def __init__(self, gm, RoP):
self.game = Game(gm, RoP)
record, rot, bxs = self.game.preprocess(load=1)
self.record = record
self.rot = rot
self.bxs = bxs
self.dist = [
' 0~ 5ft 2PT', ' 5~10ft 2PT', ' 10~15ft 2PT',
' 15~20ft 2PT', ' >20ft 2PT', ' <=25ft 3PT',
' 25~30ft 3PT', ' 30~35ft 3PT', ' 35~40ft 3PT',
' >40ft 3PT', ' restricted(RA)', ' paint(Non-RA)',
' mid range', ' left corner 3', ' right corner 3',
' above the break 3'
]
def detectgame(self, gm, playeres, season=0):
game = Game(gm[:-7], 'playoff' if self.RoP else 'regular')
for qtr in range(3, game.quarters):
for ply in game.yieldPlay(qtr):
play = Play(ply, qtr)
if play.time() <= MPTime(self.lm): # 在规定时间内
rec, ind = play.record()
s = play.score(ind=ind)
if s and play.diffbeforescore(s) <= self.dp:
try:
p = self.pm2pn[rec.split(' ')[0]]
except KeyError:
p = rec.split(' ')[0]
if season:
p += ' %d_%d' % (season, season + 1)
if p not in playeres:
playeres[p] = np.zeros((1, len(self.columns)))
if 'makes' in rec: # 得分增加命中数
playeres[p][0, 0] += s
playeres[p][0, s * 3 - 1] += 1
if 'assist' in rec: # 受助攻
playeres[p][0, s + 11] += 1
playeres[p][0, 15] += 1
try:
astp = self.pm2pn[rec.split(' ')[-1][:-1]]
except:
astp = rec.split(' ')[-1][:-1]
if season:
astp += ' %d_%d' % (season, season + 1)
if astp not in playeres:
playeres[astp] = np.zeros(
(1, len(self.columns)))
playeres[astp][0, s + 14] += 1
playeres = self.onlycountonce(
astp, gm[:-7], playeres)
playeres[p][0, s * 3] += 1 # 得分或投失增加出手数
playeres = self.onlycountonce(p, gm[:-7], playeres)
# if 'LeBron James' in p and season == 2019:
# print(s, play.play, gm[:-7], self.plyrcgs['LeBron James 2019_2020'])
return playeres
def inline(r):
line = r['R'][RoH]
return sorted(line, reverse=True)
lines_all = LoadPickle('./data/Lineups/anaSeason5Lineups.pickle')
for season in range(2020, 2021):
ss = '%d_%d' % (season, season + 1)
for i in range(2):
lines_all[i][ss] = {}
lines = {
} # {'tm1': {'line1': ['sum_time', '+/-'], 'line2': ['sum_time', '+/-'] ...}, 'tm2': ...}
gms = os.listdir('D:/sunyiwu/stat/data/seasons/%s/%s/' %
(ss, regularOrPlayoffs[i]))
for gm in tqdm(gms):
game = Game(gm, regularOrPlayoffs[i])
record = LoadPickle(gameMarkToDir(gm, regularOrPlayoffs[i], tp=3))
rot = game.rotation(record)
bxs, rot = game.replayer(record, rot)
et = '%d:00.0' % (48 + 5 * (game.quarters - 4))
for tmix, tm in enumerate(list(game.bxscr[0])): # 分别回溯两支球队
if tm not in lines:
lines[tm] = {}
RoH = list(game.bxscr[0]).index(tm)
for ix, r in enumerate(rot):
line = inline(r)
if ' '.join(line) not in lines[tm]:
lines[tm][' '.join(line)] = [
MPTime('0:00.0'), 0,
[np.zeros((21, )),
np.zeros((21, ))]
# ================ 球员单场数据准备 ================
mpsum += mp
pts = float(sgm['PTS'])
fta, ft, fga, fg, tpa, tp = float(
sgm['FTA']), float(sgm['FT']), float(
sgm['FGA']), float(sgm['FG']), float(
sgm['3PA']), float(sgm['3P'])
drb, orb, trb = float(sgm['DRB']), float(
