Name Age Nationality Overall Potential Club Value
1 L. Messi 31 Argentina 94 94 FC Barcelona 110.5
2 Cristiano Ronaldo 33 Portugal 94 94 Juventus 77
3 Neymar Jr 26 Brazil 92 93 Paris Saint-Germain 118.5
4 De Gea 27 Spain 91 93 Manchester United 72
5 K. De Bruyne 27 Belgium 91 92 Manchester City 102
6 E. Hazard 27 Belgium 91 91 Chelsea 93
Wage Special Preferred.Foot International.Reputation Weak.Foot Skill.Moves
1 565 2202 Left 5 4 4
2 405 2228 Right 5 4 5
3 290 2143 Right 5 5 5
4 260 1471 Right 4 3 1
5 355 2281 Right 4 5 4
6 340 2142 Right 4 4 4
Work.Rate Position Jersey.Number Contract.Valid.Until Height Weight
1 Medium/ Medium RF 10 2021 170.18 72.12119
2 High/ Low ST 7 2022 187.96 83.00740
3 High/ Medium LW 10 2022 175.26 68.03886
4 Medium/ Medium GK 1 2020 193.04 76.20352
5 High/ High RCM 7 2023 180.34 69.85322
6 High/ Medium LF 10 2020 172.72 73.93556
LS ST RS LW LF CF RF RW LAM CAM RAM LM LCM CM RCM
1 88+2 88+2 88+2 92+2 93+2 93+2 93+2 92+2 93+2 93+2 93+2 91+2 84+2 84+2 84+2
2 91+3 91+3 91+3 89+3 90+3 90+3 90+3 89+3 88+3 88+3 88+3 88+3 81+3 81+3 81+3
3 84+3 84+3 84+3 89+3 89+3 89+3 89+3 89+3 89+3 89+3 89+3 88+3 81+3 81+3 81+3
4
5 82+3 82+3 82+3 87+3 87+3 87+3 87+3 87+3 88+3 88+3 88+3 88+3 87+3 87+3 87+3
6 83+3 83+3 83+3 89+3 88+3 88+3 88+3 89+3 89+3 89+3 89+3 89+3 82+3 82+3 82+3
RM LWB LDM CDM RDM RWB LB LCB CB RCB RB Crossing Finishing
1 91+2 64+2 61+2 61+2 61+2 64+2 59+2 47+2 47+2 47+2 59+2 84 95
2 88+3 65+3 61+3 61+3 61+3 65+3 61+3 53+3 53+3 53+3 61+3 84 94
3 88+3 65+3 60+3 60+3 60+3 65+3 60+3 47+3 47+3 47+3 60+3 79 87
4 17 13
5 88+3 77+3 77+3 77+3 77+3 77+3 73+3 66+3 66+3 66+3 73+3 93 82
6 89+3 66+3 63+3 63+3 63+3 66+3 60+3 49+3 49+3 49+3 60+3 81 84
HeadingAccuracy ShortPassing Volleys Dribbling Curve FKAccuracy LongPassing
1 70 90 86 97 93 94 87
2 89 81 87 88 81 76 77
3 62 84 84 96 88 87 78
4 21 50 13 18 21 19 51
5 55 92 82 86 85 83 91
6 61 89 80 95 83 79 83
BallControl Acceleration SprintSpeed Agility Reactions Balance ShotPower
1 96 91 86 91 95 95 85
2 94 89 91 87 96 70 95
3 95 94 90 96 94 84 80
4 42 57 58 60 90 43 31
5 91 78 76 79 91 77 91
6 94 94 88 95 90 94 82
Jumping Stamina Strength LongShots Aggression Interceptions Positioning
1 68 72 59 94 48 22 94
2 95 88 79 93 63 29 95
3 61 81 49 82 56 36 89
4 67 43 64 12 38 30 12
5 63 90 75 91 76 61 87
6 56 83 66 80 54 41 87
Vision Penalties Composure Marking StandingTackle SlidingTackle GKDiving
1 94 75 96 33 28 26 6
2 82 85 95 28 31 23 7
3 87 81 94 27 24 33 9
4 68 40 68 15 21 13 90
5 94 79 88 68 58 51 15
6 89 86 91 34 27 22 11
GKHandling GKKicking GKPositioning GKReflexes Release.Clause
1 11 15 14 8 226.5
2 11 15 14 11 127.1
3 9 15 15 11 228.1
4 85 87 88 94 138.6
5 13 5 10 13 196.4
6 12 6 8 8 172.1Lab 3.1
Ismael Hernandez
Introduction
This analysis examines FIFA player data from 2019 to categorize Manchester United players based on their penalty and volley shooting performance with their left and right feet. The data is sourced from the International Center for Research (ICR) library. The purpose of this analysis is to determine if there are differences between left-footed and right-footed Manchester United players in their shooting abilities. Players will be categorized by their preferred shooting foot. Their penalty and volley statistics will then be examined to identify any performance differences between left-footed and right-footed players. Gathering this data will allow for a comparison of the number of left-footed versus right-footed players on the team. Additionally, it will reveal which footedness leads to better shooting results on penalties and volleys. This analysis will provide insight into the shooting tendencies and abilities of Manchester United players based on their preferred foot.
