231 -0.558181774 -0.69327366 -0.73687854
## [27,] 1.129196666 -0.99405388 -0.929437558 -0.85267681 -0.89306475
## [28,] 0.446907413 1.84185046 2.040608718 1.97938585 2.57557071
## [29,] 2.038915670 -0.99405388 -0.929437558 -0.85267681 -0.89306475
## [30,] -0.007952089 -0.99405388 -0.929437558 -0.85267681 -0.89306475
## [31,] -0.576526467 -0.99405388 -0.929437558 -0.85267681 -0.89306475
## [32,] 0.333192537 1.13287438 0.926841364 -0.00252667 -0.03404058
## [33,] -0.576526467 1.31011840 1.298097149 0.82105628 0.86403013
## [34,] 0.560622288 1.31011840 1.483725041 1.40022107 1.58639137
## [35,] -0.007952089 -0.10783378 -0.001298097 0.06654803 0.19373097
## [36,] -0.576526467 -0.99405388 -0.929437558 -0.85267681 -0.89306475
## [37,] 1.242911541 -0.99405388 -0.929437558 -0.85267681 -0.89306475
## [38,] 0.105762786 -0.99405388 -0.929437558 -0.85267681 -0.89306475
## [39,] -0.576526467 1.66460644 1.854980825 1.81466926 2.26970604
## [40,] -0.690241342 0.77838633 0.926841364 1.19299697 1.22195687
## [41,] 1.697771043 -0.99405388 -0.929437558 -0.85267681 -0.89306475
## [42,] -0.576526467 0.06941025 -0.558181774 -0.69327366 -0.65878543
## [43,] 2.493775172 -0.99405388 -0.929437558 -0.85267681 -0.89306475
## [44,] 0.788052039 -0.99405388 -0.929437558 -0.85267681 -0.89306475
## [45,] -0.462811591 -0.99405388 -0.929437558 -0.85267681 -0.89306475
## [46,] 0.105762786 -0.99405388 -0.929437558 -0.85267681 -0.89306475
## [47,] 0.333192537 1.66460644 1.854980825 1.27269854 1.13735601
## [48,] 0.446907413 1.48736242 1.483725041 1.27801198 0.95513876
## [49,] 2.948634674 -0.99405388 -0.929437558 -0.85267681 -0.89306475
## [50,] -0.690241342 -0.63956584 -0.558181774 -0.69327366 -0.71735526
## [51,] -0.235381840 -0.99405388 -0.929437558 -0.85267681 -0.89306475
## [52,] 0.560622288 -0.99405388 -0.929437558 -0.85267681 -0.89306475
## [53,] -0.121666965 -0.99405388 -0.929437558 -0.85267681 -0.89306475
## [54,] -0.576526467 -0.10783378 -0.743809666 -0.79954243 -0.82798716
## [55,] -0.235381840 0.24665427 0.184329795 -0.26819859 -0.11213369
## [56,] -0.121666965 -0.99405388 -0.929437558 -0.85267681 -0.89306475
## [57,] -0.690241342 1.48736242 1.669352933 1.48523608 1.50179050
## [58,] -0.690241342 0.06941025 0.184329795 0.26314525 0.51261116
## [59,] -0.007952089 -0.10783378 -0.001298097 -0.15661638 -0.26181214
## [60,] -0.690241342 -0.63956584 -0.558181774 -0.58700489 -0.58720009
## [61,] -0.576526467 0.95563035 1.112469256 1.32583293 1.49528274
## [62,] 2.721204923 -0.99405388 -0.929437558 -0.85267681 -0.89306475
## [63,] 0.901766915 1.66460644 -0.001298097 -0.52855707 -0.60672336
## [64,] -0.235381840 -0.99405388 -0.929437558 -0.85267681 -0.89306475
## [65,] -0.576526467 1.66460644 1.854980825 1.75090800 1.78813189
## [66,] 0.446907413 -0.99405388 -0.929437558 -0.85267681 -0.89306475
## [67,] 0.219477662 -0.99405388 -0.929437558 -0.85267681 -0.89306475
## [68,] -0.576526467 0.42389829 0.555585580 0.31627963 