Name Age Nationality Overall Potential Club
1 L. Messi 31 Argentina 94 94 FC Barcelona
2 Cristiano Ronaldo 33 Portugal 94 94 Juventus
3 Neymar Jr 26 Brazil 92 93 Paris Saint-Germain
4 De Gea 27 Spain 91 93 Manchester United
5 K. De Bruyne 27 Belgium 91 92 Manchester City
6 E. Hazard 27 Belgium 91 91 Chelsea
7 L. Modri 32 Croatia 91 91 Real Madrid
8 L. Surez 31 Uruguay 91 91 FC Barcelona
9 Sergio Ramos 32 Spain 91 91 Real Madrid
10 J. Oblak 25 Slovenia 90 93 Atletico Madrid
Value Wage Special Preferred.Foot International.Reputation Weak.Foot
1 110.5 565 2202 Left 5 4
2 77 405 2228 Right 5 4
3 118.5 290 2143 Right 5 5
4 72 260 1471 Right 4 3
5 102 355 2281 Right 4 5
6 93 340 2142 Right 4 4
7 67 420 2280 Right 4 4
8 80 455 2346 Right 5 4
9 51 380 2201 Right 4 3
10 68 94 1331 Right 3 3
Skill.Moves Work.Rate Position Jersey.Number Contract.Valid.Until
1 4 Medium/ Medium RF 10 2021
2 5 High/ Low ST 7 2022
3 5 High/ Medium LW 10 2022
4 1 Medium/ Medium GK 1 2020
5 4 High/ High RCM 7 2023
6 4 High/ Medium LF 10 2020
7 4 High/ High RCM 10 2020
8 3 High/ Medium RS 9 2021
9 3 High/ Medium RCB 15 2020
10 1 Medium/ Medium GK 1 2021
Height Weight LS ST RS LW LF CF RF RW LAM CAM RAM LM
1 170.18 72.12119 88+2 88+2 88+2 92+2 93+2 93+2 93+2 92+2 93+2 93+2 93+2 91+2
2 187.96 83.00740 91+3 91+3 91+3 89+3 90+3 90+3 90+3 89+3 88+3 88+3 88+3 88+3
3 175.26 68.03886 84+3 84+3 84+3 89+3 89+3 89+3 89+3 89+3 89+3 89+3 89+3 88+3
4 193.04 76.20352
5 180.34 69.85322 82+3 82+3 82+3 87+3 87+3 87+3 87+3 87+3 88+3 88+3 88+3 88+3
6 172.72 73.93556 83+3 83+3 83+3 89+3 88+3 88+3 88+3 89+3 89+3 89+3 89+3 89+3
7 172.72 66.22449 77+3 77+3 77+3 85+3 84+3 84+3 84+3 85+3 87+3 87+3 87+3 86+3
8 182.88 86.18255 87+5 87+5 87+5 86+5 87+5 87+5 87+5 86+5 85+5 85+5 85+5 84+5
9 182.88 82.10022 73+3 73+3 73+3 70+3 71+3 71+3 71+3 70+3 71+3 71+3 71+3 72+3
10 187.96 87.08974
LCM CM RCM RM LWB LDM CDM RDM RWB LB LCB CB RCB RB
1 84+2 84+2 84+2 91+2 64+2 61+2 61+2 61+2 64+2 59+2 47+2 47+2 47+2 59+2
2 81+3 81+3 81+3 88+3 65+3 61+3 61+3 61+3 65+3 61+3 53+3 53+3 53+3 61+3
3 81+3 81+3 81+3 88+3 65+3 60+3 60+3 60+3 65+3 60+3 47+3 47+3 47+3 60+3
4
5 87+3 87+3 87+3 88+3 77+3 77+3 77+3 77+3 77+3 73+3 66+3 66+3 66+3 73+3
6 82+3 82+3 82+3 89+3 66+3 63+3 63+3 63+3 66+3 60+3 49+3 49+3 49+3 60+3
7 88+3 88+3 88+3 86+3 82+3 81+3 81+3 81+3 82+3 79+3 71+3 71+3 71+3 79+3
8 79+5 79+5 79+5 84+5 69+5 68+5 68+5 68+5 69+5 66+5 63+5 63+5 63+5 66+5
9 75+3 75+3 75+3 72+3 81+3 84+3 84+3 84+3 81+3 84+3 87+3 87+3 87+3 84+3
10
Crossing Finishing HeadingAccuracy ShortPassing Volleys Dribbling Curve
1 84 95 70 90 86 97 93
2 84 94 89 81 87 88 81
3 79 87 62 84 84 96 88
4 17 13 21 50 13 18 21
5 93 82 55 92 82 86 85
6 81 84 61 89 80 95 83
7 86 72 55 93 76 90 85
8 77 93 77 82 88 87 86
9 66 60 91 78 66 63 74
10 13 11 15 29 13 12 13
FKAccuracy LongPassing BallControl Acceleration SprintSpeed Agility
1 94 87 96 91 86 91
2 76 77 94 89 91 87
3 87 78 95 94 90 96
4 19 51 42 57 58 60
5 83 91 91 78 76 79
6 79 83 94 94 88 95
7 78 88 93 80 72 93
8 84 64 90 86 75 82
9 72 77 84 76 75 78
10 14 26 16 43 60 67
Reactions Balance ShotPower Jumping Stamina Strength LongShots Aggression
1 95 95 85 68 72 59 94 48
2 96 70 95 95 88 79 93 63
