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首先，我们需要获得每个球员的投篮数据。利用 Savvas Tjortjoglou 贴出的代码，笔者62616964757a686964616fe4b893e5b19e31333363383437从 NBA.com 网站 API 上获取了数据。在此不会贴出这个函数的结果。如果你感兴趣，推荐你去看看 Savvas Tjortjoglou 的博客。

def aqcuire_shootingData(PlayerID,Season):

import requests

shot_chart_url = 'http://stats.nba.com/stats/shotchartdetail?CFID=33&CFPARAMS='+Season+'&ContextFilter='\

'&ContextMeasure=FGA&DateFrom=&DateTo=&GameID=&GameSegment=&LastNGames=0&LeagueID='\

'00&Location=&MeasureType=Base&Month=0&OpponentTeamID=0&Outcome=&PaceAdjust='\

'N&PerMode=PerGame&Period=0&PlayerID='+PlayerID+'&PlusMinus=N&Position=&Rank='\

'N&RookieYear=&Season='+Season+'&SeasonSegment=&SeasonType=Regular+Season&TeamID='\

'0&VsConference=&VsDivision=&mode=Advanced&showDetails=0&showShots=1&showZones=0'

response = requests.get(shot_chart_url)

headers = response.json()['resultSets'][0]['headers']

shots = response.json()['resultSets'][0]['rowSet']

shot_df = pd.DataFrame(shots, columns=headers)

return shot_df

接下来，我们需要绘制一个包含得分图的篮球场图。该篮球场图例必须使用与NBA.com API 相同的坐标系统。例如，3分位置的投篮距篮筐必须为 X 单位，上篮距离篮筐则是 Y 单位。同样，笔者再次使用了 Savvas Tjortjoglou 的代码(哈哈，否则的话，搞明白 NBA.com 网站的坐标系统肯定会耗费不少的时间)。

def draw_court(ax=None, color='black', lw=2, outer_lines=False):

from matplotlib.patches import Circle, Rectangle, Arc

if ax is None:

ax = plt.gca()

hoop = Circle((0, 0), radius=7.5, linewidth=lw, color=color, fill=False)

backboard = Rectangle((-30, -7.5), 60, -1, linewidth=lw, color=color)

outer_box = Rectangle((-80, -47.5), 160, 190, linewidth=lw, color=color,

fill=False)

inner_box = Rectangle((-60, -47.5), 120, 190, linewidth=lw, color=color,

fill=False)

top_free_throw = Arc((0, 142.5), 120, 120, theta1=0, theta2=180,

linewidth=lw, color=color, fill=False)

bottom_free_throw = Arc((0, 142.5), 120, 120, theta1=180, theta2=0,

linewidth=lw, color=color, linestyle='dashed')

restricted = Arc((0, 0), 80, 80, theta1=0, theta2=180, linewidth=lw,

color=color)

corner_three_a = Rectangle((-220, -47.5), 0, 140, linewidth=lw,

color=color)

corner_three_b = Rectangle((220, -47.5), 0, 140, linewidth=lw, color=color)

three_arc = Arc((0, 0), 475, 475, theta1=22, theta2=158, linewidth=lw,

color=color)

center_outer_arc = Arc((0, 422.5), 120, 120, theta1=180, theta2=0,

linewidth=lw, color=color)

center_inner_arc = Arc((0, 422.5), 40, 40, theta1=180, theta2=0,

linewidth=lw, color=color)

court_elements = [hoop, backboard, outer_box, inner_box, top_free_throw,

bottom_free_throw, restricted, corner_three_a,

corner_three_b, three_arc, center_outer_arc,

center_inner_arc]

if outer_lines:

outer_lines = Rectangle((-250, -47.5), 500, 470, linewidth=lw,

color=color, fill=False)

court_elements.append(outer_lines)

for element in court_elements:

ax.add_patch(element)

ax.set_xticklabels([])

ax.set_yticklabels([])

ax.set_xticks([])

ax.set_yticks([])

return ax

我想创造一个不同位置的投篮百分比数组，因此决定利用 matplot 的 Hexbin 函数 http://matplotlib.org/api/pyplot_api.html 将投篮位置均匀地分组到六边形中。该函数会对每个六边形中每一个位置的投篮次数进行计数。

六边形是均匀的分布在 XY 网格中。「gridsize」变量控制六边形的数目。「extent」变量控制第一个和最后一个六边形的绘制位置(一般来说第一个六边形的位置基于第一个投篮的位置)。

计算命中率则需要对每个六边形中投篮的次数和投篮得分次数进行计数，因此笔者对同一位置的投篮和得分数分别运行 hexbin 函数。然后，只需用每个位置的进球数除以投篮数。

def find_shootingPcts(shot_df, gridNum):

x = shot_df.LOC_X[shot_df['LOC_Y']<425.1] #i want to make sure to only include shots I can draw

y = shot_df.LOC_Y[shot_df['LOC_Y']<425.1]

x_made = shot_df.LOC_X[(shot_df['SHOT_MADE_FLAG']==1) & (shot_df['LOC_Y']<425.1)]

y_made = shot_df.LOC_Y[(shot_df['SHOT_MADE_FLAG']==1) & (shot_df['LOC_Y']<425.1)]

#compute number of shots made and taken from each hexbin location

hb_shot = plt.hexbin(x, y, gridsize=gridNum, extent=(-250,250,425,-50));

plt.close() #don't want to show this figure!

