(lg_stats[1] - lg_stats[9] + lg_stats[15] + 0.44 * lg_stats[7]) /
lg_games / 2)
lgsum, lgames, lgmax, lgmin = 0, 0, [0, []], [30, []]
for pm in tqdm(pms):
if ss in enforce['Turnover'][pm][i][-1]:
pmdir = playerMarkToDir(pm, RoF[i])
if pmdir:
plyr = Player(pm, RoF[i])
ssgms = plyr.getSeason(ss)
num_games = ssgms.shape[0]
if num_games >= games_td[i]:
mpsum, scoresum = MPTime('0:00'), 0
for g in range(num_games):
sgm = ssgms[g:g + 1]
mp = MPTime(sgm['MP'].values[0])
if mp.mf() != 0:
# ================ 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]
# ================ 分项计算 ================
pts_ = pts
orb_ = orb * plus_minus[4]
drb_ = drb * (1 - op_drbp) * op_vop
ast_ = ast * plus_minus[3]
stl_ = stl * plus_minus[0]
blk_ = blk * plus_minus[2] * op_sp
tov_ = -tov * plus_minus[1]
# pf_ = - pf * op_stats[6] / tm_stats[16]
# bmiss_ = - tm_vop * tm_drbp * bmiss
# ================ 综合数据值 ================ 改进：1 投篮效率完全没有考虑！！！ 2 盖帽需考虑对手命中率
# score = pts_ + drb * 0.45 + orb * plus_minus[4] + ast_ + stl * plus_minus[0] + blk * plus_minus[2] * 0.4 - tov * plus_minus[1] - pf * 1.2 - \
# fmiss * 0.6 - 2 * bmiss * 0.36 - 3 * tmiss * 0.25
score = pts_ + drb_ + orb_ + ast_ + stl_ + blk_ + tov_
# score = pts + orb * orb_plus + ast * ast_plus + stl * stl_plus + blk * blk_plus - tov * tov_minus - pf * 2 - fmiss - 2 * bmiss - 3 * tmiss
score = score / mp.mf()
score *= (lg_pace / tm_pace)
lgsum += score
lgames += 1
if mp.mf() > 15:
if score > lgmax[0]:
lgmax[0] = score
lgmax[1] = [pm2pn[pm], ss]
if score < lgmin[0]:
lgmin[0] = score
lgmin[1] = [pm2pn[pm], ss]
# print(pm, num_games, score)
tmp[pm] = score
# print(tmp)
lg_ascore = np.mean(np.array(list(tmp.values())))
print('联盟平均：', lg_ascore)