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
Pull-Up Shots Pull-up jump shot statistics
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.csvdribbleshots.csv (by dribble count) Schema: Catch and Shoot File : catchshoot.csvRecords : ~27,000+ player-season recordsSource : NBA.com tracking dataFields Player Info
Game Stats
Shooting Metrics
NBA.com unique player identifier
Catch-and-shoot field goals made
Catch-and-shoot field goals attempted
Catch-and-shoot FG% (0-1 scale)
Points from catch-and-shoot attempts
Catch-and-shoot 3-pointers made
Catch-and-shoot 3-pointers attempted
Catch-and-shoot 3PT% (0-1 scale)
Catch-and-shoot effective FG%
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.csvStructure : Similar to catch-and-shoot dataFields follow the same pattern with PULL_UP_* prefix:
PULL_UP_FGM, PULL_UP_FGA, PULL_UP_FG_PCTPULL_UP_FG3M, PULL_UP_FG3A, PULL_UP_FG3_PCTPULL_UP_PTS, PULL_UP_EFG_PCT
Schema: Shot Zones (Advanced) File : shotzone.csv / shotzone_ps.csvSource : pbpstats.com APIRecords : 50,000+ player-season records
At-Rim Metrics (< 6 feet) Field goal attempts at rim
Shooting percentage at rim (0-1 scale)
Mid-Range Metrics Frequency of short mid-range shots
Three-Point Metrics Three-point attempts (excluding heaves)
Three-point makes (excluding heaves)
Corner three-point attempts
Half-court heave attempts
Advanced Efficiency True shooting percentage (accounts for FTs)
Effective field goal percentage
Average expected points per shot (xPTS model)
SecondChanceShotQualityAvg
Shot quality on second-chance opportunities
Shot quality in penalty situations
Schema: Dribble Shooting File : dribbleshot.csv / dribbleshot_ps.csvPurpose : Shot performance after dribble moves (crossovers, hesitations, etc.)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 ))
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 passPull-up : Shots taken off the dribbleShot Quality metrics from pbpstats.com use expected points modelsPercentages 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.