就离谱！使用机器学习预测2022世界杯：小组赛挺准，但冠亚季军都错了 ⛵

import pandas as pd
import re
df =  pd.read_csv("results.csv") #games between national teams
df["date"] = pd.to_datetime(df["date"])
df = df[(df["date"] >= "2018-8-1")].reset_index(drop=True) #games at the 2022 wc cycle
df_wc = df #pre-wc outcomes

rank = pd.read_csv("fifa_ranking-2022-10-06.csv") #rankings
rank["rank_date"] = pd.to_datetime(rank["rank_date"]) 
rank = rank[(rank["rank_date"] >= "2018-8-1")].reset_index(drop=True) #selecting games from the 2022 wc cycle
rank["country_full"] = rank["country_full"].str.replace("IR Iran", "Iran").str.replace("Korea Republic", "South Korea").str.replace("USA", "United States") #ajustando nomes de algumas seleções
rank = rank.set_index(['rank_date']).groupby(['country_full'], group_keys=False).resample('D').first().fillna(method='ffill').reset_index()
rank_wc = rank #dataframe with rankings

#Making the merge
df_wc_ranked = df_wc.merge(rank[["country_full", "total_points", "previous_points", "rank", "rank_change", "rank_date"]], left_on=["date", "home_team"], right_on=["rank_date", "country_full"]).drop(["rank_date", "country_full"], axis=1)
df_wc_ranked = df_wc_ranked.merge(rank[["country_full", "total_points", "previous_points", "rank", "rank_change", "rank_date"]], left_on=["date", "away_team"], right_on=["rank_date", "country_full"], suffixes=("_home", "_away")).drop(["rank_date", "country_full"], axis=1)

df = df_wc_ranked

def result_finder(home, away):
    if home > away:
        return pd.Series([0, 3, 0])
    if home < away:
        return pd.Series([1, 0, 3])
    else:
        return pd.Series([2, 1, 1])

results = df.apply(lambda x: result_finder(x["home_score"], x["away_score"]), axis=1)

df[["result", "home_team_points", "away_team_points"]] = results

df["rank_dif"] = df["rank_home"] - df["rank_away"]
df["sg"] = df["home_score"] - df["away_score"]
df["points_home_by_rank"] = df["home_team_points"]/df["rank_away"]
df["points_away_by_rank"] = df["away_team_points"]/df["rank_home"]

home_team = df[["date", "home_team", "home_score", "away_score", "rank_home", "rank_away","rank_change_home", "total_points_home", "result", "rank_dif", "points_home_by_rank", "home_team_points"]]

away_team = df[["date", "away_team", "away_score", "home_score", "rank_away", "rank_home","rank_change_away", "total_points_away", "result", "rank_dif", "points_away_by_rank", "away_team_points"]]

home_team.columns = [h.replace("home_", "").replace("_home", "").replace("away_", "suf_").replace("_away", "_suf") for h in home_team.columns]

away_team.columns = [a.replace("away_", "").replace("_away", "").replace("home_", "suf_").replace("_home", "_suf") for a in away_team.columns]

team_stats = home_team.append(away_team)

team_stats_raw = team_stats.copy()
stats_val = []

for index, row in team_stats.iterrows():
    team = row["team"]
    date = row["date"]
    past_games = team_stats.loc[(team_stats["team"] == team) & (team_stats["date"] < date)].sort_values(by=['date'], ascending=False)
    last5 = past_games.head(5)
    
    goals = past_games["score"].mean()
    goals_l5 = last5["score"].mean()
    
    goals_suf = past_games["suf_score"].mean()
    goals_suf_l5 = last5["suf_score"].mean()
    
    rank = past_games["rank_suf"].mean()
    rank_l5 = last5["rank_suf"].mean()
    
    if len(last5) > 0:
        points = past_games["total_points"].values[0] - past_games["total_points"].values[-1]#qtd de pontos ganhos
        points_l5 = last5["total_points"].values[0] - last5["total_points"].values[-1] 
    else:
        points = 0
        points_l5 = 0
        
    gp = past_games["team_points"].mean()
    gp_l5 = last5["team_points"].mean()
    
    gp_rank = past_games["points_by_rank"].mean()
    gp_rank_l5 = last5["points_by_rank"].mean()
    
    stats_val.append([goals, goals_l5, goals_suf, goals_suf_l5, rank, rank_l5, points, points_l5, gp, gp_l5, gp_rank, gp_rank_l5])