3 94 84 80 61 81 49 82 56
4 90 43 31 67 43 64 12 38
5 91 77 91 63 90 75 91 76
6 90 94 82 56 83 66 80 54
7 90 94 79 68 89 58 82 62
8 92 83 86 69 90 83 85 87
9 85 66 79 93 84 83 59 88
10 86 49 22 76 41 78 12 34
Interceptions Positioning Vision Penalties Composure Marking StandingTackle
1 22 94 94 75 96 33 28
2 29 95 82 85 95 28 31
3 36 89 87 81 94 27 24
4 30 12 68 40 68 15 21
5 61 87 94 79 88 68 58
6 41 87 89 86 91 34 27
7 83 79 92 82 84 60 76
8 41 92 84 85 85 62 45
9 90 60 63 75 82 87 92
10 19 11 70 11 70 27 12
SlidingTackle GKDiving GKHandling GKKicking GKPositioning GKReflexes
1 26 6 11 15 14 8
2 23 7 11 15 14 11
3 33 9 9 15 15 11
4 13 90 85 87 88 94
5 51 15 13 5 10 13
6 22 11 12 6 8 8
7 73 13 9 7 14 9
8 38 27 25 31 33 37
9 91 11 8 9 7 11
10 18 86 92 78 88 89
Release.Clause
1 226.5
2 127.1
3 228.1
4 138.6
5 196.4
6 172.1
7 137.4
8 164
9 104.6
10 144.5FIFA Analysis
Understanding Wage, Penalties and Preferred Foot for Professional Football Players
Data Understanding
The data which were utilized for this report were generated from The Federation Internationale de Football Association. The data were compiled by the datasetsICR package list in R. The dataset was comprised of 18207 observations and 80 variables.
Viewing the data
This summary displays the first 10 observations of the dataset.
This summary displays the last 10 observations of the dataset.
Name Age Nationality Overall Potential
18198 D. Holland 18 Republic of Ireland 47 61
18199 J. Livesey 18 England 47 70
18200 M. Baldisimo 18 Canada 47 69
18201 J. Young 18 Scotland 47 62
18202 D. Walsh 18 Republic of Ireland 47 68
18203 J. Lundstram 19 England 47 65
18204 N. Christoffersson 19 Sweden 47 63
18205 B. Worman 16 England 47 67
18206 D. Walker-Rice 17 England 47 66
18207 G. Nugent 16 England 46 66
Club Value Wage Special Preferred.Foot
18198 Cork City 60 1 1362 Right
18199 Burton Albion 60 1 792 Right
18200 Vancouver Whitecaps FC 70 1 1303 Right
18201 Swindon Town 60 1 1203 Left
18202 Waterford FC 60 1 1098 Left
18203 Crewe Alexandra 60 1 1307 Right
18204 Trelleborgs FF 60 1 1098 Right
18205 Cambridge United 60 1 1189 Right
18206 Tranmere Rovers 60 1 1228 Right
18207 Tranmere Rovers 60 1 1321 Right
International.Reputation Weak.Foot Skill.Moves Work.Rate Position
18198 1 3 2 Medium/ Medium CM
18199 1 2 1 Medium/ Medium GK
18200 1 3 2 Medium/ High CM
18201 1 2 2 Medium/ Medium ST
18202 1 3 2 Medium/ Medium RB
18203 1 2 2 Medium/ Medium CM
18204 1 2 2 Medium/ Medium ST
18205 1 3 2 Medium/ Medium ST
18206 1 3 2 Medium/ Medium RW
18207 1 3 2 Medium/ Medium CM
Jersey.Number Contract.Valid.Until Height Weight LS ST RS LW
18198 14 2018 177.80 63.95652 45+2 45+2 45+2 49+2
18199 22 2021 180.34 69.85322
18200 65 2021 167.64 68.03886 42+2 42+2 42+2 43+2
18201 21 2019 175.26 71.21400 45+2 45+2 45+2 44+2
18202 29 2018 185.42 76.20352 32+2 32+2 32+2 29+2
18203 22 2019 175.26 60.78138 42+2 42+2 42+2 44+2
18204 21 2020 190.50 77.11070 45+2 45+2 45+2 39+2
18205 33 2021 172.72 67.13167 45+2 45+2 45+2 45+2
18206 34 2019 177.80 69.85322 47+2 47+2 47+2 47+2
18207 33 2019 177.80 79.83226 43+2 43+2 43+2 45+2
LF CF RF RW LAM CAM RAM LM LCM CM RCM RM LWB LDM
18198 48+2 48+2 48+2 49+2 49+2 49+2 49+2 49+2 47+2 47+2 47+2 49+2 45+2 44+2
