Datasets:

cdechoch
/

nba-data-archive

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
2: struct<name: string, seasons: list<item: int64>, first_season: int64, last_season: int64, shot_count (... 8 chars omitted)
  child 0, name: string
  child 1, seasons: list<item: int64>
      child 0, item: int64
  child 2, first_season: int64
  child 3, last_season: int64
  child 4, shot_count: int64
3: struct<name: string, seasons: list<item: int64>, first_season: int64, last_season: int64, shot_count (... 8 chars omitted)
  child 0, name: string
  child 1, seasons: list<item: int64>
      child 0, item: int64
  child 2, first_season: int64
  child 3, last_season: int64
  child 4, shot_count: int64
7: struct<name: string, seasons: list<item: int64>, first_season: int64, last_season: int64, shot_count (... 8 chars omitted)
  child 0, name: string
  child 1, seasons: list<item: int64>
      child 0, item: int64
  child 2, first_season: int64
  child 3, last_season: int64
  child 4, shot_count: int64
9: struct<name: string, seasons: list<item: int64>, first_season: int64, last_season: int64, shot_count (... 8 chars omitted)
  child 0, name: string
  child 1, seasons: list<item: int64>
      child 0, item: int64
  child 2, first_season: int64
  child 3, last_season: int64
  child 4, shot_count: int64
12: struct<name: string, seasons: list<item: int64>, first_season: int64, last_season: int64, shot_count (... 8 chars omitted)
  child 0, name: string
  child 1, seasons: list<item: int64>
      child 0, item: int64
  child 2, first_season: int64
  child 3, last_season: int64
  c
...
child 1, seasons: list<item: int64>
      child 0, item: int64
  child 2, first_season: int64
  child 3, last_season: int64
  child 4, shot_count: int64
1643253: struct<name: string, seasons: list<item: int64>, first_season: int64, last_season: int64, shot_count (... 8 chars omitted)
  child 0, name: string
  child 1, seasons: list<item: int64>
      child 0, item: int64
  child 2, first_season: int64
  child 3, last_season: int64
  child 4, shot_count: int64
1643257: struct<name: string, seasons: list<item: int64>, first_season: int64, last_season: int64, shot_count (... 8 chars omitted)
  child 0, name: string
  child 1, seasons: list<item: int64>
      child 0, item: int64
  child 2, first_season: int64
  child 3, last_season: int64
  child 4, shot_count: int64
version: int64
interned_fields: list<item: string>
  child 0, item: string
fields: struct<g: string, dt: string, h: string, v: string, p: string, t: string, a: string, z: string, d: s (... 73 chars omitted)
  child 0, g: string
  child 1, dt: string
  child 2, h: string
  child 3, v: string
  child 4, p: string
  child 5, t: string
  child 6, a: string
  child 7, z: string
  child 8, d: string
  child 9, x: string
  child 10, y: string
  child 11, m: string
  child 12, 3: string
  child 13, s: string
  child 14, po: string
layout: string
lookup_tables: struct<g: string, h: string, v: string, z: string, a: string>
  child 0, g: string
  child 1, h: string
  child 2, v: string
  child 3, z: string
  child 4, a: string
to
{'version': Value('int64'), 'layout': Value('string'), 'interned_fields': List(Value('string')), 'lookup_tables': {'g': Value('string'), 'h': Value('string'), 'v': Value('string'), 'z': Value('string'), 'a': Value('string')}, 'fields': {'g': Value('string'), 'dt': Value('string'), 'h': Value('string'), 'v': Value('string'), 'p': Value('string'), 't': Value('string'), 'a': Value('string'), 'z': Value('string'), 'd': Value('string'), 'x': Value('string'), 'y': Value('string'), 'm': Value('string'), '3': Value('string'), 's': Value('string'), 'po': Value('string')}}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2690, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2227, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2251, in _iter_arrow
                  for key, pa_table in self.ex_iterable._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 494, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 384, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 299, in _generate_tables
                  self._cast_table(pa_table, json_field_paths=json_field_paths),
                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 128, in _cast_table
                  pa_table = table_cast(pa_table, self.info.features.arrow_schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2321, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2249, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              2: struct<name: string, seasons: list<item: int64>, first_season: int64, last_season: int64, shot_count (... 8 chars omitted)
                child 0, name: string
                child 1, seasons: list<item: int64>
                    child 0, item: int64
                child 2, first_season: int64
                child 3, last_season: int64
                child 4, shot_count: int64
              3: struct<name: string, seasons: list<item: int64>, first_season: int64, last_season: int64, shot_count (... 8 chars omitted)
                child 0, name: string
                child 1, seasons: list<item: int64>
                    child 0, item: int64
                child 2, first_season: int64
                child 3, last_season: int64
                child 4, shot_count: int64
              7: struct<name: string, seasons: list<item: int64>, first_season: int64, last_season: int64, shot_count (... 8 chars omitted)
                child 0, name: string
                child 1, seasons: list<item: int64>
                    child 0, item: int64
                child 2, first_season: int64
                child 3, last_season: int64
                child 4, shot_count: int64
              9: struct<name: string, seasons: list<item: int64>, first_season: int64, last_season: int64, shot_count (... 8 chars omitted)
                child 0, name: string
                child 1, seasons: list<item: int64>
                    child 0, item: int64
                child 2, first_season: int64
                child 3, last_season: int64
                child 4, shot_count: int64
              12: struct<name: string, seasons: list<item: int64>, first_season: int64, last_season: int64, shot_count (... 8 chars omitted)
                child 0, name: string
                child 1, seasons: list<item: int64>
                    child 0, item: int64
                child 2, first_season: int64
                child 3, last_season: int64
                c
              ...
              child 1, seasons: list<item: int64>
                    child 0, item: int64
                child 2, first_season: int64
                child 3, last_season: int64
                child 4, shot_count: int64
              1643253: struct<name: string, seasons: list<item: int64>, first_season: int64, last_season: int64, shot_count (... 8 chars omitted)
                child 0, name: string
                child 1, seasons: list<item: int64>
                    child 0, item: int64
                child 2, first_season: int64
                child 3, last_season: int64
                child 4, shot_count: int64
              1643257: struct<name: string, seasons: list<item: int64>, first_season: int64, last_season: int64, shot_count (... 8 chars omitted)
                child 0, name: string
                child 1, seasons: list<item: int64>
                    child 0, item: int64
                child 2, first_season: int64
                child 3, last_season: int64
                child 4, shot_count: int64
              version: int64
              interned_fields: list<item: string>
                child 0, item: string
              fields: struct<g: string, dt: string, h: string, v: string, p: string, t: string, a: string, z: string, d: s (... 73 chars omitted)
                child 0, g: string
                child 1, dt: string
                child 2, h: string
                child 3, v: string
                child 4, p: string
                child 5, t: string
                child 6, a: string
                child 7, z: string
                child 8, d: string
                child 9, x: string
                child 10, y: string
                child 11, m: string
                child 12, 3: string
                child 13, s: string
                child 14, po: string
              layout: string
              lookup_tables: struct<g: string, h: string, v: string, z: string, a: string>
                child 0, g: string
                child 1, h: string
                child 2, v: string
                child 3, z: string
                child 4, a: string
              to
              {'version': Value('int64'), 'layout': Value('string'), 'interned_fields': List(Value('string')), 'lookup_tables': {'g': Value('string'), 'h': Value('string'), 'v': Value('string'), 'z': Value('string'), 'a': Value('string')}, 'fields': {'g': Value('string'), 'dt': Value('string'), 'h': Value('string'), 'v': Value('string'), 'p': Value('string'), 't': Value('string'), 'a': Value('string'), 'z': Value('string'), 'd': Value('string'), 'x': Value('string'), 'y': Value('string'), 'm': Value('string'), '3': Value('string'), 's': Value('string'), 'po': Value('string')}}
              because column names don't match