sgm['ORB']), float(sgm['TRB'])
ast, stl, blk, tov, pf = float(
sgm['AST']), float(sgm['STL']), float(
sgm['BLK']), float(sgm['TOV']), float(
sgm['PF'])
# ================ 球员本队数据准备 ================
gm = sgm['Playoffs' if i else 'Date'].values[0]
game = Game(gm, RoF[i])
tm_gametime = 48 if game.quarters == 4 else 48 + 5 * (
game.quarters - 4)
plyrs = game.teamplyrs()
ts = 0 if pm in plyrs[0] else 1
os = 0 if ts else 1
ttl = [
game.bxscr[1][0][-1], game.bxscr[1][1][-1]
]
tm_ast, tm_fg = int(ttl[ts][14]), int(
ttl[ts][2])
# 0.5 * ((Tm FGA + 0.4 * Tm FTA - 1.07 * (Tm ORB / (Tm ORB + Opp DRB)) * (Tm FGA - Tm FG) + Tm TOV) +
# (Opp FGA + 0.4 * Opp FTA - 1.07 * (Opp ORB / (Opp ORB + Tm DRB)) * (Opp FGA - Opp FG) + Opp TOV))
tm_poss = int(ttl[ts][3]) + 0.4 * int(
ttl[ts][9]) - 1.07 * (
int(ttl[ts][11]) /
[MPTime('0:00.0'), MPTime('0:00.0'), MPTime('0:00.0'), MPTime('0:00.0'), MPTime('0:00.0'), MPTime('0:00.0'), MPTime('0:00.0'), MPTime('0:00.0'), MPTime('0:00.0')],
[MPTime('0:00.0'), MPTime('0:00.0'), MPTime('0:00.0'), MPTime('0:00.0'), MPTime('0:00.0'), MPTime('0:00.0'), MPTime('0:00.0'), MPTime('0:00.0'), MPTime('0:00.0')]],
[[MPTime('0:00.0'), MPTime('0:00.0'), MPTime('0:00.0'), MPTime('0:00.0'), MPTime('0:00.0'), MPTime('0:00.0'), MPTime('0:00.0'), MPTime('0:00.0'), MPTime('0:00.0')],
[MPTime('0:00.0'), MPTime('0:00.0'), MPTime('0:00.0'), MPTime('0:00.0'), MPTime('0:00.0'), MPTime('0:00.0'), MPTime('0:00.0'), MPTime('0:00.0'), MPTime('0:00.0')],
[MPTime('0:00.0'), MPTime('0:00.0'), MPTime('0:00.0'), MPTime('0:00.0'), MPTime('0:00.0'), MPTime('0:00.0'), MPTime('0:00.0'), MPTime('0:00.0'), MPTime('0:00.0')]]]
average_time = [[['', '', '', '', '', '', '', '', ''], ['', '', '', '', '', '', '', '', ''], ['', '', '', '', '', '', '', '', '']],
[['', '', '', '', '', '', '', '', ''], ['', '', '', '', '', '', '', '', ''], ['', '', '', '', '', '', '', '', '']]]
count_score = np.zeros((2, 3, 9))
ss = '%d_%d' % (season, season + 1)
# print(ss)
for i in range(2): # 分别统计常规赛、季后赛
gms = os.listdir('D:/sunyiwu/stat/data/seasons/%s/%s/' % (ss, regularOrPlayoffs[i]))
for gm in tqdm(gms):
count_games[i] += 1
# print('\t\t\t' + gm)
game = Game(gm[:-7], regularOrPlayoffs[i])
gameplyrs = game.teamplyrs()
record = LoadPickle(gameMarkToDir(gm[:-7], regularOrPlayoffs[i], tp=3))
# _, _, _, record = game.game_scanner(gm[:-7])
zoom = 0
bp = -1
pm = ''
for rec in record:
if zoom: # 处于抢断/失误观察期内
# 出现得失分
if 'MK' in rec or 'MS' in rec:
GoM = 1 if 'MK' in rec else 0
KoS = 'MK' if GoM else 'MS'
plyr_side = 0 if pm in gameplyrs[0] else 1
if tar_item:
plyr_side = 0 if plyr_side else 1
#!/usr/bin/python
# -*- coding:utf8 -*-
from klasses.Game import Game
from klasses.miscellaneous import MPTime
gm = '200102280DEN'
game = Game(gm, 'regular')
# print(game.teamplyrs())
_, _, _, record = game.game_scanner(gm)
for i in record:
