Fifa Players Analysis: Correalting players Age with Overall rating and Potential
Anuj Agarwal
30 December 2017
Email: anujagarwal237@gmail.com
College: IIT Bombay
1. Introduction
Ultimate Team is one of the most popular modes in FIFA, as people are able to build their dream squads of the best players from around the world.Building a team is the biggest challenge in FIFA 18 Ultimate Team. It doesn’t need to be the best team, this is because the budget may not allow it, but it needs to be the player’s stature for him to obtain success.Planning and building the team is half the fun for some people, while others simply want the best squad available.
2. Overview of the Study
I have been a huge soccer fan since my childhood and have grown up loving the sport. With this project, I aim to combine my knowledge of data analytics and the passion for the sport to discover insights that we may not normally come across while watching a particular game or discussing it with our friends.We will be utilizing data about the attributes of the players from the latest EA Sports FIFA 18- Soccer Video Game.This paper analysises the correlation between a players age and other variables like- overall rating and potential.
3. Dataset
The data used in this paper is obtained from a secondary source.(https://www.kaggle.com/kamils/fifa-18-data-exploration/data) This dataset contains data of over 18000 players with 75 different columns including Overall rating and Potential(variables taken into account in this analysis).
setwd("C:/Anuj")
players <- read.csv(paste("CompleteDataset.csv",sep=""))
dim(players)## [1] 17981 75head(players)## X Name Age Photo
## 1 0 Cristiano Ronaldo 32 https://cdn.sofifa.org/48/18/players/20801.png
## 2 1 L. Messi 30 https://cdn.sofifa.org/48/18/players/158023.png
## 3 2 Neymar 25 https://cdn.sofifa.org/48/18/players/190871.png
## 4 3 L. Suárez 30 https://cdn.sofifa.org/48/18/players/176580.png
## 5 4 M. Neuer 31 https://cdn.sofifa.org/48/18/players/167495.png
## 6 5 R. Lewandowski 28 https://cdn.sofifa.org/48/18/players/188545.png
## Nationality Flag Overall Potential
## 1 Portugal https://cdn.sofifa.org/flags/38.png 94 94
## 2 Argentina https://cdn.sofifa.org/flags/52.png 93 93
## 3 Brazil https://cdn.sofifa.org/flags/54.png 92 94
## 4 Uruguay https://cdn.sofifa.org/flags/60.png 92 92
## 5 Germany https://cdn.sofifa.org/flags/21.png 92 92
## 6 Poland https://cdn.sofifa.org/flags/37.png 91 91
## Club Club.Logo Value
## 1 Real Madrid CF https://cdn.sofifa.org/24/18/teams/243.png €95.5M
## 2 FC Barcelona https://cdn.sofifa.org/24/18/teams/241.png €105M
## 3 Paris Saint-Germain https://cdn.sofifa.org/24/18/teams/73.png €123M
## 4 FC Barcelona https://cdn.sofifa.org/24/18/teams/241.png €97M
## 5 FC Bayern Munich https://cdn.sofifa.org/24/18/teams/21.png €61M
## 6 FC Bayern Munich https://cdn.sofifa.org/24/18/teams/21.png €92M
## Wage Special Acceleration Aggression Agility Balance Ball.control
## 1 €565K 2228 89 63 89 63 93
## 2 €565K 2154 92 48 90 95 95
## 3 €280K 2100 94 56 96 82 95
## 4 €510K 2291 88 78 86 60 91
## 5 €230K 1493 58 29 52 35 48
## 6 €355K 2143 79 80 78 80 89
## Composure Crossing Curve Dribbling Finishing Free.kick.accuracy
## 1 95 85 81 91 94 76
## 2 96 77 89 97 95 90
## 3 92 75 81 96 89 84
## 4 83 77 86 86 94 84
## 5 70 15 14 30 13 11
## 6 87 62 77 85 91 84
## GK.diving GK.handling GK.kicking GK.positioning GK.reflexes
## 1 7 11 15 14 11
## 2 6 11 15 14 8
## 3 9 9 15 15 11
## 4 27 25 31 33 37
## 5 91 90 95 91 89
## 6 15 6 12 8 10
## Heading.accuracy Interceptions Jumping Long.passing Long.shots Marking
## 1 88 29 95 77 92 22
## 2 71 22 68 87 88 13
## 3 62 36 61 75 77 21
## 4 77 41 69 64 86 30
## 5 25 30 78 59 16 10
## 6 85 39 84 65 83 25
## Penalties Positioning Reactions Short.passing Shot.power Sliding.tackle
## 1 85 95 96 83 94 23
## 2 74 93 95 88 85 26
## 3 81 90 88 81 80 33
## 4 85 92 93 83 87 38
## 5 47 12 85 55 25 11
## 6 81 91 91 83 88 19
## Sprint.speed Stamina Standing.tackle Strength Vision Volleys CAM CB CDM
## 1 