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Overview

The player statistics collection contains comprehensive player-level data including shooting splits by defender distance, play-by-play totals, shot zone analytics, and advanced shooting metrics. Data covers both regular season and playoff games from 2014 through 2025.

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Data Files

Player Shooting

Shooting performance by closest defender distance
  • player_shooting.csv (Regular Season)
  • player_shooting_p.csv (Playoffs)

Shot Zones

Field goal attempts and accuracy by court zone
  • shotzone.csv (Regular Season)
  • shotzone_ps.csv (Playoffs)

Dribble Shots

Shot attempts categorized by dribbles before shot
  • dribbleshots.csv (Regular Season)
  • dribbleshots_ps.csv (Playoffs)

Play-by-Play Totals

Comprehensive per-game statistics
  • pbp_totals.csv (Regular Season)
  • pbp_totals_ps.csv (Playoffs)

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Schema: Player Shooting

File: player_shooting.csv / player_shooting_p.csv
Generated by: player_shooting.py
Records: ~100,000+ player-season-shot type combinations

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Core Fields

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PLAYER_ID
integer
required
NBA.com unique player identifier
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PLAYER
string
required
Player name
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TEAM
string
required
Team abbreviation (e.g., LAL, BOS, GSW)
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year
integer
required
Season ending year (e.g., 2024 for 2023-24 season)
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shot_type
string
required
Defender proximity category: very_tight, tight, open, wide_open

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Sample Data

PLAYER_ID,PLAYER,TEAM,AGE,GP,G,FREQ%,FGM,FGA,FG%,EFG%,2FG FREQ%,2FGM,2FGA,2FG%,3FG FREQ%,3PM,3PA,3P%,year,shot_type
201933,Blake Griffin,LAC,25.0,80,78,26.5,149,342,43.6,44.9,24.5,140,316,44.3,2.0,9,26,34.6,2014,wide_open
201567,Kevin Love,MIN,25.0,77,75,25.1,159,340,46.8,64.3,4.9,40,66,60.6,20.2,119,274,43.4,2014,wide_open

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Schema: Shot Zone Analytics

File: shotzone.csv / shotzone_ps.csv
Records: ~50,000+ player-season records
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Name
string
Player name
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EntityId
integer
Player ID from PBPStats API
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AtRimFGA
integer
Field goal attempts at the rim (< 6 feet)
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AtRimFGM
integer
Field goals made at the rim
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AtRimAccuracy
float
Shooting percentage at rim (0-1 scale)
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ShortMidRangeFGA
integer
Mid-range attempts (short distance)
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LongMidRangeFGA
integer
Mid-range attempts (long distance)
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Corner3FGA
integer
Corner three-point attempts
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Corner3FGM
integer
Corner three-point makes
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TsPct
float
True shooting percentage
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EfgPct
float
Effective field goal percentage
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ShotQualityAvg
float
Average expected points per shot attempt

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Schema: Dribble Shots

File: dribbleshots.csv / dribbleshots_ps.csv Shot attempts categorized by number of dribbles taken before the shot (0, 1, 2, 3-6, 7+ dribbles).
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dribbles
string
Dribble range: 0, 1, 2, 3-6, 7+
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FREQ%
float
Percentage of player’s shots in this dribble range
All other fields follow the same structure as player_shooting.csv.

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Schema: Play-by-Play Totals

File: pbp_totals.csv / pbp_totals_ps.csv
Size: 23+ MB (regular season), 7+ MB (playoffs) Detailed play-by-play aggregated statistics from pbpstats.com API.

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Usage Examples

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Find Elite Catch-and-Shoot Players (0 Dribbles)

import pandas as pd

df = pd.read_csv('dribbleshots.csv')

# Filter for 0-dribble shots with high volume
elite_catch_shoot = df[
    (df['dribbles'] == '0') & 
    (df['year'] == 2024) &
    (df['3PA'] >= 100)
].sort_values('3P%', ascending=False).head(20)

print(elite_catch_shoot[['PLAYER', 'TEAM', '3PA', '3P%', 'FREQ%']])

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Analyze Shot Selection by Defender Distance

import pandas as pd

df = pd.read_csv('player_shooting.csv')

# Compare LeBron's shooting by defender distance in 2024
lebron = df[(df['PLAYER'] == 'LeBron James') & (df['year'] == 2024)]

print(lebron[['shot_type', 'FGA', 'FG%', 'EFG%', 'FREQ%']])

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Shot Zone Analysis

import pandas as pd

df = pd.read_csv('shotzone.csv')

# Find best rim finishers (min 200 attempts)
best_rim = df[
    (df['year'] == 2024) & 
    (df['AtRimFGA'] >= 200)
].sort_values('AtRimAccuracy', ascending=False).head(20)

print(best_rim[['Name', 'AtRimFGA', 'AtRimAccuracy', 'TsPct']])

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Data Collection Scripts

player_shooting.py

Scrapes NBA.com stats API for shooting data by defender distance

scrape_shooting.py

Additional shooting data collection utilities

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Notes

  • Files with _ps or _p suffix contain playoff data
  • Percentage fields (FREQ%, FG%, etc.) are on 0-100 scale in CSV files
  • Data spans 2014-2025 seasons
  • Some 2025 data may be incomplete depending on collection date
The pbp_totals.csv file is very large (23+ MB). Consider filtering by year or specific players when loading into memory.

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