0.23277753
## [69,] 0.219477662 -0.99405388 -0.929437558 -0.85267681 -0.89306475
## [70,] -0.007952089 2.01909448 2.226236610 2.76046130 2.08748880
## [71,] -0.121666965 1.48736242 1.669352933 1.82529614 1.77511637
## [72,] -0.235381840 1.84185046 1.669352933 1.32583293 1.07878618
## [73,] -0.690241342 -0.99405388 -0.929437558 -0.85267681 -0.89306475
## [74,] 3.517209051 -0.99405388 -0.929437558 -0.85267681 -0.89306475
## [75,] 1.697771043 -0.99405388 -0.929437558 -0.85267681 -0.89306475
## [76,] 0.674337164 -0.99405388 -0.929437558 -0.85267681 -0.89306475
## [77,] -0.121666965 -0.99405388 -0.929437558 -0.85267681 -0.89306475
## [78,] 1.470341292 -0.99405388 -0.929437558 -0.85267681 -0.89306475
## [79,] -0.576526467 -0.99405388 -0.929437558 -0.85267681 -0.89306475
## [80,] -0.121666965 2.01909448 2.226236610 2.17598307 2.07447328
## [81,] 3.289779300 1.66460644 1.112469256 0.63508594 0.74038272
## [82,] -0.690241342 1.31011840 1.483725041 1.61275860 1.91177931
## [83,] 0.901766915 -0.99405388 -0.929437558 -0.85267681 -0.89306475
## [84,] -0.690241342 0.60114231 0.741213472 0.63508594 0.86403013
## [85,] -0.576526467 -0.99405388 -0.929437558 -0.85267681 -0.89306475
## [86,] -0.690241342 0.95563035 0.926841364 0.79448909 0.64276633
## [87,] 1.697771043 -0.99405388 -0.929437558 -0.85267681 -0.89306475
## [88,] -0.121666965 -0.99405388 -0.929437558 -0.85267681 -0.89306475
## [89,] -0.462811591 0.24665427 -0.743809666 -0.74640805 -0.67830871
## [90,] 1.925200794 1.84185046 2.040608718 2.38852061 2.14605863
## [91,] 0.674337164 -0.99405388 -0.929437558 -0.85267681 -0.89306475
## [92,] 1.129196666 -0.99405388 -0.929437558 -0.85267681 -0.89306475
## [93,] -0.576526467 0.42389829 0.555585580 0.68822032 0.80546031
## [94,] -0.690241342 -0.10783378 -0.001298097 -0.31601954 -0.12514921
## [95,] 0.560622288 1.31011840 -0.186925989 -0.58700489 -0.51561474
## [96,] 1.356626417 -0.99405388 -0.929437558 -0.85267681 -0.89306475
## [97,] -0.235381840 0.24665427 0.369957687 0.21001087 0.35642495
## [98,] -0.576526467 -0.99405388 -0.929437558 -0.85267681 -0.89306475
## [99,] 0.560622288 1.66460644 -0.743809666 -0.74640805 -0.80195613
## [100,] -0.462811591 0.42389829 -0.558181774 -0.74640805 -0.74338630
## [101,] -0.690241342 -0.81680986 -0.743809666 -0.64013928 -0.58720009
## [102,] -0.690241342 0.77838633 0.926841364 1.11329539 0.82498358
## [103,] -0.690241342 0.42389829 0.555585580 0.53413061 0.55816547
## [104,] -0.690241342 -0.81680986 -0.743809666 -0.79954243 -0.82147940
## [105,] -0.690241342 -0.46232182 -0.372553882 -0.64013928 -0.61973888
## [106,] -0.690241342 0.06941025 0.184329795 0.15687648 0.43451805
## [107,] -0.690241342 0.06941025 0.184329795 0.31627963 0.59721202
## [108,] -0.690241342 -0.28507780 -0.186925989 -0.31601954 -0.15118024
## [109,] -0.690241342 0.24665427 0.369957687 0.26314525 0.48007236
## [110,] -0.690241342 -0.28507780 -0.186925989 -0.00252667 -0.04705610
## [111,] -0.690241342 -0.81680986 -0.743809666 -0.64013928 -0.69132423
## [112,] -0.690241342 -0.46232182 -0.372553882 -0.32133297 -0.27482766
## [113,] -0.690241342 0.42389829 0.555585580 0.18344367 0.33039391
## [114,] -0.690241342 0.06941025 0.184329795 0.23126462 0.33039391
## [115,] -0.690241342 0.06941025 0.184329795 0.36941402 -0.01451731
## [116,] -0.690241342 1.48736242 1.669352933 2.01657992 1.52782154
## [117,] -0.690241342 -0.28507780 -0.372553882 -0.48073613 -0.31387421
## [118,] -0.690241342 -0.81680986 -0.743809666 -0.69327366 -0.67180095
## [119,] -0.690241342 0.06941025 -0.001298097 -0.35852704 -0.31387421
## [120,] -0.690241342 -0.81680986 -0.743809666 -0.69327366 -0.64576992
## [121,] -0.690241342 -0.63956584 -0.558181774 -0.53387051 -0.40498284
## [122,] -0.690241342 1.84185046 2.040608718 1.86780365 1.82717844
## [123,] -0.690241342 -0.46232182 -0.372553882 -0.29476578 -0.19673456
## [124,] -0.690241342 0.24665427 0.369957687 0.47568279 0.57118099
## [125,] -0.690241342 0.24665427 0.369957687 0.52881717 0.12214563
## [126,] -0.690241342 -0.81680986 -0.743809666 -0.64013928 -0.57418457
## [127,] -0.690241342 0.95563035 1.112469256 1.27269854 1.26751118
## [128,] -0.690241342 -0.28507780 -0.186925989 -0.13536263 0.04405252
## [129,] -0.690241342 0.60114231 0.741213472 0.70947408 1.20243359
## [130,] -0.690241342 0.95563035 1.112469256 1.33645981 0.98767755
## [131,] -0.690241342 -0.63956584 -0.558181774 -0.48073613 -0.54815353
## [132,] -0.690241342 0.24665427 0.369957687 0.59789187 0.60371978
## [133,] -0.690241342 -0.63956584 -0.558181774 -0.64013928 -0.57418457
## [134,] -0.690241342 -0.81680986 -0.743809666 -0.74640805 -0.76941733
## [135,] -0.690241342 0.24665427 0.369957687 0.63508594 0.79895255
## [136,] -0.690241342 -0.46232182 -0.372553882 -0.21506421 -0.35942853
## [137,] -0.690241342 1.13287438 1.298097149 1.48523608 1.27401894
## [138,] -0.690241342 0.06941025 0.184329795 -0.05566105 0.15468442
## [139,] -0.690241342 2.01909448 2.226236610 2.65419253 2.66667933
## [140,] -0.690241342 1.48736242 1.669352933 1.93156491 2.13955087
## [141,] -0.690241342 1.48736242 1.669352933 1.96875897 1.82717844
## [142,] -0.690241342 -0.63956584 -0.558181774 -0.66670647 -0.47006043
## [143,] -0.690241342 1.48736242 1.669352933 1.80404238 1.46925171
## Wkts Avg.y Econ SR.y X4w X5w
## [1,] -0.78334568 -1.077475169 -1.31598149 -1.130460886 -0.1810878 -0.0836242
## [2,] -0.78334568 -1.077475169 -1.31598149 -1.130460886 -0.1810878 -0.0836242
## [3,] -0.78334568 -1.077475169 -1.31598149 -1.130460886 -0.1810878 -0.0836242
## [4,] -0.78334568 -1.077475169 -1.31598149 -1.130460886 -0.1810878 -0.0836242
## [5,] -0.78334568 -1.077475169 -1.31598149 -1.130460886 -0.1810878 -0.0836242
## [6,] -0.78334568 -1.077475169 -1.31598149 -1.130460886 -0.1810878 -0.0836242
## [7,] 1.41641206 0.274363434 0.68844553 0.235960172 -0.1810878 -0.0836242
## [8,] 3.27774553 -0.153471201 0.39355115 -0.034819717 9.5297468 -0.0836242
