Fifa Analysis
NIcholas Courtney
2025-03-25
# Load the data
fifa <- read.csv("C:/Users/nicho/Downloads/Fifa_Dataset (2).csv")
# View first few rows
head(fifa)## id name full_name birth_date age height_cm
## 1 158023 L. Messi Lionel Andrés Messi Cuccittini 1987-06-24 31 170.18
## 2 190460 C. Eriksen Christian Dannemann Eriksen 1992-02-14 27 154.94
## 3 195864 P. Pogba Paul Pogba 1993-03-15 25 190.50
## 4 198219 L. Insigne Lorenzo Insigne 1991-06-04 27 162.56
## 5 201024 K. Koulibaly Kalidou Koulibaly 1991-06-20 27 187.96
## 6 203376 V. van Dijk Virgil van Dijk 1991-07-08 27 193.04
## weight_kgs positions nationality overall_rating potential value_euro
## 1 72.1 CF,RW,ST Argentina 94 94 110500000
## 2 76.2 CAM,RM,CM Denmark 88 89 69500000
## 3 83.9 CM,CAM France 88 91 73000000
## 4 59.0 LW,ST Italy 88 88 62000000
## 5 88.9 CB Senegal 88 91 60000000
## 6 92.1 CB Netherlands 88 90 59500000
## wage_euro preferred_foot international_reputation.1.5. weak_foot.1.5.
## 1 565000 Left 5 4
## 2 205000 Right 3 5
## 3 255000 Right 4 4
## 4 165000 Right 3 4
## 5 135000 Right 3 3
## 6 215000 Right 3 3
## skill_moves.1.5. work_rate body_type release_clause_euro
## 1 4 Medium/ Low Messi 226500000
## 2 4 High/ Medium Lean 133800000
## 3 5 High/ Medium Normal 144200000
## 4 4 High/ Medium Normal 105400000
## 5 2 High/ High Normal 106500000
## 6 2 Medium/ Medium Normal 114500000
## club_team club_rating club_position club_jersey_number club_join_date
## 1 FC Barcelona 86 RW 10 2004-07-01
## 2 Tottenham Hotspur 83 LCM 23 2013-08-30
## 3 Manchester United 82 LCM 6 2016-08-09
## 4 Napoli 82 LS 24 2010-07-01
## 5 Napoli 82 LCB 26 2014-07-01
## 6 Liverpool 83 LCB 4 2018-01-01
## contract_end_year national_team national_rating national_team_position
## 1 2021 Argentina 82 RF
## 2 2020 Denmark 78 CAM
## 3 2021 France 84 RDM
## 4 2022 Italy 83 LW
## 5 2021 NA
## 6 2023 Netherlands 81 LCB
## national_jersey_number crossing finishing heading_accuracy short_passing
## 1 10 86 95 70 92
## 2 10 88 81 52 91
## 3 6 80 75 75 86
## 4 10 86 77 56 85
## 5 NA 30 22 83 68
## 6 4 53 52 83 79
## volleys dribbling curve freekick_accuracy long_passing ball_control
## 1 86 97 93 94 89 96
## 2 80 84 86 87 89 91
## 3 85 87 85 82 90 90
## 4 74 90 87 77 78 93
## 5 14 69 28 28 60 63
## 6 45 70 60 70 81 76
## acceleration sprint_speed agility reactions balance shot_power jumping
## 1 91 86 93 95 95 85 68
## 2 76 73 80 88 81 84 50
## 3 71 79 76 82 66 90 83
## 4 94 86 94 83 93 75 53
## 5 70 75 50 82 40 55 81
## 6 74 77 61 87 49 81 88
## stamina strength long_shots aggression interceptions positioning vision
## 1 72 66 94 48 22 94 94
## 2 92 58 89 46 56 84 91
## 3 88 87 82 78 64 82 88
## 4 75 44 84 34 26 83 87
## 5 75 94 15 87 88 24 49
## 6 75 92 64 82 88 41 60
## penalties composure marking standing_tackle sliding_tackle GK_diving
## 1 75 96 33 28 26 6
## 2 67 88 59 57 22 9
## 3 82 87 63 67 67 5
## 4 61 83 51 24 22 8
## 5 33 80 91 88 87 7
## 6 62 87 90 89 84 13
## GK_handling GK_kicking GK_positioning GK_reflexes
## 1 11 15 14 8
## 2 14 7 7 6
## 3 6 2 4 3
## 4 4 14 9 10
## 5 11 7 13 5
## 6 10 13 11 11
## tags
## 1 #Dribbler,#Distance Shooter,#Crosser,#FK Specialist,#Acrobat,#Clinical Finisher,#Complete Forward
## 2 #Playmaker ,#Crosser,#FK Specialist,#Complete Midfielder
## 3 #Dribbler,#Playmaker ,#Strength,#Complete Midfielder
## 4 #Speedster,#Dribbler,#Crosser,#Acrobat
## 5 #Tackling ,#Tactician ,#Strength,#Complete Defender
## 6 #Tactician ,#Strength
## traits
## 1 Finesse Shot,Long Shot Taker (CPU AI Only),Speed Dribbler (CPU AI Only),Playmaker (CPU AI Only),One Club Player,Team Player,Chip Shot (CPU AI Only)
## 2 Flair,Long Shot Taker (CPU AI Only),Playmaker (CPU AI Only),Technical Dribbler (CPU AI Only),Takes Finesse Free Kicks
## 3 Flair,Long Passer (CPU AI Only),Long Shot Taker (CPU AI Only),Playmaker (CPU AI Only),Technical Dribbler (CPU AI Only)
