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WorldCup_2.R
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WorldCup_2.R
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#==============================================================================#
#-------------------------------- FUNCTIONS ---------------------------------#
#==============================================================================#
#--------------------- USEFUL TOOLS -----------------------#
##### Function to extract rating #####
extract_rating = function(team){
return (ratings[ratings$Team == team,]$Rating)
}
##### Function to get best third of groups #####
# Function used for Euro only: get the four best firds of each groups which will access the eight finals
letter_in = function(sample_3rd, scheduled_3rd){
if ((sample_3rd[1] %in% strsplit(scheduled_3rd[1],'')[[1]]) && (sample_3rd[2] %in% strsplit(scheduled_3rd[2],'')[[1]])
&& (sample_3rd[3] %in% strsplit(scheduled_3rd[3],'')[[1]]) && (sample_3rd[4] %in% strsplit(scheduled_3rd[4],'')[[1]])){
return (TRUE)
}
else{
return (FALSE)
}
}
#--------------- PREDICTIVE DISTRIBUTION -----------------#
########## i) Predictive before real data (used for group stage) ##########
# Negative binamiale distribution of part i) (before real data - group stage)
predictive_i = function(team_A, team_B, delta0, beta0, n){
S = 3
m = 3
# sum des y_ij
d = historical_data[historical_data$TeamA == team_A,]$GoalsA
y = sum(d[!(is.na(d))])
# sum des RA / ROAi
RO = 0
for (i in 1:((S+1)*m)){
if ((i-1) %% (S+1) != 0){
RO = RO + historical_data[historical_data$TeamA == team_A,][i,]$RatingA /
historical_data[historical_data$TeamA == team_A,][i,]$RatingB
}
}
# rating of A and B
RA = extract_rating(team_A)
RB = extract_rating(team_B)
# Negative Binomial
X_AB = rnbinom(n, size=y+delta0, prob=(RO+beta0)/(RO+beta0+RA/RB))
if (n > 1){
return(mean(X_AB))
}
else return (X_AB)
}
# Negative binomiale distribution of part ii) (after real data - elimination stage)
########## ii) Predictive after real data (used for elimination stage) ##########
predictive_ii = function(team_A, team_B, delta0, beta0, n, stage, extra_time=FALSE, simulation=NA, tournament){
if (stage == 'eight final'){
if (tournament == "WorldCup"){
idx_data = 1:48
} else if (tournament == "Euro"){
idx_data = 1:36
}
} else if (stage == 'quarter final'){
if (tournament == "WorldCup"){
idx_data = 1:56
} else if (tournament == "Euro"){
idx_data = 1:44
}
} else if (stage == 'semi final'){
if (tournament == "WorldCup"){
idx_data = 1:60
} else if (tournament == "Euro"){
idx_data = 1:48
}
} else if (stage == 'final'){
if (tournament == "WorldCup"){
idx_data = 1:62
} else if (tournament == "Euro"){
idx_data = 1:50
}
}
S = 3
m = 3
# sum des y_ij
d = historical_data[historical_data$TeamA == team_A,]$GoalsA
y = sum(d[!(is.na(d))])
# sum des x_l
if (typeof(simulation) == "logical"){
results_groups = results[idx_data,]
x = sum(c(results_groups[results_groups$TeamA == team_A,]$GoalsA,
results_groups[results_groups$TeamB == team_A,]$GoalsB))
} else if (typeof(simulation) == "list"){
x = sum(c(simulation[simulation$teamA == team_A,]$goalA,
simulation[simulation$teamB == team_A,]$goalB))
}
# sum des RA / ROAi => opponents in historical data
RO = 0
for (i in 1:((S+1)*m)){
if ((i-1) %% (S+1) != 0){
RO = RO + historical_data[historical_data$TeamA == team_A,][i,]$RatingA /
historical_data[historical_data$TeamA == team_A,][i,]$RatingB
}
}
# sum des RA / RCl => opponents in tournament (real data)
if (typeof(simulation) == "logical"){
r = c()
for (i in idx_data){
if (results_groups$TeamA[i] == team_A) {
r = c(r, extract_rating(as.character(results_groups$TeamB[i])))
}
else if (results_groups$TeamB[i] == team_A) {
r = c(r, extract_rating(as.character(results_groups$TeamA[i])))
}
}
RA = ratings[ratings$Team == team_A,]$Rating
RC = sum(RA / r)
} else if (typeof(simulation) == "list"){
r = c()
if (tournament == "WorldCup"){
idx_r = 63
} else if (tournament == "Euro"){
idx_r = 51
}
for (i in 1:idx_r){
if (simulation$teamA[i] == team_A) {
r = c(r, extract_rating(as.character(simulation$teamB[i])))
}
else if (simulation$teamB[i] == team_A) {
r = c(r, extract_rating(as.character(simulation$teamA[i])))
}
}
RA = ratings[ratings$Team == team_A,]$Rating
RC = sum(RA / r)
}
# rating of B
RB = ratings[ratings$Team == team_B,]$Rating
# Negative Binomial
z=1
if (extra_time == TRUE) z=0.3
X_AB = rnbinom(n, size=(y+x+delta0)*z, prob=(RO+RC+beta0)/(RO+RC+beta0+RA/RB))
