def season_retire(self):
# Decides if a player retired and if so, sets retired = True
# Returns True if retired, False if not
# & Run once per season
age_norm = HelperMethods.normalize(self.age, PLAYER_GLOBALS["MIN_AGE"], PLAYER_GLOBALS["MAX_AGE"])
skill_norm = HelperMethods.normalize(self.skill, PLAYER_GLOBALS["MIN_SKILL"], PLAYER_GLOBALS["MAX_SKILL"])
if age_norm >= 1:
self.retired = True
return True
elif age_norm >= 0.9:
if random.random() >= 0.5:
self.retired = True
return True
elif age_norm >= 0.8:
if skill_norm <= 0.15:
self.retired = True
return True
else:
if random.random() >= 0.25:
self.retired = True
return True
elif age_norm >= 0.7 and skill_norm <= 0.1:
self.retired = True
return True
return False
def season_retire(self):
# Decides if a player retired and if so, sets retired = True
# Returns True if retired, False if not
# & Run once per season
age_norm = HelperMethods.normalize(self.age, PLAYER_GLOBALS["MIN_AGE"],
PLAYER_GLOBALS["MAX_AGE"])
skill_norm = HelperMethods.normalize(self.skill,
PLAYER_GLOBALS["MIN_SKILL"],
PLAYER_GLOBALS["MAX_SKILL"])
if age_norm >= 1:
self.retired = True
return True
elif age_norm >= 0.9:
if random.random() >= 0.5:
self.retired = True
return True
elif age_norm >= 0.8:
if skill_norm <= 0.15:
self.retired = True
return True
else:
if random.random() >= 0.25:
self.retired = True
return True
elif age_norm >= 0.7 and skill_norm <= 0.1:
self.retired = True
return True
return False
def season_training_increase_per_turn(self):
# Sets increase_per_turn -> how much a player trains per turn
# Returns True if worked, False if not
# & Run once per season
if self.age >= PLAYER_GLOBALS["MIN_AGE"]:
age_int = PLAYER_GLOBALS["AGE_TRAINING_INF"]
inc_int = PLAYER_GLOBALS["TRAINING_PER_AGE"]
age_norm = HelperMethods.normalize(self.age,
PLAYER_GLOBALS["MIN_AGE"],
PLAYER_GLOBALS["MAX_AGE"])
skill_norm = HelperMethods.normalize(self.skill,
PLAYER_GLOBALS["MIN_SKILL"],
PLAYER_GLOBALS["MAX_SKILL"])
skill_inf = 1
if skill_norm >= 0.85:
skill_inf = 0.5
for index, age_range in enumerate(age_int):
if age_norm < age_range:
x1 = age_int[index]
x2 = age_int[index - 1]
y1 = inc_int[index]
y2 = inc_int[index - 1]
increase = HelperMethods.ysolver(point1=(x1, y1),
point2=(x2, y2),
x=age_norm)
break
self.increase_per_turn = (increase * skill_inf) / float(
GAME_GLOBALS["TOTAL_TURNS"])
return True
return False
def training(self):
# Trains all players in team
# & Run once per turn
ground_norm = HelperMethods.normalize(5, TEAM_GLOBALS["MIN_TRAINING_GROUND"], TEAM_GLOBALS["MAX_TRAINING_GROUND"])
for player in self.players:
skill_norm = HelperMethods.normalize(player.skill, PLAYER_GLOBALS["MIN_SKILL"], PLAYER_GLOBALS["MAX_SKILL"])
if ground_norm >= skill_norm:
ground_inf = 1
else:
ground_inf = min(max(1 - 3 * (skill_norm - ground_norm), 0), 1)
player.turn_training(ground_inf)
def calc_price(self):
apparent_skill = self.skill
starting_values = PLAYER_GLOBALS["PRICE_AGE"]
factors = PLAYER_GLOBALS["PRICE_SKILL"]
power = [0, 0, 0, 0, 0, 0]