91 92 31 80 85 88 89 53 62
## 2 87 73 28 59 90 85 92 45 59
## 3 90 78 24 53 80 83 88 46 59
## 4 77 89 45 80 84 88 87 58 65
## 5 61 44 10 83 70 11 NA NA NA
## 6 83 79 42 84 78 87 84 57 62
## CF CM ID LAM LB LCB LCM LDM LF LM LS LW LWB Preferred.Positions RAM
## 1 91 82 20801 89 61 53 82 62 91 89 92 91 66 ST LW 89
## 2 92 84 158023 92 57 45 84 59 92 90 88 91 62 RW 92
## 3 88 79 190871 88 59 46 79 59 88 87 84 89 64 LW 88
## 4 88 80 176580 87 64 58 80 65 88 85 88 87 68 ST 87
## 5 NA NA 167495 NA NA NA NA NA NA NA NA NA NA GK NA
## 6 87 78 188545 84 58 57 78 62 87 82 88 84 61 ST 84
## RB RCB RCM RDM RF RM RS RW RWB ST
## 1 61 53 82 62 91 89 92 91 66 92
## 2 57 45 84 59 92 90 88 91 62 88
## 3 59 46 79 59 88 87 84 89 64 84
## 4 64 58 80 65 88 85 88 87 68 88
## 5 NA NA NA NA NA NA NA NA NA NA
## 6 58 57 78 62 87 82 88 84 61 883.1 Descriptive statistics
library(psych)
describe(players)[,c(1:5)]## vars n mean sd median
## X 1 17981 8990.00 5190.81 8990
## Name* 2 17981 8460.96 4892.67 8422
## Age 3 17981 25.14 4.61 25
## Photo* 4 17981 8960.84 5170.09 8952
## Nationality* 5 17981 77.85 47.91 72
## Flag* 6 17981 94.16 37.81 105
## Overall 7 17981 66.25 6.99 66
## Potential 8 17981 71.19 6.10 71
## Club* 9 17981 321.15 189.04 322
## Club.Logo* 10 17981 329.41 189.94 333
## Value* 11 17981 102.93 67.10 108
## Wage* 12 17981 60.55 36.37 59
## Special 13 17981 1594.10 272.15 1633
## Acceleration* 14 17981 85.02 36.98 85
## Aggression* 15 17981 69.37 37.14 69
## Agility* 16 17981 79.50 34.22 79
## Balance* 17 17981 72.34 29.55 74
## Ball.control* 18 17981 91.87 44.98 97
## Composure* 19 17981 80.83 36.05 81
## Crossing* 20 17981 72.52 46.08 68
## Curve* 21 17981 59.01 37.59 54
## Dribbling* 22 17981 91.11 48.50 96
## Finishing* 23 17981 69.81 47.49 65
## Free.kick.accuracy* 24 17981 45.53 31.13 40
## GK.diving* 25 17981 34.64 35.25 8
## GK.handling* 26 17981 33.61 34.67 7
## GK.kicking* 27 17981 38.33 38.72 8
## GK.positioning* 28 17981 36.23 36.60 8
## GK.reflexes* 29 17981 35.28 36.09 8
## Heading.accuracy* 30 17981 67.98 37.34 65
## Interceptions* 31 17981 72.08 48.46 71
## Jumping* 32 17981 68.68 32.72 64
## Long.passing* 33 17981 83.71 45.39 85
## Long.shots* 34 17981 63.28 39.16 58
## Marking* 35 17981 69.66 51.46 61
## Penalties* 36 17981 49.10 24.77 46
## Positioning* 37 17981 72.39 41.34 70
## Reactions* 38 17981 75.50 35.20 75
## Short.passing* 39 17981 102.19 52.00 101
## Shot.power* 40 17981 70.07 36.52 70
## Sliding.tackle* 41 17981 63.91 45.97 62
## Sprint.speed* 42 17981 96.18 46.01 89
## Stamina* 43 17981 92.75 44.84 92
## Standing.tackle* 44 17981 72.51 52.40 69
## Strength* 45 17981 86.30 36.86 78
## Vision* 46 17981 72.75 38.58 70
## Volleys* 47 17981 48.41 29.13 44
## CAM 48 15952 59.25 9.88 60
## CB 49 15952 55.55 12.19 57
## CDM 50 15952 56.87 10.31 58
## CF 51 15952 59.03 9.93 60
## CM 52 15952 58.51 8.89 59
## ID 53 17981 207658.71 32291.67 214057
## LAM 54 15952 59.25 9.88 60
## LB 55 15952 56.98 9.79 58
## LCB 56 15952 55.55 12.19 57
## LCM 57 15952 58.51 8.89 59
## LDM 58 15952 56.87 10.31 58
## LF 59 15952 59.03 9.93 60
## LM 60 15952 60.06 9.35 61
## LS 61 15952 58.20 9.18 59
## LW 62 15952 59.36 9.98 60
## LWB 63 15952 57.70 9.14 58
## Preferred.Positions* 64 17981 335.92 233.08 280
## RAM 65 15952 59.25 9.88 60
## RB 66 15952 56.98 9.79 58
## RCB 67 15952 55.55 12.19 57
## RCM 68 15952 58.51 8.89 59
## RDM 69 15952 56.87 10.31 58
## RF 70 15952 59.03 9.93 60
## RM 71 15952 60.06 9.35 61
## RS 72 15952 58.20 9.18 59
## RW 73 15952 59.36 9.98 60
## RWB 74 15952 57.70 9.14 58
## ST 75 15952 58.20 9.18 593.2 Contingency tables
str(players)## 'data.frame': 17981 obs. of 75 variables:
## $ X : int 0 1 2 3 4 5 6 7 8 9 ...
## $ Name : Factor w/ 16975 levels "A. Ömür","A. Örn Arnarson",..: 3203 9648 12428 9850 11176 13779 4211 4519 15725 5805 ...
## $ Age : int 32 30 25 30 31 28 26 26 27 29 ...
## $ Photo : Factor w/ 17929 levels "https://cdn.sofifa.org/48/18/players/101317.png",..: 7185 775 4020 2122 1521 3500 4392 2738 2666 1544 ...
