NBA Data

NBA Datasets in Excel/CSV Spreadsheets

NBA Datasets for Analysis, Modeling, and Research

Work with organized, season-specific NBA data instead of collecting and cleaning files from multiple sources. BigDataBall provides NBA player and team statistics, schedules, play-by-play data, historical odds, and DFS salary datasets for basketball analysis, research, modeling, reporting, and custom dashboards.

Choose the category and season that fit your project, preview a free sample, and select the files you need. Coverage varies by dataset. Play-by-play data reaches back to the 2002-03 season, while player and team datasets reach back to 2006-07. The latest listed products extend through the 2025-26 season.

• Player and team data: Analyze game-level player and team performance, box-score statistics, trends, and season results.
• Schedules and historical results: Build season calendars, game databases, team timelines, and schedule-based analyses.
• Play-by-play data: Study lineups, possessions, events, scoring runs, substitutions, fouls, shots, and game flow.
• Historical odds data: Research spreads, moneylines, totals, player props, and other available betting-market fields.
• DFS data: Analyze historical daily fantasy salaries and player pricing across NBA seasons.

Preview the available sample before selecting a product. Each product listing shows the available season, price, sample file, and purchase options. Check the individual listing for the delivered file format and exact coverage.

What You Can Do With NBA Datasets

Use the data in Excel, Google Sheets, R, Python, SQL, or your own analytics platform. The files can support research, reporting, visualization, model development, and basketball analysis.

• Scoring trends: Compare team scoring by period, game stage, season, opponent, and lineup.
• Lineup analysis: Evaluate five-player lineups using points scored, points allowed, shooting performance, possessions, and plus-minus.
• Player evaluation: Study points, assists, rebounds, steals, blocks, fouls, turnovers, shooting results, and game-by-game performance.
• Event analysis: Examine shots, fouls, free throws, turnovers, rebounds, substitutions, steals, blocks, and other game events.
• Play-by-play sequences: Identify scoring runs, momentum changes, late-game patterns, transition opportunities, and possessions that changed the result.
• Shot analysis: Use shot distance, shot results, and court coordinates to evaluate shot selection and shooting performance by location.
• Game dynamics: Study how remaining time, event duration, scoring state, and period affect team and player behavior.
• Foul and free-throw analysis: Measure which players draw fouls, how teams defend, and how foul situations affect scoring and game outcomes.
• Assist analysis: Examine relationships between assists, assisted field goals, scorers, and team offensive performance.
• Substitution analysis: Compare team performance, scoring, and lineup results before and after players enter or leave the game.

What’s Included in NBA Play-by-Play Data

NBA play-by-play files are organized around game information, lineups, timing, scores, events, players, and shot locations. Depending on the season and file version, columns may include:

1. Game ID: A unique identifier for the game.
2. Season: The season associated with the game.
3. Game date: The date on which the game was played.
4. Away lineup players 1-5: The five away-team players on the court during the event.
5. Home lineup players 1-5: The five home-team players on the court during the event.
6. Period: The period in which the event occurred.
7. Away team accumulated score: The away team’s score at the time of the event.
8. Home team accumulated score: The home team’s score at the time of the event.
9. Remaining time in period: The time remaining when the event occurred.
10. Elapsed time in period: The time that had passed since the start of the period.
11. Event duration: The duration associated with the event.
12. Event ID: A unique identifier for the event.
13. Executing team: The team associated with the event.
14. Event type: The classification of the event, such as a shot, foul, free throw, turnover, rebound, or substitution.
15. Assist player: The player credited with an assist, where applicable.
16. Away jump-ball player: The away-team player participating in a jump ball.
17. Home jump-ball player: The home-team player participating in a jump ball.
18. Shot blocker: The player credited with blocking a shot, where applicable.
19. Substitution player in: The player entering the game.
20. Substitution player out: The player leaving the game.
21. Free-throw sequence: The position of the free throw in the sequence.
22. Player fouled: The player who drew the foul.
23. Free throws awarded: The number of free throws associated with the event.
24. Primary event player: The player directly associated with the event.
25. Points scored: The points recorded for the event.
26. Rebound player: The player credited with the rebound, where applicable.
27. Play details: Additional information describing how the event occurred.
28. Shot result: Whether the shot was made or missed.
29. Steal player: The player credited with the steal, where applicable.
30. Additional event classification: An additional event or play classification, where available.
31. Shot distance: The distance of the shot in feet.
32. Shot X value: The original X-axis value associated with the shot.
33. Shot Y value: The original Y-axis value associated with the shot.
34. Converted court X coordinate: The shot’s X coordinate converted to an NBA court coordinate.
35. Converted court Y coordinate: The shot’s Y coordinate converted to an NBA court coordinate.
36. Play description: A text description of the play or event.

With these fields, you can analyze lineup effectiveness, player contributions, shot selection, scoring runs, defensive events, substitutions, game timing, and possession-level strategy.

Combine NBA Play-by-Play and Odds Data

Historical NBA odds data can be used as an additional research layer alongside player, team, schedule, and play-by-play datasets. Combining these sources allows you to compare market expectations with what happened on the court.

1. Compare market expectations with results: Compare pregame spreads, moneylines, and totals with final scores and game outcomes.
2. Study team performance against historical lines: Analyze how teams performed when favored, when listed as underdogs, or against different spread and totals ranges.
3. Compare player performance with player props: Evaluate historical points, rebounds, assists, and other player statistics against available prop lines.
4. Analyze in-game events: Study how scoring runs, turnovers, fouls, fast breaks, and lineup changes relate to available in-game odds.
5. Measure relationships between events and markets: Examine how specific game situations correspond with changes in spreads, totals, or other available market fields.
6. Backtest historical models: Combine historical odds with player, team, schedule, and play-by-play variables to evaluate a model against past seasons.

Important: Odds data is provided for historical research and analysis. Past performance and historical patterns do not guarantee future results. Confirm the available markets, seasons, and fields in the individual product listing before purchase.

Choose the NBA Dataset for Your Project

Start with player or team data for box-score and performance analysis, play-by-play data for possession-level research, schedules for game and season databases, odds data for historical market analysis, or DFS data for salary and fantasy research.

Preview a free sample, compare available seasons, and choose the NBA dataset that matches your workflow.