# if 'MK' in i or 'MS' in i:
print(i)
# game.game_analyser(gm, record)
# game.pace(gm, record)
game.find_time_series(gm, record)
# a = MPTime('47:36.1')
# b = MPTime('47:36.0')
# print(a >= b) # True
#!/usr/bin/python
# -*- coding:utf8 -*-
from klasses.Game import Game
from klasses.Player import Player
from util import LoadPickle, playerMarkToDir, writeToPickle, gameMarkToSeason
from stats_nba.Game_nba import Game_nba
import pandas as pd
import numpy as np
np.set_printoptions(suppress=True)
pd.options.display.expand_frame_repr = False
pd.options.display.width = 50
RoF = 0
gm = '201201070NJN'
game = Game(gm, 'playoff' if RoF else 'regular')
record = game.game_scanner()
# for i in record:
# print(i)
record = game.game_analyser(record)
record = game.game_analyser(record, T=1)
ss = gameMarkToSeason(gm)
rof = 'playoff' if RoF else 'regular'
season_dir = 'D:/sunyiwu/stat/data/seasons_scanned/%s/%s/' % (ss, rof)
writeToPickle(season_dir + gm + '_scanned.pickle', record)
for i in record:
print(i)
# print()
game.find_time_series(record)
game.start_of_quarter(record)
inter = 600 # 设置按时间归并区间的间隔
inter_s = inter // 10
whole = 34800 # 整场比赛时间（双加时为止）
max_diff = 40
diff_inter = 5
# 1、2、3分 x 命中数、出手数 x -40~40分差 x 间隔统计时间
shoots = np.zeros((3, 2, max_diff * 2 // diff_inter + 1, whole // inter))
prctg = np.zeros((3, max_diff * 2 // diff_inter + 1, whole // inter))
for season in player.yieldSeasons():
for gameInf in player.yieldGames(season):
# score += int(gameInf[26])
gm = gameInf[1]
team = gameInf[3]
op = gameInf[5]
game = Game(gm, ROP, team, op)
for qtr in range(game.quarters):
for playInf in game.yieldPlay(qtr):
play = Play(playInf, qtr, pm=pm, HOA=game.HOA)
if play.playRecord() and play.teamPlay():
if play.playerAst(): # 助攻
asts += 1
elif play.playerMadeShoot(): # 命中投篮
s, d, t = score_diff_time(play, game, max_diff)
d -= s # 关注的是球员得分前的分差
score += s
t = tailTime(t, inter_s)
d = groupDiff(d + 40, max_diff, diff_inter)
shoots[s - 1, 0, d, t] += 1
shoots[s - 1, 1, d, t] += 1
import os
from tqdm import tqdm
from klasses.Game import Game
from util import writeToPickle, gameMarkToDir, LoadPickle, plus_minus
regularOrPlayoffs = ['regular', 'playoffs']
plyrs = {} # {'pm': [[gm], [节次], [+/-], [胜负], [是否比赛最后一节]]}
for season in range(2000, 2020):
ss = '%d_%d' % (season, season + 1)
# print(ss)
for i in range(1):
gms = os.listdir('D:/sunyiwu/stat/data/seasons/%s/%s/' %
(ss, regularOrPlayoffs[i]))
for gmf in tqdm(gms):
gm = gmf[:-7]
game = Game(gm, regularOrPlayoffs[i])
_, _, _, record = game.game_scanner()
rot = game.rotation(record)
qtrs = rot[-1]['Q'] + 1
# 判断胜者
scores = [game.bxscr[0][x][0] for x in game.bxscr[0]]
winner = int(scores[0] < scores[1])
for tm in range(2): # 分别回溯两支球队
# if tm not in plyrs:
# plyrs[pm] = {}
q = 0
tick = 0
SoQ = 0
for qtr in range(qtrs):
# 定位至本届首发
st_pms = rot[tick]['R'][tm]
import numpy as np
from klasses.Game import Game
