Once the probability is calculated, the user might want to simulate, say, 1000 attempts to get the expected success rate (like, on average, how many attempts are needed).
def calculate_probability(distance, club_power, wind, accuracy, bonus_skill): # Apply wind to effective distance adjusted_distance = distance + wind # Calculate the difference between club power and adjusted distance difference = abs(club_power - adjusted_distance) # Base probability could be inversely proportional to the difference base_prob = 1 - (difference / (adjusted_distance ** 0.5)) # Clamp probability between 0 and 1 base_prob = max(0, min(1, base_prob)) # Multiply by accuracy and skill modifiers total_prob = base_prob * accuracy * (1 + bonus_skill) # Clamp again in case modifiers go over 1 total_prob = max(0, min(1, total_prob)) return total_prob * 100 # Convert to percentage holeinonepangyacalculator 2021
Another approach: Maybe in the game, the probability is determined by the strength of the shot. If you hit the ball at the perfect power for the distance, you get a higher chance. So the calculator could compare the power used to the required distance and adjust the probability accordingly. Once the probability is calculated, the user might
Wait, maybe the user wants a tool to calculate something related to Pangya's game mechanics for Hole-in-One. Maybe the probability depends on factors like club power, distance, wind direction and strength, or maybe it's based on in-game mechanics like the skill points, equipment, or player statistics. So the calculator could compare the power used