Data visualization graduate course final project paper. Paper seeks to identify characteristics of successful NCAA men's basketball teams and how it impacts their recruiting. Research uses three datasets to perform exploratory data analysis and build different dashboards in Tableau.

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Jonathan Stryer
11 May 2020
NCAA March Madness: Recruiting for Success
ABSTRACT
Today there are 353 Division I college basketball teams, and each of them work hard just for a
chance to be invited to the NCAA Division I Men’s Basketball tournament let alone win the
national title. This paper builds on previous work analyzing NCAA tournament success and high
school basketball recruiting. It is easy to find resources that explain what makes teams the best of
the best in any given year, but it becomes harder when we attempt to understand teams outside of
the top. Facilitated through data dashboards, we offer an interactable visualization system to drill
down on important aspects that make up different college basketball teams. Our visualization
system bridges the gap in previous work by helping teams understand what it takes to get to the
next level rather than trying to jump to the very top over the course of one season.
INTRODUCTION
One of the most exciting times of the year in the sports world is March Madness which is the
name coined to refer to the NCAA Division I Men’s Basketball Tournament. March Madness is
a single-elimination tournament that invites 68 Division I college basketball programs to
compete for the national championship title. 32 of the teams receive automatic bids if they win
their conference tournament, and the other 36 teams wait until Selection Sunday. For the other

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36 bids, the selection committee will determine what teams performance during the regular
season were deserving of an invitation to the tournament.
This paper seeks to understand the different factors that make up a successful Division I
college basketball program. There is no one size fits all solution to improving a college
basketball program, but we will look for thresholds on what types of teams make it to a certain
round in the NCAA tournament. Understanding these factors will fuel our research objective
which is to improve the recruiting process for college basketball programs. While predicting a
potential high school recruit’s impact on a college basketball program is important, we should
offer visualizations that gauge the likelihood a quality recruit will commit to a given program.
The next section of this paper - ‘Background’ - presents previous work completed in
college basketball program success analysis and recruiting analysis. The ‘Approach’ section
discusses the data collected, the exploration of the data, and the visualizations instrumented to
address our research problem. In the ‘Results’ section, we look at the visualizations built to
address our research problem, and we learn about what could be guiding principles for teams
aspiring for greater success. Lastly, the ‘Conclusion’ section offers improvements to this project
noting limitations due to the scope of this work.
BACKGROUND
To narrow focus on previous work, I looked for contributions in two areas - NCAA tournament
success and college basketball recruiting.
Teams invited to March Madness have their regular season performance reviewed by a
committee to give them a seed in one of four regions. The committee grants higher seeds, lower
numbers, to better performing teams. In a Bleacher Report article ‘NCAA Bracket 2019: The

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Secret Formula for Picking a Winner’ by Michelle Bruton, Bruton mentions that “every
champion since 1999 has been a top-three seed” besides for “No. 7 Connecticut in 2014”. This
trend suggests that only 12 out of the 68 teams have an actual shot at winning the national title.
Common characteristics of tournament champions include above-average three point shooting,
ranking top 20 in Ken Pom’s adjusted defensive efficiency, and having a guard win Most
Outstanding Player (Paruk, “The 7 Attributes of a March Madness Champion”).
Outside of the winner of the national championship, seeds can often become subjective
when predicting a team’s success in the NCAA tournament (Neudorfer and Rosset 181). Ranking
systems such as Pomeroy and Sagarin offer a more objective approach (174). The Pomeroy
ranking system offers statistics for a team such as their adjusted offensive efficiency and adjusted
defensive efficiency. A team looking to go far in the NCAA tournament should not disregard
either of these rankings because being “disproportionately dominant on one side of the ball . . .
shows it's probably not going to fare well in the NCAA tourney” (Bruton, “Secret Formula for
Picking a Winner”).
The recruiting process can impact both the short and long term success of a college
basketball program. Short term success in recruiting usually comes in elite talent at the guard
position that “significantly and immediately impacts victories” (Treme et al. 798). With a limited
amount of elite recruits available, college programs should be focusing more on long term
success when recruiting. Elite recruits often leave for the NBA after one year anyways, so
recruiting less talented players that will remain in a program longer will give teams a better
chance to go farther in the NCAA tournament. Treme concluded that “experience trumps
freshman talent in post-season play in the NCAA tournament” (795).