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NBA Data Archive

Parquet mirror of NBA play-by-play, shot detail, and player-matchup data from the 1996/97 season through 2025/26.

Built and maintained as the data layer for HoopsMatic.com analytics tools and HoopsHype editorial automation. Public so other researchers and tinkerers don't have to rebuild the same pipeline.

Layout

Two complementary layouts; pick whichever fits your query pattern.

`per_season/`

One Parquet file per (data type, season, season type). Use this when you only need a slice — a single season, a recent few years, regular season only, etc.

per_season/shotdetail/2024.parquet     # 2024/25 regular season shots
per_season/shotdetail/po_2023.parquet  # 2023/24 playoff shots
per_season/matchups/2017.parquet
...

`merged/`

One Parquet file per data type, all seasons + season types concatenated. Use this when you want to sweep the whole history at once (career-long shot charts, all-time leaderboards, multi-season trend analysis).

merged/shotdetail.parquet
merged/matchups.parquet
merged/nbastats.parquet
merged/nbastatsv3.parquet
merged/pbpstats.parquet
merged/datanba.parquet
merged/cdnnba.parquet

Every row in both layouts carries two provenance columns:

_season — int, e.g. 2024 for the 2024/25 season
_season_type — "rg" (regular) or "po" (playoffs)

Data types

Type	Coverage	Description
`shotdetail`	1996/97-2025/26	Every shot with X/Y court coordinates, distance, make/miss, period, game context
`matchups`	2017/18-2025/26	Player-vs-player matchup possessions and box-score lines
`nbastats`	1996/97-2024/25	Classic play-by-play from stats.nba.com
`nbastatsv3`	2020/21-2025/26	Newer play-by-play schema
`pbpstats`	2000/01-2024/25	Possession-level data with start-type tags
`datanba`	2016/17-2024/25	Play-by-play with on-court action coordinates
`cdnnba`	2016/17-2025/26	Lightweight play-by-play from cdn.nba.com

Playoff coverage runs through 2024/25; 2025/26 playoffs don't exist yet. Some sources (nbastats, pbpstats, datanba) don't yet have 2025/26 backfilled upstream.

Quick start

from datasets import load_dataset

# Load all shots from a single season
ds = load_dataset(
    "cdechoch/nba-data-archive",
    data_files="per_season/shotdetail/2024.parquet",
    split="train",
)

# Or load the full career history of every shot ever
ds = load_dataset(
    "cdechoch/nba-data-archive",
    data_files="merged/shotdetail.parquet",
    split="train",
)

Or skip the datasets library and read Parquet directly with pandas or pyarrow — every file is standard, snappy-compressed Parquet.

Source and license

Underlying data collected and published by Vladislav Shufinskiy at shufinskiy/nba_data under the Apache License 2.0. This dataset is the same data converted to Parquet and redistributed under the same license.

Original upstream sources, in turn: stats.nba.com, data.nba.com, cdn.nba.com, pbpstats.com.

Statistics themselves are not copyrightable (Feist Publications v. Rural Telephone Service, 1991). Player names, team names, and game data are factual information used in a nominative reference capacity. No NBA trademarks, logos, or proprietary content are included.

Raw archives

The original .tar.xz files are mirrored at jsierrahoopshype/nba-data-archive on GitHub Releases, one release per data type, in case anyone prefers to work from the CSVs.

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Total file size:

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