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Sports Simulation

A python based application that simulates two scenarios from two different kinds of sports, soccer fouls and tennis aces. Having each sport in a different tab, we implemented dynamic graphs to visually demonstrate the simulations in both conditions.


Controls


Free Kick

The foul simulation is controlled using three parameters; distance from the goal, the firing angle and the firing velocity, through which we get the maximum height of the ball and the height at the goal plane. It's important to note the we neglected drag forces and assumed the the shooting spots are as much as possible perpendicular to the goal line.

Samples 1


Ace

For the ace simulation, the purpose was to calculate the player's chance to make an Ace (a service which cannot not be returned by the opponent), knowing that it increases with an increasing ball hitting speed. Having that in mind, we considered that this chance obeys cumulative Gaussian probability distribution of the ball hit speed with a mean set to a default of 160 km/h and a standard deviation with a default of 20 km/h.

Samples 2 and 3



Samples











Tools

  • Python
  • PyQt



Code Snippets

The Ace chance function in a simplified form

def score_probability(self):

        mu = self.doubleSpinBox_mu.value()
        std = self.doubleSpinBox_std.value()
        speed = self.doubleSpinBox_spd.value()
        
        x = np.linspace(mu - 3*std, mu + 3*std, int(6*std))
        self.graphicsView_normal.clear()
        self.graphicsView_normal.plot(x, norm.pdf(x, mu, std))
    
        cdf = norm.cdf(speed, loc=mu, scale=std)

Contributors

Name Github
Ahmed Wael Ahmedwael-afk
Doaa Salah
Mohamed Osama osama51
Mohamed Abdel Galeel