stats_cols = ["goals_mean", "goals_mean_l5", "goals_suf_mean", "goals_suf_mean_l5", "rank_mean", "rank_mean_l5", "points_mean", "points_mean_l5", "game_points_mean", "game_points_mean_l5", "game_points_rank_mean", "game_points_rank_mean_l5"]

stats_df = pd.DataFrame(stats_val, columns=stats_cols)

full_df = pd.concat([team_stats.reset_index(drop=True), stats_df], axis=1, ignore_index=False)

home_team_stats = full_df.iloc[:int(full_df.shape[0]/2),:]
away_team_stats = full_df.iloc[int(full_df.shape[0]/2):,:]

home_team_stats = home_team_stats[home_team_stats.columns[-12:]]
away_team_stats = away_team_stats[away_team_stats.columns[-12:]]

home_team_stats.columns = ['home_'+str(col) for col in home_team_stats.columns]
away_team_stats.columns = ['away_'+str(col) for col in away_team_stats.columns]

match_stats = pd.concat([home_team_stats, away_team_stats.reset_index(drop=True)], axis=1, ignore_index=False)

full_df = pd.concat([df, match_stats.reset_index(drop=True)], axis=1, ignore_index=False)

def find_friendly(x):
    if x == "Friendly":
        return 1
    else: return 0

full_df["is_friendly"] = full_df["tournament"].apply(lambda x: find_friendly(x)) 

full_df = pd.get_dummies(full_df, columns=["is_friendly"])

base_df = full_df[["date", "home_team", "away_team", "rank_home", "rank_away","home_score", "away_score","result", "rank_dif", "rank_change_home", "rank_change_away", 'home_goals_mean',
       'home_goals_mean_l5', 'home_goals_suf_mean', 'home_goals_suf_mean_l5',
       'home_rank_mean', 'home_rank_mean_l5', 'home_points_mean',
       'home_points_mean_l5', 'away_goals_mean', 'away_goals_mean_l5',
       'away_goals_suf_mean', 'away_goals_suf_mean_l5', 'away_rank_mean',
       'away_rank_mean_l5', 'away_points_mean', 'away_points_mean_l5','home_game_points_mean', 'home_game_points_mean_l5',
       'home_game_points_rank_mean', 'home_game_points_rank_mean_l5','away_game_points_mean',
       'away_game_points_mean_l5', 'away_game_points_rank_mean',
       'away_game_points_rank_mean_l5',
       'is_friendly_0', 'is_friendly_1']]

base_df.tail()

base_df_no_fg = base_df.dropna()

df = base_df_no_fg

def no_draw(x):
    if x == 2:
        return 1
    else:
        return x
    
df["target"] = df["result"].apply(lambda x: no_draw(x))
import matplotlib.pyplot as plt

data1 = df[list(df.columns[8:20].values) + ["target"]]

scaled = (data1[:-1] - data1[:-1].mean()) / data1[:-1].std()
scaled["target"] = data1["target"]
violin1 = pd.melt(scaled,id_vars="target", var_name="features", value_name="value")

plt.figure(figsize=(15,10))
sns.violinplot(x="features", y="value", hue="target", data=violin1,split=True, inner="quart")
plt.xticks(rotation=90)
plt.show()

data2 = df[df.columns[20:]]

scaled = (data2[:-1] - data2[:-1].mean()) / data2[:-1].std()
scaled["target"] = data2["target"]
violin2 = pd.melt(scaled,id_vars="target", var_name="features", value_name="value")

plt.figure(figsize=(15,10))
sns.violinplot(x="features", y="value", hue="target", data=violin2,split=True, inner="quart")
plt.xticks(rotation=90)
plt.show()

dif = df.copy()
dif.loc[:, "goals_dif"] = dif["home_goals_mean"] - dif["away_goals_mean"]
dif.loc[:, "goals_dif_l5"] = dif["home_goals_mean_l5"] - dif["away_goals_mean_l5"]
dif.loc[:, "goals_suf_dif"] = dif["home_goals_suf_mean"] - dif["away_goals_suf_mean"]
dif.loc[:, "goals_suf_dif_l5"] = dif["home_goals_suf_mean_l5"] - dif["away_goals_suf_mean_l5"]
dif.loc[:, "goals_made_suf_dif"] = dif["home_goals_mean"] - dif["away_goals_suf_mean"]
dif.loc[:, "goals_made_suf_dif_l5"] = dif["home_goals_mean_l5"] - dif["away_goals_suf_mean_l5"]
dif.loc[:, "goals_suf_made_dif"] = dif["home_goals_suf_mean"] - dif["away_goals_mean"]
dif.loc[:, "goals_suf_made_dif_l5"] = dif["home_goals_suf_mean_l5"] - dif["away_goals_mean_l5"]