## $ Nationality : Factor w/ 165 levels "Afghanistan",..: 122 6 19 159 59 121 139 13 59 6 ...
## $ Flag : Factor w/ 165 levels "https://cdn.sofifa.org/flags/1.png",..: 109 125 127 134 88 108 117 140 88 125 ...
## $ Overall : int 94 93 92 92 92 91 90 90 90 90 ...
## $ Potential : int 94 93 94 92 92 91 92 91 90 90 ...
## $ Club : Factor w/ 648 levels ""," SSV Jahn Regensburg",..: 472 226 437 226 229 229 384 148 472 339 ...
## $ Club.Logo : Factor w/ 679 levels "https://cdn.sofifa.org/24/18/teams/1.png",..: 467 465 610 465 448 448 62 549 467 528 ...
## $ Value : Factor w/ 207 levels "€0","€1.1M",..: 203 13 21 206 168 202 170 198 187 186 ...
## $ Wage : Factor w/ 142 levels "€0","€100K",..: 94 94 54 88 42 68 37 57 66 52 ...
## $ Special : int 2228 2154 2100 2291 1493 2143 1458 2096 2165 1961 ...
## $ Acceleration : Factor w/ 161 levels "11","12","13",..: 151 156 159 150 57 130 55 158 63 126 ...
## $ Aggression : Factor w/ 151 levels "11","12","13",..: 79 44 59 124 20 129 31 57 72 48 ...
## $ Agility : Factor w/ 146 levels "14","15","16",..: 139 140 146 136 49 118 64 143 101 110 ...
## $ Balance : Factor w/ 135 levels "11","15","16",..: 63 134 115 57 23 109 31 130 82 82 ...
## $ Ball.control : Factor w/ 184 levels "10","11","12",..: 182 184 184 180 49 177 40 181 177 172 ...
## $ Composure : Factor w/ 161 levels "11","12","13",..: 160 161 159 150 123 154 97 154 152 153 ...
## $ Crossing : Factor w/ 176 levels "10","11","12",..: 168 155 153 155 6 100 8 162 168 130 ...
## $ Curve : Factor w/ 153 levels "10","11","12",..: 141 150 141 147 5 133 12 143 146 126 ...
## $ Dribbling : Factor w/ 194 levels "10","11","11+6",..: 190 194 193 183 23 182 10 192 172 180 ...
## $ Finishing : Factor w/ 187 levels "10","11","12",..: 186 187 182 186 4 185 4 176 161 185 ...
## $ Free.kick.accuracy : Factor w/ 136 levels "10","11","12",..: 116 134 127 127 2 127 10 120 127 76 ...
## $ GK.diving : Factor w/ 100 levels "1","10","11",..: 66 45 98 17 100 7 99 3 2 30 ...
## $ GK.handling : Factor w/ 97 levels "1","10","11",..: 3 3 95 14 96 47 91 4 3 4 ...
## $ GK.kicking : Factor w/ 103 levels "1","10","11",..: 7 7 7 22 103 4 100 58 5 81 ...
## $ GK.positioning : Factor w/ 99 levels "1","10","11",..: 6 6 7 21 99 88 95 88 75 35 ...
## $ GK.reflexes : Factor w/ 102 levels "1","10","11",..: 3 85 3 22 100 2 102 85 2 2 ...
## $ Heading.accuracy : Factor w/ 158 levels "10","11","12",..: 151 118 87 136 16 147 12 68 61 149 ...
## $ Interceptions : Factor w/ 174 levels "10","10-11","11",..: 28 18 37 47 29 43 29 47 166 15 ...
## $ Jumping : Factor w/ 140 levels "13","15","16",..: 140 74 50 80 115 127 69 42 11 118 ...
## $ Long.passing : Factor w/ 188 levels "10","11","12",..: 168 182 162 119 98 122 67 174 188 98 ...
## $ Long.shots : Factor w/ 156 levels "10","11","12",..: 156 152 136 150 7 147 3 146 154 146 ...
## $ Marking : Factor w/ 183 levels "10","11","12",..: 16 4 15 30 1 23 4 23 108 3 ...
## $ Penalties : Factor w/ 117 levels "10","11","12",..: 109 96 104 109 41 104 32 110 95 88 ...
## $ Positioning : Factor w/ 171 levels "10","10-3","11",..: 171 170 167 169 4 168 4 161 151 169 ...
## $ Reactions : Factor w/ 158 levels "28","30","31",..: 158 157 151 156 148 154 151 148 149 151 ...
## $ Short.passing : Factor w/ 209 levels "10","11","12",..: 199 205 195 199 71 199 57 203 207 179 ...
## $ Shot.power : Factor w/ 153 levels "10","11","12",..: 153 143 134 146 17 147 24 131 146 147 ...
## $ Sliding.tackle : Factor w/ 169 levels "10","11","12",..: 18 22 33 40 2 12 4 17 123 10 ...
## $ Sprint.speed : Factor w/ 194 levels "11","12","13",..: 188 178 185 143 69 165 61 178 48 155 ...