## [9,] -0.27570928 2.520495267 0.47475395 2.939063456 -0.1810878 -0.0836242
## [10,] -0.44492141 3.082028225 0.79529132 2.860803372 -0.1810878 -0.0836242
## [11,] 0.57035139 0.797767611 0.43201564 1.041256431 -0.1810878 -0.0836242
## [12,] 0.73956352 0.870063742 0.32089601 1.277601883 -0.1810878 -0.0836242
## [13,] -0.78334568 -1.077475169 -1.31598149 -1.130460886 -0.1810878 -0.0836242
## [14,] 0.06271499 -0.146538695 0.65852871 -0.175687867 -0.1810878 -0.0836242
## [15,] -0.78334568 -1.077475169 -1.31598149 -1.130460886 -0.1810878 -0.0836242
## [16,] -0.78334568 -1.077475169 -1.31598149 -1.130460886 -0.1810878 -0.0836242
## [17,] -0.27570928 1.282547828 0.86580954 1.060821452 -0.1810878 -0.0836242
## [18,] 0.57035139 0.098574898 0.89786328 -0.054384738 -0.1810878 -0.0836242
## [19,] -0.44492141 2.116429223 0.52176610 2.391242871 -0.1810878 -0.0836242
## [20,] -0.78334568 -1.077475169 -1.31598149 -1.130460886 -0.1810878 -0.0836242
## [21,] -0.27570928 0.820545844 1.51756887 0.225786361 -0.1810878 -0.0836242
## [22,] -0.61413355 -0.136635116 0.03668621 0.278220617 -0.1810878 -0.0836242
## [23,] -0.10649715 0.172851744 0.50680769 0.258655596 -0.1810878 -0.0836242
## [24,] -0.78334568 -1.077475169 -1.31598149 -1.130460886 -0.1810878 -0.0836242
## [25,] 0.90877565 0.299122383 0.23969321 0.661695026 -0.1810878 -0.0836242
## [26,] -0.78334568 0.000000000 0.39355115 0.000000000 -0.1810878 -0.0836242
## [27,] -0.78334568 -1.077475169 -1.31598149 -1.130460886 -0.1810878 -0.0836242
## [28,] 1.58562419 0.807671191 0.81238665 0.663260228 -0.1810878 -0.0836242
## [29,] -0.78334568 -1.077475169 -1.31598149 -1.130460886 -0.1810878 -0.0836242
## [30,] -0.78334568 -1.077475169 -1.31598149 -1.130460886 -0.1810878 -0.0836242
## [31,] -0.78334568 -1.077475169 -1.31598149 -1.130460886 -0.1810878 -0.0836242
## [32,] 0.06271499 0.229797326 0.44697405 0.372132717 -0.1810878 -0.0836242
## [33,] 0.40113925 0.832430140 0.49612311 1.004474192 -0.1810878 -0.0836242
## [34,] 2.26247272 -0.029676457 0.59014741 -0.017602499 -0.1810878 -0.0836242
## [35,] 0.40113925 0.103526688 0.72263619 0.043440366 -0.1810878 -0.0836242
## [36,] -0.78334568 -1.077475169 -1.31598149 -1.130460886 -0.1810878 -0.0836242
## [37,] -0.78334568 -1.077475169 -1.31598149 -1.130460886 -0.1810878 -0.0836242
## [38,] -0.78334568 -1.077475169 -1.31598149 -1.130460886 -0.1810878 -0.0836242
## [39,] 1.07798779 1.110225545 0.74614226 1.017778406 -0.1810878 -0.0836242
## [40,] 1.75483632 -0.004917508 0.47048012 0.084918211 -0.1810878 -0.0836242
## [41,] -0.78334568 -1.077475169 -1.31598149 -1.130460886 -0.1810878 -0.0836242
## [42,] -0.78334568 0.000000000 1.24831747 0.000000000 -0.1810878 -0.0836242
## [43,] -0.78334568 -1.077475169 -1.31598149 -1.130460886 -0.1810878 -0.0836242
## [44,] -0.78334568 -1.077475169 -1.31598149 -1.130460886 -0.1810878 -0.0836242
## [45,] -0.78334568 -1.077475169 -1.31598149 -1.130460886 -0.1810878 -0.0836242
## [46,] -0.78334568 -1.077475169 -1.31598149 -1.130460886 -0.1810878 -0.0836242
## [47,] 1.07798779 0.326852405 0.35081284 0.576391535 -0.1810878 -0.0836242
## [48,] 1.24719992 0.094118287 0.19481798 0.441001591 -0.1810878 -0.0836242
## [49,] -0.78334568 -1.077475169 -1.31598149 -1.130460886 -0.1810878 -0.0836242
## [50,] -0.78334568 0.000000000 0.60724273 0.000000000 -0.1810878 -0.0836242
## [51,] -0.78334568 -1.077475169 -1.31598149 -1.130460886 -0.1810878 -0.0836242
## [52,] -0.78334568 -1.077475169 -1.31598149 -1.130460886 -0.1810878 -0.0836242
## [53,] -0.78334568 -1.077475169 -1.31598149 -1.130460886 -0.1810878 -0.0836242
## [54,] -0.78334568 0.000000000 0.82093431 0.000000000 -0.1810878 -0.0836242
## [55,] -0.27570928 0.903240733 1.01325674 0.591260951 -0.1810878 -0.0836242
## [56,] -0.78334568 -1.077475169 -1.31598149 -1.130460886 -0.1810878 -0.0836242
## [57,] 1.75483632 0.137198858 0.47048012 0.246916583 3.0558571 -0.0836242
## [58,] -0.44492141 4.270457767 0.88076796 3.799924374 -0.1810878 -0.0836242
## [59,] -0.27570928 0.523438459 0.25678854 0.930127112 -0.1810878 -0.0836242
## [60,] -0.61413355 1.249866016 0.69271936 1.217341619 -0.1810878 -0.0836242
## [61,] 1.07798779 0.574441893 0.59655815 0.619434581 -0.1810878 -0.0836242
## [62,] -0.78334568 -1.077475169 -1.31598149 -1.130460886 -0.1810878 -0.0836242
## [63,] -0.10649715 -0.532778296 0.20763948 -0.406555114 -0.1810878 -0.0836242
## [64,] -0.78334568 -1.077475169 -1.31598149 -1.130460886 -0.1810878 -0.0836242
## [65,] 1.58562419 0.379341377 0.47902778 0.513000867 -0.1810878 -0.0836242
## [66,] -0.78334568 -1.077475169 -1.31598149 -1.130460886 -0.1810878 -0.0836242
## [67,] -0.78334568 -1.077475169 -1.31598149 -1.130460886 -0.1810878 -0.0836242
## [68,] 0.23192712 0.350125817 0.36363433 0.591260951 -0.1810878 -0.0836242
## [69,] -0.78334568 -1.077475169 -1.31598149 -1.130460886 -0.1810878 -0.0836242
## [70,] 2.77010913 0.002014998 0.12216284 0.389349935 -0.1810878 -0.0836242
## [71,] 0.90877565 0.952758631 0.41278339 1.248645652 -0.1810878 -0.0836242
## [72,] 1.07798779 0.286247729 0.26319929 0.619434581 -0.1810878 -0.0836242
## [73,] -0.78334568 -1.077475169 -1.31598149 -1.130460886 -0.1810878 -0.0836242
## [74,] -0.78334568 -1.077475169 -1.31598149 -1.130460886 -0.1810878 -0.0836242
## [75,] -0.78334568 -1.077475169 -1.31598149 -1.130460886 -0.1810878 -0.0836242
## [76,] -0.78334568 -1.077475169 -1.31598149 -1.130460886 -0.1810878 -0.0836242
## [77,] -0.78334568 -1.077475169 -1.31598149 -1.130460886 -0.1810878 -0.0836242
## [78,] -0.78334568 -1.077475169 -1.31598149 -1.130460886 -0.1810878 -0.0836242
## [79,] -0.78334568 -1.077475169 -1.31598149 -1.130460886 -0.1810878 -0.0836242
## [80,] 1.58562419 0.535322754 0.39355115 0.780650353 -0.1810878 -0.0836242
## [81,] 0.23192712 0.993858486 0.59869507 1.060821452 -0.1810878 -0.0836242
## [82,] 1.92404846 0.256041812 0.65639180 0.239090575 -0.1810878 -0.0836242
## [83,] -0.78334568 -1.077475169 -1.31598149 -1.130460886 -0.1810878 -0.0836242
## [84,] 0.06271499 1.596491299 0.74400534 1.499077919 -0.1810878 -0.0836242
## [85,] -0.78334568 -1.077475169 -1.31598149 -1.130460886 -0.1810878 -0.0836242
## [86,] 1.07798779 -0.015316267 0.31021144 0.192134525 3.0558571 -0.0836242
## [87,] -0.78334568 -1.077475169 -1.31598149 -1.130460886 -0.1810878 -0.0836242
## [88,] -0.78334568 -1.077475169 -1.31598149 -1.130460886 -0.1810878 -0.0836242
## [89,] -0.78334568 0.000000000 2.20992959 0.000000000 -0.1810878 -0.0836242
## [90,] 2.09326059 0.282781477 0.31875910 0.553696111 -0.1810878 -0.0836242
## [91,] -0.78334568 -1.077475169 -1.31598149 -1.130460886 -0.1810878 -0.0836242
## [92,] -0.78334568 -1.077475169 -1.31598149 -1.130460886 -0.1810878 -0.0836242
## [93,] 0.06271499 1.507359083 0.60724273 1.592990020 -0.1810878 -0.0836242
## [94,] 0.23192712 -0.103953303 1.16284084 -0.335338438 -0.1810878 -0.0836242
## [95,] -0.61413355 1.794562889 1.16284084 1.217341619 -0.1810878 -0.0836242
## [96,] -0.78334568 -1.077475169 -1.31598149 -1.130460886 -0.1810878 -0.0836242
## [97,] -0.10649715 1.299383913 0.73545768 1.217341619 -0.1810878 -0.0836242
## [98,] -0.78334568 -1.077475169 -1.31598149 -1.130460886 -0.1810878 -0.0836242
## [99,] -0.61413355 -0.384224603 0.17985957 -0.191339884 -0.1810878 -0.0836242
## [100,] -0.78334568 0.000000000 1.14147168 0.000000000 -0.1810878 -0.0836242
## [101,] -0.78334568 0.000000000 1.19489458 0.000000000 -0.1810878 -0.0836242
## [102,] 1.24719992 0.011918577 0.20763948 0.317350659 -0.1810878 -0.0836242
## [103,] 1.07798779 -0.073747386 0.50467077 -0.013689495 -0.1810878 11.8746365
## [104,] -0.78334568 0.000000000 1.03462589 0.000000000 -0.1810878 -0.0836242
## [105,] -0.61413355 1.002276528 0.92778010 0.747781118 -0.1810878 -0.0836242
## [106,] -0.10649715 1.447937606 0.97692917 1.099951494 -0.1810878 -0.0836242
## [107,] -0.10649715 1.757424466 0.90641095 1.452121869 -0.1810878 -0.0836242
## [108,] 0.06271499 0.051532895 1.07950113 -0.175687867 -0.1810878 -0.0836242
## [109,] 0.73956352 0.083224350 0.82948198 -0.034819717 -0.1810878 -0.0836242
## [110,] 0.06271499 0.209990167 0.41919414 0.372132717 -0.1810878 -0.0836242
## [111,] -0.78334568 0.000000000 0.34012826 0.000000000 -0.1810878 -0.0836242
## [112,] -0.44492141 1.274624965 0.71408852 1.217341619 -0.1810878 -0.0836242
## [113,] -0.10649715 1.249866016 0.70767778 1.197776598 -0.1810878 -0.0836242
## [114,] 0.23192712 0.473920561 0.62647497 0.486392439 -0.1810878 -0.0836242
## [115,] 0.06271499 0.259508065 -0.06374883 1.029517418 -0.1810878 -0.0836242
## [116,] 2.09326059 0.005976429 0.15421658 0.360393705 -0.1810878 -0.0836242
## [117,] -0.44492141 1.126071272 1.40003850 0.513000867 -0.1810878 -0.0836242
## [118,] -0.78334568 0.000000000 1.10514412 0.000000000 -0.1810878 -0.0836242
## [119,] -0.10649715 0.024298051 0.68417170 -0.015254696 -0.1810878 -0.0836242
## [120,] -0.78334568 0.000000000 1.38935392 