nfl-data-py 0.3.3
python library for interacting with NFL data sourced from nflfastR
License: MIT
Requires Python: >=3.6.0

#### nfl_data_py

nfl_data_py is a Python library for interacting with NFL data sourced from [nflfastR](https://github.com/nflverse/nflfastR-data/), [nfldata](https://github.com/nflverse/nfldata/), [dynastyprocess](https://raw.githubusercontent.com/dynastyprocess/), and [Draft Scout](https://draftscout.com/).

Includes import functions for play-by-play data, weekly data, seasonal data, rosters, win totals, scoring lines, officials, draft picks, draft pick values, schedules, team descriptive info, combine results and id mappings across various sites.

### Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install nfl_data_py.

```bash
pip install nfl_data_py
```

## Usage

```python
import nfl_data_py as nfl
```

**Working with play-by-play data**

```python
nfl.import_pbp_data(years, columns, downcast=True, cache=False, alt_path=None)
```

Returns play-by-play data for the years and columns specified

years
: required, list of years to pull data for (earliest available is 1999)

columns
: optional, list of columns to pull data for

downcast
: optional, converts float64 columns to float32, reducing memory usage by ~30%. Will slow down initial load speed ~50%

cache
: optional, determines whether to pull pbp data from github repo or local cache generated by nfl.cache_pbp()

alt_path
: optional, required if nfl.cache_pbp() is called using an alternate path to the default cache

```python
nfl.see_pbp_cols()
```

returns list of columns available in play-by-play dataset

**Working with weekly data**

```python
nfl.import_weekly_data(years, columns, downcast)
```

Returns weekly data for the years and columns specified

years
: required, list of years to pull data for (earliest available is 1999)

columns
: optional, list of columns to pull data for

downcast
: converts float64 columns to float32, reducing memory usage by ~30%. Will slow down initial load speed ~50%

```python
nfl.see_weekly_cols()
```

returns list of columns available in weekly dataset

**Working with seasonal data**

```python
nfl.import_seasonal_data(years, s_type)
```

Returns seasonal data, including various calculated market share stats specific to receivers

years (List[int])
: required, list of years to pull data for (earliest available is 1999)

s_type (str)
: optional (default 'REG') season type to include in average ('ALL','REG','POST')

calculated receiving market share stats include:

| Column   | is short for                                                                                                                                  |
| -------- | --------------------------------------------------------------------------------------------------------------------------------------------- |
| tgt_sh   | target share                                                                                                                                  |
| ay_sh    | air yards share                                                                                                                               |
| yac_sh   | yards after catch share                                                                                                                       |
| wopr     | [weighted opportunity rating](https://www.nbcsports.com/fantasy/football/news/article-numbers-why-receiver-air-yards-matter)                  |
| ry_sh    | receiving yards share                                                                                                                         |
| rtd_sh   | receiving TDs share                                                                                                                           |
| rfd_sh   | receiving 1st Downs share                                                                                                                     |
| rtdfd_sh | receiving TDs + 1st Downs share                                                                                                               |
| dom      | [dominator rating](https://www.pff.com/news/fantasy-football-predicting-breakout-rookie-wide-receivers-using-pff-grades-and-dominator-rating) |
| w8dom    | dominator rating, but weighted in favor of receiving yards over TDs                                                                           |
| yptmpa   | receiving yards per team pass attempt                                                                                                         |
| ppr_sh   | PPR fantasy points share                                                                                                                      |

**Additional data imports**

```python
nfl.import_seasonal_rosters(years, columns)
```

Returns yearly roster information for the seasons specified

years
: required, list of years to pull data for (earliest available is 1999)

columns
: optional, list of columns to pull data for

```python
nfl.import_weekly_rosters(years, columns)
```

Returns per-game roster information for the seasons specified

years
: required, list of years to pull data for (earliest available is 1999)

columns
: optional, list of columns to pull data for

```python
nfl.import_win_totals(years)
```

Returns win total lines for years specified

years
: optional, list of years to pull

```python
nfl.import_sc_lines(years)
```

Returns scoring lines for years specified

years
: optional, list of years to pull

```python
nfl.import_officials(years)
```

Returns official information by game for the years specified

years
: optional, list of years to pull

```python
nfl.import_draft_picks(years)
```

