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Analyzing basketball strategy
I need to offer some caution here: the three-point shooting threshold might seem arbitrary. It's often easier to hit threes when leading because of defensive patterns. Instead, I suggest focusing on three-point attempt (3PA) rates and opponent three-point percentages allowed.
To improve, let's generate quality looks, increase corner three-point attempts, and push the pace. Considering player lineups, I can analyze their 3PA rate and league averages for effective decision-making. I'll keep it concise and actionable!Improving shot quality
I want to enhance the quality of our attempts while making sure to follow the final answer style rules. We should be concise and maybe use bullet points. I can start by asking for clarification: Do you want a title, a graphic caption, or just analysis?
Here are five bullet points to consider:
I want to help the user with their game data. I can offer to pull the game log or run a quick split if they provide data or permission to fetch it. However, since the network status is uncertain, it might be better to ask if they want a CSV instead.
I could suggest a quick Python snippet to compute record splits by threshold using columns like date, team, three-point percentage, and result. Here's how I can summarize:
有意思的分割。几点快速解读与建议：
需要的话，我可以：
示例脚本（有一列three_pt_pct、three_pt_att、result，结果‘W’/‘L’）：
import pandas as pd
df = pd.read_csv("celtics_games.csv")
df["hit_36"] = df["three_pt_pct"] >= 0.36
split = df.groupby("hit_36")["result"].value_counts().unstack(fill_value=0)
print(split)
# 稳健性：滑动阈值
for t in [0.34, 0.35, 0.36, 0.37, 0.38]:
m = df["three_pt_pct"] >= t
w = (df[m]["result"] == "W").mean()
l = (df[~m]["result"] == "W").mean()
print(f"thr={t:.2f} >=thr win%={w:.3f} <thr win%={l:.3f}")
# 考虑出手量
bins = pd.cut(df["three_pt_att"], bins=[0,25,35,50,100], right=False)
pivot = pd.pivot_table(df, index=bins, columns=pd.cut(df["three_pt_pct"], [0,0.33,0.36,0.39,1]),
values="result", aggfunc=lambda x: (x=="W").mean())
print(pivot)
你想要哪种输出（分析报告/可视化/社媒文案），或者把数据给我我直接跑一版？
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