## 4 Finesse Shot,Long Shot Taker (CPU AI Only),Speed Dribbler (CPU AI Only),Takes Finesse Free Kicks
## 5 Power Header
## 6 Injury Free,Leadership,Power Header
## LS ST RS LW LF CF RF RW LAM CAM RAM LM LCM CM RCM
## 1 89+2 89+2 89+2 93+2 93+2 93+2 93+2 93+2 93+2 93+2 93+2 91+2 85+2 85+2 85+2
## 2 79+3 79+3 79+3 85+3 84+3 84+3 84+3 85+3 86+3 86+3 86+3 86+3 85+3 85+3 85+3
## 3 81+3 81+3 81+3 82+3 83+3 83+3 83+3 82+3 84+3 84+3 84+3 83+3 84+3 84+3 84+3
## 4 78+3 78+3 78+3 86+3 85+3 85+3 85+3 86+3 86+3 86+3 86+3 86+3 78+3 78+3 78+3
## 5 53+3 53+3 53+3 53+3 54+3 54+3 54+3 53+3 55+3 55+3 55+3 57+3 61+3 61+3 61+3
## 6 68+3 68+3 68+3 66+3 67+3 67+3 67+3 66+3 68+3 68+3 68+3 68+3 73+3 73+3 73+3
## RM LWB LDM CDM RDM RWB LB LCB CB RCB RB
## 1 91+2 64+2 61+2 61+2 61+2 64+2 59+2 48+2 48+2 48+2 59+2
## 2 86+3 71+3 71+3 71+3 71+3 71+3 66+3 57+3 57+3 57+3 66+3
## 3 83+3 76+3 77+3 77+3 77+3 76+3 74+3 72+3 72+3 72+3 74+3
## 4 86+3 63+3 58+3 58+3 58+3 63+3 58+3 44+3 44+3 44+3 58+3
## 5 57+3 73+3 77+3 77+3 77+3 73+3 76+3 85+3 85+3 85+3 76+3
## 6 68+3 78+3 82+3 82+3 82+3 78+3 80+3 86+3 86+3 86+3 80+3# Check for missing values
colSums(is.na(fifa))## id name
## 0 0
## full_name birth_date
## 0 0
## age height_cm
## 0 0
## weight_kgs positions
## 0 0
## nationality overall_rating
## 0 0
## potential value_euro
## 0 255
## wage_euro preferred_foot
## 246 0
## international_reputation.1.5. weak_foot.1.5.
## 0 0
## skill_moves.1.5. work_rate
## 0 0
## body_type release_clause_euro
## 0 1837
## club_team club_rating
## 0 14
## club_position club_jersey_number
## 0 14
## club_join_date contract_end_year
## 0 0
## national_team national_rating
## 0 17097
## national_team_position national_jersey_number
## 0 17097
## crossing finishing
## 0 0
## heading_accuracy short_passing
## 0 0
## volleys dribbling
## 0 0
## curve freekick_accuracy
## 0 0
## long_passing ball_control
## 0 0
## acceleration sprint_speed
## 0 0
## agility reactions
## 0 0
## balance shot_power
## 0 0
## jumping stamina
## 0 0
## strength long_shots
## 0 0
## aggression interceptions
## 0 0
## positioning vision
## 0 0
## penalties composure
## 0 0
## marking standing_tackle
## 0 0
## sliding_tackle GK_diving
## 0 0
## GK_handling GK_kicking
## 0 0
## GK_positioning GK_reflexes
## 0 0
## tags traits
## 0 0
## LS ST
## 0 0
## RS LW
## 0 0
## LF CF
## 0 0
## RF RW
## 0 0
## LAM CAM
## 0 0
## RAM LM
## 0 0
## LCM CM
## 0 0
## RCM RM
## 0 0
## LWB LDM
## 0 0
## CDM RDM
## 0 0
## RWB LB
## 0 0
## LCB CB
## 0 0
## RCB RB
## 0 0fifa %>%
select(age, overall_rating, potential, value_euro, wage_euro) %>%
summary()## age overall_rating potential value_euro
