Predicting NBA standings - Who will make the 2019-2020 NBA playoffs in the Western Conference?

Overview

This is the final project for Advanced Data Science @ JHU. This project built a prediction model for winning percentage of the opposing teams in a basketball game based on the team-level and player-level statistics. After simulating the rest games in the 2019-2020 season, we present the playoffs in the Western Conference.

Data

The data folder contains our training and testing data scraped from BASKETBALL REFERENCE

• game_stats_all.csv - The game results (scores) for all regular season games from season 2015-16 to 2018-19 and for regular season games already played in season 2019-20 and the upcoming schedule for the remaining games in season 2019-2020.
• team_stats_all.csv - The team statistics of first 20 games from season 2015-16 to 2019-20.
• pre_20_games.rds - the results for the first 20 games for each team during season 2019-20
• post_62_games_pred.rds - predictions for the rest of season 2019-2020 We only need the first two csv files to run the rmarkdown code.

Exploratory Data Analysis

The plot folder contains plots from our EDA.

• back2back.png - boxplot: the proportions of games won stratified by whether it is a back-to-back game
• home_away.png - histogram: the total wins of each team stratified by home/away and conference during season 2018-2019
• player_ws_plot.png - boxplot: the players' win share rankings over season 2014-15 to season 2018-19
• team_cluster_plot.png - cluster: five clusters of the teams from 2015-16 to 2019-20 using k-means algorithm
• team_def_off_plot.png - scatter plot: points scored of each team vs opponent in the first 20 games during season 2018-19
• team_pc_plot.png - principle component: two PCs of all the teams from 2015-16 to 2019-20
• team_rank_plot.png - scatter plot: final conference standing vs conference standing after 20 games during season 2018-19
• team_win_plot.png - scatter plot: final total wins vs total wins after 20 games during season 2018-19
• team_ws_plot.png - line segment: win shares of of the best players in each team
• west_east.png - histogram: win/lose when the teams face opposing conference
• year_dist.png - heatmap: the distance between every pair of seasons

Source code

The source folder contains source code of the functions used in the analysis

• data_scraping.R - functions to scrape team/player statistics, game results, injury information, and game schedule
• data_wrangling.R - functions to tidy data and extract information from raw data
• EDA_visualize.R - functions to generate the plots in the analysis
• data_integration.R - functions to explore the yearly effect
• feature_eng.R - functions to create team features
• model_build.R - functions to build and test the prediction model
• simulation.R - based on the predicted probability, simulate the games for B times

Shinyapp

The Shinyapp presents one simulation based on the predicted probabilities from our model. User could see the the final simulation results as well as daily game predictions.

To get a quick overview of the project, checkout our website and Youtube video. For the full analysis, read our final report.

Contributors

Kate Li (yli324@jhu.edu)

Runzhe Li (rli51@jhmi.edu)

Yifan Zhang (yzhan170@jhu.edu)

Linda Zhou (lzhou54@jhu.edu)