Load the Data
Look at the first 10 objects
Name Club Penalties Volleys Preferred.Foot
4 De Gea Manchester United 40 13 Right
46 P. Pogba Manchester United 81 85 Right
48 R. Lukaku Manchester United 78 79 Left
94 A. Snchez Manchester United 77 83 Right
117 A. Martial Manchester United 81 74 Right
133 N. Mati Manchester United 64 73 Left
212 Juan Mata Manchester United 81 71 Left
251 Fred Manchester United 65 70 Left
255 J. Lingard Manchester United 65 70 Right
320 M. Rashford Manchester United 74 72 RightLook at the last 10 objects
Name Club Penalties Volleys Preferred.Foot
2562 L. Grant Manchester United 20 15 Right
3452 Diogo Dalot Manchester United 43 44 Right
4514 S. McTominay Manchester United 59 53 Right
8192 A. Gomes Manchester United 66 57 Right
10088 T. Chong Manchester United 44 59 Left
10458 E. Hamilton Manchester United 51 52 Left
10462 C. Gribbin Manchester United 56 44 Left
11082 R. Poole Manchester United 37 25 Right
11423 R. Williams Manchester United 49 37 Right
12546 J. Bohui Manchester United 64 48 RightSummary statistics (x): Penalties
Descriptive Statistics
mydata$Penalties
N: 33
Penalties
----------------- -----------
Mean 57.85
Std.Dev 16.23
Min 16.00
Q1 49.00
Median 59.00
Q3 66.00
Max 81.00
MAD 14.83
IQR 17.00
CV 0.28
Skewness -0.59
SE.Skewness 0.41
Kurtosis 0.07
N.Valid 33.00
Pct.Valid 100.00Summary statistics (y): Volleys
Descriptive Statistics
mydata$Volleys
N: 33
Volleys
----------------- ---------
Mean 55.03
Std.Dev 19.32
Min 13.00
Q1 44.00
Median 57.00
Q3 71.00
Max 85.00
MAD 19.27
IQR 27.00
CV 0.35
Skewness -0.58
SE.Skewness 0.41
Kurtosis -0.52
N.Valid 33.00
Pct.Valid 100.00Table for the factor variable (Preferred Foot)
'data.frame': 18207 obs. of 80 variables:
$ Name : chr "L. Messi" "Cristiano Ronaldo" "Neymar Jr" "De Gea" ...
$ Age : int 31 33 26 27 27 27 32 31 32 25 ...
$ Nationality : Factor w/ 164 levels "Afghanistan",..: 7 124 21 141 14 14 36 159 141 138 ...
$ Overall : int 94 94 92 91 91 91 91 91 91 90 ...
$ Potential : int 94 94 93 93 92 91 91 91 91 93 ...
$ Club : Factor w/ 652 levels ""," SSV Jahn Regensburg",..: 218 333 438 380 379 142 475 218 475 65 ...
$ Value : chr "110.5" "77" "118.5" "72" ...
$ Wage : chr "565" "405" "290" "260" ...
$ Special : int 2202 2228 2143 1471 2281 2142 2280 2346 2201 1331 ...
$ Preferred.Foot : Factor w/ 3 levels "","Left","Right": 2 3 3 3 3 3 3 3 3 3 ...
$ International.Reputation: int 5 5 5 4 4 4 4 5 4 3 ...
$ Weak.Foot : int 4 4 5 3 5 4 4 4 3 3 ...
$ Skill.Moves : int 4 5 5 1 4 4 4 3 3 1 ...
$ Work.Rate : Factor w/ 10 levels "","High/ High",..: 10 3 4 10 2 4 2 4 4 10 ...