## Min. :17.00 Min. :47.00 Min. :48.00 Min. : 10000
## 1st Qu.:22.00 1st Qu.:62.00 1st Qu.:67.00 1st Qu.: 325000
## Median :25.00 Median :66.00 Median :71.00 Median : 700000
## Mean :25.57 Mean :66.24 Mean :71.43 Mean : 2479280
## 3rd Qu.:29.00 3rd Qu.:71.00 3rd Qu.:75.00 3rd Qu.: 2100000
## Max. :46.00 Max. :94.00 Max. :95.00 Max. :110500000
## NA's :255
## wage_euro
## Min. : 1000
## 1st Qu.: 1000
## Median : 3000
## Mean : 9902
## 3rd Qu.: 9000
## Max. :565000
## NA's :246Insights: This quick scan reveals the structure of the dataset, showing player attributes such as age, ratings, nationality, club info, wages, and value. We also verify if any variables have significant missing values to address before deeper analysis.
Summary statistics show that most players are in their early to mid-20s. There’s a wide range in player wages and market value, indicating a mix of elite and lower-tier players.
Visualizations and Analysis
Insight: Player ages are clustered between 18 and 30, with the peak around 21–25 years old. Very few players are above 35.
## Warning: Removed 246 rows containing non-finite outside the scale range
## (`stat_boxplot()`).
Insight: Forwards and attacking midfielders tend to command higher wages, while goalkeepers and some defenders show lower wage medians. This aligns with how attacking players are often more marketable and in demand.
Insight: Countries like England, Brazil, and Spain have the largest number of players in the dataset, which reflects their deep football culture and large professional leagues.
Choose appropriate data analysis methods and examine the relationships between or among the variables that interest you.
## Warning: Removed 255 rows containing missing values or values outside the scale range
## (`geom_point()`).
Insight: There’s a strong positive correlation between player value and wage, but some outliers exist — players with high wages but relatively lower market value, or vice versa. This could be due to over/underperformance, club dynamics, or contract timing.
Insight: Countries like Germany, Spain, and France show the highest average player ratings, especially when filtering for nations with a meaningful player sample size (20+). These countries are known for producing technically skilled talent and world-class academies.
Insight: Central positions like CAM (Central Attacking Midfielders), CB (Center Backs), and ST (Strikers) tend to have higher median ratings, indicating their core roles in team performance.
## `geom_smooth()` using formula = 'y ~ x'
Insight: Player ratings tend to increase with age up to a point — peaking around 27–29 — and then plateau or decline, which reflects the typical prime years of a professional footballer.