Box-score data
A dataset of NBA game summaries aligned with box- and line-scores for data-to-text generation research.
What is Box-score data?
Boxscore Data is a research dataset that pairs NBA basketball game summaries with corresponding box-scores and line-scores. It was created to support data-to-document generation tasks, where models learn to produce human-like narratives from structured statistics. The dataset includes games from 2006 to 2017, sourced from Rotowire and SBNation, and is formatted in JSON for easy integration into machine learning pipelines.
Researchers and students in natural language processing, particularly those working on data-to-text generation, summarization, or sports analytics. It is also suitable for educators creating assignments on structured data processing.
This dataset provides a clean, aligned corpus of sports statistics and narratives, which is rare and valuable for training generative models. Its standardized format and preprocessed content reduce the overhead of data cleaning, allowing researchers to focus on model development and evaluation.
Overview
This dataset provides structured NBA basketball game data paired with human-written summaries, enabling research in data-to-document generation. It serves as a benchmark for training and evaluating models that convert structured statistics into coherent natural language narratives.
Key Features
- Aligned Summaries and Statistics — Each human-written game summary is paired with corresponding box-scores and line-scores.
- Dual Source Coverage — Includes data from Rotowire (2014–2017) and SBNation (2006–2017) with distinct writing styles.
- Structured JSON Format — Data is provided in a consistent JSON schema with team, player, and game details.
- Preprocessed for NLP — Summaries are tokenized and cleaned, with numeric values standardized as integers.
- Standard Splits — Data is divided into training, validation, and test sets for machine learning experiments.
Philosophy
The dataset is designed to support reproducible research in natural language generation, focusing on the challenge of transforming structured sports data into fluent, informative text.
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