0.000000000 -0.1810878 -0.0836242
## [121,] -0.27570928 0.160472270 1.35516326 -0.191339884 -0.1810878 -0.0836242
## [122,] 2.09326059 0.139674753 0.42346797 0.286829226 3.0558571 -0.0836242
## [123,] -0.44492141 1.571732350 0.79315441 1.412991828 -0.1810878 -0.0836242
## [124,] -0.10649715 1.707906568 0.60724273 1.804292245 -0.1810878 -0.0836242
## [125,] 1.07798779 -0.375311382 -0.03383201 -0.020732902 3.0558571 -0.0836242
## [126,] -0.78334568 0.000000000 1.30174037 0.000000000 -0.1810878 -0.0836242
## [127,] 1.58562419 0.096594182 0.45765863 0.210916945 -0.1810878 -0.0836242
## [128,] -0.27570928 1.299383913 0.90641095 1.034213023 -0.1810878 -0.0836242
## [129,] 0.40113925 1.200348118 1.00257216 0.858910436 -0.1810878 -0.0836242
## [130,] 1.58562419 -0.055425764 0.17772266 0.255525193 -0.1810878 -0.0836242
## [131,] -0.27570928 -0.202989098 0.30166377 -0.034819717 -0.1810878 -0.0836242
## [132,] 0.40113925 0.549187766 0.47048012 0.714129282 -0.1810878 -0.0836242
## [133,] -0.61413355 1.348901811 1.30174037 0.747781118 -0.1810878 -0.0836242
## [134,] -0.78334568 0.000000000 0.71408852 0.000000000 -0.1810878 -0.0836242
## [135,] 0.90877565 0.209990167 0.66707637 0.184308517 3.0558571 -0.0836242
## [136,] 0.06271499 -0.265381649 0.14353200 -0.003515684 -0.1810878 -0.0836242
## [137,] 1.24719992 0.296646488 0.29952686 0.591260951 -0.1810878 -0.0836242
## [138,] -0.27570928 1.579655214 0.97692917 1.217341619 -0.1810878 -0.0836242
## [139,] 2.77010913 0.211970883 0.45338479 0.344741688 -0.1810878 -0.0836242
## [140,] 2.26247272 0.204048020 0.57305208 0.243003579 -0.1810878 -0.0836242
## [141,] 2.60089699 -0.042551110 0.36363433 0.117787446 -0.1810878 -0.0836242
## [142,] -0.44492141 0.531856501 2.30609080 -0.230469926 -0.1810878 -0.0836242
## [143,] 1.24719992 0.420441232 0.23541938 0.826041201 -0.1810878 -0.0836242
## attr(,"scaled:center")
## Mat.x Inns.x NO Runs.x HS Avg.x
## 7.286713e+00 6.013986e+00 1.251748e+00 1.323497e+02 3.315385e+01 1.740893e+01
## BF SR.x X100 X50 X4s X6s
## 9.502797e+01 9.312028e+01 3.496503e-02 7.062937e-01 1.146154e+01 6.069930e+00
## Mat.y Inns.y Ov Runs.y Wkts Avg.y
## 5.608392e+00 5.006993e+00 1.604755e+01 1.372308e+02 4.629371e+00 2.175931e+01
## Econ SR.y X4w X5w
## 6.158322e+00 1.444492e+01 5.594406e-02 6.993007e-03
## attr(,"scaled:scale")
## Mat.x Inns.x NO Runs.x HS Avg.x
## 6.0776923 5.4990217 1.6292595 175.4822433 31.9696836 16.7918365
## BF SR.x X100 X50 X4s X6s
## 120.2869190 67.2028183 0.2192364 1.4478355 16.7061468 8.7939242
## Mat.y Inns.y Ov Runs.y Wkts Avg.y
## 5.6419392 5.3871214 18.8202050 153.6627317 5.9097417 20.1947185
## Econ SR.y X4w X5w
## 4.6796416 12.7779061 0.3089333 0.0836242# Print dimensions to confirm
print(dim(scaled_data_clean))## [1] 143 22print(length(constant_columns))## [1] 24print(dim(scaled_data_clean))## [1] 143 22# Apply K-means clustering
optimal_k <- 3 # Replace with the optimal number of clusters from the elbow method