data_difs = dif.iloc[:, -8:]
scaled = (data_difs - data_difs.mean()) / data_difs.std()
scaled["target"] = data2["target"]
violin = pd.melt(scaled,id_vars="target", var_name="features", value_name="value")

plt.figure(figsize=(10,10))
sns.violinplot(x="features", y="value", hue="target", data=violin,split=True, inner="quart")
plt.xticks(rotation=90)
plt.show()

dif.loc[:, "dif_points"] = dif["home_game_points_mean"] - dif["away_game_points_mean"]
dif.loc[:, "dif_points_l5"] = dif["home_game_points_mean_l5"] - dif["away_game_points_mean_l5"]
dif.loc[:, "dif_points_rank"] = dif["home_game_points_rank_mean"] - dif["away_game_points_rank_mean"]
dif.loc[:, "dif_points_rank_l5"] = dif["home_game_points_rank_mean_l5"] - dif["away_game_points_rank_mean_l5"]

dif.loc[:, "dif_rank_agst"] = dif["home_rank_mean"] - dif["away_rank_mean"]
dif.loc[:, "dif_rank_agst_l5"] = dif["home_rank_mean_l5"] - dif["away_rank_mean_l5"]

dif.loc[:, "goals_per_ranking_dif"] = (dif["home_goals_mean"] / dif["home_rank_mean"]) - (dif["away_goals_mean"] / dif["away_rank_mean"])
dif.loc[:, "goals_per_ranking_suf_dif"] = (dif["home_goals_suf_mean"] / dif["home_rank_mean"]) - (dif["away_goals_suf_mean"] / dif["away_rank_mean"])
dif.loc[:, "goals_per_ranking_dif_l5"] = (dif["home_goals_mean_l5"] / dif["home_rank_mean"]) - (dif["away_goals_mean_l5"] / dif["away_rank_mean"])
dif.loc[:, "goals_per_ranking_suf_dif_l5"] = (dif["home_goals_suf_mean_l5"] / dif["home_rank_mean"]) - (dif["away_goals_suf_mean_l5"] / dif["away_rank_mean"])

data_difs = dif.iloc[:, -10:]
scaled = (data_difs - data_difs.mean()) / data_difs.std()
scaled["target"] = data2["target"]
violin = pd.melt(scaled,id_vars="target", var_name="features", value_name="value")

plt.figure(figsize=(15,10))
sns.violinplot(x="features", y="value", hue="target", data=violin,split=True, inner="quart")
plt.xticks(rotation=90)
plt.show()

plt.figure(figsize=(15,10))
sns.boxplot(x="features", y="value", hue="target", data=violin)
plt.xticks(rotation=90)
plt.show()

sns.jointplot(data = data_difs, x = 'dif_rank_agst', y = 'dif_rank_agst_l5', kind="reg")
plt.show()

sns.jointplot(data = data_difs, x = 'goals_per_ranking_dif', y = 'goals_per_ranking_dif_l5', kind="reg")
plt.show()

sns.jointplot(data = data_difs, x = 'dif_points_rank', y = 'dif_points_rank_l5', kind="reg")
plt.show()

sns.jointplot(data = data_difs, x = 'dif_points', y = 'dif_points_l5', kind="reg")
plt.show()

def create_db(df):
    columns = ["home_team", "away_team", "target", "rank_dif", "home_goals_mean", "home_rank_mean", "away_goals_mean", "away_rank_mean", "home_rank_mean_l5", "away_rank_mean_l5", "home_goals_suf_mean", "away_goals_suf_mean", "home_goals_mean_l5", "away_goals_mean_l5", "home_goals_suf_mean_l5", "away_goals_suf_mean_l5", "home_game_points_rank_mean", "home_game_points_rank_mean_l5", "away_game_points_rank_mean", "away_game_points_rank_mean_l5","is_friendly_0", "is_friendly_1"]
    
