Metadata-Version: 1.1
Name: baseball-scraper
Version: 0.0.6
Summary: Retrieve baseball data in Python
Home-page: https://github.com/spilchen/baseball_scraper
Author: Matt Spilchen
Author-email: matt.spilchen@gmail.com
License: MIT
Description: ================
        baseball_scraper
        ================
        
        `baseball_scraper` is a Python package for baseball data analysis. This package scrapes baseball-reference.com and baseballsavant.com so you don't have to. So far, the package performs four main tasks: retrieving statcast data, pitching stats, batting stats, and division standings/team records. Data is available at the individual pitch level, as well as aggregated at the season level and over custom time periods. 
        
        Status
        ------
        
        .. image:: https://travis-ci.com/spilchen/baseball_scraper.svg?branch=master
            :target: https://travis-ci.com/spilchen/baseball_scraper
        
        Statcast
        --------
        
        *data include pitch-level features such as Perceived Velocity (PV), Spin Rate (SR), Exit Velocity (EV), pitch X, Y, and Z coordinates, and more. The function `statcast(start_dt, end_dt)` pulls this data from baseballsavant.com.*
        
        Pull advanced metrics from Major League Baseball's Statcast system.  Statcast data include pitch-level features such as Perceived Velocity (PV), Spin Rate (SR), Exit Velocity (EV), pitch X, Y, and Z coordinates, and more. The function `statcast(start_dt, end_dt)` pulls this data from baseballsavant.com. 
        
        ::
        
          >>> from baseball_scraper import statcast
          >>> data = statcast(start_dt='2017-06-24', end_dt='2017-06-27')
          >>> data.head(2)
          
             index pitch_type  game_date  release_speed  release_pos_x  release_pos_z  
          0    314         CU 2017-06-27           79.7        -1.3441         5.4075
          1    332         FF 2017-06-27           98.1        -1.3547         5.4196
          
            player_name    batter   pitcher     events     ...      release_pos_y  
          0   Matt Bush  608070.0  456713.0  field_out     ...            54.8585
          1   Matt Bush  429665.0  456713.0  field_out     ...            54.3470
          
             estimated_ba_using_speedangle  estimated_woba_using_speedangle  woba_value  
          0                          0.100                            0.137         0.0
          1                          0.269                            0.258         0.0
          
             woba_denom babip_value iso_value launch_speed_angle at_bat_number pitch_number  
          0         1.0         0.0       0.0                3.0          64.0          1.0
          1         1.0         0.0       0.0                3.0          63.0          3.0  
          [2 rows x 79 columns]
        
        
        If `start_dt` and `end_dt` are supplied, it will return all statcast data between those two dates. If not, it will return yesterday's data. The argument `team` may also be supplied with a team's city abbreviation (i.e. BOS) to obtain only observations for games containing that team. The optional argument `verbose` will control whether the library updates you on its progress while it pulls the data.
        
        For a player-specific statcast query, pull pitching or batting data using the `statcast_pitcher` and `statcast_batter` functions. These take the same `start_dt` and `end_dt` arguments as the statcast function, as well as a `player_id` argument. This ID comes from MLB Advanced Media, and can be obtained using the function `playerid_lookup`. A complete example: 
        
        ::
        
          >>> # Find Clayton Kershaw's player id
          >>> from baseball_scraper import playerid_lookup
          >>> from baseball_scraper import statcast_pitcher
          >>> playerid_lookup('kershaw', 'clayton')
          Gathering player lookup table. This may take a moment.
          
            name_last name_first  key_mlbam key_retro  key_bbref  key_fangraphs  
          0   kershaw    clayton     477132  kersc001  kershcl01           2036
          
             mlb_played_first  mlb_played_last
          0            2008.0           2017.0
          
          >>> # His MLBAM ID is 477132, so we feed that as the player_id argument to the following function 
          >>> kershaw_stats = statcast_pitcher('2017-06-01', '2017-07-01', 477132)
          >>> kershaw_stats.head(2)
            pitch_type   game_date release_speed release_pos_x release_pos_z  
          0         SL  2017-06-29          87.2        1.0865        6.4034
          1         SL  2017-06-29          86.9        1.0195        6.4324
          
                 player_name  batter  pitcher     events              description  
          0  Clayton Kershaw  458913   477132  strikeout  swinging_strike_blocked
          1  Clayton Kershaw  458913   477132       null                     ball
          