18199
18200 44+2 44+2 44+2 43+2 44+2 44+2 44+2 45+2 45+2 45+2 45+2 45+2 47+2 48+2
18201 45+2 45+2 45+2 44+2 44+2 44+2 44+2 41+2 37+2 37+2 37+2 41+2 31+2 28+2
18202 30+2 30+2 30+2 29+2 28+2 28+2 28+2 30+2 30+2 30+2 30+2 30+2 39+2 38+2
18203 44+2 44+2 44+2 44+2 45+2 45+2 45+2 44+2 45+2 45+2 45+2 44+2 44+2 45+2
18204 42+2 42+2 42+2 39+2 40+2 40+2 40+2 38+2 35+2 35+2 35+2 38+2 30+2 31+2
18205 46+2 46+2 46+2 45+2 44+2 44+2 44+2 44+2 38+2 38+2 38+2 44+2 34+2 30+2
18206 46+2 46+2 46+2 47+2 45+2 45+2 45+2 46+2 39+2 39+2 39+2 46+2 36+2 32+2
18207 44+2 44+2 44+2 45+2 45+2 45+2 45+2 46+2 45+2 45+2 45+2 46+2 46+2 46+2
CDM RDM RWB LB LCB CB RCB RB Crossing Finishing
18198 44+2 44+2 45+2 44+2 40+2 40+2 40+2 44+2 44 44
18199 14 8
18200 48+2 48+2 47+2 47+2 48+2 48+2 48+2 47+2 31 31
18201 28+2 28+2 31+2 30+2 27+2 27+2 27+2 30+2 28 47
18202 38+2 38+2 39+2 42+2 46+2 46+2 46+2 42+2 22 23
18203 45+2 45+2 44+2 45+2 45+2 45+2 45+2 45+2 34 38
18204 31+2 31+2 30+2 29+2 32+2 32+2 32+2 29+2 23 52
18205 30+2 30+2 34+2 33+2 28+2 28+2 28+2 33+2 25 40
18206 32+2 32+2 36+2 35+2 31+2 31+2 31+2 35+2 44 50
18207 46+2 46+2 46+2 46+2 47+2 47+2 47+2 46+2 41 34
HeadingAccuracy ShortPassing Volleys Dribbling Curve FKAccuracy
18198 36 53 43 50 48 46
18199 14 19 8 10 13 10
18200 41 51 26 46 35 31
18201 47 42 37 39 32 25
18202 45 25 27 21 21 27
18203 40 49 25 42 30 34
18204 52 43 36 39 32 20
18205 46 38 38 45 38 27
18206 39 42 40 51 34 32
18207 46 48 30 43 40 34
LongPassing BallControl Acceleration SprintSpeed Agility Reactions
18198 52 51 68 62 58 41
18199 21 11 18 24 22 36
18200 55 47 60 63 53 46
18201 30 41 65 48 64 54
18202 27 32 52 52 39 43
18203 45 43 54 57 60 49
18204 25 40 41 39 38 40
18205 28 44 70 69 50 47
18206 32 52 61 60 52 21
18207 44 51 57 55 55 51
Balance ShotPower Jumping Stamina Strength LongShots Aggression
18198 62 50 55 50 38 37 37
18199 47 26 56 20 38 5 25
18200 55 49 57 42 43 30 53
18201 80 44 77 31 31 51 26
18202 48 39 74 39 52 16 44
18203 76 43 55 40 47 38 46
18204 52 41 47 43 67 42 47
18205 58 45 60 55 32 45 32
18206 71 64 42 40 48 34 33
18207 63 43 62 47 60 32 56
Interceptions Positioning Vision Penalties Composure Marking
18198 28 39 48 49 52 41
18199 6 5 37 14 34 15
18200 49 35 40 36 40 48
18201 16 46 37 58 50 15
18202 45 20 31 38 43 44
18203 46 39 52 43 45 40
18204 16 46 33 43 42 22
18205 15 48 43 55 41 32
18206 22 44 47 50 46 20
18207 42 34 49 33 43 40
StandingTackle SlidingTackle GKDiving GKHandling GKKicking GKPositioning
18198 47 38 13 6 9 10
18199 11 13 46 52 58 42
18200 49 49 7 7 9 14
18201 17 14 11 15 12 12
18202 47 53 9 10 9 11
18203 48 47 10 13 7 8
18204 15 19 10 9 9 5
18205 13 11 6 5 10 6
18206 25 27 14 6 14 8
18207 43 50 10 15 9 12
GKReflexes Release.Clause
18198 15 88
18199 48 165
18200 15 175
18201 11 143
18202 13 153
18203 9 143
18204 12 113
18205 13 165
18206 9 143
18207 9 165Variable Understanding
The main categorical variable that was used in this analysis to determine its relationship with wage and penalties was the player’s preferred foot to kick with. The following table indicates that most players used their right foot during the game and very few players did not have a preference.