    base = df.loc[:, columns]
    base.loc[:, "goals_dif"] = base["home_goals_mean"] - base["away_goals_mean"]
    base.loc[:, "goals_dif_l5"] = base["home_goals_mean_l5"] - base["away_goals_mean_l5"]
    base.loc[:, "goals_suf_dif"] = base["home_goals_suf_mean"] - base["away_goals_suf_mean"]
    base.loc[:, "goals_suf_dif_l5"] = base["home_goals_suf_mean_l5"] - base["away_goals_suf_mean_l5"]
    base.loc[:, "goals_per_ranking_dif"] = (base["home_goals_mean"] / base["home_rank_mean"]) - (base["away_goals_mean"] / base["away_rank_mean"])
    base.loc[:, "dif_rank_agst"] = base["home_rank_mean"] - base["away_rank_mean"]
    base.loc[:, "dif_rank_agst_l5"] = base["home_rank_mean_l5"] - base["away_rank_mean_l5"]
    base.loc[:, "dif_points_rank"] = base["home_game_points_rank_mean"] - base["away_game_points_rank_mean"]
    base.loc[:, "dif_points_rank_l5"] = base["home_game_points_rank_mean_l5"] - base["away_game_points_rank_mean_l5"]
    
    model_df = base[["home_team", "away_team", "target", "rank_dif", "goals_dif", "goals_dif_l5", "goals_suf_dif", "goals_suf_dif_l5", "goals_per_ranking_dif", "dif_rank_agst", "dif_rank_agst_l5", "dif_points_rank", "dif_points_rank_l5", "is_friendly_0", "is_friendly_1"]]
    return model_df
 
model_db = create_db(df)
model_db

import numpy as np
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.model_selection import train_test_split, GridSearchCV

#separating the target from the features
X = model_db.iloc[:, 3:]
y = model_db[["target"]]

#dividing the database
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size= 0.2, random_state=1)

gb = GradientBoostingClassifier(random_state=5)
params = {"learning_rate": [0.01, 0.1, 0.5],
            "min_samples_split": [5, 10],
            "min_samples_leaf": [3, 5],
            "max_depth":[3,5,10],
            "max_features":["sqrt"],
            "n_estimators":[100, 200]
         } 
gb_cv = GridSearchCV(gb, params, cv = 3, n_jobs = -1, verbose = False)
gb_cv.fit(X_train.values, np.ravel(y_train))

#getting the best model
gb = gb_cv.best_estimator_

params_rf = {"max_depth": [20],
                "min_samples_split": [5, 10],
                "max_leaf_nodes": [175, 200],
                "min_samples_leaf": [5, 10],
                "n_estimators": [250],
                 "max_features": ["sqrt"],
                }

rf = RandomForestClassifier(random_state=1)
rf_cv = GridSearchCV(rf, params_rf, cv = 3, n_jobs = -1, verbose = False)
rf_cv.fit(X_train.values, np.ravel(y_train))

rf = rf_cv.best_estimator_

GridSearchCV(cv=3, estimator=RandomForestClassifier(random_state=1), n_jobs=-1,
             param_grid={'max_depth': [20], 'max_features': ['sqrt'],
                         'max_leaf_nodes': [175, 200],
                         'min_samples_leaf': [5, 10],
                         'min_samples_split': [5, 10], 'n_estimators': [250]},
             verbose=False)

from sklearn.metrics import confusion_matrix, roc_curve, roc_auc_score

def analyze(model):
    fpr, tpr, _ = roc_curve(y_test, model.predict_proba(X_test.values)[:,1]) #test AUC
    plt.figure(figsize=(15,10))
    plt.plot([0, 1], [0, 1], 'k--')
    plt.plot(fpr, tpr, label="test")

    fpr_train, tpr_train, _ = roc_curve(y_train, model.predict_proba(X_train.values)[:,1]) #train AUC
    plt.plot(fpr_train, tpr_train, label="train")
    auc_test = roc_auc_score(y_test, model.predict_proba(X_test.values)[:,1])
    auc_train = roc_auc_score(y_train, model.predict_proba(X_train.values)[:,1])
    plt.legend()
    plt.title('AUC score is %.2f on test and %.2f on training'%(auc_test, auc_train))
    plt.show()
    