                ...       release_pos_y  estimated_ba_using_speedangle  
          0     ...             54.5463                            0.0
          1     ...             54.7625                            0.0
          
             estimated_woba_using_speedangle  woba_value woba_denom babip_value  
          0                              0.0        0.00          1           0
          1                              0.0        null       null        null
          
            iso_value launch_speed_angle at_bat_number pitch_number
          0         0               null            57            6
          1      null               null            57            5
          
          [2 rows x 78 columns]
        
        
        Pitching Stats
        --------------
        
        *pitching stats for players across multiple seasons, single seasons, or during a specified time period*
        
        This library contains two main functions for obtaining pitching data. For league-wide season-level pitching data, use the function `pitching_stats(start_season, end_season)`. This will return one row per player per season, and provide all metrics made available by FanGraphs. 
        
        The second is `pitching_stats_range(start_dt, end_dt)`. This allows you to obtain pitching data over a specific time interval, allowing you to get more granular than the FanGraphs function (for example, to see which pitcher had the strongest month of May). This query pulls data from Baseball Reference. Note that all dates should be in `YYYY-MM-DD` format.
        
        If you prefer Baseball Reference to FanGraphs, there is actually a third option called `pitching_stats_bref(season)`. This works the same as `pitching_stats`, but retrieves its data from Baseball Reference instead. This is typically not recommended, however, because the Baseball Reference query currently can only retrieve one season's worth of data per request.
        
        ::
        
          >>> from baseball_scraper import pitching_stats
          >>> data = pitching_stats(2012, 2016)
          >>> data.head()
               Season             Name     Team   Age     W    L   ERA  WAR     G    GS  
          336  2015.0  Clayton Kershaw  Dodgers  27.0  16.0  7.0  2.13  8.6  33.0  33.0
          236  2014.0  Clayton Kershaw  Dodgers  26.0  21.0  3.0  1.77  7.6  27.0  27.0
          472  2014.0     Corey Kluber  Indians  28.0  18.0  9.0  2.44  7.4  34.0  34.0
          235  2015.0     Jake Arrieta     Cubs  29.0  22.0  6.0  1.77  7.3  33.0  33.0
          256  2013.0  Clayton Kershaw  Dodgers  25.0  16.0  9.0  1.83  7.1  33.0  33.0
          
                 ...      wSL/C (pi)  wXX/C (pi)  O-Swing% (pi)  Z-Swing% (pi)  
          336    ...            1.76       22.85          0.364          0.665
          236    ...            2.62         NaN          0.371          0.670
          472    ...            3.92         NaN          0.336          0.598
          235    ...            2.42         NaN          0.329          0.618
          256    ...            0.74         NaN          0.339          0.635
          
               Swing% (pi)  O-Contact% (pi)  Z-Contact% (pi)  Contact% (pi)  Zone% (pi)  
          336        0.511            0.478            0.811          0.689       0.487
          236        0.525            0.536            0.831          0.730       0.515
          472        0.468            0.485            0.886          0.744       0.505
          235        0.468            0.595            0.856          0.762       0.483
          256        0.484            0.563            0.873          0.763       0.492
          
               Pace (pi)
          336       23.4
          236       23.7
          472       24.6
          235       23.3
          256       23.4
          
          [5 rows x 299 columns]
        
        
        
        Batting Stats
        -------------
        
        *hitting stats for players within seasons or during a specified time period*
        
        Batting stats are obtained similar to pitching stats. The function call for getting a season-level stats is `batting_stats(start_season, end_season)`, and for a particular time range it is `batting_stats_range(start_dt, end_dt)`. The Baseball Reference equivalent for season-level data is `batting_stats_bref(season)`. 
        