if (n > 1){
return (mean(X_AB))
}
else return (X_AB)
}
#----------------- PROBA OF WIN/DRAW/LOSS -----------------#
# Proba win/draw/loss for group stage (used for DeFinetti distance and good prediction rate)
proba_win_i = function(team_A, team_B, delta0, beta0){
S = 3
m = 3
a0=1
# sum des y_ij pour A
d_A = historical_data[historical_data$TeamA == team_A,]$GoalsA
y_A = sum(d_A[!(is.na(d_A))])
# sum des RA / ROAi
RO_A = 0
for (i in 1:((S+1)*m)){
if ((i-1) %% (S+1) != 0){
RO_A = RO_A + historical_data[historical_data$TeamA == team_A,][i,]$RatingA /
historical_data[historical_data$TeamA == team_A,][i,]$RatingB
}
}
# sum des y_ij pour B
d_B = historical_data[historical_data$TeamA == team_B,]$GoalsA
y_B = sum(d_B[!(is.na(d_B))])
# sum des RB / ROBi
RO_B = 0
for (i in 1:((S+1)*m)){
if ((i-1) %% (S+1) != 0){
RO_B = RO_B + historical_data[historical_data$TeamA == team_B,][i,]$RatingA /
historical_data[historical_data$TeamA == team_B,][i,]$RatingB
}
}
# rating of A and B
RA = extract_rating(team_A)
RB = extract_rating(team_B)
# Negative Binomial
#dnbinom(0:20, size=y_A+delta0, prob=(RO_A+beta0)/(RO_A+beta0+RA/RB)) # P(X_AB = i)
#dnbinom(0:20, size=y_B+delta0, prob=(RO_B+beta0)/(RO_B+beta0+RB/RA)) # P(X_BA = i)
p_win = 0
for (i in 1:20){
for (j in 0:(i-1)){
p_win = p_win + dnbinom(i, size=a0*y_A+delta0, prob=(a0*RO_A+beta0)/(a0*RO_A+beta0+RA/RB)) *
dnbinom(j, size=a0*y_B+delta0, prob=(a0*RO_B+beta0)/(a0*RO_B+beta0+RB/RA))
}
}
p_draw = 0
for (i in 0:20){
p_draw = p_draw + dnbinom(i, size=a0*y_A+delta0, prob=(a0*RO_A+beta0)/(a0*RO_A+beta0+RA/RB)) *
dnbinom(i, size=a0*y_B+delta0, prob=(a0*RO_B+beta0)/(a0*RO_B+beta0+RB/RA))
}
p_loss = 0
for (j in 1:20){
for (i in 0:(j-1)){
p_loss = p_loss + dnbinom(i, size=a0*y_A+delta0, prob=(a0*RO_A+beta0)/(a0*RO_A+beta0+RA/RB)) *
dnbinom(j, size=a0*y_B+delta0, prob=(a0*RO_B+beta0)/(a0*RO_B+beta0+RB/RA))
}
}
return (c(p_win, p_draw, p_loss))
}
# Proba win/draw/loss for elimination stage (used for DeFinetti distance and good prediction rate)
proba_win_ii = function(team_A, team_B, delta0, beta0, stage, tournament){
if (stage == 'eight final'){
if (tournament == "Euro"){
idx_data = 1:36
} else if (tournament == "WorldCup"){
idx_data = 1:48
}
}
else if (stage == 'quarter final'){
if (tournament == "Euro"){
idx_data = 1:44
} else if (tournament == "WorldCup"){
idx_data = 1:56
}
}
else if (stage == 'semi final'){
if (tournament == "Euro"){
idx_data = 1:48
} else if (tournament == "WorldCup"){
idx_data = 1:60
}
}
else if (stage == 'final'){
idx_data = 1:50
} else if (tournament == "WorldCup"){
idx_data = 1:60
}
S = 3
m = 3
a0=1
# sum des y_ij pour A
d_A = historical_data[historical_data$TeamA == team_A,]$GoalsA
y_A = sum(d_A[!(is.na(d_A))])
# sum des y_ij pour B
d_B = historical_data[historical_data$TeamA == team_B,]$GoalsA
y_B = sum(d_B[!(is.na(d_B))])
# sum des x_l pour A
results_groups = results[idx_data,]
x_A = sum(c(results_groups[results_groups$TeamA == team_A,]$GoalsA,
results_groups[results_groups$TeamB == team_A,]$GoalsB))
# sum des x_l pour B
results_groups = results[idx_data,]
x_B = sum(c(results_groups[results_groups$TeamA == team_B,]$GoalsA,
results_groups[results_groups$TeamB == team_B,]$GoalsB))
# sum des RA / ROAi => opponents in historical data
RO_A = 0
for (i in 1:((S+1)*m)){
if ((i-1) %% (S+1) != 0){
RO_A = RO_A + historical_data[historical_data$TeamA == team_A,][i,]$RatingA /
historical_data[historical_data$TeamA == team_A,][i,]$RatingB
}
}
# sum des RB / ROBi => opponents in historical data
RO_B = 0
for (i in 1:((S+1)*m)){
if ((i-1) %% (S+1) != 0){
RO_B = RO_B + historical_data[historical_data$TeamA == team_B,][i,]$RatingA /
historical_data[historical_data$TeamA == team_B,][i,]$RatingB
}
}
# sum des RA / RCl => opponents in tournament (real data)
r_A = c()
for (i in idx_data){
if (results_groups$TeamA[i] == team_A) {
r_A = c(r_A, extract_rating(as.character(results_groups$TeamB[i])))
}
else if (results_groups$TeamB[i] == team_A) {
r_A = c(r_A, extract_rating(as.character(results_groups$TeamA[i])))
}
}
RA = ratings[ratings$Team == team_A,]$Rating
RC_A = sum(RA / r_A)
# sum des RB / RCl => opponents in tournament (real data)
r_B = c()
for (i in idx_data){
if (results_groups$TeamA[i] == team_B) {
r_B = c(r_B, extract_rating(as.character(results_groups$TeamB[i])))
}
else if (results_groups$TeamB[i] == team_B) {
r_B = c(r_B, extract_rating(as.character(results_groups$TeamA[i])))
}
}