from util import writeToPickle, gameMarkToDir, LoadPickle
regularOrPlayoffs = ['regular', 'playoffs']
seasons = {}
plyrs = {}
for season in range(1996, 2020):
ss = '%d_%d' % (season, season + 1)
# print(ss)
seasons[ss] = [[0, 0, np.zeros((3, 7, 19))], [0, 0, np.zeros((3, 7, 19))]]
for i in range(2):
gms = os.listdir('D:/sunyiwu/stat/data/seasons/%s/%s/' % (ss, regularOrPlayoffs[i]))
for gmf in tqdm(gms):
gm = gmf[:-7]
game = Game(gm, regularOrPlayoffs[i])
_, _, _, record = game.game_scanner()
tmplyrs = game.teamplyrs()
bx = game.boxscores(record)['STAT']
gametime = 48 if game.quarters == 4 else 48 + 5 * (game.quarters - 4)
# 联盟赛季总和
seasons[ss][i][0] += 1
seasons[ss][i][1] += gametime
seasons[ss][i][-1][0, 6, :] += bx[0]
seasons[ss][i][-1][1, 6, :] += bx[1]
seasons[ss][i][-1][2, 6, :] += (bx[0] + bx[1])
# 球员赛季总和
for rh in range(2):
for pm in tmplyrs[rh]:
if pm not in plyrs:
plyrs[pm] = {}
class GameReviewer(object):
def __init__(self, gm, RoP):
self.game = Game(gm, RoP)
record, rot, bxs = self.game.preprocess(load=1)
self.record = record
self.rot = rot
self.bxs = bxs
self.dist = [
' 0~ 5ft 2PT', ' 5~10ft 2PT', ' 10~15ft 2PT',
' 15~20ft 2PT', ' >20ft 2PT', ' <=25ft 3PT',
' 25~30ft 3PT', ' 30~35ft 3PT', ' 35~40ft 3PT',
' >40ft 3PT', ' restricted(RA)', ' paint(Non-RA)',
' mid range', ' left corner 3', ' right corner 3',
' above the break 3'
]
# for r in self.rot:
# print(r)
#
# print(list(bxs.tdbxs[0][0]['team']))
# print(list(bxs.tdbxs[0][1]['team']))
# print(list(bxs.tdbxs[0][2]['team']))
# print(list(bxs.tdbxs[0][3]['team']))
# print(list(bxs.tdbxs[0][4]['team']))
# print()
# print(list(bxs.tdbxs[1][0]['team']))
# print(list(bxs.tdbxs[1][1]['team']))
# print(list(bxs.tdbxs[1][2]['team']))
# print(list(bxs.tdbxs[1][3]['team']))
# print(list(bxs.tdbxs[1][4]['team']))
# print()
# for tm in range(2):
# for k in bxs.tdbxs[tm][0]:
# if k != 'team':
# print(k, 2880 / bxs.tdbxs[tm][0][k][0][-2] * bxs.tdbxs[tm][0][k][0][-3])
# print('all', list(bxs.tdbxs[tm][0][k][0]))
# if k in bxs.tdbxs[tm][1]:
# print(1, list(bxs.tdbxs[tm][1][k][0])[-3], list(bxs.tdbxs[tm][1][k][0])[-3] * 2880 / list(bxs.tdbxs[tm][1][k][0])[-2], list(bxs.tdbxs[tm][1][k][0]))
# if k in bxs.tdbxs[tm][2]:
# print(2, list(bxs.tdbxs[tm][2][k][0])[-3], list(bxs.tdbxs[tm][2][k][0])[-3] * 2880 / list(bxs.tdbxs[tm][2][k][0])[-2], list(bxs.tdbxs[tm][2][k][0]))
# if k in bxs.tdbxs[tm][3]:
# print(3, list(bxs.tdbxs[tm][3][k][0])[-3], list(bxs.tdbxs[tm][3][k][0])[-3] * 2880 / list(bxs.tdbxs[tm][3][k][0])[-2], list(bxs.tdbxs[tm][3][k][0]))
# if k in bxs.tdbxs[tm][4]:
# print(4, list(bxs.tdbxs[tm][4][k][0])[-3], list(bxs.tdbxs[tm][4][k][0])[-3] * 2880 / list(bxs.tdbxs[tm][4][k][0])[-2], list(bxs.tdbxs[tm][4][k][0]))
# print()
# print(list(bxs.tdbxs[0][0][k][1][0]))
# print(list(bxs.tdbxs[0][0][k][1][1]))
# print('\n')
# 0FG 1FGA 2FG% 33P 43PA 53P% 6FT 7FTA 8FT% 9ORB 10DRB 11TRB 12AST 13STL 14BLK 15TOV 16PF 17PTS 18BP 19MP 20+/-
def eFGperc(self): # 四要素之一：有效命中率
