In pssguy/epldata: Datasets from English Premier League 1992-2018

I have used several packages

Basics

library(epldata)

data(package="epldata")
## not best

Let's look at one of them

library(tidyverse) # overkill but easier add suppress warning s o block

glimpse(players)

Let's get say percentage distribution of players born from 1990 by birth country

players %>% 
  filter(birth_date>"1989-12-31") %>% 
  group_by(birth_country) %>% 
  tally() %>% 
  mutate(pc=round(100*n/sum(n),2)) %>% 
  arrange(desc(pc))

Predictably England dominates but Scotland only scrapes into the top 10

Combining tables

Much more commonly you will need to combine tables

Which player has scored the most for each team

df <- players %>% 
  #filter(player_id!="OWNGOAL") %>% 
  left_join(player_team) %>% #7080 7107 - may be some that never played
  left_join(player_game) %>% 
  right_join(goals) %>% 
  mutate(name=paste(first_name,last_name)) %>% 
  group_by(player_id,name,team) %>% 
  tally() %>% 
  arrange(desc(n)) %>% 
  group_by(team) %>% 
  slice(1) %>% 
  ungroup() %>% 
  filter(!(is.na(team))) %>% ## still to explain
  select(team,name,goals=n)



df

Derived tables

The above example included quite a few joins which you may not wish to do for every analysis For instance you might want to have the standings for a team at any point of a season

## goals by team for individual match

goals_by_team <- game_team %>% 
  left_join(player_game) %>% 
  right_join(goals) %>% 
  group_by(team,team_game_id,game_id) %>% 
  tally() %>% 
  right_join(game_team) %>% 
  mutate(GF=ifelse(is.na(n),0,n)) %>% 
  select(-c(venue,n))

goals_by_team

So we now have the goals scored by each team. The next step is to mach theis table with itself to obtain goals against but only by the game_id

df <-goals_by_team %>% 
  inner_join(goals_by_team,by="game_id") 

head(df)

We have duplication and wish to remove all those where team.x= team.y. as well as tidy up column names and calculate the points accrued for each match

df <- df %>% 
  filter(team.x!=team.y) %>% 
  select(team=team.x,team_game_id=team_game_id.x,game_id,GF=GF.x,opponents=team.y,GA=GF.y) %>% 
  mutate(points=case_when(
       GF >GA  ~ 3,
       GF==GA ~ 1,
       GF<GA ~ 0

  ))

For the standings at any time, we need to add the date in order and split the results into seasons

years <- c(1992:2018)


library(lubridate)
df_next <- df %>% 
  left_join(game) %>% 
  mutate(year=year(game_date),month=month(game_date)) %>% 
  mutate(season= case_when(
    month<=7 ~ paste(year-1,year,sep="/"),
    month>7 ~ paste(year,year+1,sep="/")
         )
  ) %>% 
  arrange(game_date) %>% 
  group_by(season,team) %>% 
  mutate(year_game_order=row_number())

  df_next

We can now create a standings data.frame for each round of matches based on points, Goal difference , and Goals For

standings <- df_next %>% 
  select(team,season,game_date,year_game_order,GF,GA,points) %>% 
  group_by(team,season,year_game_order) %>% 
  mutate(cum_points=cumsum(points),cum_GF=cumsum(GF),cum_GA=cumsum(GA),cum_GD=cum_GF-cum_GA) %>% 
  group_by(season) %>% 
  arrange(desc(cum_points),desc(cum_GD),desc(cum_GF),team) %>% 
  mutate(position=row_number()) %>% 
  select(season,team,round=year_game_order,position,GF=cum_GF,GA=cum_GA,GD=cum_GD,points=cum_points)

standings

It is then a simple matter to create a function to get a table for any round of any year. e.g after 20 games in 1994/1995

table_year_round <- function(x,y){
  standings %>% 
    filter(season==x,round==y)
}

table_year_round("1994/1995",20)

Obviously you can vary what is in the standings table

and there may be many other derived tables you want to create and save

pssguy/epldata documentation built on May 12, 2019, 7:36 a.m.