RB = ratings[ratings$Team == team_B,]$Rating
RC_B = sum(RB / r_B)
# Negative Binomial
p_win = 0
for (i in 1:20){
for (j in 0:(i-1)){
p_win = p_win + dnbinom(i, size=a0*y_A+x_A+delta0, prob=(a0*RO_A+RC_A+beta0)/(a0*RO_A+RC_A+beta0+RA/RB)) *
dnbinom(j, size=a0*y_B+x_B+delta0, prob=(a0*RO_B+RC_B+beta0)/(a0*RO_B+RC_B+beta0+RB/RA))
}
}
p_draw = 0
for (i in 0:20){
p_draw = p_draw + dnbinom(i, size=a0*y_A+x_A+delta0, prob=(a0*RO_A+RC_A+beta0)/(a0*RO_A+RC_A+beta0+RA/RB)) *
dnbinom(i, size=a0*y_B+x_B+delta0, prob=(a0*RO_B+RC_B+beta0)/(a0*RO_B+RC_B+beta0+RB/RA))
}
p_loss = 0
for (j in 1:20){
for (i in 0:(j-1)){
p_loss = p_loss + dnbinom(i, size=a0*y_A+x_B+delta0, prob=(a0*RO_A+RC_A+beta0)/(a0*RO_A+RC_A+beta0+RA/RB)) *
dnbinom(j, size=a0*y_B+x_B+delta0, prob=(a0*RO_B+RC_B+beta0)/(a0*RO_B+RC_B+beta0+RB/RA))
}
}
return (c(p_win, p_draw, p_loss))
}
############ Predictions group stage & final rounds ############
# Table of predictions (proba win/draw/loss, DeFinetti distance, good prediction rate)
prediction_table = function(delta, beta, tournament){
if (tournament == "WorldCup"){
n_total = 63
n_groups = 48
} else if (tournament == "Euro"){
n_total = 51
n_groups = 36
}
matches_df = data.frame("teamA"=rep(0, n_total), "score"=rep(0,n_total),
"teamB"=rep(0, n_total), "pred1"=rep(0, n_total), "deFi1"=rep(0, n_total), "good_pred1"=rep(0, n_total),
"pred2"=rep(0, n_total), "deFi2"=rep(0, n_total), "good_pred2"=rep(0, n_total))
# group stage
for (i in 1:n_groups){
teamA = as.character(results$TeamA[i])
matches_df$teamA[i] = teamA
teamB = as.character(results$TeamB[i])
matches_df$teamB[i] = teamB
matches_df$score[i] = paste(results$GoalsA[i], results$GoalsB[i], sep="-")
if (results$GoalsA[i] > results$GoalsB[i]){
res = c(1,0,0)
} else if (results$GoalsA[i] < results$GoalsB[i]){
res = c(0,0,1)
} else res = c(0,1,0)
pred1 = proba_win_i(teamA, teamB, delta, beta)
matches_df$pred1[i] = paste0('(', round(pred1[1], 2), ',', round(pred1[2], 2), ',', round(pred1[3], 2), ')')
matches_df$deFi1[i] = round(sum((res-pred1)^2), 3)
if ((which.max(pred1) == which.max(res))){
matches_df$good_pred1[i] = 1
}
pred2 = c(as.numeric(extract_rating(teamA) > extract_rating(teamB)), 0, as.numeric(extract_rating(teamA) < extract_rating(teamB)))
matches_df$pred2[i] = paste0('(', pred2[1], ',', 0, ',', pred2[3], ')')
matches_df$deFi2[i] = sum((res-pred2)^2)
if ((which.max(pred2) == which.max(res))){
matches_df$good_pred2[i] = 1
}
}
# final rounds
idx_start = n_groups+1
k_x = 0
stage_vec = c("eight final", "quarter final", "semi final", "final")
for (k in c(8, 4, 2, 1)){
idx_end = idx_start+k-1
idx_data = idx_start:(idx_end)
k_x = k_x + 1
stage = stage_vec[k_x]
for (i in idx_data){
teamA = as.character(results$TeamA[i])
matches_df$teamA[i] = teamA
teamB = as.character(results$TeamB[i])
matches_df$teamB[i] = teamB
matches_df$score[i] = paste(results$GoalsA[i], results$GoalsB[i], sep="-")
if (results$GoalsA[i] > results$GoalsB[i]){
res = c(1,0,0)
} else if (results$GoalsA[i] < results$GoalsB[i]){
res = c(0,0,1)
} else res = c(0,1,0)
pred1 = proba_win_ii(teamA, teamB, delta, beta, stage, tournament)
matches_df$pred1[i] = paste0('(', round(pred1[1], 2), ',', round(pred1[2], 2), ',', round(pred1[3], 2), ')')
matches_df$deFi1[i] = round(sum((res-pred1)^2), 3)
if ((which.max(pred1) == which.max(res))){
matches_df$good_pred1[i] = 1
}
pred2 = c(as.numeric(extract_rating(teamA) > extract_rating(teamB)), 0, as.numeric(extract_rating(teamA) < extract_rating(teamB)))
matches_df$pred2[i] = paste0('(', pred2[1], ',', 0, ',', pred2[3], ')')
matches_df$deFi2[i] = sum((res-pred2)^2)
if ((which.max(pred2) == which.max(res))){
matches_df$good_pred2[i] = 1
}
}
idx_start = idx_end + 1
}
return (matches_df)
}
#----------------- TOURNAMENT SIMULATION ----------------#
# WorldCup (2014 & 2018)
tournament_simulation_WorldCup = function(delta, beta, tournament="WorldCup"){
# group stage
list_team = union(results$TeamA, results$TeamB)
teamA_vec = c()
teamB_vec = c()
score_df = data.frame("team"=list_team, "points"=rep(0, 32), "diff"=rep(0, 32), "nbr_goals"=rep(0, 32))
matches_df = data.frame("teamA"=rep(0, 63), "goalA"=rep(0,63), "teamB"=rep(0, 63), "goalB"=rep(0,63))
for (i in 1:48){
teamA = as.character(results$TeamA[i])
matches_df$teamA[i] = teamA
teamB = as.character(results$TeamB[i])
matches_df$teamB[i] = teamB
teamA_vec = c(teamA_vec, teamA)
teamB_vec = c(teamB_vec, teamB)
goalsA = predictive_i(teamA, teamB, delta, beta, 1)
matches_df$goalA[i] = goalsA
