FIFA 19 DATASET

Setup

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(ggplot2)
library(tidyverse)

## ── Attaching packages ────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──

## ✓ tibble  3.0.3     ✓ purrr   0.3.4
## ✓ tidyr   1.1.2     ✓ stringr 1.4.0
## ✓ readr   1.3.1     ✓ forcats 0.5.0

## ── Conflicts ───────────────────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

Setting Working Directory & Importing Data File

setwd("~/Dropbox/Imperial/MSc CCM&F/R Programming/Lecture 3 - Visions")
FIFA19 <- read_csv("FIFA19.csv")

## Warning: Missing column names filled in: 'X1' [1]

## Parsed with column specification:
## cols(
##   .default = col_character(),
##   X1 = col_double(),
##   ID = col_double(),
##   Age = col_double(),
##   Overall = col_double(),
##   Potential = col_double(),
##   Special = col_double(),
##   `International Reputation` = col_double(),
##   `Weak Foot` = col_double(),
##   `Skill Moves` = col_double(),
##   `Jersey Number` = col_double(),
##   Crossing = col_double(),
##   Finishing = col_double(),
##   HeadingAccuracy = col_double(),
##   ShortPassing = col_double(),
##   Volleys = col_double(),
##   Dribbling = col_double(),
##   Curve = col_double(),
##   FKAccuracy = col_double(),
##   LongPassing = col_double(),
##   BallControl = col_double()
##   # ... with 24 more columns
## )

## See spec(...) for full column specifications.

First rows of data of FIFA19 Dataset

head(FIFA19)

## # A tibble: 6 x 89
##      X1     ID Name    Age Photo Nationality Flag  Overall Potential Club 
##   <dbl>  <dbl> <chr> <dbl> <chr> <chr>       <chr>   <dbl>     <dbl> <chr>
## 1     0 158023 L. M…    31 http… Argentina   http…      94        94 FC B…
## 2     1  20801 Cris…    33 http… Portugal    http…      94        94 Juve…
## 3     2 190871 Neym…    26 http… Brazil      http…      92        93 Pari…
## 4     3 193080 De G…    27 http… Spain       http…      91        93 Manc…
## 5     4 192985 K. D…    27 http… Belgium     http…      91        92 Manc…
## 6     5 183277 E. H…    27 http… Belgium     http…      91        91 Chel…
## # … with 79 more variables: `Club Logo` <chr>, Value <chr>, Wage <chr>,
## #   Special <dbl>, `Preferred Foot` <chr>, `International Reputation` <dbl>,
## #   `Weak Foot` <dbl>, `Skill Moves` <dbl>, `Work Rate` <chr>, `Body
## #   Type` <chr>, `Real Face` <chr>, Position <chr>, `Jersey Number` <dbl>,
## #   Joined <chr>, `Loaned From` <chr>, `Contract Valid Until` <chr>,
## #   Height <chr>, Weight <chr>, LS <chr>, ST <chr>, RS <chr>, LW <chr>,
## #   LF <chr>, CF <chr>, RF <chr>, RW <chr>, LAM <chr>, CAM <chr>, RAM <chr>,
## #   LM <chr>, LCM <chr>, CM <chr>, RCM <chr>, RM <chr>, LWB <chr>, LDM <chr>,
## #   CDM <chr>, RDM <chr>, RWB <chr>, LB <chr>, LCB <chr>, CB <chr>, RCB <chr>,
## #   RB <chr>, Crossing <dbl>, Finishing <dbl>, HeadingAccuracy <dbl>,
## #   ShortPassing <dbl>, Volleys <dbl>, Dribbling <dbl>, Curve <dbl>,
## #   FKAccuracy <dbl>, LongPassing <dbl>, BallControl <dbl>, Acceleration <dbl>,
## #   SprintSpeed <dbl>, Agility <dbl>, Reactions <dbl>, Balance <dbl>,
## #   ShotPower <dbl>, Jumping <dbl>, Stamina <dbl>, Strength <dbl>,
## #   LongShots <dbl>, Aggression <dbl>, Interceptions <dbl>, Positioning <dbl>,
## #   Vision <dbl>, Penalties <dbl>, Composure <dbl>, Marking <dbl>,
## #   StandingTackle <dbl>, SlidingTackle <dbl>, GKDiving <dbl>,
## #   GKHandling <dbl>, GKKicking <dbl>, GKPositioning <dbl>, GKReflexes <dbl>,
## #   `Release Clause` <chr>

Plotting Overall Rating vs Age

ggplot(FIFA19, aes(Age,Overall)) +
geom_smooth() +
theme_light() +
labs(title="Overall Rating vs Age",
subtitle="FIFA 19 Data",
caption="Source: www.kaggle.com",
x = "Age",
y = "Overall Rating")

## `geom_smooth()` using method = 'gam' and formula 'y ~ s(x, bs = "cs")'

Discussion

Above shows the relationship between all football players’ overall FIFA 19 rating and their age. As may be expected we see footballers have a low overall rating at a younger age with this gradually increasing until they reach what can be described as the peak of their careers around the age of 30. In their early 30’s the overall rating of football players appears to gradually reduce yet remains considerably higher than when they started their football career.

This trend is to be expected as players mature and improve throughout their career while physically struggling more at the end of their career when they might strategically play the game better than when they started out.