NBA Stat Scraper

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This project has two main components: a web scraper, and a database for the scraped data.

The web scraper is for NBA statistics on games, players, standings, and teams. This project uses information available on Basketball Reference. I wrote this module to learn BeautifulSoup, and use it for analytics to annoy friends with stats nobody asked for.

The database uses the stores the scraped data. There are currently 5 tables: players, players.perGame, teams, teams.perGame, standings.

Usage

This section describes the methods supported by this project.

Installation

Clone the repository: $ git clone https://github.com/NBAStatScraper.git Install the requirements: $ pip install -r requirements.txt

Player

get_season_projection(name)

Parameters:

• name: The first and last name of a player (e.g. 'Joel Embiid')

Returns:

A Pandas dataframe containing the player's stat projections for the current season.

get_career_player_stats(name, per):

Parameters:

• name: The first and last name of a player (e.g. OG Anunoby)
• per: The method in which the statistics are calculated. Can be any one of:

['game', 'total', 'min', 'pos', 'shooting', 'playoffTotal', 'playoffGame', 'playoffMin', 'playoffPos', 'playoffShooting', 'careerHighs', 'playoffCareerHighs', 'college', 'salary', 'contract']

Returns:

A Pandas dataframe containing the player stats.

Team

get_roster(team, year)

Parameters:

• team: The 3 letter abbreviation of an NBA team (e.g. 'TOR', 'POR')
• year: The year for the roster to get (e.g. 2020)

Returns:

A Pandas dataframe containing the roster and player information for the year.

get_team_stats(team, year, per)

Parameters:

• team: The 3 letter abbreviation of an NBA team (e.g. 'TOR', 'POR')
• year: The year for the roster to get (e.g. 2020)
• per: The method in which the statistics are calculated. Can be any one of:

['game', 'total', 'min', 'pos', 'shooting', 'playoffTotal', 'playoffGame', 'playoffMin', 'playoffPos', 'playoffShooting']

Returns:

A Pandas dataframe containing the team stats for a chosen year.

Game

get_team_season_results

Parameters:

• team: The 3 letter abbreviation of a team (e.g. 'TOR', 'BOS')
• year: The year of the season to get game scores for (e.g. '2019', '2002')

Returns:

A Pandas dataframe containing the information of each game for the team in the given year.

get_team_playoff_results

Parameters:

• team: The 3 letter abbreviation of a team (e.g. 'TOR', 'BOS')
• year: The year of the season to get playoff scores for (e.g. '2019', '2002')

Returns:

A Pandas dataframe containing the information of each playoff game for a team in a given year.

These following two are currently broken. I suspect it is an issue with the WSL pyppeteer port.

get_shot_chart(home, away, date)

Parameters:

• home: The 3 letter abbreviation of the home team (e.g. 'TOR', 'BOS')
• away: The 3 letter abbreviation of the away team (e.g. 'TOR', 'BOS')
• date: The date of the game (e.g. 2020-12-23)

Returns:

A Pandas dataframe containing the locations and information of each shot taken in the game.

get_team_shooting(home, team, date)

Parameters:

• home: The 3 letter abbreviation of the home team (e.g. 'TOR', 'BOS')
• team: The 3 letter abbreviation of the team to get the shooting stats for (e.g. 'TOR', 'BOS')
• date: The date of the game (e.g. 2018-11-20)

Returns:

A Pandas dataframe containing the team's shooting information for the game.

Standings

get_conference_standings(conference, year)

Parameters:

• conference: The conference to get the standings for. Can be one of ['E', 'W']
• year: The year to get the conference standings for (e.g. '2019`)

Returns:

A Pandas dataframe containing the conference standings.

get_league_standings(year)

Parameters:

• year: The year to get the league standings for (e.g. '2000')

Returns:

A Pandas dataframe containing the league standings for a year.

get_team_v_team(year)

Parameters:

• year: The year to get the team vs. team records (e.g. '2018')

Returns:

• A Pandas dataframe containing the records of each team against every other team.