FIFA2019
David Huddleston
9/24/2019
R Markdown
What is the distribution of wage across all of 2019 fifa employees?
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Is there a difference in wage based on a player’s position?
What is there the affect of value on wage by player position?
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Does overall player rating affect the players wage?
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
Does overall player rating affect the players value?
## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'
What is the typical wage for a player from each club? Top 10:
## # A tibble: 648 x 6
## Club AvgWage MinWage MaxWage St.dev NumberOfPlayers
## <fct> <dbl> <int> <int> <dbl> <int>
## 1 Real Madrid 154069. 9000 420000 130716. 29
## 2 FC Barcelona 153379. 11000 565000 137560. 29
## 3 Juventus 143476. 32000 405000 77246. 21
## 4 Manchester City 141240 6000 355000 90932. 25
## 5 Chelsea 121083. 8000 340000 67029. 24
## 6 Liverpool 110917. 17000 255000 53923. 24
## 7 Manchester United 100033. 8000 230000 65690. 30
## 8 Tottenham Hotspur 90077. 6000 205000 52551. 26
## 9 Arsenal 87296. 10000 265000 63082. 27
## 10 FC Bayern München 83857. 5000 205000 52694. 21
## # ... with 638 more rowsWhat is the typical value for a player from each club? Top 10:
## # A tibble: 648 x 6
## Club AvgValue MinValue MaxValue St.dev NumberOfPlayers
## <fct> <dbl> <int> <int> <dbl> <int>
## 1 Juventus 30485714. 4200000 89000000 2.07e7 21
## 2 Manchester City 29614000 550000 102000000 2.59e7 25
## 3 Real Madrid 26985345. 700000 76500000 2.50e7 29
## 4 FC Barcelona 26745690. 850000 110500000 2.73e7 29
## 5 FC Bayern München 26627381. 575000 77000000 2.01e7 21
## 6 Chelsea 22512500 1100000 93000000 2.01e7 24
## 7 Liverpool 22390625 975000 69500000 1.71e7 24
## 8 Paris Saint-Germa~ 22380556. 450000 118500000 2.82e7 27
## 9 Napoli 22113636. 2500000 62000000 1.60e7 22
## 10 Tottenham Hotspur 21993269. 800000 83500000 2.08e7 26
## # ... with 638 more rowsWhat is the expected cost to pay for players from a each country? Top 10:
## # A tibble: 153 x 6
## Nationality AvgWage MinWage MaxWage St.dev NumberOfPlayers
## <fct> <dbl> <int> <int> <dbl> <int>
## 1 Dominican Republic 71000 2000 140000 97581. 2
## 2 Gabon 39300 1000 265000 80690. 10
## 3 United Arab Emirates 39000 39000 39000 NaN 1
## 4 Egypt 28611. 1000 255000 59246. 18
## 5 St Kitts Nevis 26000 26000 26000 NaN 1
## 6 Croatia 24284. 1000 420000 56745. 88
## 7 Belgium 22922. 1000 355000 49852. 166
## 8 Equatorial Guinea 21250 2000 65000 29398. 4
## 9 Armenia 20000 1000 145000 46904. 9
## 10 Uruguay 19875 1000 455000 49210. 112
## # ... with 143 more rowsWhat is the typical value for players from each country? Top 10:
## # A tibble: 153 x 6
## Nationality AvgValue MinValue MaxValue St.dev NumberOfPlayers
## <fct> <dbl> <int> <int> <dbl> <int>
## 1 United Arab Emira~ 10500000 10500000 10500000 NaN 1
## 2 Dominican Republic 10400000 800000 20000000 1.36e7 2
## 3 Central African R~ 10050000 950000 27500000 1.51e7 3
## 4 Gabon 9790000 625000 59000000 1.77e7 10
## 5 Egypt 7340278. 300000 69500000 1.59e7 18
## 6 Belgium 6052380. 120000 102000000 1.34e7 166
## 7 Uzbekistan 6000000 6000000 6000000 NaN 1
## 8 Croatia 5993352. 220000 67000000 1.02e7 88
## 9 Slovakia 5839531. 150000 46500000 1.12e7 32
## 10 Uruguay 5682679. 130000 80000000 1.11e7 112
## # ... with 143 more rowsWhat is the expected cost to pay for players in a each position? Top 10:
## # A tibble: 26 x 6
## Position AvgWage MinWage MaxWage St.dev NumberOfPlayers
## <fct> <dbl> <int> <int> <dbl> <int>
## 1 RF 60923. 2000 565000 155520. 13
## 2 LF 44667. 1000 340000 96329. 15
## 3 RW 19491. 1000 215000 38962. 232
## 4 RAM 19095. 1000 150000 30898. 21
## 5 LW 16275. 1000 340000 39888. 262
## 6 RCM 16118. 1000 420000 38009. 313
## 7 LS 15802. 1000 200000 23186. 162
## 8 CF 15366. 1000 71000 18984. 41
## 9 LCM 15226. 1000 355000 31784. 314
## 10 RS 14957. 1000 455000 37976. 161
## # ... with 16 more rowsWhat is the expected value for players in a each position? Top 10:
## # A tibble: 26 x 6
## Position AvgValue MinValue MaxValue St.dev NumberOfPlayers
## <fct> <dbl> <int> <int> <dbl> <int>
## 1 LF 17153333. 300000 93000000 30825643. 15
## 2 RF 14857308. 220000 110500000 31114378. 13
## 3 LS 5353735. 230000 60000000 8222681. 162
## 4 RAM 5329762. 525000 32500000 8682161. 21
## 5 RW 5180991. 120000 59500000 9436938. 232
## 6 CF 5128659. 350000 28500000 6937760. 41
## 7 LCM 4884459. 140000 76500000 8817474. 314
## 8 RCM 4801821. 220000 102000000 9465717. 313
## 9 LW 4574981. 150000 118500000 11620897. 262
## 10 RS 4438075. 250000 80000000 7748532. 161
## # ... with 16 more rows