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Overview

Team-level statistics aggregate performance metrics at the team level. These datasets are useful for analyzing team strategies, tendencies, and overall effectiveness.

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Core Team Files

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team_shooting.csv

Team shooting statistics by shot coverage type. File: team_shooting.csv / team_shooting_ps.csv
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TEAM
string
required
Team abbreviation (e.g., “LAL”, “BOS”, “GSW”)
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TEAMNAME
string
Full team name (e.g., “Los Angeles Lakers”)
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year
integer
required
Season year
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shot_coverage
string
required
Shot coverage category:
  • Open: Defender 4+ feet away
  • Tight: Defender 2-4 feet away
  • Very Tight: Defender 0-2 feet away
  • Wide Open: Defender 6+ feet away
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GP
integer
Games played
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G
integer
Total games in dataset
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Freq%
float
Frequency percentage - what portion of team’s shots fall into this category
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FGM
integer
Field goals made
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FGA
integer
Field goals attempted
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FG%
float
Field goal percentage (0-100 scale)
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eFG%
float
Effective field goal percentage: (FGM + 0.5 * 3PM) / FGA
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2FG Freq%
float
Frequency of 2-point attempts
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2FGM
integer
2-point field goals made
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2FGA
integer
2-point field goals attempted
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2FG%
float
2-point field goal percentage
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3FG Freq%
float
Frequency of 3-point attempts
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3PM
integer
3-pointers made
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3PA
integer
3-pointers attempted
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3P%
float
3-point percentage
Example: 
TEAM,GP,G,Freq%,FGM,FGA,FG%,eFG%,2FG Freq%,2FGM,2FGA,2FG%,3FG Freq%,3PM,3PA,3P%,shot_coverage,year,TEAMNAME
ATL,82,82,45.2,1850,3987,46.4,48.1,35.6,1420,3140,45.2,9.6,430,847,50.8,wide_open,2014,Atlanta Hawks

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team_shotzone.csv

Team shooting performance by court zone. File: team_shotzone.csv / team_shotzone_ps.csv
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Name
string
required
Team abbreviation
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EntityId
integer
required
Team identifier
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TeamId
integer
Team ID (same as EntityId)
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TeamAbbreviation
string
Team abbreviation (same as Name)
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year
integer
required
Season year
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GamesPlayed
integer
Games played
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OffPoss
integer
Offensive possessions
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DefPoss
integer
Defensive possessions
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Points
integer
Total points scored
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FtPoints
integer
Points from free throws
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FTA
integer
Free throw attempts
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AtRimFGA
integer
Field goal attempts at the rim
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AtRimFGM
integer
Field goals made at the rim
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AtRimAccuracy
float
Shooting accuracy at the rim (percentage)
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ShortMidRangeFGA
integer
Short mid-range field goal attempts
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ShortMidRangeFGM
integer
Short mid-range field goals made
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ShortMidRangeAccuracy
float
Short mid-range accuracy percentage
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ShortMidRangeFrequency
float
Frequency of short mid-range attempts
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LongMidRangeFGA
integer
Long mid-range field goal attempts
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LongMidRangeFGM
integer
Long mid-range field goals made
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FG2A
integer
Total 2-point attempts
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FG2M
integer
Total 2-point makes
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FG3A
integer
Total 3-point attempts
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NonHeaveFg3Pct
float
3-point percentage excluding heaves
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NonHeaveArc3FGA
integer
Non-heave 3-point attempts from the arc
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NonHeaveArc3FGM
integer
Non-heave 3-point makes from the arc
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HeaveAttempts
integer
Number of heave attempts (end-of-clock desperation shots)
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Corner3FGA
integer
Corner 3-point attempts
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Corner3FGM
integer
Corner 3-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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SecondChanceEfgPct
float
eFG% on second chance opportunities
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PenaltyEfgPct
float
eFG% in the penalty (bonus free throw situation)
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SecondChanceTsPct
float
True shooting % on second chance opportunities
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PenaltyTsPct
float
True shooting % in the penalty
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SecondChanceShotQualityAvg
float
Average shot quality on second chance attempts
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PenaltyShotQualityAvg
float
Average shot quality in penalty situations
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ShotQualityAvg
float
Overall average shot quality

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teamplay.csv

Team play-type statistics (offensive). File: teamplay.csv / teamplay_p.csv
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Team
string
required
Team abbreviation
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full_name
string
Full team name
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year
integer
required
Season year
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playtype
string
required
Type of offensive play:
  • Transition: Fast break plays
  • Isolation: 1-on-1 isolation plays
  • PRBallHandler: Pick and roll ball handler
  • PRRollMan: Pick and roll roll man
  • Postup: Post-up plays
  • Spotup: Spot-up shooting
  • Handoff: Handoff plays
  • Cut: Cutting to the basket
  • OffScreen: Off-ball screens
  • OffRebound: Offensive rebound putbacks
  • Misc: Miscellaneous plays
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GP
integer
Games played
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POSS
integer
Total possessions of this play type
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FREQ%
float
Frequency percentage - portion of team’s plays that are this type
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PPP
float
Points per possession for this play type
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Points
integer
Total points scored on this play type
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FGM
integer
Field goals made
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FGA
integer
Field goals attempted
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FG%
float
Field goal percentage
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EFG%
float
Effective field goal percentage
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FTFREQ%
float
Free throw frequency - possessions ending in free throws
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TOVFREQ%
float
Turnover frequency - possessions ending in turnovers
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SFFREQ%
float
Shooting foul frequency
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AND ONEFREQ%
float
And-one frequency - made shot with foul
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SCOREFREQ%
float
Scoring frequency - possessions resulting in points
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PERCENTILE
float
Percentile rank among all teams for this play type
Example: 
Team,GP,POSS,FREQ%,PPP,Points,FGM,FGA,FG%,EFG%,FTFREQ%,TOVFREQ%,SFFREQ%,AND ONEFREQ%,SCOREFREQ%,PERCENTILE,year,playtype,full_name
ATL,82,1456,18.5,1.12,1631,589,1098,53.6,55.2,12.4,8.9,10.1,4.2,58.1,76.2,2014,Transition,Atlanta Hawks