        ::
        
          >>> from baseball_scraper import batting_stats_range
          >>> data = batting_stats_range('2017-05-01', '2017-05-08')
          >>> data.head()
                    Name  Age  #days     Lev          Tm  G  PA  AB  R  H  ...    HBP  
          1   Jose Abreu   30     69  MLB-AL     Chicago  7  31  30  5  9  ...      0
          2   Lane Adams   27     69  MLB-NL     Atlanta  6   6   6  0  2  ...      0
          3   Matt Adams   28     68  MLB-NL   St. Louis  6   9   9  2  4  ...      0
          4   Jim Adduci   32     69  MLB-AL     Detroit  6  24  21  3  5  ...      0
          5  Tim Adleman   29     72  MLB-NL  Cincinnati  1   2   2  0  0  ...      0
          
             SH  SF  GDP  SB  CS     BA    OBP    SLG    OPS  mlb_ID
          1   0   0    1   0   0  0.300  0.323  0.667  0.989  547989
          2   0   0    1   1   0  0.333  0.333  0.333  0.667  572669
          3   0   0    0   0   0  0.444  0.444  0.778  1.222  571431
          4   0   0    0   0   0  0.238  0.333  0.381  0.714  451192
          5   0   0    0   0   0  0.000  0.000  0.000  0.000  534947
          
          [5 rows x 28 columns]
        
        
        Fangraphs
        ---------
        
        Various baseball projections are available at fangraphs.com.  You can scrape that site using the fangraphs API.  You supply it the fangraph player ID to lookup and the projection system.  It will return a DataFrame with the projections.
        
        ::
        
          >>> from baseball_scraper import fangraphs
          >>> from baseball_id import Lookup
          >>> player_id = Lookup.from_names(['Khris Davis']).iloc[0].fg_id
          >>> fg = fangraphs.Scraper(player_id=player_id)
          >>> fg.instances()
          ['ZiPS (R)', 'Steamer (R)', 'Depth Charts (R)', 'THE BAT (R)']
          >>> df = proj.scrape(instance='ZiPS (R)')
          >>> df.columns
          Index(['G', 'AB', 'PA', 'H', '1B', '2B', '3B', 'HR', 'R', 'RBI', 'BB', 'IBB',
                 'SO', 'HBP', 'SF', 'SH', 'GDP', 'SB', 'CS', 'AVG', 'BB%', 'K%', 'BB/K',
                 'OBP', 'SLG', 'OPS', 'ISO', 'Spd', 'BABIP', 'UBR', 'wGDP', 'wSB', 'wRC',
                 'wRAA', 'wOBA', 'wRC+'],
               dtype='object')
          >>> df
              G   AB   PA   H  1B  2B  3B  HR   R  RBI  BB  ...    OPS    ISO  Spd  BABIP  UBR  wGDP  wSB  wRC  wRAA   wOBA  wRC+
          0  88  323  363  79  42  13   0  24  49   64  34  ...  0.834  0.263  2.3   0.28  0.0   NaN -0.2   54   8.9  0.347   120
        
          [1 rows x 36 columns]
          >>> player_id = Lookup.from_names(['Hyun-Jin Ryu']).iloc[0].fg_id
          >>> fg = fangraphs.Scraper(player_id=player_id)
          >>> df = fg.scrape(instance='ZiPS')
          >>> df.columns
          Index(['W', 'L', 'ERA', 'G', 'GS', 'CG', 'ShO', 'SV', 'HLD', 'BS', 'IP', 'TBF',
               'H', 'R', 'ER', 'HR', 'BB', 'IBB', 'HBP', 'WP', 'BK', 'SO', 'K/9',
               'BB/9', 'K/BB', 'HR/9', 'K%', 'BB%', 'K-BB%', 'AVG', 'WHIP', 'BABIP',
               'LOB%', 'ERA-', 'FIP-', 'FIP'],
              dtype='object')
          >>> df
             W  L   ERA   G  GS  CG  ShO  SV  HLD  BS    IP  ...  HR/9  K%  BB%  K-BB%    AVG  WHIP  BABIP   LOB%  ERA-  FIP-   FIP
          0  6  5  3.89  17  17 NaN  NaN   0  NaN NaN  88.0  ...  1.43 NaN  NaN    NaN  0.264  1.25  0.311  0.763   NaN   NaN  4.15
        