$ Position : Factor w/ 28 levels "","CAM","CB",..: 23 28 16 7 21 13 21 25 20 7 ...
$ Jersey.Number : int 10 7 10 1 7 10 10 9 15 1 ...
$ Contract.Valid.Until : Factor w/ 37 levels "","2018","2019",..: 5 6 6 4 7 4 4 5 4 5 ...
$ Height : num 170 188 175 193 180 ...
$ Weight : num 72.1 83 68 76.2 69.9 ...
$ LS : Factor w/ 94 levels "","31+2","32+2",..: 93 94 88 1 85 87 75 92 67 1 ...
$ ST : Factor w/ 94 levels "","31+2","32+2",..: 93 94 88 1 85 87 75 92 67 1 ...
$ RS : Factor w/ 94 levels "","31+2","32+2",..: 93 94 88 1 85 87 75 92 67 1 ...
$ LW : Factor w/ 106 levels "","25+2","27+2",..: 106 105 105 1 104 105 101 103 71 1 ...
$ LF : Factor w/ 103 levels "","27+2","29+2",..: 103 102 101 1 98 100 95 99 68 1 ...
$ CF : Factor w/ 103 levels "","27+2","29+2",..: 103 102 101 1 98 100 95 99 68 1 ...
$ RF : Factor w/ 103 levels "","27+2","29+2",..: 103 102 101 1 98 100 95 99 68 1 ...
$ RW : Factor w/ 106 levels "","25+2","27+2",..: 106 105 105 1 104 105 101 103 71 1 ...
$ LAM : Factor w/ 102 levels "","27+2","28+2",..: 102 100 101 1 100 101 99 97 69 1 ...
$ CAM : Factor w/ 102 levels "","27+2","28+2",..: 102 100 101 1 100 101 99 97 69 1 ...
$ RAM : Factor w/ 102 levels "","27+2","28+2",..: 102 100 101 1 100 101 99 97 69 1 ...
$ LM : Factor w/ 101 levels "","27+2","28+2",..: 101 99 99 1 99 100 98 96 70 1 ...
$ LCM : Factor w/ 93 levels "","30+2","31+2",..: 88 83 83 1 92 85 93 79 70 1 ...
$ CM : Factor w/ 93 levels "","30+2","31+2",..: 88 83 83 1 92 85 93 79 70 1 ...
$ RCM : Factor w/ 93 levels "","30+2","31+2",..: 88 83 83 1 92 85 93 79 70 1 ...
$ RM : Factor w/ 101 levels "","27+2","28+2",..: 101 99 99 1 99 100 98 96 70 1 ...
$ LWB : Factor w/ 96 levels "","30+2","31+2",..: 55 58 58 1 83 60 93 67 91 1 ...
$ LDM : Factor w/ 100 levels "","28+2","29+2",..: 50 51 49 1 84 55 92 66 97 1 ...
$ CDM : Factor w/ 100 levels "","28+2","29+2",..: 50 51 49 1 84 55 92 66 97 1 ...
$ RDM : Factor w/ 100 levels "","28+2","29+2",..: 50 51 49 1 84 55 92 66 97 1 ...
$ RWB : Factor w/ 96 levels "","30+2","31+2",..: 55 58 58 1 83 60 93 67 91 1 ...
$ LB : Factor w/ 99 levels "","29+2","30+2",..: 49 54 52 1 79 52 91 65 99 1 ...
$ LCB : Factor w/ 109 levels "","25+2","27+2",..: 30 44 31 1 71 35 81 65 109 1 ...
$ CB : Factor w/ 109 levels "","25+2","27+2",..: 30 44 31 1 71 35 81 65 109 1 ...
$ RCB : Factor w/ 109 levels "","25+2","27+2",..: 30 44 31 1 71 35 81 65 109 1 ...
$ RB : Factor w/ 99 levels "","29+2","30+2",..: 49 54 52 1 79 52 91 65 99 1 ...
$ Crossing : int 84 84 79 17 93 81 86 77 66 13 ...
$ Finishing : int 95 94 87 13 82 84 72 93 60 11 ...
$ HeadingAccuracy : int 70 89 62 21 55 61 55 77 91 15 ...
$ ShortPassing : int 90 81 84 50 92 89 93 82 78 29 ...
$ Volleys : int 86 87 84 13 82 80 76 88 66 13 ...
$ Dribbling : int 97 88 96 18 86 95 90 87 63 12 ...
$ Curve : int 93 81 88 21 85 83 85 86 74 13 ...