hb_made = plt.hexbin(x_made, y_made, gridsize=gridNum, extent=(-250,250,425,-50),cmap=plt.cm.Reds);

plt.close()

#compute shooting percentage

ShootingPctLocs = hb_made.get_array() / hb_shot.get_array()

ShootingPctLocs[np.isnan(ShootingPctLocs)] = 0 #makes 0/0s=0

return (ShootingPctLocs, hb_shot)

笔者非常喜欢 Savvas Tjortjoglou 在他的得分图中加入了球员头像的做法，因此也顺道用了他的这部分代码。球员照片会出现在得分图的右下角。

def acquire_playerPic(PlayerID, zoom, offset=(250,400)):

from matplotlib import offsetbox as osb

import urllib

pic = urllib.urlretrieve("http://stats.nba.com/media/players/230x185/"+PlayerID+".png",PlayerID+".png")

player_pic = plt.imread(pic[0])

img = osb.OffsetImage(player_pic, zoom)

#img.set_offset(offset)

img = osb.AnnotationBbox(img, offset,xycoords='data',pad=0.0, box_alignment=(1,0), frameon=False)

return img

笔者想用连续的颜色图来描述投篮进球百分比，红圈越多代表着更高的进球百分比。虽然「红」颜色图示效果不错，但是它会将0%的投篮进球百分比显示为白色http://matplotlib.org/users/colormaps.html，而这样显示就会不明显，所以笔者用淡粉红色代表0%的命中率，因此对红颜色图做了下面的修改。

#cmap = plt.cm.Reds

#cdict = cmap._segmentdata

cdict = {

'blue': [(0.0, 0.6313725709915161, 0.6313725709915161), (0.25, 0.4470588266849518, 0.4470588266849518), (0.5, 0.29019609093666077, 0.29019609093666077), (0.75, 0.11372549086809158, 0.11372549086809158), (1.0, 0.05098039284348488, 0.05098039284348488)],

'green': [(0.0, 0.7333333492279053, 0.7333333492279053), (0.25, 0.572549045085907, 0.572549045085907), (0.5, 0.4156862795352936, 0.4156862795352936), (0.75, 0.0941176488995552, 0.0941176488995552), (1.0, 0.0, 0.0)],

'red': [(0.0, 0.9882352948188782, 0.9882352948188782), (0.25, 0.9882352948188782, 0.9882352948188782), (0.5, 0.9843137264251709, 0.9843137264251709), (0.75, 0.7960784435272217, 0.7960784435272217), (1.0, 0.40392157435417175, 0.40392157435417175)]

}

mymap = mpl.colors.LinearSegmentedColormap('my_colormap', cdict, 1024)

好了，现在需要做的就是将它们合并到一块儿。下面所示的较大函数会利用上文描述的函数来创建一个描述投篮命中率的得分图，百分比由红圈表示(红色越深 = 更高的命中率)，投篮次数则由圆圈的大小决定(圆圈越大 = 投篮次数越多)。需要注意的是，圆圈在交叠之前都能增大。一旦圆圈开始交叠，就无法继续增大。

在这个函数中，计算了每个位置的投篮进球百分比和投篮次数。然后画出在该位置投篮的次数(圆圈大小)和进球百分比(圆圈颜色深浅)。

def shooting_plot(shot_df, plot_size=(12,8),gridNum=30):

from matplotlib.patches import Circle

x = shot_df.LOC_X[shot_df['LOC_Y']<425.1]

y = shot_df.LOC_Y[shot_df['LOC_Y']<425.1]

#compute shooting percentage and # of shots

(ShootingPctLocs, shotNumber) = find_shootingPcts(shot_df, gridNum)

#draw figure and court

fig = plt.figure(figsize=plot_size)#(12,7)

cmap = mymap #my modified colormap

ax = plt.axes([0.1, 0.1, 0.8, 0.8]) #where to place the plot within the figure

draw_court(outer_lines=False)

plt.xlim(-250,250)

plt.ylim(400, -25)

#draw player image

zoom = np.float(plot_size[0])/(12.0*2) #how much to zoom the player's pic. I have this hackily dependent on figure size

img = acquire_playerPic(PlayerID, zoom)

ax.add_artist(img)

#draw circles

for i, shots in enumerate(ShootingPctLocs):

restricted = Circle(shotNumber.get_offsets()[i], radius=shotNumber.get_array()[i],

color=cmap(shots),alpha=0.8, fill=True)

if restricted.radius > 240/gridNum: restricted.radius=240/gridNum

ax.add_patch(restricted)

#draw color bar

ax2 = fig.add_axes([0.92, 0.1, 0.02, 0.8])

cb = mpl.colorbar.ColorbarBase(ax2,cmap=cmap, orientation='vertical')

cb.set_label('Shooting %')

cb.set_ticks([0.0, 0.25, 0.5, 0.75, 1.0])

cb.set_ticklabels(['0%','25%', '50%','75%', '100%'])

plt.show()

return ax

好了，大功告成！因为笔者是森林狼队的粉丝，在下面用几分钟跑出了森林狼队前六甲的得分图。

PlayerID = '203952' #andrew wiggins

shot_df = aqcuire_shootingData(PlayerID,'2015-16')

ax = shooting_plot(shot_df, plot_size=(12,8));

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