goalsB = predictive_i(teamB, teamA, delta, beta, 1)
matches_df$goalB[i] = goalsB
score_df$diff[score_df$team == teamA] = score_df$diff[score_df$team == teamA] + (goalsA-goalsB)
score_df$diff[score_df$team == teamB] = score_df$diff[score_df$team == teamB] + (goalsB-goalsA)
score_df$nbr_goals[score_df$team == teamA] = score_df$nbr_goals[score_df$team == teamA] + goalsA
score_df$nbr_goals[score_df$team == teamB] = score_df$nbr_goals[score_df$team == teamB] + goalsB
if (goalsA > goalsB){
score_df$points[score_df$team == teamA] = score_df$points[score_df$team == teamA] + 3
}
else if (goalsA == goalsB){
score_df$points[score_df$team == teamA] = score_df$points[score_df$team == teamA] + 1
score_df$points[score_df$team == teamB] = score_df$points[score_df$team == teamB] + 1
}
else{
score_df$points[score_df$team == teamB] = score_df$points[score_df$team == teamB] + 3
}
}
first_qualified = c()
second_qualified = c()
for (i in 1:8){
group_df = score_df[(i*4-3):(i*4),]
group_df = group_df[order(group_df$points, group_df$diff, group_df$nbr_goals, decreasing=TRUE),]
first_qualified = c(first_qualified, as.character(group_df[1,]$team))
second_qualified = c(second_qualified, as.character(group_df[2,]$team))
#print(group_df)
}
team_qualified_group = c(first_qualified, second_qualified)
#print("---------------------------------------------------------------------------")
#print(" --------------------- EIGHT FINAL ----------------------------")
#print("---------------------------------------------------------------------------")
# eight final
team_A_ef = rep(NA, 8)
team_B_ef = rep(NA, 8)
for (i in 1:4){
team_A_ef[i] = first_qualified[i*2-1]
team_B_ef[i] = second_qualified[i*2]
team_A_ef[i+4] = first_qualified[i*2]
team_B_ef[i+4] = second_qualified[i*2-1]
}
goalsA_ef_vec = c()
goalsB_ef_vec = c()
score_string = c()
team_qualified_ef = rep(NA, 8)
for (i in 1:8){
teamA = team_A_ef[i]
matches_df$teamA[i+48] = teamA
teamB = team_B_ef[i]
matches_df$teamB[i+48] = teamB
goalsA = predictive_ii(teamA, teamB, delta, beta, 1, stage="eight final", simulation=matches_df, tournament=tournament)
matches_df$goalA[i+48] = goalsA
goalsB = predictive_ii(teamB, teamA, delta, beta, 1, stage="eight final", simulation=matches_df, tournament=tournament)
matches_df$goalB[i+48] = goalsB
goalsA_ef_vec = c(goalsA_ef_vec, goalsA)
goalsB_ef_vec = c(goalsB_ef_vec, goalsB)
if (goalsA > goalsB){
team_qualified_ef[i] = teamA
}
else if (goalsA < goalsB){
team_qualified_ef[i] = teamB
}
else if (goalsA == goalsB){
goalsA = goalsA + predictive_ii(teamA, teamB, delta, beta, 1, stage="eight final", extra_time=TRUE, simulation=matches_df, tournament=tournament)
matches_df$goalA[i+48] = goalsA
goalsB = goalsB + predictive_ii(teamB, teamA, delta, beta, 1, stage="eight final", extra_time=TRUE, simulation=matches_df, tournament=tournament)
matches_df$goalB[i+48] = goalsB
if (goalsA > goalsB){
team_qualified_ef[i] = teamA
}
else if (goalsA < goalsB){
team_qualified_ef[i] = teamB
}
else if (goalsA == goalsB){
team_qualified_ef[i] = sample(c(teamA, teamB), 1)
}
}
score_string = c(score_string, paste(goalsA, goalsB, sep="-"))
}
score_ef_df = data.frame("teamA"=team_A_ef, "score"=score_string, "teamB"=team_B_ef, "goalsA"=goalsA_ef_vec, "goalsB"=goalsB_ef_vec)
#print(score_ef_df[,1:3])
#print("---------------------------------------------------------------------------")
#print(" --------------------- QUARTER FINAL ----------------------------")
#print("---------------------------------------------------------------------------")
# quarter final
team_A_qf = rep(NA, 4)
team_B_qf = rep(NA, 4)
for (i in 1:4){
team_A_qf[i] = team_qualified_ef[i*2-1]
team_B_qf[i] = team_qualified_ef[2*i]
}
goalsA_qf_vec = c()
goalsB_qf_vec = c()
score_string = c()
team_qualified_qf = rep(NA, 4)
for (i in 1:4){
teamA = team_A_qf[i]
matches_df$teamA[i+56] = teamA
teamB = team_B_qf[i]
matches_df$teamB[i+56] = teamB
goalsA = predictive_ii(teamA, teamB, delta, beta, 1, stage="quarter final", simulation=matches_df, tournament=tournament)
matches_df$goalA[i+56] = goalsA
goalsB = predictive_ii(teamB, teamA, delta, beta, 1, stage="quarter final", simulation=matches_df, tournament=tournament)
matches_df$goalB[i+56] = goalsB
goalsA_qf_vec = c(goalsA_qf_vec, goalsA)
goalsB_qf_vec = c(goalsB_qf_vec, goalsB)
if (goalsA > goalsB){
team_qualified_qf[i] = teamA
}
else if (goalsA < goalsB){
team_qualified_qf[i] = teamB
}
else if (goalsA == goalsB){