## $ Stamina : Factor w/ 180 levels "12","13","14",..: 177 124 144 173 38 149 31 149 142 117 ...
## $ Standing.tackle : Factor w/ 187 levels "10","11","12",..: 28 24 18 53 1 49 15 22 174 16 ...
## $ Strength : Factor w/ 168 levels "12","20","21",..: 137 65 48 137 144 148 72 73 114 151 ...
## $ Vision : Factor w/ 173 levels "10","11","12",..: 165 170 160 164 127 154 119 166 168 127 ...
## $ Volleys : Factor w/ 125 levels "10","11","12",..: 121 118 116 121 2 120 4 111 115 121 ...
## $ CAM : num 89 92 88 87 NA 84 NA 88 83 81 ...
## $ CB : num 53 45 46 58 NA 57 NA 47 72 46 ...
## $ CDM : num 62 59 59 65 NA 62 NA 61 82 52 ...
## $ CF : num 91 92 88 88 NA 87 NA 87 81 84 ...
## $ CM : num 82 84 79 80 NA 78 NA 81 87 71 ...
## $ ID : int 20801 158023 190871 176580 167495 188545 193080 183277 182521 167664 ...
## $ LAM : num 89 92 88 87 NA 84 NA 88 83 81 ...
## $ LB : num 61 57 59 64 NA 58 NA 59 76 51 ...
## $ LCB : num 53 45 46 58 NA 57 NA 47 72 46 ...
## $ LCM : num 82 84 79 80 NA 78 NA 81 87 71 ...
## $ LDM : num 62 59 59 65 NA 62 NA 61 82 52 ...
## $ LF : num 91 92 88 88 NA 87 NA 87 81 84 ...
## $ LM : num 89 90 87 85 NA 82 NA 87 81 79 ...
## $ LS : num 92 88 84 88 NA 88 NA 82 77 87 ...
## $ LW : num 91 91 89 87 NA 84 NA 88 80 82 ...
## $ LWB : num 66 62 64 68 NA 61 NA 64 78 55 ...
## $ Preferred.Positions: Factor w/ 802 levels "CAM ","CAM CB ",..: 781 650 428 736 280 736 280 428 122 736 ...
## $ RAM : num 89 92 88 87 NA 84 NA 88 83 81 ...
## $ RB : num 61 57 59 64 NA 58 NA 59 76 51 ...
## $ RCB : num 53 45 46 58 NA 57 NA 47 72 46 ...
## $ RCM : num 82 84 79 80 NA 78 NA 81 87 71 ...
## $ RDM : num 62 59 59 65 NA 62 NA 61 82 52 ...
## $ RF : num 91 92 88 88 NA 87 NA 87 81 84 ...
## $ RM : num 89 90 87 85 NA 82 NA 87 81 79 ...
## $ RS : num 92 88 84 88 NA 88 NA 82 77 87 ...
## $ RW : num 91 91 89 87 NA 84 NA 88 80 82 ...
## $ RWB : num 66 62 64 68 NA 61 NA 64 78 55 ...
## $ ST : num 92 88 84 88 NA 88 NA 82 77 87 ...mytable <- with(players,table(Preferred.Positions))
head(mytable)## Preferred.Positions
## CAM CAM CB CAM CB CDM CAM CB CM CAM CB CM CDM
## 320 1 2 1 1
## CAM CDM
## 18mytable <- xtabs(~Preferred.Positions+Nationality,data=players)
head(mytable)## Nationality
## Preferred.Positions Afghanistan Albania Algeria Angola Antigua & Barbuda
## CAM 0 1 1 0 0
## CAM CB 0 0 0 0 0
## CAM CB CDM 0 0 0 0 0
## CAM CB CM 0 0 0 0 0
## CAM CB CM CDM 0 0 0 0 0
## CAM CDM 0 0 0 0 0
## Nationality
## Preferred.Positions Argentina Armenia Australia Austria Azerbaijan
## CAM 24 1 9 5 0
## CAM CB 0 0 0 0 0
## CAM CB CDM 0 0 0 0 0
## CAM CB CM 0 0 0 0 0
## CAM CB CM CDM 0 0 0 0 0
## CAM CDM 0 0 0 0 0
## Nationality
## Preferred.Positions Barbados Belarus Belgium Belize Benin Bermuda Bolivia
## CAM 0 0 7 0 0 0 1
## CAM CB 0 0 0 0 0 0 0
## CAM CB CDM 0 0 0 0 0 0 0
## CAM CB CM 0 0 1 0 0 0 0
## CAM CB CM CDM 0 0 0 0 0 0 0
## CAM CDM 0 0 0 0 0 0 0
## Nationality
## Preferred.Positions Bosnia Herzegovina Brazil Brunei Darussalam Bulgaria
## CAM 2 38 0 0
## CAM CB 0 0 0 0
## CAM CB CDM 0 0 0 0
## CAM CB CM 0 0 0 0
## CAM CB CM CDM 0 0 0 0
## CAM CDM 1 0 0 0
## Nationality
## Preferred.Positions Burkina Faso Burundi Cameroon Canada Cape Verde
## CAM 0 0 1 0 1
## CAM CB 0 0 0 0 0
## CAM CB CDM 0 0 0 0 0
## CAM CB CM 0 0 0 0 0
## CAM CB CM CDM 0 0 0 0 0
## CAM CDM 0 0 0 0 0
## Nationality
## Preferred.Positions Central African Rep. Chad Chile China PR Colombia
## CAM 0 0 20 0 27
## CAM CB 0 0 0 0 0
## CAM CB CDM 0 0 0 0 0
## CAM CB CM 0 0 0 0 0
## CAM CB CM CDM 0 0 0 0 0
## CAM CDM 0 0 0 0 0
## Nationality
## Preferred.Positions Comoros