kmeans_result <- kmeans(scaled_data_clean, centers = optimal_k, nstart = 10)
kmeans_result## K-means clustering with 3 clusters of sizes 41, 60, 42
##
## Cluster means:
## Mat.x Inns.x NO Runs.x HS Avg.x BF
## 1 0.3942637 0.02850429 0.3844081 -0.2923422 -0.2458943 -0.2142711 -0.3155356
## 2 -0.8479172 -0.76631558 -0.5125529 -0.6248476 -0.6481301 -0.5406564 -0.6291455
## 3 0.8264338 1.06691093 0.3569628 1.1780211 1.1659398 0.9815356 1.2068021
## SR.x X100 X50 X4s X6s Mat.y Inns.y
## 1 0.1991672 -0.1594855 -0.3867513 -0.3196181 -0.1993259 1.2539191 1.3207347
## 2 -0.6245514 -0.1594855 -0.4648044 -0.5922893 -0.5784217 -0.2939400 -0.3044903
## 3 0.6977911 0.3835247 1.0415492 1.1581357 1.0208968 -0.8041496 -0.8543025
## Ov Runs.y Wkts Avg.y Econ SR.y X4w
## 1 1.2861765 1.2643367 1.1646574 0.6990217 0.4787672 0.8310231 0.29261143
## 2 -0.3139827 -0.2723330 -0.2785295 0.2214131 0.5117938 0.1592183 -0.07318966
## 3 -0.8070065 -0.8451862 -0.7390282 -0.9986828 -1.1985020 -1.0386916 -0.18108783
## X5w
## 1 -0.0836242
## 2 0.1156801
## 3 -0.0836242
##
## Clustering vector:
## [1] 3 3 3 3 3 3 1 1 1 1 1 1 3 2 3 3 2 2 1 2 2 2 2 2 1 2 3 1 3 3 2 1 1 1 2 2 3
## [38] 3 1 1 3 2 3 3 2 3 1 1 3 2 2 3 3 2 2 3 1 1 2 2 1 3 3 3 1 3 3 1 3 1 1 1 2 3
## [75] 3 3 3 3 2 1 3 1 3 1 2 1 3 3 2 1 3 3 1 2 1 3 1 3 3 2 2 2 2 2 2 2 2 2 2 2 2
## [112] 2 2 2 2 1 2 2 2 2 2 1 2 2 2 2 1 2 2 1 2 2 2 2 2 2 1 2 1 1 1 2 1
##
## Within cluster sum of squares by cluster:
## [1] 546.7575 507.7763 575.3275
## (between_SS / total_SS = 47.8 %)
##
## Available components:
##
## [1] "cluster" "centers" "totss" "withinss" "tot.withinss"
## [6] "betweenss" "size" "iter" "ifault"# Visualize the clusters
fviz_cluster(kmeans_result, data = scaled_data_clean,
geom = "point", stand = FALSE) +
labs(title = "Cricket Player Performance Clusters")
Conclusion
Just used EDA to analyze the dataset before using K-means
Three separate clusters of cricket player performance data are revealed by the K-means clustering results. This is a brief conclusion:
Cluster 1: The majority of metrics show somewhat positive or negative values for players in this group, who typically perform in a moderate manner. Their balanced capabilities can be seen in their batting and bowling statistics, which are comparatively average.
Cluster 2: The players that perform well are represented in this cluster. In terms of performance metrics, these players are noticeably superior. They bat with greater runs, averages, and strike rates, and bowl with better economy rates and wicket totals.
Cluster 3: Players that perform below average are in this group. In comparison to the other two clusters, their batting and bowling statistics are typically lower, indicating a worse overall performance.
The clustering suggests that players naturally fall into groups according to how well they perform, which may be useful in identifying both players who excel and those who might use some improvement.