goalsA = goalsA + predictive_ii(teamA, teamB, delta, beta, 1, stage="quarter final", extra_time=TRUE, simulation=matches_df, tournament=tournament)
matches_df$goalA[i+56] = goalsA
goalsB = goalsB + predictive_ii(teamB, teamA, delta, beta, 1, stage="quarter final", extra_time=TRUE, simulation=matches_df, tournament=tournament)
matches_df$goalB[i+56] = goalsB
if (goalsA > goalsB){
team_qualified_qf[i] = teamA
}
else if (goalsA < goalsB){
team_qualified_qf[i] = teamB
}
else if (goalsA == goalsB){
team_qualified_qf[i] = sample(c(teamA, teamB), 1)
}
}
score_string = c(score_string, paste(goalsA, goalsB, sep="-"))
}
score_qf_df = data.frame("teamA"=team_A_qf, "score"=score_string, "teamB"=team_B_qf, "goalsA"=goalsA_qf_vec, "goalsB"=goalsB_qf_vec)
#print(score_qf_df[,1:3])
#print("---------------------------------------------------------------------------")
#print(" --------------------- SEMI FINAL ----------------------------")
#print("---------------------------------------------------------------------------")
# semi final
team_A_sf = rep(NA, 2)
team_B_sf = rep(NA, 2)
for (i in 1:2){
team_A_sf[i] = team_qualified_qf[i*2-1]
team_B_sf[i] = team_qualified_qf[2*i]
}
goalsA_sf_vec = c()
goalsB_sf_vec = c()
score_string = c()
team_qualified_sf = rep(NA, 2)
for (i in 1:2){
teamA = team_A_sf[i]
matches_df$teamA[i+60] = teamA
teamB = team_B_sf[i]
matches_df$teamB[i+60] = teamB
goalsA = predictive_ii(teamA, teamB, delta, beta, 1, stage="semi final", simulation=matches_df, tournament=tournament)
matches_df$goalA[i+60] = goalsA
goalsB = predictive_ii(teamB, teamA, delta, beta, 1, stage="semi final", simulation=matches_df, tournament=tournament)
matches_df$goalB[i+60] = goalsB
goalsA_sf_vec = c(goalsA_sf_vec, goalsA)
goalsB_sf_vec = c(goalsB_sf_vec, goalsB)
if (goalsA > goalsB){
team_qualified_sf[i] = teamA
}
else if (goalsA < goalsB){
team_qualified_sf[i] = teamB
}
else if (goalsA == goalsB){
goalsA = goalsA + predictive_ii(teamA, teamB, delta, beta, 1, stage="semi final", extra_time=TRUE, simulation=matches_df, tournament=tournament)
matches_df$goalA[i+60] = goalsA
goalsB = goalsB + predictive_ii(teamB, teamA, delta, beta, 1, stage="semi final", extra_time=TRUE, simulation=matches_df, tournament=tournament)
matches_df$goalB[i+60] = goalsB
if (goalsA > goalsB){
team_qualified_sf[i] = teamA
}
else if (goalsA < goalsB){
team_qualified_sf[i] = teamB
}
else if (goalsA == goalsB){
team_qualified_sf[i] = sample(c(teamA, teamB), 1)
}
}
score_string = c(score_string, paste(goalsA, goalsB, sep="-"))
}
score_sf_df = data.frame("teamA"=team_A_sf, "score"=score_string, "teamB"=team_B_sf, "goalsA"=goalsA_sf_vec, "goalsB"=goalsB_sf_vec)
#print(score_sf_df[,1:3])
#print("---------------------------------------------------------------------------")
#print(" ------------------------ FINAL -------------------------------")
#print("---------------------------------------------------------------------------")
# final
team_A_f = team_qualified_sf[1]
matches_df$teamA[63] = team_A_f
team_B_f = team_qualified_sf[2]
matches_df$teamB[63] = team_B_f
goalsA_f = predictive_ii(team_A_f, team_B_f, delta, beta, 1, stage="final", simulation=matches_df, tournament=tournament)
matches_df$goalA[63] = goalsA_f
goalsB_f = predictive_ii(team_B_f, team_A_f, delta, beta, 1, stage="final", simulation=matches_df, tournament=tournament)
matches_df$goalB[63] = goalsB_f
if (goalsA_f > goalsB_f){
team_winner = team_A_f
} else if (goalsA_f < goalsB_f){
team_winner = team_B_f
} else if (goalsA_f == goalsB_f){
goalsA_f = goalsA_f + predictive_ii(team_A_f, team_B_f, delta, beta, 1, stage="final", extra_time=TRUE, simulation=matches_df, tournament=tournament)
matches_df$goalA[63] = goalsA_f
goalsB_f = goalsB_f + predictive_ii(team_B_f, team_A_f, delta, beta, 1, stage="final", extra_time=TRUE, simulation=matches_df, tournament=tournament)
matches_df$goalB[63] = goalsB_f
if (goalsA_f > goalsB_f){
team_winner = team_A_f
}
else if (goalsA_f < goalsB_f){
team_winner = team_B_f
}
else if (goalsA_f == goalsB_f){
team_winner = sample(c(team_A_f, team_B_f), 1)
}
}
score_string = paste(goalsA_f, goalsB_f, sep="-")
score_sf_df = data.frame("teamA"=team_A_f, "score"=score_string, "teamB"=team_B_f, "goalsA"=goalsA_f, "goalsB"=goalsB_f)
#print(score_sf_df[,1:3])
#print(paste("Winner: ", team_winner))
return(list(team_winner, team_qualified_sf, team_qualified_qf, team_qualified_ef, team_qualified_group))
}
result_tournament_WorldCup = function(delta, beta, n){