    plt.figure(figsize=(15, 10))
    cm = confusion_matrix(y_test, model.predict(X_test.values))
    sns.heatmap(cm, annot=True, fmt="d")

analyze(gb)

analyze(rf)

dfs = pd.read_html(r"https://en.wikipedia.org/wiki/2022_FIFA_World_Cup#Teams")

from collections.abc import Iterable

for i in range(len(dfs)):
    df = dfs[i]
    cols = list(df.columns.values)
    
    if isinstance(cols[0], Iterable):
        if any("Tie-breaking criteria" in c for c in cols):
            start_pos = i+1

        if any("Match 46" in c for c in cols):
            end_pos = i+1
matches = []
groups = ["A", "B", "C", "D", "E", "F", "G", "H"]
group_count = 0 

table = {}
#TABLE -> TEAM, POINTS, WIN PROBS (CRITERIO DE DESEMPATE)
table[groups[group_count]] = [[a.split(" ")[0], 0, []] for a in list(dfs[start_pos].iloc[:, 1].values)]

for i in range(start_pos+1, end_pos, 1):
    if len(dfs[i].columns) == 3:
        team_1 = dfs[i].columns.values[0]
        team_2 = dfs[i].columns.values[-1]
        
        matches.append((groups[group_count], team_1, team_2))
    else:
        group_count+=1
        table[groups[group_count]] = [[a, 0, []] for a in list(dfs[i].iloc[:, 1].values)]

table

matches[:10]

def find_stats(team_1):
#team_1 = "Qatar"
    past_games = team_stats_raw[(team_stats_raw["team"] == team_1)].sort_values("date")
    last5 = team_stats_raw[(team_stats_raw["team"] == team_1)].sort_values("date").tail(5)

    team_1_rank = past_games["rank"].values[-1]
    team_1_goals = past_games.score.mean()
    team_1_goals_l5 = last5.score.mean()
    team_1_goals_suf = past_games.suf_score.mean()
    team_1_goals_suf_l5 = last5.suf_score.mean()
    team_1_rank_suf = past_games.rank_suf.mean()
    team_1_rank_suf_l5 = last5.rank_suf.mean()
    team_1_gp_rank = past_games.points_by_rank.mean()
    team_1_gp_rank_l5 = last5.points_by_rank.mean()

    return [team_1_rank, team_1_goals, team_1_goals_l5, team_1_goals_suf, team_1_goals_suf_l5, team_1_rank_suf, team_1_rank_suf_l5, team_1_gp_rank, team_1_gp_rank_l5]

def find_features(team_1, team_2):
    rank_dif = team_1[0] - team_2[0]
    goals_dif = team_1[1] - team_2[1]
    goals_dif_l5 = team_1[2] - team_2[2]
    goals_suf_dif = team_1[3] - team_2[3]
    goals_suf_dif_l5 = team_1[4] - team_2[4]
    goals_per_ranking_dif = (team_1[1]/team_1[5]) - (team_2[1]/team_2[5])
    dif_rank_agst = team_1[5] - team_2[5]
    dif_rank_agst_l5 = team_1[6] - team_2[6]
    dif_gp_rank = team_1[7] - team_2[7]
    dif_gp_rank_l5 = team_1[8] - team_2[8]
    
    return [rank_dif, goals_dif, goals_dif_l5, goals_suf_dif, goals_suf_dif_l5, goals_per_ranking_dif, dif_rank_agst, dif_rank_agst_l5, dif_gp_rank, dif_gp_rank_l5, 1, 0]

advanced_group = []
last_group = ""

for k in table.keys():
    for t in table[k]:
        t[1] = 0
        t[2] = []
        
for teams in matches:
    draw = False
    team_1 = find_stats(teams[1])
    team_2 = find_stats(teams[2])

    features_g1 = find_features(team_1, team_2)
    features_g2 = find_features(team_2, team_1)

    probs_g1 = gb.predict_proba([features_g1])
    probs_g2 = gb.predict_proba([features_g2])
    
    team_1_prob_g1 = probs_g1[0][0]
    team_1_prob_g2 = probs_g2[0][1]
    team_2_prob_g1 = probs_g1[0][1]
    team_2_prob_g2 = probs_g2[0][0]

    team_1_prob = (probs_g1[0][0] + probs_g2[0][1])/2
    team_2_prob = (probs_g2[0][0] + probs_g1[0][1])/2
    
