'''
Divegrass Sim - 01/23/18
 
This model assumes that gameplay can be broken down into a fixed number of potential attacks.
Seizing Ability describes a team’s ability to mount an attack, i.e. the highest number on their die.
An attack is awarded if one team has a higher die roll than the other. If the dice rolls are equal, the attack is not
awarded to either team. Implications of an attack’s duration and outcome are considered to be negible.
Success Rate describes the quality and thus the chances of a goal during an average attack.
'''
 
from random import randint
from random import random
 
 
# Team stats
home_seizing_ability = 10
away_seizing_ability = 10
home_success_rate = 0.25
away_success_rate = 0.25
home_penalty_ability = 1
away_penalty_ability = 1
 
# Simulation settings
potential_attacks = 15
games = 50
overtime = False  # TODO: implement properly
penalties = False  # TODO: implement properly
 
# Evaluation settings
mislead_threshold = 0.05  # Deviation from the average at which a test run can be considered misleading
 
 
# Simulate the matches
def simulate():
    results = []
    for game in range(0, games):
        attacks_home = 0
        attacks_away = 0
        goals_home = 0
        goals_away = 0
        for attack in range(0, potential_attacks):
            home_roll = randint(1, home_seizing_ability)
            away_roll = randint(1, away_seizing_ability)
            if home_roll > away_roll:
                # Home starts an attack
                attacks_home += 1
                if home_success_rate > random():
                    goals_home += 1
            elif home_roll < away_roll:
                # Away starts an attack
                attacks_away += 1
                if away_success_rate > random():
                    goals_away += 1
            else:
                # No attack
                pass
 
        # Overtime
        if overtime and goals_home == goals_away:
            for attack in range(0, int(potential_attacks / 3)):  # A third of the game time
                home_roll = randint(1, home_seizing_ability)
                away_roll = randint(1, away_seizing_ability)
                if home_roll > away_roll:
                    # Home starts an attack
                    attacks_home += 1
                    if home_success_rate > random():
                        goals_home += 1
                elif home_roll < away_roll:
                    # Away starts an attack
                    attacks_away += 1
                    if away_success_rate > random():
                        goals_away += 1
                else:
                    # No attack
                    pass
 
        # Penalties
        if penalties and goals_home == goals_away:
            home_roll = random() * home_penalty_ability
            away_roll = random() * away_penalty_ability
            if home_roll >= away_roll:  # Note: this is almost never equal, so this simplification is okay
                goals_home += 1
            else:
                goals_away += 1
 
        result = {
            'attacks_home': attacks_home,
            'attacks_away': attacks_away,
            'goals_home': goals_home,
            'goals_away': goals_away}
        results.append(result)
        #print(goals_home, '-', goals_away)
    return results
 
 
if __name__ == '__main__':
    runs = 1000
    print('Games:', games)
    print('Potential attacks:', potential_attacks)
    print('Runs:', runs)
    print('Home Seizing Ability:', home_seizing_ability)
    print('Away Seizing Ability:', away_seizing_ability)
    print('Home Success Rate:', home_success_rate)
    print('Away Success Rate', away_success_rate)
 
    win_chances = []
    for _ in range(0, runs):
        results = simulate()
        # Tally results
        wins = sum(1 for result in results if result['goals_home'] > result['goals_away'])
        losses = sum(1 for result in results if result['goals_home'] < result['goals_away'])
        draws = sum(1 for result in results if result['goals_home'] == result['goals_away'])
        goals_home = sum(result['goals_home'] for result in results)
        goals_away = sum(result['goals_away'] for result in results)
        goal_delta = goals_home - goals_away
        avg_goals_home = goals_home / games
        avg_goals_away = goals_away / games
        avg_goal_delta = avg_goals_home - avg_goals_away
        win_chance = wins / games
        draw_chance = draws / games
        loss_chance = losses / games
 
        # Output results
        print('Win Chance:', '{:.2%}'.format(win_chance),
              'Wins:', wins,
              'Losses:', losses,
              'Draws:', draws,
              'Avg. goal delta:', '{:.2f}'.format(avg_goal_delta))
        win_chances.append(win_chance)
 
    mean_win_chance = sum(win_chances) / len(win_chances)
    temp = 0
    misleading_runs = 0
    for win_chance in win_chances:
        temp += abs(win_chance - mean_win_chance)
        if abs(win_chance - mean_win_chance) >= mislead_threshold:
            misleading_runs += 1
    mean_absolute_difference = temp / runs
    mislead_rate = misleading_runs / runs
 
    print('Mean win rate:', '{:.2%}'.format(mean_win_chance))
    print('Mean absolute difference (percentile points):', mean_absolute_difference * 100)
    print('Ratio of misleading test runs ({:.2%} points deviation from the average)'.format(mislead_threshold),
          '{:.2%}'.format(mislead_rate))