winner = c()
finalists = c()
semi_finalists = c()
quarter_finalists = c()
eight_finalists = c()
for (i in 1:n){
print(i)
if (i%%10 == 0){
#print(i)
}
result_simu = tournament_simulation_WorldCup(delta, beta)
winner = c(winner, result_simu[[1]])
for (i in 1:2){
finalists = c(finalists, result_simu[[2]][i])
}
for (i in 1:4){
semi_finalists = c(semi_finalists, result_simu[[3]][i])
}
for (i in 1:8){
quarter_finalists = c(quarter_finalists, result_simu[[4]][i])
}
for (i in 1:16){
eight_finalists = c(eight_finalists, result_simu[[5]][i])
}
}
list_team = union(results$TeamA, results$TeamB)
df_outcome = data.frame(list_team, matrix(NA, length(list_team), 4))
colnames(df_outcome) = c("Team", "final", "semi final", "quarter final", "eight final")
df_outcome = data.frame("Team"=list_team)
df1 = as.data.frame(table(winner, dnn=c("Team"))/n, responseName="winner")
df2 = as.data.frame(table(finalists, dnn=c("Team"))/n, responseName="final")
df3 = as.data.frame(table(semi_finalists, dnn=c("Team"))/n, responseName="semi final")
df4 = as.data.frame(table(quarter_finalists, dnn=c("Team"))/n, responseName="quarter final")
df5 = as.data.frame(table(eight_finalists, dnn=c("Team"))/n, responseName="eigth final")
df_outcome = merge(x=df_outcome, y=df1, by='Team', all.x=TRUE)
df_outcome = merge(x=df_outcome, y=df2, by='Team', all.x=TRUE)
df_outcome = merge(x=df_outcome, y=df3, by='Team', all.x=TRUE)
df_outcome = merge(x=df_outcome, y=df4, by='Team', all.x=TRUE)
df_outcome = merge(x=df_outcome, y=df5, by='Team', all.x=TRUE)
df_outcome[is.na(df_outcome)] = 0
df_outcome = df_outcome[order(df_outcome$winner, decreasing=TRUE),]
rownames(df_outcome) <- NULL
print(df_outcome)
}
# Euro 2016
tournament_simulation_Euro_2016 = function(delta, beta){
# group stage
list_team = union(results$TeamA, results$TeamB)
teamA_vec = c()
teamB_vec = c()
score_df = data.frame("team"=list_team, "points"=rep(0, 24), "diff"=rep(0, 24), "nbr_goals"=rep(0, 24))
matches_df = data.frame("teamA"=rep(0, 51), "goalA"=rep(0,51), "teamB"=rep(0, 51), "goalB"=rep(0,51))
for (i in 1:36){
teamA = as.character(results$TeamA[i])
matches_df$teamA[i] = teamA
teamB = as.character(results$TeamB[i])
matches_df$teamB[i] = teamB
teamA_vec = c(teamA_vec, teamA)
teamB_vec = c(teamB_vec, teamB)
goalsA = predictive_i(teamA, teamB, delta, beta, 1)
matches_df$goalA[i] = goalsA
goalsB = predictive_i(teamB, teamA, delta, beta, 1)
matches_df$goalB[i] = goalsB
score_df$diff[score_df$team == teamA] = score_df$diff[score_df$team == teamA] + (goalsA-goalsB)
score_df$diff[score_df$team == teamB] = score_df$diff[score_df$team == teamB] + (goalsB-goalsA)
score_df$nbr_goals[score_df$team == teamA] = score_df$nbr_goals[score_df$team == teamA] + goalsA
score_df$nbr_goals[score_df$team == teamB] = score_df$nbr_goals[score_df$team == teamB] + goalsB
if (goalsA > goalsB){
score_df$points[score_df$team == teamA] = score_df$points[score_df$team == teamA] + 3
}
else if (goalsA == goalsB){
score_df$points[score_df$team == teamA] = score_df$points[score_df$team == teamA] + 1
score_df$points[score_df$team == teamB] = score_df$points[score_df$team == teamB] + 1
}
else{
score_df$points[score_df$team == teamB] = score_df$points[score_df$team == teamB] + 3
}
}
first_qualified = c()
second_qualified = c()
third_teams = c()
for (i in 1:6){
group_df = score_df[(i*4-3):(i*4),]
group_df = group_df[order(group_df$points, group_df$diff, group_df$nbr_goals, decreasing=TRUE),]
first_qualified = c(first_qualified, as.character(group_df[1,]$team))
second_qualified = c(second_qualified, as.character(group_df[2,]$team))
third_teams = c(third_teams, as.character(group_df[3,]$team))
}
# Meilleurs 3iemes de groupes
third_teams_df = score_df[score_df$team %in% third_teams,]
third_teams_df["group"] = c("A", "B", "C", "D", "E", "F")
third_teams_df_ordered = third_teams_df[order(third_teams_df$points, third_teams_df$diff, third_teams_df$nbr_goals, decreasing=TRUE),]
third_qualified = as.character(third_teams_df_ordered$team)[1:4]
scheduled_3rd = c("BEF", "ACD", "ABF", "CDE") # specific to Euro 2016
best_third = third_teams_df_ordered[1:4,]
match = FALSE
while (!match){
sample_3rd = sample(best_third$group)
match = letter_in(sample_3rd, scheduled_3rd)
}
team_qualified_group = c(first_qualified, second_qualified, third_qualified)
#print("---------------------------------------------------------------------------")
#print(" --------------------- EIGHT FINAL ----------------------------")