    if ((team_1_prob_g1 > team_2_prob_g1) & (team_2_prob_g2 > team_1_prob_g2)) | ((team_1_prob_g1 < team_2_prob_g1) & (team_2_prob_g2 < team_1_prob_g2)):
        draw=True
        for i in table[teams[0]]:
            if i[0] == teams[1] or i[0] == teams[2]:
                i[1] += 1
                
    elif team_1_prob > team_2_prob:
        winner = teams[1]
        winner_proba = team_1_prob
        for i in table[teams[0]]:
            if i[0] == teams[1]:
                i[1] += 3
                
    elif team_2_prob > team_1_prob:  
        winner = teams[2]
        winner_proba = team_2_prob
        for i in table[teams[0]]:
            if i[0] == teams[2]:
                i[1] += 3
    
    for i in table[teams[0]]: #adding criterio de desempate (probs por jogo)
            if i[0] == teams[1]:
                i[2].append(team_1_prob)
            if i[0] == teams[2]:
                i[2].append(team_2_prob)

    if last_group != teams[0]:
        if last_group != "":
            print("\n")
            print("Group %s advanced: "%(last_group))
            
            for i in table[last_group]: #adding crieterio de desempate
                i[2] = np.mean(i[2])
            
            final_points = table[last_group]
            final_table = sorted(final_points, key=itemgetter(1, 2), reverse = True)
            advanced_group.append([final_table[0][0], final_table[1][0]])
            for i in final_table:
                print("%s -------- %d"%(i[0], i[1]))
        print("\n")
        print("-"*10+" Starting Analysis for Group %s "%(teams[0])+"-"*10)
        
    if draw == False:
        print("Group %s - %s vs. %s: Winner %s with %.2f probability"%(teams[0], teams[1], teams[2], winner, winner_proba))
    else:
        print("Group %s - %s vs. %s: Draw"%(teams[0], teams[1], teams[2]))
    last_group =  teams[0]


print("\n")
print("Group %s advanced: "%(last_group))

for i in table[last_group]: #adding crieterio de desempate
    i[2] = np.mean(i[2])
            
final_points = table[last_group]
final_table = sorted(final_points, key=itemgetter(1, 2), reverse = True)
advanced_group.append([final_table[0][0], final_table[1][0]])
for i in final_table:
    print("%s -------- %d"%(i[0], i[1]))

---------- Starting Analysis for Group A ----------
Group A - Qatar vs. Ecuador: Winner Ecuador with 0.62 probability
Group A - Senegal vs. Netherlands: Winner Netherlands with 0.62 probability
Group A - Qatar vs. Senegal: Winner Senegal with 0.60 probability
Group A - Netherlands vs. Ecuador: Winner Netherlands with 0.73 probability
Group A - Ecuador vs. Senegal: Draw
Group A - Netherlands vs. Qatar: Winner Netherlands with 0.78 probability


Group A advanced: 
Netherlands -------- 9
Senegal -------- 4
Ecuador -------- 4
Qatar -------- 0




---------- Starting Analysis for Group B ----------
Group B - England vs. Iran: Winner England with 0.62 probability
Group B - United States vs. Wales: Draw
Group B - Wales vs. Iran: Draw
Group B - England vs. United States: Winner England with 0.61 probability
Group B - Wales vs. England: Winner England with 0.64 probability
Group B - Iran vs. United States: Winner United States with 0.58 probability




Group B advanced: 
England -------- 9
United States -------- 4
Wales -------- 2
Iran -------- 1




---------- Starting Analysis for Group C ----------
Group C - Argentina vs. Saudi Arabia: Winner Argentina with 0.79 probability
Group C - Mexico vs. Poland: Draw
Group C - Poland vs. Saudi Arabia: Winner Poland with 0.70 probability
Group C - Argentina vs. Mexico: Winner Argentina with 0.67 probability
Group C - Poland vs. Argentina: Winner Argentina with 0.71 probability
Group C - Saudi Arabia vs. Mexico: Winner Mexico with 0.71 probability




Group C advanced: 
Argentina -------- 9
Poland -------- 4
Mexico -------- 4
Saudi Arabia -------- 0




---------- Starting Analysis for Group D ----------
Group D - Denmark vs. Tunisia: Winner Denmark with 0.68 probability
Group D - France vs. Australia: Winner France with 0.71 probability
Group D - Tunisia vs. Australia: Draw
Group D - France vs. Denmark: Draw
Group D - Australia vs. Denmark: Winner Denmark with 0.71 probability
Group D - Tunisia vs. France: Winner France with 0.69 probability