tmp = [0, 0]
for i in range(2):
bxsc = self.bxs.tdbxs[i][0]['team']
tmp[i] = (bxsc[0] + 0.5 * bxsc[3]) / bxsc[1]
return tmp
def TOVperc(self): # 四要素之一：失误率
tmp = [0, 0]
for i in range(2):
bxsc = self.bxs.tdbxs[i][0]['team']
tmp[i] = bxsc[15] / bxsc[18]
return tmp
def ORBperc(self): # 四要素之一：进攻篮板率
tmp = [0, 0]
for i in range(2):
bxsc = self.bxs.tdbxs[i][0]['team']
tmp[i] = bxsc[9] / (bxsc[9] + self.bxs.tdbxs[i - 1][0]['team'][10])
return tmp
def FTr(self): # 四要素之一：造罚球率
tmp = [0, 0]
for i in range(2):
bxsc = self.bxs.tdbxs[i][0]['team']
tmp[i] = bxsc[6] / bxsc[1]
return tmp
def FTPtPTsperc(self): # 罚球得分占比
tmp = [0, 0]
for i in range(2):
bxsc = self.bxs.tdbxs[i][0]['team']
tmp[i] = bxsc[6] / bxsc[17]
return tmp
def FTprop(self): # 罚球出手/运动战出手比
tmp = [0, 0]
for i in range(2):
bxsc = self.bxs.tdbxs[i][0]['team']
tmp[i] = bxsc[7] / bxsc[1]
return tmp
def FTperc(self): # 罚球命中率
tmp = [0, 0]
for i in range(2):
bxsc = self.bxs.tdbxs[i][0]['team']
tmp[i] = bxsc[6] / bxsc[7]
return tmp
def twoPtperc(self): # 两分球命中率
tmp = [0, 0]
for i in range(2):
bxsc = self.bxs.tdbxs[i][0]['team']
tmp[i] = (bxsc[0] - bxsc[3]) / (bxsc[1] - bxsc[4])
return tmp
def twoPtprop(self): # 两分球出手占比
tmp = [0, 0]
for i in range(2):
bxsc = self.bxs.tdbxs[i][0]['team']
tmp[i] = (bxsc[1] - bxsc[4]) / bxsc[1]
return tmp
def twoPtPTsperc(self): # 两分球得分占比
tmp = [0, 0]
for i in range(2):
bxsc = self.bxs.tdbxs[i][0]['team']
tmp[i] = (bxsc[0] - bxsc[3]) * 2 / bxsc[17]
return tmp
def threePtperc(self): # 三分球命中率
tmp = [0, 0]
for i in range(2):
bxsc = self.bxs.tdbxs[i][0]['team']
tmp[i] = bxsc[3] / bxsc[4]
return tmp
def threePtprop(self): # 三分球出手占比
tmp = [0, 0]
for i in range(2):
bxsc = self.bxs.tdbxs[i][0]['team']
tmp[i] = bxsc[4] / bxsc[1]
return tmp
def threePtPTsperc(self): # 三分球得分占比
tmp = [0, 0]
for i in range(2):
bxsc = self.bxs.tdbxs[i][0]['team']
tmp[i] = bxsc[3] * 3 / bxsc[17]
return tmp
def fourfactors(self):
return pd.DataFrame(
[self.eFGperc(),
self.TOVperc(),
self.ORBperc(),
self.FTr()],
index=[' eFG%', ' TOV%', ' ORB%', ' FT/FGA'],
columns=list(self.game.bxscr[0]))
def shooting(self):
asts = [[0, 0], [0, 0]]
for ix, rec in enumerate(self.game.gn.record):
if 'MK' in rec and 'AST' in rec:
asts[rec['MK'][1] - 2][rec['MK'][2]] += 1
asts[0][0] = asts[0][0] / (self.bxs.tdbxs[0][0]['team'][0] -
self.bxs.tdbxs[0][0]['team'][3])
asts[0][1] = asts[0][1] / (self.bxs.tdbxs[1][0]['team'][0] -
self.bxs.tdbxs[1][0]['team'][3])
asts[1][0] = asts[1][0] / self.bxs.tdbxs[0][0]['team'][
3] if self.bxs.tdbxs[0][0]['team'][3] else -1
asts[1][1] = asts[1][1] / self.bxs.tdbxs[1][0]['team'][
3] if self.bxs.tdbxs[1][0]['team'][3] else -1
return pd.DataFrame([
self.twoPtperc(),
self.twoPtprop(),
self.twoPtPTsperc(), asts[0],
self.threePtperc(),
self.threePtprop(),
self.threePtPTsperc(), asts[1],