#print("---------------------------------------------------------------------------")
# eight final
team_A_ef = rep(NA, 8)
team_B_ef = rep(NA, 8)
team_A_ef[1] = team_qualified_group[7] # 2A
team_B_ef[1] = team_qualified_group[9] # 2C
team_A_ef[2] = team_qualified_group[4] # 1D
team_B_ef[2] = as.character(best_third[best_third$group == sample_3rd[1],]$team) # 3BEF
team_A_ef[3] = team_qualified_group[2] # 1B
team_B_ef[3] = as.character(best_third[best_third$group == sample_3rd[2],]$team) # 3ACD
team_A_ef[4] = team_qualified_group[6] # 1F
team_B_ef[4] = team_qualified_group[11] # 2E
team_A_ef[5] = team_qualified_group[3] # 1C
team_B_ef[5] = as.character(best_third[best_third$group == sample_3rd[3],]$team) # 3ABF
team_A_ef[6] = team_qualified_group[5] # 1E
team_B_ef[6] = team_qualified_group[10] # 2D
team_A_ef[7] = team_qualified_group[1] # 1A
team_B_ef[7] = as.character(best_third[best_third$group == sample_3rd[4],]$team) # 3CDE
team_A_ef[8] = team_qualified_group[8] # 2B
team_B_ef[8] = team_qualified_group[12] # 2F
goalsA_ef_vec = c()
goalsB_ef_vec = c()
score_string = c()
team_qualified_ef = rep(NA, 8)
for (i in 1:8){
teamA = team_A_ef[i]
matches_df$teamA[i+36] = teamA
teamB = team_B_ef[i]
matches_df$teamB[i+36] = teamB
goalsA = predictive_ii(teamA, teamB, delta, beta, 1, stage="eight final", simulation=matches_df, tournament="Euro")
matches_df$goalA[i+36] = goalsA
goalsB = predictive_ii(teamB, teamA, delta, beta, 1, stage="eight final", simulation=matches_df, tournament="Euro")
matches_df$goalB[i+36] = goalsB
goalsA_ef_vec = c(goalsA_ef_vec, goalsA)
goalsB_ef_vec = c(goalsB_ef_vec, goalsB)
if (goalsA > goalsB){
team_qualified_ef[i] = teamA
}
else if (goalsA < goalsB){
team_qualified_ef[i] = teamB
}
else if (goalsA == goalsB){
goalsA = goalsA + predictive_ii(teamA, teamB, delta, beta, 1, stage="eight final", extra_time=TRUE, simulation=matches_df, tournament="Euro")
matches_df$goalA[i+36] = goalsA
goalsB = goalsB + predictive_ii(teamB, teamA, delta, beta, 1, stage="eight final", extra_time=TRUE, simulation=matches_df, tournament="Euro")
matches_df$goalB[i+36] = goalsB
if (goalsA > goalsB){
team_qualified_ef[i] = teamA
}
else if (goalsA < goalsB){
team_qualified_ef[i] = teamB
}
else if (goalsA == goalsB){
team_qualified_ef[i] = sample(c(teamA, teamB), 1)
}
}
score_string = c(score_string, paste(goalsA, goalsB, sep="-"))
}
score_ef_df = data.frame("teamA"=team_A_ef, "score"=score_string, "teamB"=team_B_ef, "goalsA"=goalsA_ef_vec, "goalsB"=goalsB_ef_vec)
#print(score_ef_df[,1:3])
#print("---------------------------------------------------------------------------")
#print(" --------------------- QUARTER FINAL ----------------------------")
#print("---------------------------------------------------------------------------")
# quarter final
team_A_qf = rep(NA, 4)
team_B_qf = rep(NA, 4)
for (i in 1:4){
team_A_qf[i] = team_qualified_ef[i*2-1]
team_B_qf[i] = team_qualified_ef[2*i]
}
goalsA_qf_vec = c()
goalsB_qf_vec = c()
score_string = c()
team_qualified_qf = rep(NA, 4)
for (i in 1:4){
teamA = team_A_qf[i]
matches_df$teamA[i+44] = teamA
teamB = team_B_qf[i]
matches_df$teamB[i+44] = teamB
goalsA = predictive_ii(teamA, teamB, delta, beta, 1, stage="quarter final", simulation=matches_df, tournament="Euro")
matches_df$goalA[i+44] = goalsA
goalsB = predictive_ii(teamB, teamA, delta, beta, 1, stage="quarter final", simulation=matches_df, tournament="Euro")
matches_df$goalB[i+44] = goalsB
goalsA_qf_vec = c(goalsA_qf_vec, goalsA)
goalsB_qf_vec = c(goalsB_qf_vec, goalsB)
if (goalsA > goalsB){
team_qualified_qf[i] = teamA
}
else if (goalsA < goalsB){
team_qualified_qf[i] = teamB
}
else if (goalsA == goalsB){
goalsA = goalsA + predictive_ii(teamA, teamB, delta, beta, 1, stage="quarter final", extra_time=TRUE, simulation=matches_df, tournament="Euro")
matches_df$goalA[i+44] = goalsA
goalsB = goalsB + predictive_ii(teamB, teamA, delta, beta, 1, stage="quarter final", extra_time=TRUE, simulation=matches_df, tournament="Euro")
matches_df$goalB[i+44] = goalsB
if (goalsA > goalsB){
team_qualified_qf[i] = teamA
}
else if (goalsA < goalsB){
team_qualified_qf[i] = teamB
}
else if (goalsA == goalsB){
team_qualified_qf[i] = sample(c(teamA, teamB), 1)
}
}
score_string = c(score_string, paste(goalsA, goalsB, sep="-"))
}
score_qf_df = data.frame("teamA"=team_A_qf, "score"=score_string, "teamB"=team_B_qf, "goalsA"=goalsA_qf_vec, "goalsB"=goalsB_qf_vec)
#print(score_qf_df[,1:3])
