Reviewing data sharing in the NBA
I’m looking into the availability of SportVU sample datasets from 2015 and the leaked tracking data from 2016/2017. There’s a blog by Savvas Tjortjoglou offering sample games, which is helpful.
Regarding wearables, Catapult GPS and IMU are used in practice but kept private during games, with data ownership issues governed by the NBPA guidelines. Injury reports are published but don’t reveal full medical records. I need to compile links and APIs for coding purposes, focusing on NBA stats.Exploring data retrieval options
I can provide sample Python code to fetch NBA combine data, using the nba_api library for tracking. Basketball-Reference offers stats like per game but not fitness details. I’ll also note potential data scraping issues.
If the user prefers a response in Chinese, I would format it with concise bullet points. For next steps, I could propose writing a script to download combine data and tracking speed/distance for multiple seasons. If they want the script right away, we can create it later, keeping things explanatory!Preparing Python code for data retrieval
I can include a sample Python snippet to fetch combine data along with speed and distance for a specified year. I’ll keep the formatting light, using code blocks for clarity.
Using the nba_api, I can show how to retrieve 'draftcombine' data and 'leaguedashptstats' with PtMeasureType='SpeedDistance'. There are also endpoints for tracking, like leaguedashptstats.LeagueDashPtStats, and several draft combine endpoints, such as draftcombineanthro and draftcombinedrillresults. I need to be precise with class names and parameters to avoid any errors.Crafting a Python script
I need to clarify that the PtMeasureType options for the endpoint include various metrics like 'Drives', 'Defense', 'Rebounding', and 'SpeedDistance', among others.
I'll create a script to fetch combine anthropometric data and drills for 2024, along with speed and distance for the 2023-24 season, and save the results as a CSV file. I’ll include the necessary imports from the nba_api, while reminding the user to set headers and possibly configure a proxy or timeout to avoid issues.
Lastly, I’ll ask if they want to fetch the data now or write the script later and provide a concise conclusion and sources list as well as a follow-up regarding preferred metrics and time ranges.Clarifying linking guidelines
It seems I need to clarify that while there are guidelines about links, the restrictions mainly apply to local files. I can provide plain names but not actual embedded links. Since the CLI permits plain text, it's perfectly fine for me to include domain names without causing issues. I’ll proceed with crafting my response while keeping this in mind. It’s important to follow these guidelines to ensure clarity and compliance!
结论：NBA 球员“体能/生理”类原始数据（心率、GPS/IMU、训练负荷、睡眠等）基本不公开。公开的主要是：
- 选秀联合试训（Draft Combine）的体测数据
- 比赛中通过光学追踪生成的衍生运动量数据（速度、跑动距离等）
- 身高/体重、出勤/伤病状态等基础信息
下面按可获得性与用途梳理来源与边界。
可公开获取的数据
Draft Combine 体测（最接近“体能”）
- 包含：身高/臂展/体脂、原地/助跑弹跳、三分之三场冲刺、折返跑、卧推等
- 覆盖：历年参加联合试训的球员（不是所有NBA球员）
- 去哪找：NBA Stats 的 Draft Combine 页面；ESPN/DraftExpress 汇总；第三方镜像数据集
- 编程访问：nba_api 提供
DraftCombineAnthro、DraftCombineDrillResults等端点
比赛追踪衍生指标（Second Spectrum）
- 包含：平均速度、场上奔跑距离、Touches、Drives、防守对位影响、篮板争抢位置等
- 去哪找：stats.nba.com 的 Tracking（Speed & Distance、Drives、Defense…）
- 编程访问：nba_api 的
LeagueDashPtStats（PtMeasureType='SpeedDistance'等） - 注意：不是生理数据；是基于光学定位的统计汇总
基础信息与出勤/伤病
- 身高体重年龄：球队媒体指南、Basketball-Reference
- 伤病/出勤：联盟每日伤病报告；InStreetClothes、ProSportsTransactions、ManGamesLost 等民间汇总
- 这些能辅助推断负荷与健康，但不含可穿戴/医疗细节
受限或不公开的数据
- 可穿戴与训练数据：Catapult、Kinexon、Polar、力平台、HR/HRV、GPS/IMU、睡眠、练习课数据
- 受球员工会与联盟关于可穿戴设备的协议限制（数据归属偏向球员/球队，未经同意不得用于公开或合同谈判）
- 比赛期间一般不允许使用可穿戴，常用于训练与恢复，球队内部持有
常见误区
- “原始轨迹数据”基本不公开：历史上有少量 SportVU 示例赛数据流出，规模很小且不完整；现行 Second Spectrum 原始XY数据对公众与学界都不开放，公开的多为聚合统计表
- “完整体能画像”不存在公开渠道：想要心率、负荷、睡眠等，需要球员授权或球队合作
要不要我帮你写个脚本把能公开的“体能相关”数据一次性拉成 CSV？
- 方案1：抓 Draft Combine（体测）+ Tracking 的 Speed & Distance（比赛运动量）
- 方案2：再加上伤病/出勤与生涯统计，方便做回归或可视化
如果你需要，下面这个 Python 示例用 nba_api 抓 2024 年 Draft Combine 和 2023-24 赛季的 Speed & Distance，并保存 CSV：
# pip install nba_api pandas
import pandas as pd
from nba_api.stats.endpoints import draftcombineanthro, draftcombinedrillresults, leaguedashptstats
# 1) Draft Combine 2024
anth = draftcombineanthro.DraftCombineAnthro(league_id='00', season_year='2024')
drill = draftcombinedrillresults.DraftCombineDrillResults(league_id='00', season_year='2024')
pd.DataFrame(anth.get_data_frames()[0]).to_csv('draft_combine_2024_anthro.csv', index=False)
pd.DataFrame(drill.get_data_frames()[0]).to_csv('draft_combine_2024_drills.csv', index=False)
# 2) League Tracking - Speed & Distance (2023-24)
spd = leaguedashptstats.LeagueDashPtStats(
season='2023-24',
per_mode_simple='PerGame',
pt_measure_type='SpeedDistance'
)
pd.DataFrame(spd.get_data_frames()[0]).to_csv('tracking_2023_24_speed_distance.csv', index=False)
print('done')
提示
- nba_api 访问 stats.nba.com 可能对网络环境较敏感；若超时，重试或设置更长
timeout/代理。 - 如果你需要其它年份或更多 Tracking 类别（
Drives、Defense、Rebounding等），告诉我具体清单和赛季范围，我直接扩展脚本。