Returns list of draft picks for the years specified

years
: optional, list of years to pull

```python
nfl.import_draft_values()
```

Returns relative values by generic draft pick according to various popular valuation methods

```python
nfl.import_team_desc()
```

Returns dataframe with color/logo/etc information for all NFL team

```python
nfl.import_schedules(years)
```

Returns dataframe with schedule information for years specified

years
: required, list of years to pull data for (earliest available is 1999)

```python
nfl.import_combine_data(years, positions)
```

Returns dataframe with combine results for years and positions specified

years
: optional, list or range of years to pull data from

positions
: optional, list of positions to be pulled (standard format - WR/QB/RB/etc.)

```python
nfl.import_ids(columns, ids)
```

Returns dataframe with mapped ids for all players across most major NFL and fantasy football data platforms

columns
: optional, list of columns to return

ids
: optional, list of ids to return

```python
nfl.import_ngs_data(stat_type, years)
```

Returns dataframe with specified NGS data

stat_type (str)
: required, type of stats to pull (passing, rushing, receiving)

years
: optional, list of years to return data for

```python
nfl.import_depth_charts(years)
```

Returns dataframe with depth chart data

years
: optional, list of years to return data for

```python
nfl.import_injuries(years)
```

Returns dataframe of injury reports

years
: optional, list of years to return data for

```python
nfl.import_qbr(years, level, frequency)
```

Returns dataframe with QBR history

years
: optional, years to return data for

level
: optional, competition level to return data for, nfl or college, default nfl

frequency
: optional, frequency to return data for, weekly or season, default season

```python
nfl.import_seasonal_pfr(s_type, years)
```

Returns a dataframe of season-aggregated data sourced from players' pages on pro-football-reference.com. E.g. [Patrick Mahomes](https://www.pro-football-reference.com/players/M/MahoPa00.htm#all_passing_detailed)

s_type (str)
: required, the type of stat data to request. Must be one of pass, rec, or rush.

years (List[int])
: optional, years to return data for

```python
nfl.import_weekly_pfr(s_type, years)
```

Returns a dataframe of per-game data sourced from players' advanced gamelog pages on pro-football-reference.com. E.g. [Mahomes in 2022](https://www.pro-football-reference.com/players/M/MahoPa00/gamelog/2022/advanced/#all_advanced_passing)

s_type (str)
: required, the type of stat data to request. Must be one of pass, rec, or rush.

years (List[int])
: optional, years to return data for

```python
nfl.import_snap_counts(years)
```

Returns dataframe with snap count records

years
: optional, list of years to return data for


```python
nfl.import_ftn_data(years, columns=None, downcast=True, thread_requests=False)
```
Returns dataframe with FTN charting data
    
FTN Data manually charts plays and has graciously provided a subset of their
charting data to be published via the nflverse. Data is available from the 2022
season onwards and is charted within 48 hours following each game. This data
is released under the [CC-BY-SA 4.0](https://creativecommons.org/licenses/by-sa/4.0/)
Creative Commons license and attribution must be made to **FTN Data via nflverse**

years (List[int])
    : required, years to get weekly data for
columns (List[str])
    : optional, only return these columns
downcast (bool)
    : optional, convert float64 to float32, default True
thread_requests (bool)
    : optional use thread pool to read files, default False



**Additional features**

```python
nfl.cache_pbp(years, downcast=True, alt_path=None)
```

Caches play-by-play data locally to speed up download time. If years specified have already been cached they will be overwritten, so if using in-season must cache 1x per week to catch most recent data

years
: required, list or range of years to cache

downcast
: optional, converts float64 columns to float32, reducing memory usage by ~30%. Will slow down initial load speed ~50%

alt_path
:optional, alternate path to store pbp cache - default is in program created user Local folder

```python
nfl.clean_nfl_data(df)
```

Runs descriptive data (team name, player name, etc.) through various cleaning processes

df
: required, dataframe to be cleaned

## Recognition

I'd like to recognize all of [Ben Baldwin](https://twitter.com/benbbaldwin), [Sebastian Carl](https://twitter.com/mrcaseb), and [Lee Sharpe](https://twitter.com/LeeSharpeNFL) for making this data freely available and easy to access. I'd also like to thank [Tan Ho](https://twitter.com/_TanH), who has been an invaluable resource as I've worked through this project, and Josh Kazan for the resources and assistance he's provided.

## Contributing

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.

## License

[MIT](https://choosealicense.com/licenses/mit/)