#print("---------------------------------------------------------------------------")
#print(" --------------------- SEMI FINAL ----------------------------")
#print("---------------------------------------------------------------------------")
# semi final
team_A_sf = rep(NA, 2)
team_B_sf = rep(NA, 2)
for (i in 1:2){
team_A_sf[i] = team_qualified_qf[i*2-1]
team_B_sf[i] = team_qualified_qf[2*i]
}
goalsA_sf_vec = c()
goalsB_sf_vec = c()
score_string = c()
team_qualified_sf = rep(NA, 2)
for (i in 1:2){
teamA = team_A_sf[i]
matches_df$teamA[i+48] = teamA
teamB = team_B_sf[i]
matches_df$teamB[i+48] = teamB
goalsA = predictive_ii(teamA, teamB, delta, beta, 1, stage="semi final", simulation=matches_df, tournament="Euro")
matches_df$goalA[i+48] = goalsA
goalsB = predictive_ii(teamB, teamA, delta, beta, 1, stage="semi final", simulation=matches_df, tournament="Euro")
matches_df$goalB[i+48] = goalsB
goalsA_sf_vec = c(goalsA_sf_vec, goalsA)
goalsB_sf_vec = c(goalsB_sf_vec, goalsB)
if (goalsA > goalsB){
team_qualified_sf[i] = teamA
}
else if (goalsA < goalsB){
team_qualified_sf[i] = teamB
}
else if (goalsA == goalsB){
goalsA = goalsA + predictive_ii(teamA, teamB, delta, beta, 1, stage="semi final", extra_time=TRUE, simulation=matches_df, tournament="Euro")
matches_df$goalA[i+48] = goalsA
goalsB = goalsB + predictive_ii(teamB, teamA, delta, beta, 1, stage="semi final", extra_time=TRUE, simulation=matches_df, tournament="Euro")
matches_df$goalB[i+48] = goalsB
if (goalsA > goalsB){
team_qualified_sf[i] = teamA
}
else if (goalsA < goalsB){
team_qualified_sf[i] = teamB
}
else if (goalsA == goalsB){
team_qualified_sf[i] = sample(c(teamA, teamB), 1)
}
}
score_string = c(score_string, paste(goalsA, goalsB, sep="-"))
}
score_sf_df = data.frame("teamA"=team_A_sf, "score"=score_string, "teamB"=team_B_sf, "goalsA"=goalsA_sf_vec, "goalsB"=goalsB_sf_vec)
#print(score_sf_df[,1:3])
#print("---------------------------------------------------------------------------")
#print(" ------------------------ FINAL -------------------------------")
#print("---------------------------------------------------------------------------")
# final
team_A_f = team_qualified_sf[1]
matches_df$teamA[51] = team_A_f
team_B_f = team_qualified_sf[2]
matches_df$teamB[51] = team_B_f
goalsA_f = predictive_ii(team_A_f, team_B_f, delta, beta, 1, stage="final", simulation=matches_df, tournament="Euro")
matches_df$goalA[51] = goalsA_f
goalsB_f = predictive_ii(team_B_f, team_A_f, delta, beta, 1, stage="final", simulation=matches_df, tournament="Euro")
matches_df$goalB[51] = goalsB_f
if (goalsA_f > goalsB_f){
team_winner = team_A_f
} else if (goalsA_f < goalsB_f){
team_winner = team_B_f
} else if (goalsA_f == goalsB_f){
goalsA_f = goalsA_f + predictive_ii(team_A_f, team_B_f, delta, beta, 1, stage="final", extra_time=TRUE, simulation=matches_df, tournament="Euro")
matches_df$goalA[51] = goalsA_f
goalsB_f = goalsB_f + predictive_ii(team_B_f, team_A_f, delta, beta, 1, stage="final", extra_time=TRUE, simulation=matches_df, tournament="Euro")
matches_df$goalB[51] = goalsB_f
if (goalsA_f > goalsB_f){
team_winner = team_A_f
}
else if (goalsA_f < goalsB_f){
team_winner = team_B_f
}
else if (goalsA_f == goalsB_f){
team_winner = sample(c(team_A_f, team_B_f), 1)
}
}
score_string = paste(goalsA_f, goalsB_f, sep="-")
score_sf_df = data.frame("teamA"=team_A_f, "score"=score_string, "teamB"=team_B_f, "goalsA"=goalsA_f, "goalsB"=goalsB_f)
#print(score_sf_df[,1:3])
#print(paste("Winner: ", team_winner))
return(list(team_winner, team_qualified_sf, team_qualified_qf, team_qualified_ef, team_qualified_group))
}
result_tournament_Euro_2016 = function(delta, beta, n){
winner = c()
finalists = c()
semi_finalists = c()
quarter_finalists = c()
eight_finalists = c()
for (i in 1:n){
if (i%%10 == 0){
#print(i)
}
result_simu = tournament_simulation_Euro_2016(delta, beta)
winner = c(winner, result_simu[[1]])
for (i in 1:2){
finalists = c(finalists, result_simu[[2]][i])
}
for (i in 1:4){
semi_finalists = c(semi_finalists, result_simu[[3]][i])
}
for (i in 1:8){
quarter_finalists = c(quarter_finalists, result_simu[[4]][i])
}
for (i in 1:16){
eight_finalists = c(eight_finalists, result_simu[[5]][i])
}
}
list_team = union(results$TeamA, results$TeamB)
df_outcome = data.frame(list_team, matrix(NA, length(list_team), 4))
colnames(df_outcome) = c("Team", "final", "semi final", "quarter final", "eight final")
df_outcome = data.frame("Team"=list_team)