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12/11/2019

  • Added front-end web client to allow users to simulate games! Need to fix designs a bit though and add actual text description in place of the generic lorem ipsum. However, so far the structure and functionalities are taken care of!
  • Frontend web client is implemented using HTML/CSS/Javascript
  • Simulation functions in simulation.py now returns dictionaries of games results!

11/28/2019

  • Fixed backend endpoints:

    • /teams endpoint now contains different versions of a team from the 2014-2015 season to present.
    • Each team object in /teams endpoint now contain a list of embedded players who was/is in the team's roster for that respective season.
    • /players endpoint created. It now has 3627 versions of players who played/plays in the NBA from the 2014-2015 season to now.
    • Each player document keep track of the following stats:
      • Name
      • Position
      • Season
      • PER (Player Efficiency Rating)
      • TS% (True Shooting Percentage)
      • DWS (Defensive Win Shares)
      • OWS (Offensive Win Shares)
      • Points
      • Rebounds
      • Assists
      • Offensive Rating
      • Defensive Rating

  • Allow simulation between teams in different seasons! For example, 2015 Warriors vs 2019 Lakers! '

  • Allow more dynamic searching for teams and players by using regex matching:

    • you can search for players just by typing substring of their names

    • you can simulate games between teams without having to type the full team out! For example,

      c = client()
# 2015 Brooklyn Nets vs 2019 Lakers
c.simulate_game(team_a="brk", team_b="lakers", season_of_A=2015, season_of_B=2019)
# 2015 Warriors vs 2019 Raptors
c.simulate_game(team_a="warriors", team_b="tor", season_of_A=2015, season_of_B=2019)

  • Allow more accurate simulate_games_on_date(year, month, day) by using season-appropriate versions of teams to simulate. For example, simulate_games_on_date(2016, 12, 28) would use the 2017-season version of teams to simulate the matchup instead of current 2020 version.

11/27/2019

  • Set up backend database using MongoDB
  • Added scraping functions to extract data about:
    • Team statistics
    • Game schedules
  • Added Monte Carlos simulation algorithm.