Congo Costa Rica Croatia Cuba Curacao Cyprus
## CAM 0 0 0 5 0 0 0
## CAM CB 0 0 0 0 0 0 0
## CAM CB CDM 0 0 0 0 0 0 0
## CAM CB CM 0 0 0 0 0 0 0
## CAM CB CM CDM 0 0 0 0 0 0 0
## CAM CDM 1 0 0 0 0 0 0
## Nationality
## Preferred.Positions Czech Republic Denmark Dominican Republic DR Congo
## CAM 0 3 0 0
## CAM CB 0 1 0 0
## CAM CB CDM 0 0 0 0
## CAM CB CM 0 0 0 0
## CAM CB CM CDM 0 0 0 0
## CAM CDM 0 1 0 0
## Nationality
## Preferred.Positions Ecuador Egypt El Salvador England Equatorial Guinea
## CAM 0 0 0 20 0
## CAM CB 0 0 0 0 0
## CAM CB CDM 0 0 0 0 0
## CAM CB CM 0 0 0 0 0
## CAM CB CM CDM 0 0 0 0 0
## CAM CDM 0 0 0 0 0
## Nationality
## Preferred.Positions Eritrea Estonia Ethiopia Faroe Islands Fiji Finland
## CAM 0 2 0 0 0 1
## CAM CB 0 0 0 0 0 0
## CAM CB CDM 0 0 0 0 0 0
## CAM CB CM 0 0 0 0 0 0
## CAM CB CM CDM 0 0 0 0 0 0
## CAM CDM 0 0 0 0 0 0
## Nationality
## Preferred.Positions France FYR Macedonia Gabon Gambia Georgia Germany
## CAM 20 0 0 0 2 20
## CAM CB 0 0 0 0 0 0
## CAM CB CDM 1 0 0 0 0 1
## CAM CB CM 0 0 0 0 0 0
## CAM CB CM CDM 0 0 0 0 0 0
## CAM CDM 1 0 0 0 1 2
## Nationality
## Preferred.Positions Ghana Gibraltar Greece Grenada Guam Guatemala Guinea
## CAM 1 0 1 0 0 0 0
## CAM CB 0 0 0 0 0 0 0
## CAM CB CDM 0 0 0 0 0 0 0
## CAM CB CM 0 0 0 0 0 0 0
## CAM CB CM CDM 0 0 0 0 0 0 0
## CAM CDM 0 0 0 0 0 0 0
## Nationality
## Preferred.Positions Guinea Bissau Guyana Haiti Honduras Hong Kong Hungary
## CAM 0 0 1 0 0 0
## CAM CB 0 0 0 0 0 0
## CAM CB CDM 0 0 0 0 0 0
## CAM CB CM 0 0 0 0 0 0
## CAM CB CM CDM 0 0 0 0 0 0
## CAM CDM 1 0 0 0 0 0
## Nationality
## Preferred.Positions Iceland India Iran Iraq Israel Italy Ivory Coast
## CAM 0 0 1 0 0 5 1
## CAM CB 0 0 0 0 0 0 0
## CAM CB CDM 0 0 0 0 0 0 0
## CAM CB CM 0 0 0 0 0 0 0
## CAM CB CM CDM 0 0 0 0 0 0 0
## CAM CDM 0 0 0 0 1 0 0
## Nationality
## Preferred.Positions Jamaica Japan Kazakhstan Kenya Korea DPR
## CAM 1 12 0 0 0
## CAM CB 0 0 0 0 0
## CAM CB CDM 0 0 0 0 0
## CAM CB CM 0 0 0 0 0
## CAM CB CM CDM 0 0 0 0 0
## CAM CDM 0 1 0 0 0
## Nationality
## Preferred.Positions Korea Republic Kosovo Kuwait Kyrgyzstan Latvia Lebanon
## CAM 1 1 0 0 0 0
## CAM CB 0 0 0 0 0 0
## CAM CB CDM 0 0 0 0 0 0
## CAM CB CM 0 0 0 0 0 0
## CAM CB CM CDM 0 0 0 0 0 0
## CAM CDM 0 0 0 0 0 0
## Nationality
## Preferred.Positions Liberia Libya Liechtenstein Lithuania Luxembourg
## CAM 0 0 0 0 0
## CAM CB 0 0 0 0 0
## CAM CB CDM 0 0 0 0 0
## CAM CB CM 0 0 0 0 0
## CAM CB CM CDM 0 0 0 0 0
## CAM CDM 0 0 0 0 0
## Nationality
## Preferred.Positions Madagascar Mali Malta Mauritania Mauritius Mexico
## CAM 0 1 0 0 0 5
## CAM CB 0 0 0 0 0 0
## CAM CB CDM 0 0 0 0 0 0
## CAM CB CM 0 0 0 0 0 0
## CAM CB CM CDM 0 0 0 0 0 0
## CAM CDM 0 0 0 0 0 0
## Nationality
## Preferred.Positions Moldova Montenegro Montserrat Morocco Mozambique
## CAM 0 0 0 2 0
## CAM CB 0 0 0 0 0
## CAM CB CDM 0 0 0 0 0
## CAM CB CM 0 0 0 0 0
## CAM CB CM CDM 0 0 0 0 0
## CAM CDM 0 0 0 0 0
## Nationality
## Preferred.Positions Namibia Netherlands New Caledonia New Zealand Niger
## CAM 0 9 0 0 0
## CAM CB 0 0 0 0 0
## CAM CB CDM 0 0 0 0 0
## CAM CB CM 0 0 0 0 0
## CAM CB CM CDM 0 0 0 0 0
## CAM CDM 0 0 0 0 0
## Nationality
## Preferred.Positions Nigeria Northern Ireland Norway Oman Palestine Panama
## CAM 1 1 2 0 0 0
## CAM CB 0 0 0 0 0 0
## CAM CB CDM 0 0 0 0 0 0
## CAM CB CM 0 0 0 0 0 0
## CAM CB CM CDM 0 0 0 0 0 0
## CAM CDM 0 0 0 0 0 0
## Nationality
## Preferred.Positions Paraguay Peru Philippines Poland Portugal Puerto Rico
## CAM 3 2 0 14 1 0
## CAM CB 0 0 0 0 0 0
## CAM CB CDM 0 0 0 0 0 0
## CAM CB CM 0 0 0 0 0 