Shooting Analytics

Overview

Shooting analytics datasets provide granular shot-level detail including shot types (catch-and-shoot, pull-up, dribble moves), shot zones (at-rim, mid-range, corner 3s), and contextual shooting metrics. These datasets enable deep-dive analysis of shooting efficiency and shot selection.

Data Files

Catch & Shoot

Catch-and-shoot performance metrics

catchshoot.csv

Pull-Up Shots

Pull-up jump shot statistics

pullup.csv

Shot Zones

Court zone analytics (rim, paint, mid-range, 3PT)

shotzone.csv / shotzone_ps.csv

Dribble Shooting

Shots after dribble moves

dribbleshot.csv / dribbleshot_ps.csv
dribbleshots.csv (by dribble count)

Schema: Catch and Shoot

File: catchshoot.csv
Records: ~27,000+ player-season records
Source: NBA.com tracking data

Fields

Player Info
Game Stats
Shooting Metrics

PLAYER_ID

integer

required

NBA.com unique player identifier

PLAYER_NAME

string

required

Player name

TEAM_ID

integer

Team ID from NBA.com

TEAM_ABBREVIATION

string

Team abbreviation

Season

string

Season format: “2023-24”

Sample Data

PLAYER_ID,PLAYER_NAME,TEAM_ID,TEAM_ABBREVIATION,GP,W,L,MIN,CATCH_SHOOT_FGM,CATCH_SHOOT_FGA,CATCH_SHOOT_FG_PCT,CATCH_SHOOT_PTS,CATCH_SHOOT_FG3M,CATCH_SHOOT_FG3A,CATCH_SHOOT_FG3_PCT,CATCH_SHOOT_EFG_PCT,Season
201985,AJ Price,1610612739,CLE,26,11,15,323.0,10,31,0.323,30,10.0,28.0,0.357,0.484,2014-15
201166,Aaron Brooks,1610612741,CHI,82,50,32,1885.0,67,157,0.427,198,64.0,152.0,0.421,0.631,2014-15

Schema: Pull-Up Shots

File: pullup.csv
Structure: Similar to catch-and-shoot data Fields follow the same pattern with PULL_UP_* prefix:

PULL_UP_FGM, PULL_UP_FGA, PULL_UP_FG_PCT
PULL_UP_FG3M, PULL_UP_FG3A, PULL_UP_FG3_PCT
PULL_UP_PTS, PULL_UP_EFG_PCT

Schema: Shot Zones (Advanced)

File: shotzone.csv / shotzone_ps.csv
Source: pbpstats.com API
Records: 50,000+ player-season records

Core Shot Zone Fields

Name

string

Player name

EntityId

integer

pbpstats.com player ID

TeamId

integer

pbpstats.com team ID

GamesPlayed

integer

Games played

Minutes

float

Total minutes

year

integer

Season ending year

At-Rim Metrics (< 6 feet)

AtRimFGA

integer

Field goal attempts at rim

AtRimFGM

integer

Field goals made at rim

AtRimAccuracy

float

Shooting percentage at rim (0-1 scale)

Mid-Range Metrics

ShortMidRangeFGA

integer

Short mid-range attempts

ShortMidRangeFGM

integer

Short mid-range makes

ShortMidRangeAccuracy

float

Short mid-range FG%

ShortMidRangeFrequency

float

Frequency of short mid-range shots

LongMidRangeFGA

integer

Long mid-range attempts

LongMidRangeFGM

integer

Long mid-range makes

Three-Point Metrics

NonHeaveArc3FGA

integer

Three-point attempts (excluding heaves)

NonHeaveArc3FGM

integer

Three-point makes (excluding heaves)

NonHeaveFg3Pct

float

Three-point percentage

Corner3FGA

integer

Corner three-point attempts

Corner3FGM

integer

Corner three-point makes

HeaveAttempts

integer

Half-court heave attempts

Advanced Efficiency

TsPct

float

True shooting percentage (accounts for FTs)

EfgPct

float

Effective field goal percentage

ShotQualityAvg

float

Average expected points per shot (xPTS model)

SecondChanceShotQualityAvg

float

Shot quality on second-chance opportunities

PenaltyShotQualityAvg

float

Shot quality in penalty situations

Schema: Dribble Shooting

File: dribbleshot.csv / dribbleshot_ps.csv
Purpose: Shot performance after dribble moves (crossovers, hesitations, etc.)

See Tracking Data for detailed dribble drive metrics

Usage Examples

Elite Catch-and-Shoot Three-Point Shooters

import pandas as pd

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

# Find elite catch-and-shoot 3PT shooters (min 100 attempts)
df['year'] = df['Season'].str.split('-').str[1].astype(int) + 2000

elite_cs = df[
    (df['year'] == 2024) & 
    (df['CATCH_SHOOT_FG3A'] >= 100)
].sort_values('CATCH_SHOOT_FG3_PCT', ascending=False)

print(elite_cs[['PLAYER_NAME', 'TEAM_ABBREVIATION', 'CATCH_SHOOT_FG3A', 'CATCH_SHOOT_FG3_PCT']].head(20))