age_norm = HelperMethods.normalize(self.age, PLAYER_GLOBALS["MIN_AGE"], PLAYER_GLOBALS["MAX_AGE"])
age_inf_price = int(age_norm * (len(starting_values) - 2))
point1 = (age_inf_price, starting_values[age_inf_price])
point2 = (age_inf_price + 1, starting_values[age_inf_price + 1])
start_value = Line.Line((point1, point2)).solve_for_y(age_norm * (len(starting_values) - 2))
get_lastpower = (apparent_skill - PLAYER_GLOBALS["MIN_SKILL"]) / 10
get_howmuchpower = (apparent_skill - PLAYER_GLOBALS["MIN_SKILL"]) % 10
for i in range(get_lastpower + 1):
power[i] = 3
if i == get_lastpower:
power[i] = get_howmuchpower / 3.0
price = start_value * pow(factors[0],power[0]) * pow(factors[1],power[1]) * pow(factors[2],power[2]) * pow(factors[3],power[3]) * pow(factors[4],power[4]) * 1000
if price / 100 >= 1:
return int(price) - int(price) % 10
else:
return int(price)
def calc_price(self):
apparent_skill = self.skill
starting_values = PLAYER_GLOBALS["PRICE_AGE"]
factors = PLAYER_GLOBALS["PRICE_SKILL"]
power = [0, 0, 0, 0, 0, 0]
age_norm = HelperMethods.normalize(self.age, PLAYER_GLOBALS["MIN_AGE"],
PLAYER_GLOBALS["MAX_AGE"])
age_inf_price = int(age_norm * (len(starting_values) - 2))
point1 = (age_inf_price, starting_values[age_inf_price])
point2 = (age_inf_price + 1, starting_values[age_inf_price + 1])
start_value = Line.Line(
(point1,
point2)).solve_for_y(age_norm * (len(starting_values) - 2))
get_lastpower = (apparent_skill - PLAYER_GLOBALS["MIN_SKILL"]) / 10
get_howmuchpower = (apparent_skill - PLAYER_GLOBALS["MIN_SKILL"]) % 10
for i in range(get_lastpower + 1):
power[i] = 3
if i == get_lastpower:
power[i] = get_howmuchpower / 3.0
price = start_value * pow(factors[0], power[0]) * pow(
factors[1], power[1]) * pow(factors[2], power[2]) * pow(
factors[3], power[3]) * pow(factors[4], power[4]) * 1000
if price / 100 >= 1:
return int(price) - int(price) % 10
else:
return int(price)
def minute(self):
if self.minutes < MATCH_GLOBALS["TOTAL_TURNS"]:
self.home.update_skill_tactic()
self.away.update_skill_tactic()
self.minutes += 1
self.events = {"Home Possession": False, "Goal": False}
home_tactic_inf = [(self.home.tactic[0] - self.away.tactic[2]) * MATCH_GLOBALS["TACTIC_INFLUENCE"],
(self.home.tactic[1] - self.away.tactic[1]) * MATCH_GLOBALS["TACTIC_INFLUENCE"],
(self.home.tactic[2] - self.away.tactic[0]) * MATCH_GLOBALS["TACTIC_INFLUENCE"]]
home_concedes_rat = (self.home.skill[0] - self.away.skill[2]) / float(PLAYER_GLOBALS["MAX_SKILL"]) + home_tactic_inf[0]
home_possession_rat = (self.home.skill[1] - self.away.skill[1]) / float(PLAYER_GLOBALS["MAX_SKILL"]) + home_tactic_inf[1]
home_scores_rat = (self.home.skill[2] - self.away.skill[0]) / float(PLAYER_GLOBALS["MAX_SKILL"]) + home_tactic_inf[2]
home_scores_prob = Line.Line(((-1, MATCH_GLOBALS["MIN_GOAL_PER_POSS"]), (1, MATCH_GLOBALS["MAX_GOAL_PER_POSS"]))).solve_for_y(home_scores_rat)
home_possession_prob = Line.Line(((-1, 1 - MATCH_GLOBALS["MAX_POSSESSION"]),(1, MATCH_GLOBALS["MAX_POSSESSION"]))).solve_for_y(home_possession_rat)
