{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Introducing space occupation \n", "\n", "We focus on the space occupation of basketball players. We'd like to know how we can quantify the occupation and how we can visualize this occupation of the court.\n", "\n", "## Voronoi cutting\n", "----\n", "\n", "First, we can use Voronoi diagram to partitionate the court into cells which belong to a unique player.\n", "\n", "### Voronoi diagram\n", "\n", "The first thing we have to do is to extract the data from json files. The function *json_extracter* is available in the python file *data_extracter* in the folder python_file." ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "from python_files.data_extracter import json_extracter\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import math as m\n", "\n", "data,events=json_extracter('data/game1.json')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We compute a function that plot voroni diagram of a moment of a play" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "from scipy.spatial import Voronoi, voronoi_plot_2d\n", " \n", "def voronoi(events,event_id,mom_id): \n", " event=events[event_id]\n", " moment=event['moments'][mom_id][5] # we only take the five index of moment info because it contains players' and ball's position\n", " points = np.array([[player[2],player[3]] for player in moment[1:]])\n", " vor = Voronoi(points)\n", " fig = voronoi_plot_2d(vor, show_vertices=False, line_colors='black',line_width=1, line_alpha=0.6, point_size=0)\n", " plt.xlim(0,94) #force the plt.show to adapt the size\n", " plt.ylim(0,50)\n", " plt.xlabel('x in feet')\n", " plt.ylabel('y in feet')\n", "\n", "voronoi(events,0,25)\n", "field = plt.imread(\"Images/fullcourt.png\")\n", "plt.imshow(field, extent=[0,94,0,50])\n", "plt.plot(63,39,'o')\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we want to add ball's position, player's velocity vector and to distinguish defenders and attackers" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "def players_ball_speed_position(moment1,moment2):\n", " \n", " dt=moment1[2]-moment2[2]\n", " mom_infos={}\n", " mom_infos['ball']={}\n", " mom_infos['team1']={}\n", " mom_infos['team2']={}\n", " for i in range(11) :\n", " if i==0:\n", " mom_infos['ball']['xy']=np.array(moment1[5][i][2:4])\n", " mom_infos['ball']['z']=moment1[5][i][4]\n", " mom_infos['ball']['v']=np.array([(moment2[5][i][2]-moment1[5][i][2])/dt,(moment2[5][i][3]-moment1[5][i][3])/dt])\n", " if 6<=i<=11:\n", " mom_infos['team2'][str(moment1[5][i][1])]={'xy':np.array(moment1[5][i][2:4]),'v':np.array([(moment2[5][i][2]-moment1[5][i][2])/dt,(moment2[5][i][3]-moment1[5][i][3])/dt])}\n", " if 1<=i<=5:\n", " mom_infos['team1'][str(moment1[5][i][1])]={'xy':np.array(moment1[5][i][2:4]),'v':np.array([(moment2[5][i][2]-moment1[5][i][2])/dt,(moment2[5][i][3]-moment1[5][i][3])/dt])}\n", " return(mom_infos)" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "def print_court_teams_occupation(events,event_id,mom_id,voronoi_cut=False,player_info=False):\n", " \"This function return a visualization of the court for the moment mom_id of the event event_id. If voronoi=True, voronoi cutting is plotted.\"\n", " if voronoi_cut:\n", " voronoi(events,event_id,mom_id)\n", " event=events[event_id]\n", " moment=event['moments'][mom_id]\n", " moment1=moment\n", " moment2=event['moments'][mom_id+1]\n", " \n", " # separation of ball, team1 and team2 and calculation of the speed\n", " mom_infos=players_ball_speed_position(moment1,moment2)\n", " \n", " p=1\n", " for player in mom_infos['team2'].keys():\n", " x=mom_infos['team2'][player]['xy'][0]\n", " y=mom_infos['team2'][player]['xy'][1]\n", " vx=mom_infos['team2'][player]['v'][0]\n", " vy=mom_infos['team2'][player]['v'][1]\n", " plt.plot(x,y,'bo',markersize=12,alpha=0.6)\n", " plt.arrow(x,y,vx,vy,shape='full',lw=1.5,head_width=1)\n", " if