0
## CAM CB CM CDM 0 0 0 0 0 0
## CAM CDM 0 0 0 1 0 0
## Nationality
## Preferred.Positions Qatar Republic of Ireland Romania Russia
## CAM 0 3 1 3
## CAM CB 0 0 0 0
## CAM CB CDM 0 0 0 0
## CAM CB CM 0 0 0 0
## CAM CB CM CDM 0 0 0 0
## CAM CDM 0 0 0 2
## Nationality
## Preferred.Positions São Tomé & PrÃÂncipe San Marino Saudi Arabia
## CAM 0 0 2
## CAM CB 0 0 0
## CAM CB CDM 0 0 0
## CAM CB CM 0 0 0
## CAM CB CM CDM 0 0 0
## CAM CDM 0 0 0
## Nationality
## Preferred.Positions Scotland Senegal Serbia Sierra Leone Slovakia Slovenia
## CAM 2 0 2 0 2 2
## CAM CB 0 0 0 0 0 0
## CAM CB CDM 0 0 0 0 0 0
## CAM CB CM 0 0 0 0 0 0
## CAM CB CM CDM 0 0 0 0 0 0
## CAM CDM 0 0 1 0 0 0
## Nationality
## Preferred.Positions Somalia South Africa Spain Sri Lanka St Kitts Nevis
## CAM 0 1 5 0 0
## CAM CB 0 0 0 0 0
## CAM CB CDM 0 0 0 0 0
## CAM CB CM 0 0 0 0 0
## CAM CB CM CDM 0 0 0 0 0
## CAM CDM 0 0 2 0 0
## Nationality
## Preferred.Positions St Lucia Sudan Suriname Swaziland Sweden Switzerland
## CAM 0 1 1 0 3 2
## CAM CB 0 0 0 0 0 0
## CAM CB CDM 0 0 0 0 0 0
## CAM CB CM 0 0 0 0 0 0
## CAM CB CM CDM 0 0 0 0 0 1
## CAM CDM 0 0 0 0 0 0
## Nationality
## Preferred.Positions Syria Tanzania Thailand Togo Trinidad & Tobago Tunisia
## CAM 0 0 1 0 0 0
## CAM CB 0 0 0 0 0 0
## CAM CB CDM 0 0 0 0 0 0
## CAM CB CM 0 0 0 0 0 0
## CAM CB CM CDM 0 0 0 0 0 0
## CAM CDM 0 0 0 0 0 0
## Nationality
## Preferred.Positions Turkey Turkmenistan Uganda Ukraine United States
## CAM 3 0 0 1 3
## CAM CB 0 0 0 0 0
## CAM CB CDM 0 0 0 0 0
## CAM CB CM 0 0 0 0 0
## CAM CB CM CDM 0 0 0 0 0
## CAM CDM 2 0 0 0 0
## Nationality
## Preferred.Positions Uruguay Uzbekistan Venezuela Vietnam Wales Zambia
## CAM 1 0 2 0 1 1
## CAM CB 0 0 0 0 0 0
## CAM CB CDM 0 0 0 0 0 0
## CAM CB CM 0 0 0 0 0 0
## CAM CB CM CDM 0 0 0 0 0 0
## CAM CDM 0 0 0 0 0 0
## Nationality
## Preferred.Positions Zimbabwe
## CAM 0
## CAM CB 0
## CAM CB CDM 0
## CAM CB CM 0
## CAM CB CM CDM 0
## CAM CDM 03.3 Boxplot
boxplot(Age~Overall, data=players,col=(c("magenta","green")),
main="Player Age and Overall rating", xlab="Overall rating")
boxplot(Age~Potential, data=players,col=(c("magenta","green")),
main="Player Age and Potential", xlab="Player Potential")
3.4 Histogram
hist(players$Age,
main="Histogram for players Age",
xlab="Age",
border="blue",
col="green",
las=1,
breaks=40)
hist(players$Overall,
main="Histogram for players Overall Rating",
xlab="Overall rating",
border="blue",
col="magenta",
las=1,
breaks=20)
hist(players$Potential,
main="Histogram for players Potential",
xlab="Potential",
border="black",
col="red",
las=1,
breaks=20)
3.5 Correlation Matrix
str(players)## 'data.frame': 17981 obs. of 75 variables:
## $ X : int 0 1 2 3 4 5 6 7 8 9 ...
## $ Name : Factor w/ 16975 levels "A. Ömür","A. Örn Arnarson",..: 3203 9648 12428 9850 11176 13779 4211 4519 15725 5805 ...
## $ Age : int 32 30 25 30 31 28 26 26 27 29 ...
## $ Photo : Factor w/ 17929 levels "https://cdn.sofifa.org/48/18/players/101317.png",..: 7185 775 4020 2122 1521 3500 4392 2738 2666 1544 ...
## $ Nationality : Factor w/ 165 levels "Afghanistan",..: 122 6 19 159 59 121 139 13 59 6 ...
## $ Flag : Factor w/ 165 levels "https://cdn.sofifa.org/flags/1.png",..: 109 125 127 134 88 108 117 140 88 125 ...
## $ Overall : int 94 93 92 92 92 91 90 90 90 90 ...
## $ Potential : int 94 93 94 92 92 91 92 91 90 90 ...
## $ Club : Factor w/ 648 levels ""," SSV Jahn Regensburg",..: 472 226 437 226 229 229 384 148 472 339 ...
## $ Club.Logo : Factor w/ 679 levels "https://cdn.sofifa.org/24/18/teams/1.png",..: 467 465 610 465 448 448 62 549 467 528 ...
