README.md

---
language:
- en
license: mit
task_categories:
- time-series-forecasting
tags:
- temporal-point-process
- event-sequences
- nba
- sports
- play-by-play
- marked-temporal-point-process
size_categories:
- 1K<n<10K
pretty_name: NBA Quarter Events
dataset_description: >-
  Curated per-quarter NBA play-by-play sequences from the 2024-25 regular season,
  designed for temporal point process (TPP) and marked temporal point process (MTPP)
  modeling. Each sequence represents one quarter (Q1-Q4) of a single game, with the
  prediction target being the play type of the next event. Sequences are filtered to
  keep only quarters with 90-110 events, ensuring consistent sequence lengths. Rich
  narrative type_text descriptions provide fully descriptive context — shot style and
  distance, make/miss results, offensive/defensive rebound classification, full team
  names on every event, and timeout types — enabling an LLM to predict the next event
  type from the sequence alone. Quarter and time information are captured in metadata
  and time_since_start fields rather than repeated in type_text, reducing token waste.
---

# NBA Quarter Events

## Dataset Description

Curated per-quarter NBA play-by-play sequences from the 2024-25 regular season, designed for temporal point process (TPP) and marked temporal point process (MTPP) modeling. Each sequence represents one quarter (Q1–Q4) of a single game, with the prediction target being the **play type** of the next event. Sequences are filtered to keep only quarters with 90–110 events, ensuring consistent sequence lengths.

Rich narrative `type_text` descriptions provide fully descriptive context for each event — shot style and distance, free throw make/miss with sequence position, offensive/defensive rebound classification, team identification on every play, and timeout types — enabling an LLM to predict the next event type from the sequence alone. Quarter and time-remaining information is not repeated in `type_text` since it is already captured in `metadata.quarter` and `time_since_start`.

- **Source:** [NBA Stats API](https://www.nba.com/stats/)
- **Season:** 2024–25 Regular Season
- **Grouping:** Events grouped by quarter (Q1–Q4) within each game
- **Sequences:** 2,868
- **Train / Validation / Test:** 2,296 / 286 / 286
- **Sequence length:** 90–110 events per quarter (mean: 101.5)
- **Total events:** 291,091
- **Event types:** 9 play categories
- **Time unit:** minutes (from quarter start)
- **Source games:** 1,225

## Dataset Summary

| Property | Value |
|---|---|
| Total sequences | 2,868 |
| Train / Validation / Test | 2,296 / 286 / 286 |
| Event types | 9 |
| Total events | 291,091 |
| Events per quarter | 90–110 (mean 101.5) |
| Time unit | minutes (from quarter start) |
| Duration per quarter | 11.2–12.0 minutes |
| Season | 2024-25 Regular Season |
| Source games | 1,225 |

## Schema

Each record follows the canonical 8-field TPP schema:

| Field | Type | Description |
|---|---|---|
| `seq_idx` | int | Sequence index (per split) |
| `seq_len` | int | Number of events in the sequence |
| `description` | str | Game summary (teams, date, quarter, player rosters) |
| `metadata` | str (JSON) | `game_id`, `home_team`, `away_team`, `home_players`, `away_players`, `game_date`, `season`, `season_type`, `time_unit`, `quarter` |
| `time_since_start` | list[float] | Elapsed minutes from the first event in the quarter |
| `time_since_last_event` | list[float] | Minutes since the previous event (first is 0.0) |
| `type_event` | list[str] | Play type label — the prediction target |
| `type_text` | list[str] | Rich narrative description of each event |

## Event Types (9 categories)

| type_event | Description | Share |
|---|---|---|
| `rebound` | Offensive and defensive rebounds | 24.3% |
| `missed_fg` | Missed field goals (2pt or 3pt) | 22.4% |
| `made_2pt` | Made 2-point field goals | 13.8% |
| `free_throw` | Free throw attempts (make/miss) | 8.9% |
| `foul` | Personal, shooting, and offensive fouls | 8.3% |
| `made_3pt` | Made 3-point field goals | 7.0% |
| `turnover` | Turnovers (bad pass, travel, shot clock, etc.) | 6.7% |
| `steal_or_block` | Steals and blocked shots | 5.9% |
| `timeout` | Team timeouts (regular, mandatory) | 2.6% |

Administrative events (substitution, jump ball, period start/end, violation, ejection, instant replay) are excluded.

## type_text (Rich Narrative)

Each event's `type_text` is a natural language sentence combining multiple context signals. The description is designed to be **fully informative** so an LLM reading a sequence of events can predict the next event type:

- **Player identity**: Name, team full name, position (guard / forward / center)
- **Team identification**: Full team name on every event (e.g., "for the Orlando Magic") for unambiguous possession tracking
- **Shot style**: Specific shot type from API subType (driving layup, fadeaway jump, pullup jump, cutting dunk, etc.)
- **Shot distance & zone**: Distance in feet from the API `shotDistance` field, plus court zone (paint, left wing, corner three, top of arc, mid-range, etc.)
- **Free throw detail**: Make/miss result parsed from description, plus sequence position (e.g., "1 of 2", "2 of 3")
- **Rebound classification**: Offensive or defensive, classified via subType and description parsing (covers ~95%+ of rebounds)
- **Timeout team & type**: Team identified via `location` field (home/visitor), with timeout subtype (regular, mandatory)
- **Foul subtype**: Lowercased foul type (shooting, personal, offensive, loose ball, etc.)
- **Score context**: Leading team, margin, game state (tight game, comfortable margin, blowout)
- **Momentum**: Scoring runs with full team name (e.g., "Orlando Magic currently on a 6-0 scoring run"), pattern signals (e.g., "shooting 3 for 5 recently")