player_info:\n", " plt.annotate(str(p),(x,y),xytext=(3,3),textcoords='offset points')\n", " p+=1\n", " \n", " for player in mom_infos['team1'].keys():\n", " x=mom_infos['team1'][player]['xy'][0]\n", " y=mom_infos['team1'][player]['xy'][1]\n", " vx=mom_infos['team1'][player]['v'][0]\n", " vy=mom_infos['team1'][player]['v'][1]\n", " plt.plot(x,y,'ro',markersize=12,alpha=0.6)\n", " plt.arrow(x,y,vx,vy,shape='full',lw=1.5,head_width=1)\n", " if player_info:\n", " plt.annotate(str(p),(x,y),xytext=(3,3),textcoords='offset points')\n", " p+=1\n", " \n", " plt.plot(mom_infos['ball']['xy'][0],mom_infos['ball']['xy'][1],'yo')\n", " plt.xlabel('x in feet')\n", " plt.ylabel('y in feet')\n", "\n", " field = plt.imread(\"Images/fullcourt.png\")\n", " plt.imshow(field, extent=[0,94,0,50])\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "print_court_teams_occupation(events,0,25,voronoi_cut=True,player_info=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Add a valuation to voronoi diagram\n", "\n", "The aim is to add visual tool to highlight the fact a player doesn't occupy his cell uniformly : the closest of a point he is, the more he control the space. To do so, we cut the court into cells and we attribute a value to each cell by introducing a continuous measure, taking into account the relative distance of a point to a player. This quantity, $\\delta_d$ is calculated as the difference between the distance $d$ from the point $(x,y)$ to the closest player and the distance from the same point to the closest opponent,\n", "\\begin{equation}\\label{eq:deltad}\n", " \\delta_d(x,y)=d_\\textrm{closest player}-d_\\textrm{closest opponent}.\n", "\\end{equation}" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "def distance(a,b): #a = (x,y) departure point ; b = (i,j) arrival point\n", " return m.sqrt((a[0]-b[0])**2+(a[1]-b[1])**2)" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "def print_court_teams_occupation(events,event_id,mom_id,voronoi_cut=False,value=False,player_info=False,n=50,p=94):\n", " \"This function return a visualization of the court for the moment mom_id of the event event_id. If voronoi_cut=True, voronoi cutting is plotted. Then, if value=True, a heat-map giving a value to space occupation is drawn.\"\n", " if voronoi_cut:\n", " voronoi(events,event_id,mom_id)\n", " event=events[event_id]\n", " moment=event['moments'][mom_id]\n", " moment1=moment\n", " moment2=event['moments'][mom_id+1]\n", " \n", " # separation of ball, team1 and team2 and calculation of the speed\n", " mom_infos=players_ball_speed_position(moment1,moment2)\n", " \n", " ind=1\n", " for player in mom_infos['team2'].keys():\n", " x=mom_infos['team2'][player]['xy'][0]\n", " y=mom_infos['team2'][player]['xy'][1]\n", " vx=mom_infos['team2'][player]['v'][0]\n", " vy=mom_infos['team2'][player]['v'][1]\n", " plt.plot(x,y,'bo',markersize=12,alpha=0.6)\n", " plt.arrow(x,y,vx,vy,shape='full',lw=1.5,head_width=1)\n", " if player_info:\n", " plt.annotate(str(ind),(x,y),xytext=(3,3),textcoords='offset points')\n", " ind+=1\n", " \n", " for player in mom_infos['team1'].keys():\n", " x=mom_infos['team1'][player]['xy'][0]\n", " y=mom_infos['team1'][player]['xy'][1]\n", " vx=mom_infos['team1'][player]['v'][0]\n", " vy=mom_infos['team1'][player]['v'][1]\n", " plt.plot(x,y,'ro',markersize=12,alpha=0.6)\n", " plt.arrow(x,y,vx,vy,shape='full',lw=1.5,head_width=1)\n", " if player_info:\n", " plt.annotate(str(ind),(x,y),xytext=(3,3),textcoords='offset points')\n", " ind+=1\n", " if value:\n", " court=np.zeros((n,p))\n", " for i in range(n):\n", " for j in range(p):\n", " b=np.array([j,i]) # point