--- 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- 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} } ```