Player Shooting

Overview

The player_shooting.py script collects shooting statistics categorized by closest defender distance. Data is collected in four defender distance buckets: Very Tight, Tight, Open, and Wide Open.

Data Sources

NBA.com Stats API: leaguedashplayerptshot endpoint
Defender Distance Classifications:
- Very Tight: 0-2 feet
- Tight: 2-4 feet
- Open: 4-6 feet
- Wide Open: 6+ feet

Core Function

get_playershots()

Collects shooting data for all defender distance categories.

years

list[int]

required

List of years to scrape (e.g., [2024, 2025])

boolean

default:"False"

Playoffs mode - if True, fetches playoff data

Returns: Saves four CSV files per year (one for each distance category)

# From player_shooting.py:65
def get_playershots(years, ps=False):
    shots = [
        "0-2%20Feet%20-%20Very%20Tight",
        "2-4%20Feet%20-%20Tight",
        "4-6%20Feet%20-%20Open",
        "6%2B%20Feet%20-%20Wide%20Open"
    ]
    terms = ['very_tight.csv', 'tight.csv', 'open.csv', 'wide_open.csv']
    stype = "Playoffs" if ps else "Regular%20Season"
    
    for year in years:
        for shot in shots:
            season = str(year) + '-' + str(year + 1 - 2000)
            url = f"https://stats.nba.com/stats/leaguedashplayerptshot?CloseDefDistRange={shot}&Season={season}&SeasonType={stype}"

master_shooting()

Combines individual year files into master dataset.

playoffs

boolean

default:"False"

If True, processes playoff data

Returns: pd.DataFrame with all years and shot types combined

# From player_shooting.py:126
def master_shooting(playoffs=False):
    data = []
    for i in range(2014, 2026):
        p = '/playoffs' if playoffs else ''
        path = str(i) + p + '/player_shooting/'
        files = ['wide_open', 'open', 'tight', 'very_tight']
        
        for file in files:
            df = pd.read_csv(path + file + '.csv')
            df['year'] = i
            df['shot_type'] = file
            data.append(df)
    
    master = pd.concat(data)
    return master

Statistics Collected

PLAYER_ID

integer

NBA player ID

PLAYER

string

Player name

TEAM

string

Team abbreviation

AGE

integer

Player age

integer

Games played

integer

Games started

FREQ%

float

Frequency percentage - how often player takes shots in this defender distance range

FGM

integer

Field goals made

FGA

integer

Field goal attempts

FG%

float

Field goal percentage (0-100 scale)

EFG%

float

Effective field goal percentage (accounts for 3-pointers)

2FG FREQ%

float

Two-point shot frequency within this defender distance

2FGM

integer

Two-point field goals made

2FGA

integer

Two-point field goal attempts

2FG%

float

Two-point field goal percentage

3FG FREQ%

float

Three-point shot frequency within this defender distance

3PM

integer

Three-pointers made

3PA

integer

Three-point attempts

3P%

float

Three-point percentage

Output Files

Individual Year Files

very_tight.csv

CSV

Shots with defender 0-2 feet awayPath: {year}/player_shooting/very_tight.csv or {year}/playoffs/player_shooting/very_tight.csv

tight.csv

CSV

Shots with defender 2-4 feet awayPath: {year}/player_shooting/tight.csv

open.csv

CSV

Shots with defender 4-6 feet awayPath: {year}/player_shooting/open.csv

wide_open.csv

CSV

Shots with defender 6+ feet awayPath: {year}/player_shooting/wide_open.csv

Master Files

player_shooting.csv

CSV

All regular season data combined (2014-2025)Additional columns: year, shot_type

player_shooting_p.csv

CSV

All playoff data combined (2014-2025)Additional columns: year, shot_type

Column Mapping

The script renames NBA.com columns to more readable names:

# From player_shooting.py:97-110
new_columns = {
    'FG2A_FREQUENCY': '2FG FREQ%',
    'FG2_PCT': '2FG%',
    'FG2A': '2FGA',
    'FG2M': '2FGM',
    'FG3A_FREQUENCY': '3FG FREQ%',
    'FG3_PCT': '3P%',
    'FG3A': '3PA',
    'FG3M': '3PM',
    'EFG_PCT': 'EFG%',
    'FG_PCT': 'FG%',
    'FGA_FREQUENCY': 'FREQ%',
    'PLAYER_NAME': 'PLAYER',
    'PLAYER_LAST_TEAM_ABBREVIATION': 'TEAM'
}

Percentages are multiplied by 100 for display:

# From player_shooting.py:116-118
for col in df.columns:
    if '%' in col or 'PERC' in col:
        df[col] *= 100

Usage Example

# Set playoff mode
ps = True

# Collect data for 2024-25 season
get_playershots([2024], ps=ps)

# Create master file with all years
master = master_shooting(playoffs=ps)

# Save to CSV
if ps:
    master.to_csv('player_shooting_p.csv', index=False)
else:
    master.to_csv('player_shooting.csv', index=False)

Output directory structure:

2025/player_shooting/
├── very_tight.csv
├── tight.csv
├── open.csv
└── wide_open.csv

2025/playoffs/player_shooting/
├── very_tight.csv
├── tight.csv
├── open.csv
└── wide_open.csv

Analysis Use Cases

Shot quality analysis: Compare FG% across defender distances
Player evaluation: Identify players who create their own shot (high very_tight volume)
Catch-and-shoot specialists: High FG% on wide_open shots
Contested shooting ability: Performance on very_tight and tight shots
Shot selection: Frequency distribution across defender distances

Data Scripts

Overview

Data Sources

Core Function

get_playershots()

master_shooting()

Statistics Collected

Output Files

Individual Year Files

Master Files

Column Mapping

Usage Example

Analysis Use Cases

Build docs developers (and LLMs) love

Data Scripts

​Overview

​Data Sources

​Core Function

​get_playershots()

​master_shooting()

​Statistics Collected

​Output Files

​Individual Year Files

​Master Files

​Column Mapping

​Usage Example

​Analysis Use Cases

Build docs developers (and LLMs) love

Overview

Data Sources

Core Function

get_playershots()

master_shooting()

Statistics Collected

Output Files

Individual Year Files

Master Files

Column Mapping

Usage Example

Analysis Use Cases