FFS-BOT

FFS-BOT is a python selenium based script to go and fetch match data from Fantasy Football Scout match stats page.

Prerequisites:

Python version 3.7+
Google Chrome
ChromeWebdriver

How to run?

Create a virtualenv (optional but recommended) It'll be helpful for installing dependencies in a isolated place. Then activate the virtualenv to run the script. Otherwise, you need to install the libraries from requirement.txt file manually and run the script.
Inside project directory: create a .env file. Put 3 environment variables:
1. FFS_USERNAME=<YOUR_FFS_USERNAME>
2. FFS_PASSWORD=<YOUR_FFS_PASSWORD>
3. WEBDRIVER_PATH=<YOUR_CHOMEDRIVER_PATH>
Remember: Google Chrome version and ChromeDriver version must be same. Current Google Chrome is 92. So, download ChromeDriver version 92 and install.
From project directory run: python3 ffs_bot.py 212123. Here, 212123 = the match ID from which you want to extract the data.
Results will be written in match_stats.json file.

Implementation

First login to FFS site using credentials from .env file. Then sleep for 3 seconds to load the webpage.
Next, we initialize every players data with empty Hash/Dictionary
Then for each player, go through Tab by Tab. Tabs definition can be found on constants.py file.
For each tab, go through every row of the table, find the row associated with Player Name, then extract and assign the data to empty player stats.

To Extend

Currently, we're extracting data from 3 tabs. If you want to collect data from other tabs, define the tab info in constants.py file.
For additional/new tab data, initialize the build_player_stats of player_stats.py file with new dictionary key-value pair.
In player_stats.py file, extend player function with for new tab_type

Example Contribution

constants.py

constants = {
  '#player-tabs-2': 'involvement',
  '#player-tabs-7': 'keeping',
  '#player-tabs-11': 'expected',
  '#player-tabs-1': 'fantasy' # < Added new tab >
}

player_stats.py

Initialize empty data dictionary:

def build_player_stats(page_source):
  ...... other codes
  categories = ['touches', 'passes', 'expected', 'keeping', 'assist_potential', 'goal_threat', 'fantasy'] # newly added dict key
  ...... other codes

Define new extraction method:

def player(page_source, stats_type, stats):
  if stats_type == 'involvement':
    stats = involvement(page_source, stats)
  elif stats_type == 'expected':
    stats = expected(page_source, stats)
  elif stats_type == 'keeping':
    stats = keeping(page_source, stats)
  # Added following 2 lines to extract info from fantasy tab
  elif stats_type == 'fantasy':
    stats = fantasy(page_source, stats)

  return stats

Write extract data method:

def fantasy(page_source, stats):
  for indx, row_data in enumerate(page_source.find_elements_by_css_selector('td')):
    if indx == 0:
      stats['fantasy'][X] = row_data.text.strip()
    if indx == 3:
      stats['fantasy'][Y] = row_data.text.strip()
  # Here X, Y can be 'goals', 'assists' etc.
  return stats

License

FFPB

Free Software, Hell Yeah!