Housekeeping

Introduction

The purpose of this exercise is to give you additional practice working with logistic regression. The exercise is based on the NBA Team Game Stats dataset found here: https://www.kaggle.com/ionaskel/nba-games-stats-from-2014-to-2018/. Read more about the problem and dataset under the description section of the link.

Kaggle is a great online community of data scientists. To learn more about Kaggle, follow this link: https://www.kaggle.com/getting-started/44916.

For this exercise, you will need to select ONLY ONE NBA team in the data. Also, you will need to set aside data for the 2017/2018 season as test data. Write the name of the team you selected at the top of your report

The data

Download the data (named nba_games_stats.csv) from Sakai and save it locally to the same directory as your R markdown file. To find the data file on Sakai, go to Resources \(\rightarrow\) Datasets \(\rightarrow\) In-Class Analyses. Once you have downloaded the data file into the SAME folder as your R markdown file, load and clean the data by using the following R code.

⊕Again, you are expected to select ONLY ONE NBA team in the data. Also, set aside data for the 2017/2018 season as test data.

nba <- read.csv("nba_games_stats.csv",header = TRUE,sep = ",",stringsAsFactors = FALSE)

# Set factor variables
nba$Home <- factor(nba$Home)
nba$Team <- factor(nba$Team)
nba$WINorLOSS <- factor(nba$WINorLOSS)

# Convert date to the right format
nba$Date <- as.Date(nba$Date, "%Y-%m-%d")

# Also create a binary variable from WINorLOSS. 
# This is not always necessary but can be useful 
#particularly for R functions that prefer numeric binary variables
#to the original factor variables
nba$Win <- rep(0,nrow(nba))
nba$Win[nba$WINorLOSS=="W"] <- 1

# I picked the Charlotte Hornets (CHO) as an example,
#you should pick any team you want
nba_reduced <- nba[nba$Team == "CHO", ]

# Set aside the 2017/2018 season as your test data
nba_reduced_train <- nba_reduced[nba_reduced$Date < "2017-10-01",]
nba_reduced_test <- nba_reduced[nba_reduced$Date >= "2017-10-01",]

You will only use the nba_reduced_train data today, but will use the nba_reduced_test data in the next in-class exercise.

Exercises

Treat the variable Win (or WinorLoss) as your response variable and the other variables as potential predictors. Answer all questions using only the nba_reduced_train data. Leave the `nba_reduced_test data for next time.

1. Make exploratory plots to explore the relationships between Win and the following variables: Home, TeamPoints, FieldGoals., Assists, Steals, Blocks and Turnovers. Don’t include any of the plots, just briefly describe the relationships.

2. Just by looking at the code book, there are several combinations of variables we should not include as predictors in the logistic model. Identify at least two pairs and explain in at most two sentences, why we should not include them in the model at the same time.

3. Fit a logistic regression model for Win (or WinorLoss) using Home, TeamPoints, FieldGoals., Assists, Steals, Blocks and Turnovers. as your predictors. Using the vif function, are there are any concerns regarding multicollinearity in this model?

4. Present the output of the fitted model and interpret the significant coefficients in terms of the odds of your team winning an NBA game.

5. Using 0.5 as your cutoff for predicting wins or losses (1 vs 0) from the predicted probabilities, what is the accuracy of this model? Plot the roc curve for the fitted model. What is the AUC value?

Acknowledgement

This exercise is based on ideas proposed by Sam Voisin.