## $ Value : Factor w/ 207 levels "€0","€1.1M",..: 203 13 21 206 168 202 170 198 187 186 ...
## $ Wage : Factor w/ 142 levels "€0","€100K",..: 94 94 54 88 42 68 37 57 66 52 ...
## $ Special : int 2228 2154 2100 2291 1493 2143 1458 2096 2165 1961 ...
## $ Acceleration : Factor w/ 161 levels "11","12","13",..: 151 156 159 150 57 130 55 158 63 126 ...
## $ Aggression : Factor w/ 151 levels "11","12","13",..: 79 44 59 124 20 129 31 57 72 48 ...
## $ Agility : Factor w/ 146 levels "14","15","16",..: 139 140 146 136 49 118 64 143 101 110 ...
## $ Balance : Factor w/ 135 levels "11","15","16",..: 63 134 115 57 23 109 31 130 82 82 ...
## $ Ball.control : Factor w/ 184 levels "10","11","12",..: 182 184 184 180 49 177 40 181 177 172 ...
## $ Composure : Factor w/ 161 levels "11","12","13",..: 160 161 159 150 123 154 97 154 152 153 ...
## $ Crossing : Factor w/ 176 levels "10","11","12",..: 168 155 153 155 6 100 8 162 168 130 ...
## $ Curve : Factor w/ 153 levels "10","11","12",..: 141 150 141 147 5 133 12 143 146 126 ...
## $ Dribbling : Factor w/ 194 levels "10","11","11+6",..: 190 194 193 183 23 182 10 192 172 180 ...
## $ Finishing : Factor w/ 187 levels "10","11","12",..: 186 187 182 186 4 185 4 176 161 185 ...
## $ Free.kick.accuracy : Factor w/ 136 levels "10","11","12",..: 116 134 127 127 2 127 10 120 127 76 ...
## $ GK.diving : Factor w/ 100 levels "1","10","11",..: 66 45 98 17 100 7 99 3 2 30 ...
## $ GK.handling : Factor w/ 97 levels "1","10","11",..: 3 3 95 14 96 47 91 4 3 4 ...
## $ GK.kicking : Factor w/ 103 levels "1","10","11",..: 7 7 7 22 103 4 100 58 5 81 ...
## $ GK.positioning : Factor w/ 99 levels "1","10","11",..: 6 6 7 21 99 88 95 88 75 35 ...
## $ GK.reflexes : Factor w/ 102 levels "1","10","11",..: 3 85 3 22 100 2 102 85 2 2 ...
## $ Heading.accuracy : Factor w/ 158 levels "10","11","12",..: 151 118 87 136 16 147 12 68 61 149 ...
## $ Interceptions : Factor w/ 174 levels "10","10-11","11",..: 28 18 37 47 29 43 29 47 166 15 ...
## $ Jumping : Factor w/ 140 levels "13","15","16",..: 140 74 50 80 115 127 69 42 11 118 ...
## $ Long.passing : Factor w/ 188 levels "10","11","12",..: 168 182 162 119 98 122 67 174 188 98 ...
## $ Long.shots : Factor w/ 156 levels "10","11","12",..: 156 152 136 150 7 147 3 146 154 146 ...
## $ Marking : Factor w/ 183 levels "10","11","12",..: 16 4 15 30 1 23 4 23 108 3 ...
## $ Penalties : Factor w/ 117 levels "10","11","12",..: 109 96 104 109 41 104 32 110 95 88 ...
## $ Positioning : Factor w/ 171 levels "10","10-3","11",..: 171 170 167 169 4 168 4 161 151 169 ...
## $ Reactions : Factor w/ 158 levels "28","30","31",..: 158 157 151 156 148 154 151 148 149 151 ...
## $ Short.passing : Factor w/ 209 levels "10","11","12",..: 199 205 195 199 71 199 57 203 207 179 ...
## $ Shot.power : Factor w/ 153 levels "10","11","12",..: 153 143 134 146 17 147 24 131 146 147 ...
## $ Sliding.tackle : Factor w/ 169 levels "10","11","12",..: 18 22 33 40 2 12 4 17 123 10 ...
## $ Sprint.speed : Factor w/ 194 levels "11","12","13",..: 188 178 185 143 69 165 61 178 48 155 ...
## $ Stamina : Factor w/ 180 levels "12","13","14",..: 177 124 144 173 38 149 31 149 142 117 ...
## $ Standing.tackle : Factor w/ 187 levels "10","11","12",..: 28 24 18 53 1 49 15 22 174 16 ...
## $ Strength : Factor w/ 168 levels "12","20","21",..: 137 65 48 137 144 148 72 73 114 151 ...
## $ Vision : Factor w/ 173 levels "10","11","12",..: 165 170 160 164 127 154 119 166 168 127 ...
## $ Volleys : Factor w/ 125 levels "10","11","12",..: 121 118 116 121 2 120 4 111 115 121 ...
## $ CAM : num 89 92 88 87 NA 84 NA 88 83 81 ...
## $ CB : num 53 45 46 58 NA 57 NA 47 72 46 ...
## $ CDM : num 62 59 59 65 NA 62 NA 61 82 52 ...
## $ CF : num 91 92 88 88 NA 87 NA 87 81 84 ...
## $ CM : num 82 84 79 80 NA 78 NA 81 87 71 ...
## $ ID : int 20801 158023 190871 176580 167495 188545 193080 183277 182521 167664 ...
## $ LAM : num 89 92 88 87 NA 84 NA 88 83 81 ...
## $ LB : num 61 57 59 64 NA 58 NA 59 76 51 ...
## $ LCB : num 53 45 46 58 NA 57 NA 47 72 46 ...
## $ LCM : num 82 84 79 80 NA 78 NA 81 87 71 ...
## $ LDM : num 62 59 59 65 NA 62 NA 61 82 52 ...
## $ LF : num 91 92 88 88 NA 87 NA 87 81 84 ...
## $ LM : num 89 90 87 85 NA 82 NA 87 81 79 ...
## $ LS : num 92 88 84 88 NA 88 NA 82 77 87 ...
## $ LW : num 91 91 89 87 NA 84 NA 88 80 82 ...
## $ LWB : num 66 62 64 68 NA 61 NA 64 78 55 ...
