computeELORanking <- function(games, k = 30, initialValue = 1500)
{
  ranking <- data.frame(Date=as.Date(character()), Team=character(), Points=numeric())
  elos <- numeric() # current elo values
  names(elos) <- character() # named vector ("dictionary")
  games <- na.omit(games)
  games <- games[order(games$Date),]
  for (i in 1:nrow(games)) # loop over all rows (games)
  {
    homeTeam <- games$HomeTeam[i]
    awayTeam <- games$AwayTeam[i]
    # set initial ELO value if necessary
    if (!(homeTeam %in% names(elos)))
    {
      elos[homeTeam]=initialValue
    }
    if (!(awayTeam %in% names(elos)))
    {
      elos[awayTeam]=initialValue
    }
    # compute elo rating as described on https://en.wikipedia.org/wiki/Elo_rating_system
    gd <- games$FTHG[i] - games$FTAG[i]
    W_home <- 0.5
    if (gd > 0)
    {
      W_home <- 1
    }
    else if (gd < 0)
    {
      W_home <- 0
    }
    We_home <- 1/(1+10^((elos[awayTeam]-elos[homeTeam])/400)) # expected result
    P <- k*(W_home-We_home) # change
    elos[homeTeam] <- elos[homeTeam] + P
    elos[awayTeam] <- elos[awayTeam] - P
    ranking <- tibble::add_row(ranking, Date = games$Date[i], Team = homeTeam, Points = elos[homeTeam])
    ranking <- tibble::add_row(ranking, Date = games$Date[i], Team = awayTeam, Points = elos[awayTeam])
  }
  return(list(ranking, elos))
}