$ FKAccuracy : int 94 76 87 19 83 79 78 84 72 14 ...
$ LongPassing : int 87 77 78 51 91 83 88 64 77 26 ...
$ BallControl : int 96 94 95 42 91 94 93 90 84 16 ...
$ Acceleration : int 91 89 94 57 78 94 80 86 76 43 ...
$ SprintSpeed : int 86 91 90 58 76 88 72 75 75 60 ...
$ Agility : int 91 87 96 60 79 95 93 82 78 67 ...
$ Reactions : int 95 96 94 90 91 90 90 92 85 86 ...
$ Balance : int 95 70 84 43 77 94 94 83 66 49 ...
$ ShotPower : int 85 95 80 31 91 82 79 86 79 22 ...
$ Jumping : int 68 95 61 67 63 56 68 69 93 76 ...
$ Stamina : int 72 88 81 43 90 83 89 90 84 41 ...
$ Strength : int 59 79 49 64 75 66 58 83 83 78 ...
$ LongShots : int 94 93 82 12 91 80 82 85 59 12 ...
$ Aggression : int 48 63 56 38 76 54 62 87 88 34 ...
$ Interceptions : int 22 29 36 30 61 41 83 41 90 19 ...
$ Positioning : int 94 95 89 12 87 87 79 92 60 11 ...
$ Vision : int 94 82 87 68 94 89 92 84 63 70 ...
$ Penalties : int 75 85 81 40 79 86 82 85 75 11 ...
$ Composure : int 96 95 94 68 88 91 84 85 82 70 ...
$ Marking : int 33 28 27 15 68 34 60 62 87 27 ...
$ StandingTackle : int 28 31 24 21 58 27 76 45 92 12 ...
$ SlidingTackle : int 26 23 33 13 51 22 73 38 91 18 ...
$ GKDiving : int 6 7 9 90 15 11 13 27 11 86 ...
$ GKHandling : int 11 11 9 85 13 12 9 25 8 92 ...
$ GKKicking : int 15 15 15 87 5 6 7 31 9 78 ...
$ GKPositioning : int 14 14 15 88 10 8 14 33 7 88 ...
$ GKReflexes : int 8 11 11 94 13 8 9 37 11 89 ...
$ Release.Clause : chr "226.5" "127.1" "228.1" "138.6" ...
Left Right
0 9 24 Statistics (left-foot)
Descriptive Statistics
left_footed_data
N: 9
Penalties Volleys
----------------- ----------- ---------
Mean 61.78 61.11
Std.Dev 12.22 12.45
Min 44.00 44.00
Q1 53.00 52.00
Median 64.00 59.00
Q3 65.00 71.00
Max 81.00 79.00
MAD 16.31 16.31
IQR 12.00 19.00
CV 0.20 0.20
Skewness 0.22 0.04
SE.Skewness 0.72 0.72
Kurtosis -1.38 -1.79
N.Valid 9.00 9.00
Pct.Valid 100.00 100.00Statistics (right-foot)
Descriptive Statistics
right_footed_data
N: 24
Penalties Volleys
----------------- ----------- ---------
Mean 56.38 52.75
Std.Dev 17.50 21.10
Min 16.00 13.00
Q1 45.50 39.50
Median 57.00 55.00
Q3 68.00 70.50
Max 81.00 85.00
MAD 17.05 22.98
IQR 21.25 29.50
CV 0.31 0.40
Skewness -0.55 -0.41
SE.Skewness 0.47 0.47
Kurtosis -0.33 -0.97
N.Valid 24.00 24.00
Pct.Valid 100.00 100.00Chart 1
Interpret the data
- From the 18,207 players in the FIFA data set, only 33 players were from the Manchester United Football Club,9 players were left-foot and 24 right-foot. The left-foot players in group have better results when comparing volleys and penalties, with the lowest score achieved by a left footed of approximately 44 volleys and 44 scored penalties (look at the summary of data). While for right footed players, although numerous, seemed to have a higher variance in both penalties and volleys. This can be interpreted that left-foot players are more consistent in performance, also there are less left-footed players, which addresses to genetics than training skills or method, however it is important to emphasize that right-footed players, unlike left-footed players, are numerous and this shows results more spread and can achieved a less biased. In conclusion, despite the low number of left-foot players, to show what players are better in performance, the left-foot players show a more consistent performance, and right-foot players tend to variate around 40 to 70 goals in average per each variable.
Chart 2
Chart 3 (general overview)