Group D advanced: 
France -------- 7
Denmark -------- 7
Tunisia -------- 1
Australia -------- 1




---------- Starting Analysis for Group E ----------
Group E - Germany vs. Japan: Winner Germany with 0.62 probability
Group E - Spain vs. Costa Rica: Winner Spain with 0.76 probability
Group E - Japan vs. Costa Rica: Winner Japan with 0.63 probability
Group E - Spain vs. Germany: Draw
Group E - Japan vs. Spain: Winner Spain with 0.67 probability
Group E - Costa Rica vs. Germany: Winner Germany with 0.65 probability




Group E advanced: 
Spain -------- 7
Germany -------- 7
Japan -------- 3
Costa Rica -------- 0




---------- Starting Analysis for Group F ----------
Group F - Morocco vs. Croatia: Winner Croatia with 0.58 probability
Group F - Belgium vs. Canada: Winner Belgium with 0.75 probability
Group F - Belgium vs. Morocco: Winner Belgium with 0.67 probability
Group F - Croatia vs. Canada: Winner Croatia with 0.64 probability
Group F - Croatia vs. Belgium: Winner Belgium with 0.64 probability
Group F - Canada vs. Morocco: Draw




Group F advanced: 
Belgium -------- 9
Croatia -------- 6
Morocco -------- 1
Canada -------- 1




---------- Starting Analysis for Group G ----------
Group G - Switzerland vs. Cameroon: Winner Switzerland with 0.69 probability
Group G - Brazil vs. Serbia: Winner Brazil with 0.72 probability
Group G - Cameroon vs. Serbia: Winner Serbia with 0.66 probability
Group G - Brazil vs. Switzerland: Draw
Group G - Serbia vs. Switzerland: Winner Switzerland with 0.57 probability
Group G - Cameroon vs. Brazil: Winner Brazil with 0.81 probability




Group G advanced: 
Brazil -------- 7
Switzerland -------- 7
Serbia -------- 3
Cameroon -------- 0




---------- Starting Analysis for Group H ----------
Group H - Uruguay vs. South Korea: Winner Uruguay with 0.62 probability
Group H - Portugal vs. Ghana: Winner Portugal with 0.81 probability
Group H - South Korea vs. Ghana: Winner South Korea with 0.76 probability
Group H - Portugal vs. Uruguay: Winner Portugal with 0.60 probability
Group H - Ghana vs. Uruguay: Winner Uruguay with 0.77 probability
Group H - South Korea vs. Portugal: Winner Portugal with 0.67 probability




Group H advanced: 
Portugal -------- 9
Uruguay -------- 6
South Korea -------- 3
Ghana -------- 0

advanced = advanced_group


playoffs = {"Round of 16": [], "Quarter-Final": [], "Semi-Final": [], "Final": []}


for p in playoffs.keys():
    playoffs[p] = []


actual_round = ""
next_rounds = []


for p in playoffs.keys():
    if p == "Round of 16":
        control = []
        for a in range(0, len(advanced*2), 1):
            if a < len(advanced):
                if a % 2 == 0:
                    control.append((advanced*2)[a][0])
                else:
                    control.append((advanced*2)[a][1])
            else:
                if a % 2 == 0:
                    control.append((advanced*2)[a][1])
                else:
                    control.append((advanced*2)[a][0])


        playoffs[p] = [[control[c], control[c+1]] for c in range(0, len(control)-1, 1) if c%2 == 0]
        
        for i in range(0, len(playoffs[p]), 1):
            game = playoffs[p][i]
            
            home = game[0]
            away = game[1]
            team_1 = find_stats(home)
            team_2 = find_stats(away)


            features_g1 = find_features(team_1, team_2)
            features_g2 = find_features(team_2, team_1)
            
            probs_g1 = gb.predict_proba([features_g1])
            probs_g2 = gb.predict_proba([features_g2])
            
            team_1_prob = (probs_g1[0][0] + probs_g2[0][1])/2
            team_2_prob = (probs_g2[0][0] + probs_g1[0][1])/2
            
            if actual_round != p:
                print("-"*10)
                print("Starting simulation of %s"%(p))
                print("-"*10)
                print("\n")
            