Shot Zone Distribution Analysis

import pandas as pd

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

# Calculate shot distribution for a player
player_name = 'Stephen Curry'
player = df[(df['Name'] == player_name) & (df['year'] == 2024)].iloc[0]

total_fga = player['AtRimFGA'] + player['ShortMidRangeFGA'] + player['LongMidRangeFGA'] + player['NonHeaveArc3FGA']

print(f"Shot Distribution for {player_name} (2024):")
print(f"At Rim: {player['AtRimFGA']/total_fga*100:.1f}% ({player['AtRimAccuracy']*100:.1f}% FG)")
print(f"Short Mid: {player['ShortMidRangeFGA']/total_fga*100:.1f}%")
print(f"Long Mid: {player['LongMidRangeFGA']/total_fga*100:.1f}%")
print(f"Three-Point: {player['NonHeaveArc3FGA']/total_fga*100:.1f}% ({player['NonHeaveFg3Pct']*100:.1f}% 3PT)")
print(f"Corner 3s: {player['Corner3FGA']} attempts ({player['Corner3FGM']} makes)")

Compare Catch-and-Shoot vs Pull-Up Efficiency

import pandas as pd

cs = pd.read_csv('catchshoot.csv')
pu = pd.read_csv('pullup.csv')

# Extract year from Season column
cs['year'] = cs['Season'].str.split('-').str[1].astype(int) + 2000
pu['year'] = pu['Season'].str.split('-').str[1].astype(int) + 2000

# Filter 2024, min volume
cs_2024 = cs[(cs['year'] == 2024) & (cs['CATCH_SHOOT_FG3A'] >= 100)]
pu_2024 = pu[(pu['year'] == 2024) & (pu['PULL_UP_FG3A'] >= 100)]

# Merge on player
merged = cs_2024.merge(
    pu_2024[['PLAYER_ID', 'PULL_UP_FG3_PCT', 'PULL_UP_FG3A']], 
    on='PLAYER_ID'
)

merged['CS_vs_PU_Diff'] = merged['CATCH_SHOOT_FG3_PCT'] - merged['PULL_UP_FG3_PCT']

print("Players with Biggest Catch-and-Shoot Advantage:")
print(merged.sort_values('CS_vs_PU_Diff', ascending=False)[['PLAYER_NAME', 'CATCH_SHOOT_FG3_PCT', 'PULL_UP_FG3_PCT', 'CS_vs_PU_Diff']].head(10))

Shot Quality vs Actual Performance

import pandas as pd

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

# Compare xPTS (ShotQualityAvg) to actual efficiency
df_2024 = df[df['year'] == 2024].copy()

# Calculate points per FGA
df_2024['ActualPPFGA'] = df_2024['Points'] / (df_2024['FG2A'] + df_2024['FG3A'])
df_2024['OverPerformance'] = df_2024['ActualPPFGA'] - df_2024['ShotQualityAvg']

# Find players beating expected performance
print("Players Exceeding Shot Quality Expectations:")
print(df_2024.sort_values('OverPerformance', ascending=False)[['Name', 'ShotQualityAvg', 'ActualPPFGA', 'OverPerformance']].head(20))

Data Collection Scripts

new_tracking.py

Collects catch-and-shoot, pull-up, and tracking data from NBA.com

scrape_shooting.py

Shot zone and shot quality data scraper

Notes

Catch-and-shoot: Shots taken within 2 seconds of receiving a pass
Pull-up: Shots taken off the dribble
Shot Quality metrics from pbpstats.com use expected points models
Percentages in catchshoot.csv and pullup.csv are on 0-1 scale
Percentages in shotzone.csv are on 0-1 scale

The shotzone.csv file contains data from pbpstats.com which may use different player IDs than NBA.com. Cross-reference using player names when merging with other datasets.

Get Started

Data Collections

Scripts & Automation

Data Schema

Overview

Data Files

Catch & Shoot

Pull-Up Shots

Shot Zones

Dribble Shooting

Schema: Catch and Shoot

Fields

Sample Data

Schema: Pull-Up Shots

Schema: Shot Zones (Advanced)

At-Rim Metrics (< 6 feet)

Mid-Range Metrics

Three-Point Metrics

Advanced Efficiency

Schema: Dribble Shooting

Usage Examples

Elite Catch-and-Shoot Three-Point Shooters

Shot Zone Distribution Analysis

Compare Catch-and-Shoot vs Pull-Up Efficiency

Shot Quality vs Actual Performance

Data Collection Scripts

new_tracking.py

scrape_shooting.py

Notes

Build docs developers (and LLMs) love

Get Started

Data Collections

Scripts & Automation

Data Schema

Documentation Index

​Overview

​Data Files

Catch & Shoot

Pull-Up Shots

Shot Zones

Dribble Shooting

​Schema: Catch and Shoot

​Fields

​Sample Data

​Schema: Pull-Up Shots

​Schema: Shot Zones (Advanced)

​At-Rim Metrics (< 6 feet)

​Mid-Range Metrics

​Three-Point Metrics

​Advanced Efficiency

​Schema: Dribble Shooting

​Usage Examples

​Elite Catch-and-Shoot Three-Point Shooters

​Shot Zone Distribution Analysis

​Compare Catch-and-Shoot vs Pull-Up Efficiency

​Shot Quality vs Actual Performance

​Data Collection Scripts

new_tracking.py

scrape_shooting.py

​Notes

Build docs developers (and LLMs) love

Overview

Data Files

Schema: Catch and Shoot

Fields

Sample Data

Schema: Pull-Up Shots

Schema: Shot Zones (Advanced)

At-Rim Metrics (< 6 feet)

Mid-Range Metrics

Three-Point Metrics

Advanced Efficiency

Schema: Dribble Shooting

Usage Examples

Elite Catch-and-Shoot Three-Point Shooters

Shot Zone Distribution Analysis

Compare Catch-and-Shoot vs Pull-Up Efficiency

Shot Quality vs Actual Performance

Data Collection Scripts

Notes