Clarifying assumptions and providing templates
I should avoid making assumptions, but in this case, I definitely need to clarify. I want to outline the analysis and create a checklist for the necessary data. Including suggested metrics is a good idea. I could also provide a template and a code snippet in Python to compute ORtg/DRtg from CSV files, which would be quite useful—even without the actual data, I can still share valuable information. Ultimately, the final answer should be concise and to the point.Structuring an analysis framework
First, I should ask for specifics, like the league, season span, and teams of interest, as well as whether they want high-level or deep stats, and data source languages. Then, I can lay out a general analysis framework.
This includes assessing team form, looking at key stats like eFG% and TOV%, analyzing pace and shot profiles, and considering defensive metrics. Additionally, I’ll touch on clutch performance, player impacts, rebounding battles, and special teams. This structured approach will help in providing a comprehensive analysis.Creating a basketball analysis framework
Basketball data isn’t widely tracked, so I need to consider factors like injuries, travel fatigue, and video tendencies. I’ll provide deliverables such as one-page reports, team dashboards, and game previews.
I plan to include a Python/pandas code snippet to compute the four factors from a basic box score dataset. I should also mention next steps: I'll need the league and data from the user, or ask for permission to fetch data from APIs, depending on the approval process.
Keeping lists to 4-6 bullets is important and I’ll adapt the code for Chinese as well.Calculating basketball metrics
To create a more robust analysis, I’ll compute several metrics, including eFG% using the formula (FGM + 0.53PM)/FGA, and TOV% as TOV / (FGA + 0.44FTA + TOV). For ORB%, if OppDRB isn't available, I'll use ORB/(FGA - FGM). Additionally, I’ll calculate FTr as FTA/FGA and estimate possessions using a specific formula.
I plan to provide a snippet that groups data by team and season. Sample interpretations will also help clarify the results. I'll ask the user to specify which league and season they’re interested in.
想法对上了！要做“女子篮球联赛战况分析”，我需要一点上下文来定方法和深度。
请先告诉我：
- 哪个联赛/赛季：WNBA、WCBA、NCAA、欧冠女篮，还是具体国家/地区联赛？
- 分析范围：整季走势、近10场趋势、季后赛对阵、单场赛后复盘？
- 关注对象：全联盟、某几支队、某几位球员？
- 数据来源：是否已有数据文件/链接；是否允许我抓取公开数据？
如果你还在确定方向，下面是一个通用分析框架，选你需要的部分即可：
- 战绩与赛程强度：胜率/净效率差、主客场/背靠背、最近5–10场趋势、对强队战绩
- 四要素与节奏：eFG%、TOV%、ORB%、FTr、PACE；对手四要素（防守剖面）
- 投篮版图：篮下/中距离/三分出手占比与命中、二次进攻与转换效率
- 战术类型与克制：P&R持球/顺下、手递手、定点、低位、转换每回合得分（PPP）及对手抑制效率
- 关键时刻：分差≤5分、最后5分钟的攻防净效率与回合分配
- 阵容与搭配：五人组净效率、核心球员on/off、轮换稳定度
- 球员影响：使用率vs效率、助攻制造（潜在助攻/二次助攻）、防守干扰（对位命中率抑制、干扰投篮） 
- 伤病/体能：出勤、旅途负荷、连续客场的影响
交付形式可做：
- 球队一页纸战况卡（净效率、四要素雷达、最近趋势）
- 对阵前瞻/复盘（优势-隐患-X因素）
- 可复用的分析脚本与可视化 
如果你手头有CSV/Excel比赛数据，我可以立刻跑一次基础指标。示例脚本（Python/pandas），读取逐场数据快速汇总四要素与攻防效率：
import pandas as pd
# 期望列示例：['team','opponent','pts','fga','fgm','fg3m','fta','ftm','orb','drb','tov',
# 'opp_pts','opp_fga','opp_fgm','opp_fg3m','opp_fta','opp_ftm','opp_orb','opp_drb','opp_tov','date','season']
df = pd.read_csv('games.csv')
def efg(fgm, fg3m, fga):
return (fgm + 0.5*fg3m) / fga if fga else 0.0
def tov_rate(fga, fta, tov):
denom = fga + 0.44*fta + tov
return tov / denom if denom else 0.0
def orb_rate(orb, opp_drb, fga=None, fgm=None):
# 优先用ORB / (ORB + 对手DRB)，无对手DRB时用近似 ORB / 进攻篮板机会
if pd.notna(opp_drb):
denom = orb + opp_drb
return orb / denom if denom else 0.0
elif fga is not None and fgm is not None:
chances = max(fga - fgm, 0)
return orb / chances if chances else 0.0
return 0.0
def ftr(fta, fga):
return fta / fga if fga else 0.0
def poss(row):
t = row['fga'] + 0.44*row['fta'] + row['tov']
o = row['opp_fga'] + 0.44*row['opp_fta'] + row['opp_tov']
return 0.5 * (t + o)
# 单场计算
df['poss'] = df.apply(poss, axis=1)
df['ortg'] = 100 * df['pts'] / df['poss']
df['drtg'] = 100 * df['opp_pts'] / df['poss']
df['netrtg'] = df['ortg'] - df['drtg']
df['efg'] = df.apply(lambda r: efg(r['fgm'], r['fg3m'], r['fga']), axis=1)
df['tov%'] = df.apply(lambda r: tov_rate(r['fga'], r['fta'], r['tov']), axis=1)
df['orb%'] = df.apply(lambda r: orb_rate(r['orb'], r.get('opp_drb', float('nan')), r['fga'], r['fgm']), axis=1)
df['ftr'] = df.apply(lambda r: ftr(r['fta'], r['fga']), axis=1)
df['pace'] = 48 * df['poss'] / 5 # 若每场48分钟；按联赛规则调整
# 汇总到球队赛季层面（可换成最近N场滚动）
agg = df.groupby(['season','team']).agg(
gp=('team','count'),
ortg=('ortg','mean'),
drtg=('drtg','mean'),
netrtg=('netrtg','mean'),
efg=('efg','mean'),
tov_rate=('tov%','mean'),
orb_rate=('orb%','mean'),
ftr=('ftr','mean'),
pace=('pace','mean')
).reset_index().sort_values(['season','netrtg'], ascending=[True, False])
print(agg.head(20).to_string(index=False))
你可以把现有数据存成 games.csv 按注释列名提供；或者告诉我联赛与赛季，我来给出抓取与清洗方案。你希望我先按哪个联赛/球队开工？