JoGall

Data on set piece goals scored and conceded in 2016-17 from [here](https://objective-football.blogspot.co.uk/2016/08/201617-open-playset-piecepenalty-data.html).

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# Get data
```{r}
library(dplyr)
library(XML)
library(RCurl)
library(engsoccerdata)

JoGall

require(dplyr)
require(ggplot2)

# get week-by-week data for current season and last (data originally taken from official API)
d17 <- read.csv(url("https://github.com/JoGall/FantasyPL/blob/master/17-18.csv"))
d16 <- read.csv(url("https://github.com/JoGall/FantasyPL/blob/master/16-17_formatted.csv"))

# compute price changes
ss16 <- d16 %>%
  group_by(player_id) %>%

JoGall

# dataframe 'dat' w/ x,y-coords in columns 'x' and 'y' and timestamp in column, 'ts', e.g.:
library(soccermatics)
attach(tromso)
dat <- subset(tromso, id == 8)[,c(1,3:4)]

#calculate direction of each vector angle (in radians)
v <- diff(complex(real = dat$x, imaginary = dat$y))
dat$direction <- c(NA, diff(Arg(v)) %% (2*pi), NA) - pi

# calculate speed (distance / time interval)

JoGall

library(ggplot2)
library(dplyr)
library(scales)

# SAMPLE DATA
set.seed(123)
# get all dates in a range
start_date <- as.Date("2017-01-01")
end_date <- as.Date("2017-06-01")
dates <- seq(start_date, end_date, 1)

JoGall

library(ggplot2)
library(ggforce) # gives us `geom_circle` and `geom_arc` functions

# pitch dimensions
courtLength <- 30.5
courtWidth <- 15.25
courtRunoff <- 3.05

# aesthetic parameters
pitch_col <- "grey90"

JoGall

# Computes number of neighbours in common between first degree neighbourhood of each vertex in an igraph network
# Not sure if this is meaningful metric yet but seems like it could be interesting!

# FUNCTION --------------------------------------------------------------------

common.neighbours <- function(graph) {
  
  lapply(unique(V(graph)), function(v) {
    
    # get vertices of 1st-order neighbourhood for focal vertex (and omit self)

JoGall

library(tidyverse)
library(soccermatics)
library(StatsBombR)

# get data from StatsBomb
fixture <- FreeMatches(37) %>% 
  filter(home_team.home_team_name == "Reading WFC" & away_team.away_team_name == "Yeovil Town LFC")

# helper function from my fork of statsbombr package: https://github.com/JoGall/StatsBombR
# or code here: https://github.com/JoGall/StatsBombR/blob/master/R/allinfo.R

JoGall

#devtools::install_github("statsbomb/StatsBombR") #install package
#devtools::install_github("jogall/StatsBombR") #or fork for R v<5.0 if necessary
library(StatsBombR)

# see all free competitions
FreeCompetitions()

# e.g. get all fixtures from FIFA World Cup
fixtures <- FreeMatches(43)

JoGall

# Compute total minutes played per player -------------------------------

statsbombMinsPlayed <- function(dat) {
  
  dat <- dat %>% 
    group_by(match_id) %>% 
    mutate(end = max(ElapsedTime))
  
  # starting XI
  minutes <- dat %>% 

JoGall