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opp_team_shooting.csv

Opponent team shooting statistics (defensive perspective). File: opp_team_shooting.csv / opp_team_shooting_ps.csv Schema is identical to team_shooting.csv, but represents the shooting performance allowed by the defense rather than offensive shooting.
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TEAM
string
required
Team abbreviation (the defensive team)
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year
integer
required
Season year
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shot_coverage
string
required
Shot coverage provided by defense
All other fields match team_shooting.csv schema, representing opponent shooting performance.

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team_shotzone_vs.csv

Team shooting performance by zone (defensive/opponent view). File: team_shotzone_vs.csv / team_shotzone_vs_ps.csv Schema matches team_shotzone.csv, representing opponent shooting performance by zone that the team allowed.

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Usage Examples

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Analyzing Team 3-Point Tendencies

import pandas as pd

# Load team shooting data
team_shooting = pd.read_csv('team_shooting.csv')

# Filter for 2023 season and wide open 3s
wide_open_2023 = team_shooting[
    (team_shooting['year'] == 2023) &
    (team_shooting['shot_coverage'] == 'Wide Open')
]

# Calculate 3-point volume and efficiency
wide_open_2023['3PA_per_game'] = wide_open_2023['3PA'] / wide_open_2023['GP']

print(wide_open_2023[['TEAM', '3PA_per_game', '3P%', 'Freq%']].sort_values('3PA_per_game', ascending=False))

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Comparing Offensive and Defensive Efficiency by Play Type

# Load offensive and defensive play-type data
team_offense = pd.read_csv('teamplay.csv')
team_defense = pd.read_csv('teamplayd.csv')

# Filter for transition plays in 2023
transition_off = team_offense[
    (team_offense['year'] == 2023) &
    (team_offense['playtype'] == 'Transition')
]

transition_def = team_defense[
    (team_defense['year'] == 2023) &
    (team_defense['playtype'] == 'Transition')
]

# Merge offensive and defensive stats
transition = pd.merge(
    transition_off[['Team', 'PPP', 'FREQ%']],
    transition_def[['Team', 'PPP', 'FREQ%']],
    on='Team',
    suffixes=('_off', '_def')
)

# Calculate net efficiency
transition['net_ppp'] = transition['PPP_off'] - transition['PPP_def']

print(transition.sort_values('net_ppp', ascending=False))

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Shot Distribution Analysis

# Load team shot zone data
shot_zones = pd.read_csv('team_shotzone.csv')

# Calculate shot distribution for each team
shot_zones['rim_freq'] = shot_zones['AtRimFGA'] / (shot_zones['FG2A'] + shot_zones['FG3A'])
shot_zones['three_freq'] = shot_zones['FG3A'] / (shot_zones['FG2A'] + shot_zones['FG3A'])
shot_zones['mid_freq'] = (shot_zones['ShortMidRangeFGA'] + shot_zones['LongMidRangeFGA']) / (shot_zones['FG2A'] + shot_zones['FG3A'])

# Filter for 2023 and compare modern vs traditional shot selection
shot_dist_2023 = shot_zones[shot_zones['year'] == 2023]

print(shot_dist_2023[['Name', 'rim_freq', 'three_freq', 'mid_freq', 'TsPct']].sort_values('TsPct', ascending=False))

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Defensive Shot Quality Allowed

# Load opponent shooting data
opp_shooting = pd.read_csv('opp_team_shooting.csv')

# Pivot to compare coverage types
coverage_pivot = opp_shooting.pivot_table(
    index=['TEAM', 'year'],
    columns='shot_coverage',
    values=['FGA', 'FG%', 'Freq%']
)

# Filter for 2023 and calculate defensive quality
defense_2023 = coverage_pivot[coverage_pivot.index.get_level_values('year') == 2023]

# Teams that force the most contested shots
print(defense_2023.sort_values(('Freq%', 'Very Tight'), ascending=False))

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Play-Type Efficiency Rankings

# Load team play-type data
teamplay = pd.read_csv('teamplay.csv')

# Find most efficient play types by team
efficient_plays = teamplay[
    (teamplay['year'] == 2023) &
    (teamplay['POSS'] >= 100)  # Minimum sample size
].sort_values(['Team', 'PPP'], ascending=[True, False])

# Get top play type for each team
top_play_by_team = efficient_plays.groupby('Team').first().reset_index()

print(top_play_by_team[['Team', 'playtype', 'PPP', 'FREQ%', 'SCOREFREQ%']])

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Relationship to Player Data

Team statistics can be connected to player statistics through:
  • Team abbreviation: Join on TEAM or Team field
  • Season: Join on year field
  • Aggregation: Sum player stats to validate team totals
# Example: Verify team 3-point totals from player data
player_shooting = pd.read_csv('player_shooting.csv')

# Aggregate player 3PA by team
player_team_3pa = player_shooting[
    player_shooting['year'] == 2023
].groupby('TEAM')['3PA'].sum().reset_index()

# Compare to team totals
team_totals = team_shooting[
    team_shooting['year'] == 2023
].groupby('TEAM')['3PA'].sum().reset_index()

comparison = pd.merge(player_team_3pa, team_totals, on='TEAM', suffixes=('_player', '_team'))
print(comparison)

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