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&gt;Also I should mention, I am fluent in english, french, spanish, portuguese and german (yay multicultural parents) and proficient in hebrew and romanian (ditto)

If you have ancestry, particularly recent ancestry from any of those countries, you _may_ be able to migrate permanently (particularly if the German and Hebrew comes from Jewish parents, which might mean a right of return to both Germany &amp; Israel). 

Worth pointing out the tradeoff you'd be making, that anywhere with longer winter days would mean shorter summer days (not that extreme in most situations, but near the equator it is freaking _weird_ sitting on a beach at 6pm in a tshirt as the sun sets). 

As a Canadian citizen you have access to working holiday programs in a number of countries for short-term options (about the next decade of your life in various places). Some of these would mean more sunshine (eg Australia), some significantly less (eg UK). You would need to purchase health insurance, or otherwise pay for your healthcare, as part of those programs.##############################################################################################################################################
# Implements the Glicko algorithm from <NAME> as outlined in the papers: http://www.glicko.net/glicko/glicko.pdf (short version)
# and http://www.glicko.net/research/glicko.pdf (technical version).
# In both cases, the original papers use an artificial point scale for comparability with the Elo system, which complicates the calaculations
# considerably. In the below, we are using natural ratings, which can be related back to the Elo scale by a simple scaling factor.
#
# Natural = Elo_style * ln(10) / 400
#
# In addition, Elo are traditionally started at 1000, where in this case, we set zero as the default rating (so negative scores are possible.)
# This is in keeping with the rest of this package, including the Elo.
##############################################################################################################################################

struct Glicko
    c::Float64
    default_rating::Tuple{Float64, Float64, Int64}
    ratings::Dict
end

function Glicko(;c = 0.1, default_rating = (0, 2.0, -1), ratings = Dict{Int64, Tuple}())
    #Set up a new Elo struct, either using key words or defaults if not provided
    return Glicko(c, default_rating, ratings)
end

#Fit the Glicko rating to some new data
function fit!(glicko::Glicko, original_games::DataFrame)

    games = original_games[!, (1:5)]

    #Duplicate whole rating dataframe with reversed results
    games_duped = dupe_for_rating(games)

    #Split into days and deal with all players in them separately
    for day_games in groupby(games_duped, :Period)
        #Add glicko predictions to each day
        day_games = DataFrame(day_games)
        day = day_games.Period[1]

        #Groupby Player1 including results
        for player_day_games in groupby(day_games, :P1)
            player = player_day_games.P1[1]
            glicko.ratings[player] = update_player_rating(glicko, day, player, player_day_games[!, 2], player_day_games[!, 3], player_day_games[!, 4])
        end
    end
end

function RD_drift(RD, c, defaultRD, day, last_rated)
    #Return the drifted value of RD given the time the rating  was last updated
    if last_rated < 0
        return defaultRD
    else
        return min(sqrt(RD^2 + ((c^2) * abs(day - last_rated))), defaultRD)
    end
end

#g function from Glicko paper
function g(RD::Float64)
    return 1 / sqrt(1 + (0.304 * RD^2))
end

#E function from Glicko paper
function E(r::Float64, rj::Float64, RDj::Float64)
    return 1 / (1 + exp(g(RDj) * (rj - r)))
end

#Reciprocal of d2 function from Glicko paper
function recipd2(r, rj, RDj)
    return sum(g.(RDj).^2.* E.(r, rj, RDj).* (1.- E.(r, rj, RDj)))
end

function update_player_rating(glicko::Glicko, day::Int64, player::Int64, opponents, player_wins, opponent_wins)

    #Collect a tuple of the player being updated...
    (r, RD, last_rated) = get(glicko.ratings, player, glicko.default_rating)
    #Adjusts current RD based on when last rated
    RD = RD_drift(RD, glicko.c, glicko.default_rating[2], day, last_rated)

    #And a vector of the Tuples of the ratings and RDs for their opponents
    rj, RDj, last_rated_j = collect(zip(get.(Ref(glicko.ratings), opponents, Ref(glicko.default_rating))...))
    #Again, giving drift
    RDj = RD_drift.(RDj, Ref(glicko.c), Ref(glicko.default_rating[2]), Ref(day), last_rated_j)

    r_updated = r + ((1 / (1 / RD^2 + recipd2(r, rj, RDj))) * sum(g.(RDj).* (player_wins.- ((player_wins + opponent_wins).* E.(r, rj, RDj)))))
    RD_updated = sqrt(1/((1 / RD^2) + recipd2(r, rj, RDj)))