home_concedes_prob = Line.Line(((-1, MATCH_GLOBALS["MAX_GOAL_PER_POSS"]), (1, MATCH_GLOBALS["MIN_GOAL_PER_POSS"]))).solve_for_y(home_concedes_rat)
poss_rand = random.uniform(0, 1)
goal_rand = random.uniform(0, 1)
#ANTI GOLEADAS:
if self.result[0] >= 3 + self.result[1]:
home_scores_prob = home_scores_prob * MATCH_GLOBALS["ANTI_GOLEADAS"]
elif self.result[1] >= 3 + self.result[0]:
home_concedes_prob = home_concedes_prob * MATCH_GLOBALS["ANTI_GOLEADAS"]
#PROBABILIDADE HUMANA DE MARCAR NO FINAL
min_norm = HelperMethods.normalize(self.minutes, 0, MATCH_GLOBALS["TOTAL_TURNS"])
if min_norm <= 0.2:
if self.away.manager.human:
home_scores_prob = home_scores_prob * MATCH_GLOBALS["GOAL_BEGINNING_END_MULTI"]
if self.home.manager.human:
home_concedes_prob = home_concedes_prob * MATCH_GLOBALS["GOAL_BEGINNING_END_MULTI"]
if min_norm >= 0.8:
if self.home.manager.human:
home_scores_prob = home_scores_prob * MATCH_GLOBALS["GOAL_BEGINNING_END_MULTI"]
if self.away.manager.human:
home_concedes_prob = home_concedes_prob * MATCH_GLOBALS["GOAL_BEGINNING_END_MULTI"]
if poss_rand <= home_possession_prob:
self.events["Home Possession"] = True
self.possession[0] += 1
if goal_rand <= home_scores_prob:
self.result[0] += 1
self.events["Goal"] = True
self.set_goalscorer(True)
else:
self.events["Home Possession"] = False
self.possession[1] += 1
if goal_rand <= home_concedes_prob:
self.result[1] += 1
self.events["Goal"] = True
self.set_goalscorer(False)
return True
else:
return False
def season_training_increase_per_turn(self):
# Sets increase_per_turn -> how much a player trains per turn
# Returns True if worked, False if not
# & Run once per season
if self.age >= PLAYER_GLOBALS["MIN_AGE"]:
age_int = PLAYER_GLOBALS["AGE_TRAINING_INF"]
inc_int = PLAYER_GLOBALS["TRAINING_PER_AGE"]
age_norm = HelperMethods.normalize(self.age, PLAYER_GLOBALS["MIN_AGE"], PLAYER_GLOBALS["MAX_AGE"])
skill_norm = HelperMethods.normalize(self.skill, PLAYER_GLOBALS["MIN_SKILL"], PLAYER_GLOBALS["MAX_SKILL"])
skill_inf = 1
if skill_norm >= 0.85:
skill_inf = 0.5
for index, age_range in enumerate(age_int):
if age_norm < age_range:
x1 = age_int[index]
x2 = age_int[index - 1]
y1 = inc_int[index]
y2 = inc_int[index - 1]
increase = HelperMethods.ysolver(point1=(x1, y1), point2=(x2, y2), x = age_norm)
break
self.increase_per_turn = (increase * skill_inf) / float(GAME_GLOBALS["TOTAL_TURNS"])
return True
return False
def create_skill(skill, avg_skill):
# Returns int with player starting skill
# TODO: Add age influence!
# if avg_skill:
# skill_variation = int(round(PLAYER_GLOBALS["MAX_SKILL"] * 0.02, 0))
# random_var = random.randint(-skill_variation, skill_variation)
# skill += random_var
skill_age_inf = PLAYER_GLOBALS["MAX_SKILL"] * 0.05 + skill * 0.2
age_inf = 1 - 2 * HelperMethods.normalize(age, PLAYER_GLOBALS["MIN_AGE"], PLAYER_GLOBALS["MAX_AGE"])
if age_inf > 0:
skill -= skill_age_inf * age_inf
skill = min(max(int(round(skill, 0)), PLAYER_GLOBALS["MIN_SKILL"]), PLAYER_GLOBALS["MAX_SKILL"])
return skill
def create_skill(skill, avg_skill):
