NBA Stats Cherry Picker
'FirstName' 'LastName' is the first player since 'Good Player' in 'year' to record 'x' points, 'y' assists, 'z' rebounds and 'n' steals.
With the awesome NBA Stats API!
Requires python3.6 or above, so make sure to have that.
Data
The best data from can be found here. Also checkout here for a csv of some greats out there. This data is a csv with the following columns: ,Name,ID,Date,Team,PTS,AST,REB,STL,BLK,TOV
For users not wanting to go through the whole process of getting all of the data again (this takes a long time), we recommend using the above data.
There is a different dataset available (but beware, missing data from the way older years, such as Wilt Chamberlains's time) online here. This dataset is harder to work with than the previous one, and it is highly recommended to use the former one.
player_id,"[game_1, game_2 ... game_n], where player_id is an integer of the player's unique id (according to stats.nba.com), n denotes every game played in every season they played. Each game_i is a list that consists of the following:
[GAME_DATE, MATCHUP, WL, MIN, FGM, FGA, FG_PCT, FG3M, FG3A, FG3_PCT, FTM, FTA, FT_PCT, OREB, DREB, REB, AST, STL, BLK, TOV, PF, PTS]
Easy Run
git clone https://github.com/peasant98/NBA-Stats-Cherry-Picker
cd NBA-Stats-Cherry-Picker
# getting the data
wget https://nba-data-bucket.s3-us-west-2.amazonaws.com/filtered_players.csv
wget https://nba-data-bucket.s3-us-west-2.amazonaws.com/filtered_legends.csv
python3.6 main.py- Follow the instructions to see what shakes loose!
Usage
If you want to have some more power of controlling the Cherry Picker interface:
We need to first analyze main.py to understand how to work with the code.
If the files 'docs/legend_ids.txt' and 'docs/players_ids.txt' do not exist, you will
be prompted for --legends_path, which is the path to a file of great players. There is a default option, and the code will take care of writing to the files for the player and legends ids.
The line data_engine.create_data(is_seasons_created=True, is_gamelogs_created=True, include_legends_with_players=True, is_analysis_df_created=True)
creates data if is_seasons_created is False (the csv for all of the players' active seasons), is_gamelogs_created is False (every game for every player, ever), or is_analysis_df_created is False (the df and csv that was the first download link on this readme). The argument include_legends_with_players will toss the legends into the massive csv file with the players. Feel free to change this as you see fit. It is currently setup for the steps in Easy Run.
The other line to worry about is data_engine.run_analysis(games, game_info, name, 'filtered_legends.csv', 'filtered_players.csv', single_game_threshold=4)
In this case, single_game_threshold means that we display every cherry picked stat subset if 4 games or less include that stat. If this argument is too high, then some of the results don't look at impressive because 10+ games have had this certain, ultra-specific, statline.
Cool Fact
Stay tuned for interesting posts on real players as the 2019-20 NBA season progresses!
- Anthony Davis in LAL vs. MIN is the first player EVER to score at least 50.0 points, to dish out at least 6.0 assists, to snag at least 7.0 rebounds, to have at least 4.0 steals, to register at least 1.0 block, and to have at most 1.0 turnover.
Misc
If you went about the steps in usage to generate all_players.csv and legend_players.csv, run python3.6 random_stat_generator.py just for some random games generation!
Enjoy!!!