## $ Preferred.Positions: Factor w/ 802 levels "CAM ","CAM CB ",..: 781 650 428 736 280 736 280 428 122 736 ...
## $ RAM : num 89 92 88 87 NA 84 NA 88 83 81 ...
## $ RB : num 61 57 59 64 NA 58 NA 59 76 51 ...
## $ RCB : num 53 45 46 58 NA 57 NA 47 72 46 ...
## $ RCM : num 82 84 79 80 NA 78 NA 81 87 71 ...
## $ RDM : num 62 59 59 65 NA 62 NA 61 82 52 ...
## $ RF : num 91 92 88 88 NA 87 NA 87 81 84 ...
## $ RM : num 89 90 87 85 NA 82 NA 87 81 79 ...
## $ RS : num 92 88 84 88 NA 88 NA 82 77 87 ...
## $ RW : num 91 91 89 87 NA 84 NA 88 80 82 ...
## $ RWB : num 66 62 64 68 NA 61 NA 64 78 55 ...
## $ ST : num 92 88 84 88 NA 88 NA 82 77 87 ...cor(players$Age,players$Overall)## [1] 0.4597649plot(players$Age,players$Overall)
cor(players$Age,players$Potential)## [1] -0.2182643plot(players$Age,players$Potential)
3.6 Corrgram
library(corrgram)
corrgram(players, order=TRUE, upper.panel=panel.pie,lower.panel=panel.shade, text.panel=panel.txt,main="Correlogram")
3.7 Scatterplot
library(car) ##
## Attaching package: 'car'## The following object is masked from 'package:psych':
##
## logitscatterplot(Age ~ Overall, data=players,
xlab="Overall Rating", ylab="Age",
main="Scatter plot of Age and Overall rating",
labels=row.names(players))
3.8 T-test
H1:There is no relationship between Age and Overall Rating
H2:There is no relationship between Age and Potential
t.test(players$Age,players$Overall)##
## Welch Two Sample t-test
##
## data: players$Age and players$Overall
## t = -658.19, df = 31155, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -41.22584 -40.98104
## sample estimates:
## mean of x mean of y
## 25.14454 66.24798t.test(players$Age,players$Potential)##
## Welch Two Sample t-test
##
## data: players$Age and players$Potential
## t = -807.08, df = 33475, p-value < 2.2e-16
## alternative hypothesis: true difference in means is not equal to 0
## 95 percent confidence interval:
## -46.15810 -45.93445
## sample estimates:
## mean of x mean of y
## 25.14454 71.19081From the above T-Tests we can observe that, p-value for both the tests are less than 0.05, hence we can conclude that both the hypothesis are rejected.
3.9 Modelling the data
In order to test Hypothesis, we proposed the following model:
\[Age= \beta_0 + \beta_1 Overall + \beta_2 Potential + \epsilon\]
#Linear Regression Model
fit <- lm(formula = players$Age ~ players$Overall+players$Potential, data=players)
summary(fit)##
## Call:
## lm(formula = players$Age ~ players$Overall + players$Potential,
## data = players)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.3686 -1.5991 -0.5582 1.1223 18.0990
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 28.949723 0.207218 139.7 <2e-16 ***
## players$Overall 0.754594 0.003417 220.9 <2e-16 ***
## players$Potential -0.755652 0.003913 -193.1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.337 on 17978 degrees of freedom
## Multiple R-squared: 0.7435, Adjusted R-squared: 0.7435
## F-statistic: 2.606e+04 on 2 and 17978 DF, p-value: < 2.2e-16Model1 <- Age ~
Overall+Potential
fit1 <- lm(Model1, data = players)
summary(fit1)##
## Call:
## lm(formula = Model1, data = players)
##
## Residuals:
## Min 1Q Median 3Q Max
## -9.3686 -1.5991 -0.5582 1.1223 18.0990
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 28.949723 0.207218 139.7 <2e-16 ***
## Overall 0.754594 0.003417 220.9 <2e-16 ***
## Potential -0.755652 0.003913 -193.1 <2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 2.337 on 17978 degrees of freedom
## Multiple R-squared: 0.7435, Adjusted R-squared: 0.7435
## F-statistic: 2.606e+04 on 2 and 17978 DF, p-value: < 2.2e-16We established the effect of Overall rating and Potential on the Age of a player with the simplest model. We regressed Age on Overall rating and Potential.We estimated model, using linear regression.
3.10 Result
The model is a good fit model as p < 0.05. equation:- y(Age) = 28.94 + 0.75(Overall rating) - 0.755652(Potential)
4. Conclusion
This paper was motivated by the need for research that could improve our understanding of how Age of a player is defined by the variables like Overall rating and Potential.We can see from the above equation that our model fits nicely and the dependency of a players age can be predicted from its overall rating and potential.This briefly gives us an overview of a player regarding age while considering him for the Ultimate Team.