# Returns int with player starting skill
# TODO: Add age influence!
# if avg_skill:
# skill_variation = int(round(PLAYER_GLOBALS["MAX_SKILL"] * 0.02, 0))
# random_var = random.randint(-skill_variation, skill_variation)
# skill += random_var
skill_age_inf = PLAYER_GLOBALS["MAX_SKILL"] * 0.05 + skill * 0.2
age_inf = 1 - 2 * HelperMethods.normalize(
age, PLAYER_GLOBALS["MIN_AGE"], PLAYER_GLOBALS["MAX_AGE"])
if age_inf > 0:
skill -= skill_age_inf * age_inf
skill = min(max(int(round(skill, 0)), PLAYER_GLOBALS["MIN_SKILL"]),
PLAYER_GLOBALS["MAX_SKILL"])
return skill
def minute(self):
if self.minutes < MATCH_GLOBALS["TOTAL_TURNS"]:
self.home.update_skill_tactic()
self.away.update_skill_tactic()
self.minutes += 1
self.events = {"Home Possession": False, "Goal": False}
home_tactic_inf = [
(self.home.tactic[0] - self.away.tactic[2]) * MATCH_GLOBALS["TACTIC_INFLUENCE"],
(self.home.tactic[1] - self.away.tactic[1]) * MATCH_GLOBALS["TACTIC_INFLUENCE"],
(self.home.tactic[2] - self.away.tactic[0]) * MATCH_GLOBALS["TACTIC_INFLUENCE"],
]
home_concedes_rat = (self.home.skill[0] - self.away.skill[2]) / float(
PLAYER_GLOBALS["MAX_SKILL"]
) + home_tactic_inf[0]
home_possession_rat = (self.home.skill[1] - self.away.skill[1]) / float(
PLAYER_GLOBALS["MAX_SKILL"]
) + home_tactic_inf[1]
home_scores_rat = (self.home.skill[2] - self.away.skill[0]) / float(
PLAYER_GLOBALS["MAX_SKILL"]
) + home_tactic_inf[2]
home_scores_prob = Line.Line(
((-1, MATCH_GLOBALS["MIN_GOAL_PER_POSS"]), (1, MATCH_GLOBALS["MAX_GOAL_PER_POSS"]))
).solve_for_y(home_scores_rat)
home_possession_prob = Line.Line(
((-1, 1 - MATCH_GLOBALS["MAX_POSSESSION"]), (1, MATCH_GLOBALS["MAX_POSSESSION"]))
).solve_for_y(home_possession_rat)
home_concedes_prob = Line.Line(
((-1, MATCH_GLOBALS["MAX_GOAL_PER_POSS"]), (1, MATCH_GLOBALS["MIN_GOAL_PER_POSS"]))
).solve_for_y(home_concedes_rat)
poss_rand = random.uniform(0, 1)
goal_rand = random.uniform(0, 1)
# ANTI GOLEADAS:
if self.result[0] >= 3 + self.result[1]:
home_scores_prob = home_scores_prob * MATCH_GLOBALS["ANTI_GOLEADAS"]
elif self.result[1] >= 3 + self.result[0]:
home_concedes_prob = home_concedes_prob * MATCH_GLOBALS["ANTI_GOLEADAS"]
# PROBABILIDADE HUMANA DE MARCAR NO FINAL
min_norm = HelperMethods.normalize(self.minutes, 0, MATCH_GLOBALS["TOTAL_TURNS"])
if min_norm <= 0.2:
if self.away.manager.human:
home_scores_prob = home_scores_prob * MATCH_GLOBALS["GOAL_BEGINNING_END_MULTI"]
if self.home.manager.human:
home_concedes_prob = home_concedes_prob * MATCH_GLOBALS["GOAL_BEGINNING_END_MULTI"]
if min_norm >= 0.8:
if self.home.manager.human:
home_scores_prob = home_scores_prob * MATCH_GLOBALS["GOAL_BEGINNING_END_MULTI"]
if self.away.manager.human:
home_concedes_prob = home_concedes_prob * MATCH_GLOBALS["GOAL_BEGINNING_END_MULTI"]
if poss_rand <= home_possession_prob:
self.events["Home Possession"] = True
self.possession[0] += 1
if goal_rand <= home_scores_prob:
self.result[0] += 1
self.events["Goal"] = True
self.set_goalscorer(True)
else:
self.events["Home Possession"] = False
self.possession[1] += 1
if goal_rand <= home_concedes_prob:
self.result[1] += 1
self.events["Goal"] = True
self.set_goalscorer(False)
return True
else:
return False
