JoGall

# Calculate expected points for a FantasyPL draft, head-to-head league.
# 
# Calculate expected points (xPts) and expected rank (xRank) for each
# team in a head-to-head draft league as the average points (i.e. 0, 1, 3)
# expected against all teams in that league each gameweek, not just the actual
# opponent.
# 
# @param leagueid: integer

library(dplyr)

JoGall

Hello,

Can I please receive an explanation of the exact legislation being used by StackOverflow to prevent me from transforming, deleting and/or dissociating myself from my own submissions?

By my understanding of the CC BY-SA 4.0 license, StackOverflow may have the right to distribute, adapt, or transform my content, but only provided that attribution is granted; can OpenAI prove that attribution is given in all of its various models and applications?

Furthermore, the Privacy Policy states 'WE DO NOT AND WILL NOT SELL YOUR PERSONAL INFORMATION`; is there proof my identifying information - which may or may not include my username (either directly, or indirectly by being able to triangulate my identity if the same username exists on different sites) and references contained in submitted questions, answers, or data - has been expunged from the data stored by OpenAI and used to train its models?

Finally, my 'right to be forgotten' (Article 17, GDPR) permits my

JoGall

"""
Generates a search URL for twitter.com to retrieve tweets on this day (#OTD)
by a given user and opens results in system default browser

Usage:
    python ./twitter_otd.py

Author:
    Joe Gallagher (@joedgallagher)
"""

JoGall

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

#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

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

# 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(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

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)