d'arrivée\n", " \n", " dmin_1=np.inf\n", " for player in mom_infos['team1'].keys():\n", " a=mom_infos['team1'][player]['xy']\n", " d=distance(a,b)\n", " if d" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "print_court_teams_occupation(events,0,25,voronoi_cut=True,value=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This quantity does therefore not only take into account the distance to a player, but also the fact that only players of the other team will dispute the control of a certain area. On the boundaries of a Voronoï cell between players of different teams the value of $\\delta_d$ is per definition $\\delta_d=0$. The continuous nature of $\\delta_d$ allows to determine a _heat-map_ describing the occupation of the court by the players and thereby we are able to evaluate the free-space that each player has as a function of his position. Furthermore since the definition of $\\delta_d$ distinguishes between the two different teams we also measure how \"free\" a player is." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Looking at another way of valuing\n", "\n", "Our way of valuing only take into account the distance between closest player of each team and the point considered. Yet, players are moving and for a fast moving player, it is more difficult to control the area behind him. Therefore, we try to cut the court taking into account players'inertia. \n", "\n", "To do so, we only change our way to calculate the value : for each cell, we don't calculate a distance but a relative time. Therefore, the boundaries between a defense-controlled zone and an attack-controlled zone are more precisely defined by a quantity $\\delta_t$,\n", "\\begin{equation}\\label{eq:deltat}\n", " \\delta_{t}(x,y) = t_\\textrm{attack}-t_\\textrm{defense},\n", "\\end{equation}\n", "where $t_\\textrm{attack}$ is the time it takes for the closest attacker (in time) to join the point $(x,y)$, and $t_\\textrm{defense}$ the same time for a defender. In the unphysical case of players with zero mass, or infinite force, this $\\delta_t$ should behave as $\\delta_d$. However, inertia is expected to change this. Let's look at the notebook [2_Time_calculation](https://nbviewer.jupyter.org/github/AmigoCap/MecaFootCo/blob/master/2_Time_calculation.ipynb) to have details over the model introducing inertia." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "def time_to_point(a,b,v,F=10*3.281): \n", " \"time to go from a to b with initial speed v, F is the force parameter in feet/s-2\"\n", " x0,y0=a\n", " xf,yf=b\n", " X=x0-xf\n", " Y=y0-yf\n", " k4=1\n", " k3=0\n", " k2=4*(v[0]**2+v[1]**2)/F**2\n", " k1=8*(v[0]*X+v[1]*Y)/F**2\n", " k0=4*(X**2+Y**2)/F**2\n", " times=np.roots([k4,k3,-k2,-k1,-k0])\n", " for i in range(4): # Selection of the root real and positive\n", " if times[i].imag==0:\n", " if times[i].real>=0:\n", " return times[i].real\n", " print('error')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "def print_court_teams_occupation_inertia(events,event_id,mom_id,voronoi_cut=False,player_info=False,n=50,p=94):\n", " \"This function return a visualization of the court for the moment mom_id of the event event_id. If voronoi_cut=True, voronoi cutting is plotted. Then, if value=True, a heat-map giving a value to space occupation is drawn.