self.FTperc(),
self.FTprop(),
self.FTPtPTsperc()
],
index=[
' 2PT %', ' 2PT FGA%', ' 2PT PTS%',
' 2PT ASTed%', ' 3PT %',
' 3PT FGA%', ' 3PT PTS%',
' 3PT ASTed%', ' FT %', ' FTA/FGA',
' FT PTS%'
],
columns=list(self.game.bxscr[0]))
def distance(self, rec):
return math.sqrt(rec['C'][0]**2 + rec['C'][1]**2) / 10
def game_counter(self):
tovpts, orbpts, paintpts, leadmax, longestrun = [0, 0], [0,
0], [0, 0], [
0, 0
], [0, 0]
zoomtov, zoomorb, leaderchange, tie, run = 0, 0, 0, 0, 0
bptov, bporb, leadtm, runningtm = -1, -1, -1, -1
# ==================== 投篮分布数据 ====================
# 0客队1主队 0失手1命中 0 0~5 1 5~10 2 10~15 3 15~20 4 20~3PT 5 ~25 6 25~30 7 30~35 8 35~40 9 >40 10 限制区 11 paint限制区外 12 mid range
shots = np.zeros((2, 3, len(self.dist)))
for ix, rec in enumerate(self.game.gn.record):
if 'MK' in rec or 'MS' in rec:
GoM = 0 if 'MS' in rec else 1
item = 'MS' if 'MS' in rec else 'MK'
if rec[item][1] > 1:
d = self.distance(rec)
# print(d, rec)
# ==================== paint得分 ====================
if rec[item][1] > 1 and -80 <= rec['C'][0] <= 80 and rec[
'C'][1] < 140:
if GoM:
paintpts[rec['MK'][2]] += rec['MK'][1]
# if rec['MK'][2] == 1:
# print(rec)
if d <= 4:
xx = 10
else:
xx = 11
shots[rec[item][2], GoM, xx] += 1
if 'AST' in rec:
shots[rec[item][2], 2, xx] += 1
else:
if rec[item][1] < 3:
shots[rec[item][2], GoM, 12] += 1
if 'AST' in rec:
shots[rec[item][2], 2, 12] += 1
else:
if rec['C'][0] <= -200 and rec['C'][
1] <= 100: # left corner 3pt
xx = 13
elif rec['C'][0] >= 200 and rec['C'][
1] <= 100: # right corner 3pt
xx = 14
else:
xx = 15
shots[rec[item][2], GoM, xx] += 1
if 'AST' in rec:
shots[rec[item][2], 2, xx] += 1
# ==================== 按距离得分统计 ====================
if d <= 5:
xx = 0
elif d <= 10:
xx = 1
elif d <= 15:
xx = 2
elif d <= 20:
xx = 3
elif rec[item][1] < 3:
xx = 4
elif d <= 25:
xx = 5
elif d <= 30:
xx = 6
elif d <= 35:
xx = 7
elif d <= 40:
xx = 8
else:
xx = 9
shots[rec[item][2], GoM, xx] += 1
if 'AST' in rec:
shots[rec[item][2], 2, xx] += 1
ads = [[], []]
for i in range(len(self.dist)):
for tm in range(2):
pre = [shots[tm][1][i],
shots[tm][0][i] + shots[tm][1][i]] # FG/FGA
tail = [
shots[tm][1][i] / self.bxs.tdbxs[tm][0]['team'][0],
(shots[tm][0][i] + shots[tm][1][i]) /
self.bxs.tdbxs[tm][0]['team'][1],
shots[tm][1][i] * (3 if 5 <= i <= 9 or i > 12 else 2) /
self.bxs.tdbxs[tm][0]['team'][17]
] # 区域命中比例、出手比例、得分比例
if shots[tm][1][i]:
ads[tm].append(pre + [
shots[tm][1][i] /
(shots[tm][0][i] + shots[tm][1][i]), shots[tm][2][i] /
shots[tm][1][i]
] + tail)
else:
if shots[tm][0][i] + shots[tm][1][i]:
ads[tm].append(pre + [
shots[tm][1][i] /
(shots[tm][0][i] + shots[tm][1][i]), -1
] + tail)
else:
ads[tm].append(pre + [-1, -1] + tail)
ads = [
pd.DataFrame(
x,
index=self.dist,
columns=['FG', 'FGA', '%', 'ASTed%', 'FG%', 'FGA%', 'PTS%'])
for x in ads
]
# ==================== 基础对比数据 ====================
for ix, rec in enumerate(self.record):