**Examples:**

```
# Made 2pt — includes shot style, distance, zone, team name
"J. Robinson-Earl. makes a 2-point putback layup from 3 feet near the basket
 for the New Orleans Pelicans. Orlando Magic lead 12-6. early in the game."

# Made 3pt
"K. Ware, Heat center. makes a 3-point jump from 28 feet from the right wing
 for the Miami Heat. Milwaukee Bucks lead 81-65 with a comfortable margin."

# Missed field goal — includes 2pt/3pt distinction and shot style
"J. Robinson-Earl. misses a 2-point layup from 6 feet in the paint for the
 New Orleans Pelicans. The game is tied. early in the game."

# Free throw miss — includes make/miss and sequence position (1 of 2)
"J. Alvarado, Knicks guard. misses free throw 1 of 2 for the New Orleans
 Pelicans. The game is tied."

# Rebound — classified as offensive/defensive
"G. Bitadze, Magic center-forward. grabs an offensive rebound for the Orlando
 Magic. The game is tied. early in the game."

# Turnover — subtype integrated naturally into description
"B. Ingram, Raptors forward. commits a bad pass turnover for the New Orleans
 Pelicans. The game is tied. early in the game."

# Foul — lowercased subtype
"F. Wagner, Magic forward. commits a personal foul for the Orlando Magic.
 The game is tied. early in the game."

# Steal
"K. Caldwell-Pope, Grizzlies guard. records a steal for the Orlando Magic.
 The game is tied. early in the game."

# Timeout — team identified with full name, includes timeout type
"the New Orleans Pelicans call a regular timeout. The game is tied. early in
 the game. Orlando Magic currently on a 6-0 scoring run."
```

## Curation Filters

Quarters are selected from complete games (all 4 regulation quarters present) with diverse, non-trivial event distributions:

| Filter | Value | Description |
|---|---|---|
| `min-events` | 90 | Min events per quarter |
| `max-events` | 110 | Max events per quarter |
| `min-types` | 4 | At least 4 distinct event types of 9 |
| `max-dominant-ratio` | 0.40 | No single event type exceeds 40% of events |
| `max-repeat-ratio` | 0.30 | Consecutive same-type rate below 30% |
| Completeness | — | All 4 quarters (Q1–Q4) must be present; overtime excluded |

## Example

```json
{
  "seq_idx": 0,
  "seq_len": 96,
  "description": "NBA Regular Season game between the New York Knicks and the Brooklyn Nets on 2025-04-13. Q1 play-by-play events. New York Knicks: C. Payne, L. Shamet, M. Bridges, ...; Brooklyn Nets: D. Timme, K. Johnson, T. Etienne, ...",
  "metadata": "{\"game_id\": \"0022401188\", \"home_team\": \"BKN\", \"away_team\": \"NYK\", \"home_players\": [\"D. Timme\", ...], \"away_players\": [\"C. Payne\", ...], \"game_date\": \"2025-04-13\", \"season\": \"2024-25\", \"season_type\": \"Regular Season\", \"time_unit\": \"minutes\", \"quarter\": \"Q1\"}",
  "time_since_start": [0.0, 0.3167, 0.5333, ...],
  "time_since_last_event": [0.0, 0.3167, 0.2167, ...],
  "type_event": ["foul", "turnover", "made_2pt", ...],
  "type_text": [
    "M. Bridges, Knicks guard-forward. commits a personal take foul for the New York Knicks. The game is tied. early in the game.",
    "commits a shot clock turnover. The game is tied. early in the game.",
    "C. Payne, 76ers guard. makes a 2-point driving finger roll layup from 5 feet in the paint for the New York Knicks. New York Knicks lead 2-0 in a tight game. early in the game.",
    ...
  ]
}
```

## Data Source

Play-by-play data sourced from the NBA Stats API via the [`nba_api`](https://github.com/swar/nba_api) Python package using the `PlayByPlayV3` endpoint. Player metadata (name, position, team) fetched via `CommonPlayerInfo`.

## Usage

```python
from datasets import load_dataset

ds = load_dataset("DescribeEvents/nba_quarter_events")

# Access splits
train = ds["train"]       # 2,296 sequences
val = ds["validation"]    # 286 sequences
test = ds["test"]         # 286 sequences

print(train[0]["type_event"][:5])   # First 5 play types
print(train[0]["type_text"][:5])    # First 5 narrative descriptions
```

## Citation

If you use this dataset, please cite:

```bibtex
@dataset{nba_quarter_events_2025,
  title={NBA Quarter Events: A Temporal Point Process Dataset},
  author={DescribeEvents},
  year={2025},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/DescribeEvents/nba_quarter_events}
}
```