\"\n", " if voronoi_cut:\n", " voronoi(events,event_id,mom_id)\n", " event=events[event_id]\n", " moment=event['moments'][mom_id]\n", " moment1=moment\n", " moment2=event['moments'][mom_id+1]\n", " \n", " # separation of ball, team1 and team2 and calculation of the speed\n", " mom_infos=players_ball_speed_position(moment1,moment2)\n", " \n", " ind=1\n", " for player in mom_infos['team2'].keys():\n", " x=mom_infos['team2'][player]['xy'][0]\n", " y=mom_infos['team2'][player]['xy'][1]\n", " vx=mom_infos['team2'][player]['v'][0]\n", " vy=mom_infos['team2'][player]['v'][1]\n", " plt.plot(x,y,'bo',markersize=12,alpha=0.6)\n", " plt.arrow(x,y,vx,vy,shape='full',lw=1.5,head_width=1)\n", " if player_info:\n", " plt.annotate(str(ind),(x,y),xytext=(3,3),textcoords='offset points')\n", " ind+=1\n", " \n", " for player in mom_infos['team1'].keys():\n", " x=mom_infos['team1'][player]['xy'][0]\n", " y=mom_infos['team1'][player]['xy'][1]\n", " vx=mom_infos['team1'][player]['v'][0]\n", " vy=mom_infos['team1'][player]['v'][1]\n", " plt.plot(x,y,'ro',markersize=12,alpha=0.6)\n", " plt.arrow(x,y,vx,vy,shape='full',lw=1.5,head_width=1)\n", " if player_info:\n", " plt.annotate(str(ind),(x,y),xytext=(3,3),textcoords='offset points')\n", " ind+=1\n", " \n", " court=np.zeros((n,p))\n", " for i in range(n):\n", " for j in range(p):\n", " b=np.array([j,i]) # point d'arrivée\n", " \n", " tmin_1=np.inf\n", " for player in mom_infos['team1'].keys():\n", " a=mom_infos['team1'][player]['xy']\n", " v=mom_infos['team1'][player]['v']\n", " t=time_to_point(a,b,v)\n", " if t" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "print_court_teams_occupation_inertia(events,0,25,voronoi_cut=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The boundaries between red zones and blue ones don't fit with voronoï tesselation anymore. Let's compare the valuation :" ] }, { "cell_type": "code", "execution_count": 76, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", "text/plain": [ "" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "plt.figure(1)\n", "plt.title('Space occupation taking intertia into account')\n", "print_court_teams_occupation_inertia(events,0,25,player_info=True)\n", "plt.figure(2)\n", "plt.title('Previous space occpuation')\n", "print_court_teams_occupation(events,0,25,voronoi_cut=False,value=True,player_info=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "With the two figures above, we can compare classic space occupation and the one calculated taking into account inertia. As expected inertia introduce change the interpretation of occupation. Indeed, if we focus on players 4 and 8, as 4 is moving with high speed to the area behind 8 while 8 is moving in the opposite direction, 4 control the area behind 8. Visually, space occupation is more accurate with inertia. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "----\n", "## Conclusion\n", "\n", "In this notebook we introduced three ways to observe space occupation :\n", "* Voronoi tesselation which is the simplest way to characterize occupation\n", "* A team point of view of voronoï tesselation with the quantity $\\delta_d$ which brings the information \"how free a player is from the opponent team\".\n", "* A time-based approach in with a better visual appreciation due to the introduction of inertia.\n", "\n", "you can find the rest of the study in the following notebooks :\n", "* [2_time_calculation](https://nbviewer.jupyter.org/github/AmigoCap/MecaFootCo/blob/master/2_Time_calculation.ipynb) where we detail time calculation and where we explain visually the trajectories modelized by the calculation\n", "* [3_Comparison_of_ways_to_quantify_free_space](https://nbviewer.jupyter.org/github/AmigoCap/MecaFootCo/blob/master/3_Comparison_of_ways_to_quantify_free_space.ipynb) in which the comparison of occupancy calculations is taken further and in which a new way to quantify how \"free\" a players is is introduced.\n", "* [4_Free_space_and_3-points_efficiency](https://nbviewer.jupyter.org/github/AmigoCap/MecaFootCo/blob/master/4_Free_space_and_3-points_efficiency.ipynb) in which we focus on 3-points shot and the link between efficiency and free-space." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.4" } }, "nbformat": 4, "nbformat_minor": 2 }