NCAA Men’s Basketball 2015–2020 Seasons Data Analysis
By: Tom Petry
Tags: Data Science, College Basketball, Data Analysis, Exploratory Data Analysis, Correlation

I did the following analysis on some college basketball data as a final project in a class I recently finished. The course was an introduction to R, so all of the analysis shown below was done using R.

Import & Pre-Processing:

Included below is a snapshot of the first 5 columns of the data. The only pre-processing involved was to re-name all of the columns to more intuitive names and also to create a subset of the original data frame that is made up of only teams from the so-called Power 5 Conferences (ACC, Big 10, Big 12, PAC 12, and SEC)

First 5 Columns and First 6 Rows of the Dataset

Analysis of Categorical Variable — Conference:

The primary goal in doing this analysis is to determine what statistics ultimately translate to wins. First, we’ll look at the number of wins by each of the Conferences in the data set. The total number of wins for each Conference from the whole dataset is summed and displayed in a basic barplot using a color gradient based on the win values.

We can also see the variations in the number of wins by each of the power 5 conferences. The equivalent boxplot using the full dataset was too crowded, so only the Power 5 Conference subset was used here. As can be seen from the boxplot, the median value of wins is very similar between the conferences. The Big 12 Conference seems to have the tightest range from the minimum to the maximum while the Big 10 seems to have the widest range. The one outlier here is from the SEC and is for Kentucky in 2015. That team went 38–1 and eventually made it to the Final Four. Below the plot, we can see the average number of wins for each Conference is very similar among the power 5 conferences.

Average Wins by Conference

Analysis of Numerical Variable — Wins Correlation:

The primary goal of this analysis is to see what stats correlate closely to wins — mainly what stats should teams strive most to improve in order to increase wins and ultimately postseason success? First, we’ll take a look at the correlations between every statistical variable in the dataset compared to the Wins values.

Correlation Between Each Column and Wins

The two most closely correlated stats to wins are PWR Rating and Wins over Bubble Teams. Looking at a scatterplot of Wins vs. Power Rating, with a color gradient representing Wins Over Bubble Teams (wins over teams that are considered “on the bubble” of making it to postseason play) there is a very clear correlation between these variables.

PWR Rating and Wins Over Bubble can be misleading, however, as they do not necessarily result in wins but often times the reverse is true. Wins can lead to a better power rating and obviously more wins over bubble teams. So, although there is clearly a high correlation, it may be more telling to look at the next two highest correlated stats, Adjusted Offensive Efficiency and Adjusted Defensive Efficiency. The ACC school of Duke University, consistently one of the best college basketball teams in the country, is highlighted for reference. The conclusion is that Adjusted Offensive Efficiency and Adjusted Defensive Efficiency are two excellent stats to track when trying to project a team’s win total.

Adjusted Offensive Efficiency vs. Wins (Duke Highlighted)

Adjusted Defensive Efficiency vs. Wins (Duke Highlighted)

Analysis on 1 Set of Variables:

Focusing on the Power 5 Conferences — we can look at how the conferences compared based on the number of teams they had finish in each of the postseason stages. There is only 1 champion each year and only 1 team that finishes 2nd. There are 2 teams each year that finish in the final four, or “F4”. There are 4 other teams that finish in the elite 8 every year, or “E8”, 8 teams finish in the sweet 16, or “S16”, 16 teams finish in the round of 32, or “R32”, 32 teams finish in the round of 64, or “R64”, and only 4 teams finish each year in the round of 68, or “R68”. Any team that did not make it to the postseason has an “N/A” in that column. We can start by looking at the margin table summing the values for conferences and postseason eliminations for the power 5 conference subset, with a simple bar plot showing the data as well.

Round of Postseason Eliminations by Conference

Focusing again on just the Power 5 Conferences, a lot of variation can be seen in the number of wins by where teams finished in the postseason.