            if team_1_prob < team_2_prob:
                print("%s vs. %s: %s advances with prob %.2f"%(home, away, away, team_2_prob))
                next_rounds.append(away)
            else:
                print("%s vs. %s: %s advances with prob %.2f"%(home, away, home, team_1_prob))
                next_rounds.append(home)
            
            game.append([team_1_prob, team_2_prob])
            playoffs[p][i] = game
            actual_round = p
        
    else:
        playoffs[p] = [[next_rounds[c], next_rounds[c+1]] for c in range(0, len(next_rounds)-1, 1) if c%2 == 0]
        next_rounds = []
        for i in range(0, len(playoffs[p])):
            game = playoffs[p][i]
            home = game[0]
            away = game[1]
            team_1 = find_stats(home)
            team_2 = find_stats(away)
            
            features_g1 = find_features(team_1, team_2)
            features_g2 = find_features(team_2, team_1)
            
            probs_g1 = gb.predict_proba([features_g1])
            probs_g2 = gb.predict_proba([features_g2])
            
            team_1_prob = (probs_g1[0][0] + probs_g2[0][1])/2
            team_2_prob = (probs_g2[0][0] + probs_g1[0][1])/2
            
            if actual_round != p:
                print("-"*10)
                print("Starting simulation of %s"%(p))
                print("-"*10)
                print("\n")
            
            if team_1_prob < team_2_prob:
                print("%s vs. %s: %s advances with prob %.2f"%(home, away, away, team_2_prob))
                next_rounds.append(away)
            else:
                print("%s vs. %s: %s advances with prob %.2f"%(home, away, home, team_1_prob))
                next_rounds.append(home)
            game.append([team_1_prob, team_2_prob])
            playoffs[p][i] = game
            actual_round = p

----------
Starting simulation of Round of 16
----------




Netherlands vs. United States: Netherlands advances with prob 0.54
Argentina vs. Denmark: Argentina advances with prob 0.59
Spain vs. Croatia: Spain advances with prob 0.61
Brazil vs. Uruguay: Brazil advances with prob 0.64
Senegal vs. England: England advances with prob 0.64
Poland vs. France: France advances with prob 0.67
Germany vs. Belgium: Belgium advances with prob 0.53
Switzerland vs. Portugal: Portugal advances with prob 0.57
----------
Starting simulation of Quarter-Final
----------




Netherlands vs. Argentina: Netherlands advances with prob 0.51
Spain vs. Brazil: Brazil advances with prob 0.54
England vs. France: England advances with prob 0.51
Belgium vs. Portugal: Portugal advances with prob 0.52
----------
Starting simulation of Semi-Final
----------




Netherlands vs. Brazil: Brazil advances with prob 0.55
England vs. Portugal: England advances with prob 0.51
----------
Starting simulation of Final
----------




Brazil vs. England: Brazil advances with prob 0.56

import networkx as nx
from networkx.drawing.nx_pydot import graphviz_layout

plt.figure(figsize=(15, 10))
G = nx.balanced_tree(2, 3)


labels = []


for p in playoffs.keys():
    for game in playoffs[p]:
        label = f"{game[0]}({round(game[2][0], 2)}) \n {game[1]}({round(game[2][1], 2)})"
        labels.append(label)
    
labels_dict = {}
labels_rev = list(reversed(labels))

for l in range(len(list(G.nodes))):
    labels_dict[l] = labels_rev[l]

pos = graphviz_layout(G, prog='twopi')
labels_pos = {n: (k[0], k[1]-0.08*k[1]) for n,k in pos.items()}
center  = pd.DataFrame(pos).mean(axis=1).mean()
    

nx.draw(G, pos = pos, with_labels=False, node_color=range(15), edge_color="#bbf5bb", width=10, font_weight='bold',cmap=plt.cm.Greens, node_size=5000)
nx.draw_networkx_labels(G, pos = labels_pos, bbox=dict(boxstyle="round,pad=0.3", fc="white", ec="black", lw=.5, alpha=1),
                        labels=labels_dict)
texts = ["Round \nof 16", "Quarter \n Final", "Semi \n Final", "Final\n"]
pos_y = pos[0][1] + 55
for text in reversed(texts):
    pos_x = center
    pos_y -= 75 
    plt.text(pos_y, pos_x, text, fontsize = 18)

plt.axis('equal')
plt.show()