Left None Right
4211 48 13948 Charts
To understand the relationship between the wage a player received, the amount of penalties measured by the ” accuracy of shots from inside the penalty area” and their preferred foot, the following chart was made.
Chart 1
The data seemed to indicate that they were a clear positive relationship between the wage a player earned and the amount of penalties they were given. However, for most of the data points it seems that regardless of the amount of penalties that a player had in their careers they would expect to have the same salary. Likewise, player foot preference did not seem to have much of an effect, considering how much more right foot prevalence there was. The summary statistics for the variables tells the same story, the median wage for professional players is $3 million and the median player had 49 penalties. There are near 3 times more average earners than the players with more money and penalties, yet the higher earners pull the average upwards.
Chart2
Changes made
Because this dataset is really large, this chart was changed to now put all of the players that earned less than 30 million into a greyscale. I did this so that the user might be able to see how wage is affected by the penalties at higher wage levels. Additionally, new colors were chosen to represent their preferred kicking foot because these colors were more distinguishable from the previous colors. Finally, this chart seemed to indicate that both at lower wage levels and higher ones most players maintain around 70 penalties. Additionally, it takes a random sample of 50% of the dataset to show the true spread of the observations. Compressing these data points seemed to visually indicate that players earnings, penalties and their preferred foot were not perfectly proportional. In fact, most players made the same amount of money regardless of the amount of penalties they acquired or their foot of choice. However, it is interesting to note that the two highest earners are left footed.
Fitting a line
Attempting to fit a line through the points indicated that the more penalties a player had, the higher their wage would be and that right footed players earn less money. However, other factors such as Leonardo Messi being the highest earner and being left footed could be skewing the data against players who have a different dominant foot. Additionally, the penalty variables can also be explained by something similar. Because the more talented and higher earners are on the field more than the others, they are likely to have received more penalties during their careers. Therefore, more research is needed to fully understand the wage variable and what makes it grow.
Linear Fitting Model
Call:
lm(formula = Wage ~ Penalties + Preferred.Foot, data = cat_var)
Residuals:
Min 1Q Median 3Q Max
-20.49 -8.69 -4.90 1.21 546.68
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) -5.43504 0.61562 -8.829 <2e-16 ***
Penalties 0.31671 0.01029 30.775 <2e-16 ***
Preferred.FootRight -0.07284 0.38240 -0.190 0.849
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 21.58 on 17915 degrees of freedom
(289 observations deleted due to missingness)
Multiple R-squared: 0.05041, Adjusted R-squared: 0.05031
F-statistic: 475.5 on 2 and 17915 DF, p-value: < 2.2e-16Chart3
Changes made
For this chart a similar color change was done as the previous for the same distinction, this change revealed that the Manchester players are overwhelmingly right-footed. For this chart, wage amount was utilized to show the different levels of wages received by the Manchester United group. In addition to that, the players names were added as a label in order to be able to see the top 6 players on the team with the highest wages and penalties. Lastly the legend was added at the top in order to portray the x axis better.
##
The data also indicated that bigger clubs like Manchester United had higher wages for their players than the average amount in the league. The median wage these players received is $110 million and they had more penalties than league average.
Changes from Feedback
With the feedback that was acquired from my classmates the formatting was changed into the correct one. Additionally, clearer and more concise legends and labeling were able to be utilized. A deeper understanding and explanation of the data and its values was also added and finally there was a change in design and css formatting.
The specific things that were changed were: -The labeling of the categorical variables -More interpretation -A change in scale and quantity of variables being represented in each chart