# ==================== 交替领先、平分、连续得分 ====================
if 'MK' in rec:
if rec['S'][0] != rec['S'][1]:
li = 0 if rec['S'][0] > rec['S'][1] else 1
if li != leadtm:
if leadtm != -1:
leaderchange += 1
leadtm = li
else:
if self.record[ix - 1]['S'][0] != self.record[ix -
1]['S'][1]:
tie += 1
li = rec['MK'][2]
if li == runningtm:
run += rec['MK'][1]
else:
if runningtm == -1:
runningtm = li
run += rec['MK'][1]
else:
if longestrun[li] < run:
longestrun[li] = run
runningtm = li
run = rec['MK'][1]
# ==================== 最大领先 ====================
li = 0 if rec['S'][0] > rec['S'][1] else 1
if rec['S'][li] != rec['S'][
li - 1] and rec['S'][li] - rec['S'][li - 1] > leadmax[li]:
leadmax[li] = rec['S'][li] - rec['S'][li - 1]
# ==================== 跨节 ====================
if len(rec) == 4 or (ix > 0
and rec['Q'] != self.record[ix - 1]['Q']):
zoomtov, zoomorb = 0, 0
bptov, bporb = -1, -1
# ==================== 利用失误得分 ====================
if zoomtov:
if 'MK' in rec and rec['M'] != 'technical':
if rec['MK'][-1] == bptov:
tovpts[bptov] += rec['MK'][1]
# print(rec)
else:
if rec['M'] != 'technical':
print('失误得分存疑', self.game.gm, rec)
if rec['BP'] != bptov:
zoomtov = 0
bptov = -1
if 'TOV' in rec:
bptov = rec['BP']
zoomtov = 1
# ==================== 二次进攻 ====================
if zoomorb:
if 'MK' in rec:
if rec['MK'][-1] == bporb:
# print(rec)
orbpts[bporb] += rec['MK'][1]
else:
if rec['M'] != 'technical':
print('二次进攻得分存疑', self.game.gm, rec)
if rec['BP'] != bporb:
zoomorb = 0
bporb = -1
if 'ORB' in rec and (rec['ORB'] != 'Team' or
('MS' in self.record[ix - 1]
and self.record[ix - 1]['MS'][1] > 1) or
('MS' in self.record[ix - 1]
and self.record[ix - 1]['D'][1]
== self.record[ix - 1]['D'][0])):
bporb = rec['BP']
zoomorb = 1
# print(self.game.gm, tovpts[0], orbpts[0], paintpts[0], tovpts[1], orbpts[1], paintpts[1])
# ==================== 汇总数据 ====================
return pd.DataFrame(
[[
self.bxs.tdbxs[0][0]['team'][-3],
self.bxs.tdbxs[1][0]['team'][-3]
], tovpts,
[
tovpts[0] / self.bxs.tdbxs[0][0]['team'][-4],
tovpts[1] / self.bxs.tdbxs[1][0]['team'][-4]
], orbpts,
[
orbpts[0] / self.bxs.tdbxs[0][0]['team'][-4],
orbpts[1] / self.bxs.tdbxs[1][0]['team'][-4]
], paintpts,
[
paintpts[0] / self.bxs.tdbxs[0][0]['team'][-4],
paintpts[1] / self.bxs.tdbxs[1][0]['team'][-4]
], leadmax,
[
leadmax[0] / self.bxs.tdbxs[0][0]['team'][-4],
leadmax[1] / self.bxs.tdbxs[1][0]['team'][-4]
], longestrun,
[
longestrun[0] / self.bxs.tdbxs[0][0]['team'][-4],
longestrun[1] / self.bxs.tdbxs[1][0]['team'][-4]
], [leaderchange, leaderchange], [tie, tie]],
index=[
' pace', ' pts off tovs', ' pts% off tovs',
' 2nd chance pts', ' 2nd chance pts%',
' pts in the paint', ' pts% in the paint',
' biggest lead', ' biggest lead pts%',
' longest run', ' longest run pts%',
' lead changes', ' times tied'
],
columns=list(self.game.bxscr[0])), ads