          [1 rows x 36 columns]
        
        
        Game-by-Game Results and Schedule 
        ---------------------------------
        
        The `schedule_and_record` function returns a team's game-by-game results for a given season, including game date, home and away teams, end result (W/L/Tie), score, winning/losing/saving pitchers, attendance, and division standing at that date. The function's only two arguments are `season` and `team`, where team is the team's abbreviation (i.e. NYY for New York Yankees, SEA for Seattle Mariners). If the season argument is set to the current season, the query returns results for past games and the schedule for those that have not occurred yet. 
        
        ::
        
          >>> # Example: Let's take a look at the individual-game results of the 1927 Yankees
          >>> from baseball_scraper import schedule_and_record
          >>> data = schedule_and_record(1927, 'NYY')
          >>> data.head()
                          Date   Tm Home_Away  Opp W/L     R   RA   Inn  W-L  Rank  \
          1    Tuesday, Apr 12  NYY      Home  PHA   W   8.0  3.0   9.0  1-0   1.0
          2  Wednesday, Apr 13  NYY      Home  PHA   W  10.0  4.0   9.0  2-0   1.0
          3   Thursday, Apr 14  NYY      Home  PHA   T   9.0  9.0  10.0  2-0   1.0
          4     Friday, Apr 15  NYY      Home  PHA   W   6.0  3.0   9.0  3-0   1.0
          5   Saturday, Apr 16  NYY      Home  BOS   W   5.0  2.0   9.0  4-0   1.0
          
                 GB      Win     Loss  Save  Time D/N  Attendance  Streak
          1    Tied     Hoyt    Grove  None  2:05   D     72000.0       1
          2  up 0.5  Ruether     Gray  None  2:15   D      8000.0       2
          3    Tied     None     None  None  2:50   D      9000.0       2
          4    Tied  Pennock    Ehmke  None  2:27   D     16000.0       3
          5  up 1.0  Shocker  Ruffing  None  2:05   D     25000.0       4
        
        
        Standings
        ---------
        
        The `standings(season)` function gives division standings for a given season. If the current season is chosen, it will give the most current set of standings. Otherwise, it will give the end-of-season standings for each division for the chosen season. 
        
        This function returns a list of dataframes. Each dataframe is the standings for one of MLB's six divisions. 
        
        ::
        
          >>> from baseball_scraper import standings
          >>> data = standings(2016)[4]
          >>> print(data)
                              Tm    W   L  W-L%    GB
          1         Chicago Cubs  103  58  .640    --
          2  St. Louis Cardinals   86  76  .531  17.5
          3   Pittsburgh Pirates   78  83  .484  25.0
          4    Milwaukee Brewers   73  89  .451  30.5
          5      Cincinnati Reds   68  94  .420  35.5
        
        
        Complete Documentation
        ----------------------
        
        So far this has provided a basic overview of what this package can do and how you can use it. For full documentation on available functions and their arguments, see the [docs](https://github.com/spilchen/baseball_scraper/tree/master/docs) folder. 
        
        Installation
        ------------
        
        To install baseball_scraper, simply run 
        
        ::
        
          pip install baseball_scraper
        
        or, for the version currently on the repo (which may at times be more up to date):
        
        ::
        
          git clone https://github.com/spilchen/baseball_scraper
          cd baseball_scraper
          python setup.py install
        
        
        Testing
        -------
        
        We use pytest for testing the package.  It can be invoked as follows:
        
        ::
          python setup.py test
        
        
        Dependencies
        ------------
        
        This library depends on: Pandas, NumPy, bs4 (beautiful soup), and Requests. 
        
Keywords: baseball sabermetrics data statistics statcast web scraping
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Intended Audience :: Developers
Classifier: Topic :: Software Development :: Libraries
Classifier: License :: OSI Approved :: MIT License
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.3
Classifier: Programming Language :: Python :: 3.4
Classifier: Programming Language :: Python :: 3.5
Classifier: Programming Language :: Python :: 3.6
