Code Documentation

Rmarkdown and HTML Files

• Rmarkdown file: report.Rmd
• Github Repo link
• Compiled HTML link

./data

• game_stats_all.csv
• team_stats_all.csv
• post_62_games_pred.rds
• pre_20_games.rds

./nba_simulation_shiny

• ShinyApp source code
• ShinyApp link

./plots

./source

• EDA_visualize.R
• data_integration.R
• data_scraping.R
• data_wrangling.R
• feature_eng.R
• model_build.R
• simulation.R

Wrap-Up Functions

• Data Scraping
  • get_schedule_data(year,month): obtain the game schedules and results in specific year and month
  • combine_game_data(): combine the game schedules data
  • get_team_stats(year): obtain the team-level data (30 teams in the league) of first 20 games in specific year
• Data Wrangling and Feature Engineering
  • get_ws_data(year): obtain the win share data in specific year
  • get_team_year_feature(year): obtain the team performance feature vectors in specific year
  • get_game_year_feature(year): obtain the game results in tidy format in specific year
  • get_injury_data(year), combine_injury_data(): obtain and combine the injury data
• EDA
  • team_def_off_plot(year): show each team’s defense vs offense in specific year
  • team_rank_plot(year,Nfirst): show each team’s season rank vs first N games’ rank in specific year
  • home_way_plot(year): show each team’s home and away records in specific year; show western vs eastern records
  • team_ws_plot(year): show each team’s highest winshare in specific year
  • player_ws_plot(): show the winshare of top players in the league in recent years
• Model Building
  • get_train_test_data(year,firstN): obtain the training and test data of first N games in specific year.
  • build_logit_model_year(train_year, test_year, firstN): use the first N games of train_year to build the model, and test the model on test_year. return the prediction results (W/L) and winning probability of each game.
• Integrative Prediction
  • combine_season_data(): combine the team performance feature data of multiple seasons
  • team_pc_plot(): create the PC plot for the team performances of all the 30 teams in multiple seasons; the distance matrix of season data
  • team_cluster_plot(): create the cluster plot for each team in multiple seasons
  • win_prob_prediction(train_year, test_year,firstN,year_effect): The winning probability prediction function allow for year_effect
• Simulation
  • WL_record_cal(game_schedule,WL_record): calculate the W/L records for each team given the game schedule
  • game_simulation(pred_result,B,year,Nfirst): given the predict results in specific year, repeat the game simulation for B times