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How does a college basketball program improve its chance of recruiting players out of
high school that can bring their program to the next level? It is suggested that recruits judge a
program based on things such as “recent on-court success, historical on-court success, stadium
size, and playing in a ‘power’ conference” (Evans and Pitts 351). An interesting trend though
indicated that recent on-court success was “less important in the recruiting of guards than for the
top 150 players in general” (354).
APPROACH
I chose three datasets to support my exploration and analysis. The datasets each span from 2009
to 2017 providing data on NCAA March Madness tournament games, college basketball
program team statistics, and high school basketball recruits. Given the previous work discussed
in the ‘Background’ section as well as my own college basketball knowledge, I had a strong idea
of what I would expect to see from each of the individual datasets. Single variable exploratory
data analysis was completed on each of the datasets to confirm or deny my expectations as well
as to generate specific analytical questions. The major contribution for this research is the
development of visualization dashboards that provide guidance to a college basketball program’s
stakeholder on how it can improve the recruiting process.
Datasets.​ The first dataset collected was NCAA March Madness tournament games from 2009 to
2017. Each record for a game contains information such as who won, what seed the winning
team was, and what round the game was in. It was collected from the public BigQuery data
warehouse on the Google Cloud Platform. A query on the table and data cleaning on the team
name allowed me to easily build this dataset.

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The second dataset collected was college basketball program team statistics from 2009 to
2017. For this data, I looked towards kenpom.com which provides team level ratings by season
such as a team’s adjusted efficiency margin, strength of schedule, and win-loss record. The
website was created by Ken Pomeroy who is a respected contributor to the college basketball
statistics and analytics space. In accordance with the websites Terms of Use, HTML pages were
downloaded and parsed using the Beautiful Soup package in Python to create a usable dataset.
The third dataset procured was high school basketball recruits from 2009 to 2017. There
are multiple sources that have good data on recruiting information, but I chose 247Sports.com
which is owned by CBS Corporation. On this website, we can find data on each recruit which
includes rank, position, height, weight, rating, and college committed too. The same steps were
followed to create a usable dataset that I followed for the team statistics dataset. While building
this dataset, I created two new features - IMPRESSION_YEAR and FRESHMAN_YEAR.
IMPRESSION_YEAR is the year before the recruit graduates high school, and
FRESHMAN_YEAR is the year the recruit should be a freshman at the school they committed
too.
Analytical Questions.​ Based on the literature survey in the ‘Background’ section and my single
variable exploratory data analysis, there are analytical questions I seek to answer:
1. What common characteristics do we see in teams that make it to a certain round in the
NCAA tournament?
2. How do the non-power conferences fare in the NCAA tournament compared to their
power conference counterparts?

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3. How does regular season and postseason performance impact recruiting?
4. Can we recommend what recruits a college basketball program should go after through
visualization?
Visualization Dashboards.​ To improve the recruiting process for college basketball programs,
two data dashboards were built in Tableau to provide an interactive system to serve different
types of teams. Pappas and Whitman discuss three categories of data dashboards in ‘Riding the
Technology Wave: Effective Dashboard Data Visualization.’ The data dashboards developed for
this project will both fall under the analytical category which allows us to “see and question,
explore what-if scenarios” (Pappas and Whitman 252).
The first data dashboard addresses the first two analytical questions by producing
visualizations on common characteristics of teams that make it to a certain round in the
tournament. To improve the recruiting process for college basketball programs, a second data
dashboard was developed to answer our third and fourth analytical questions. Recruiting is a two
way street, so it is important that a college basketball program recognizes not just what recruits
to go after but also what recruits are likely to commit to their school.
RESULTS
Common characteristics of NCAA Tournament Teams. ​Here​ you can find the dashboard
published to Tableau Public.

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Figure. 1. Winning team Avg NCSOS Adjusted EM by Avg SOS Adjusted EM
There appears to be distinctions for teams that win a certain round in the NCAA
tournament based on non-conference strength of schedule adjusted efficiency margin (NCSOS
AEM) and overall strength of schedule adjusted efficiency margin (SOS AEM) (see figure. 1).
For teams that win the Round of 64 and Round of 32, we see the SOS AEM around 8.0 while
their NCSOS AEM is around 0.65. When we move to later rounds, we see the SOS AEM around
10.0 and the NCSOS AEM generally around 1.5. The average for SOS AEM moved roughly 2.0
higher between the rounds, and the average for NCSOS AEM moved roughly 0.85 higher
between the rounds.

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Figure. 2. Winning team conference composition at Round 32 (Left) and Elite Eight (Right)
Early in the tournament, there are a diverse group of conferences represented as winning
the Round of 32, but, when we look at winners of the Elite Eight, we find dominance among four
conferences (see figure. 2). It does not come as too much surprise that these major conferences
are represented well later in the tournament, and most of the conferences not represented well - 2
or 1 records - come from major conferences too.
Figure. 3. Recruits freshman year composition at Round 32 (Left) and Elite Eight (Right)
The last common characteristic we attempt to bring forth in this paper is how well a
recruit's team does in the NCAA tournament during their freshman year (see figure. 3). This
attempt comes at a high level because we do not quantitatively measure a recruit’s actual impact
to the team in the scope of this project. For winners of the Round of 32, we see many 3 and 4 star
freshman point guards on the roster, but, for winners of the Elite Eight, we see many more 4 and
5 star freshman point guards on the roster than 3 star point guards. One position teams may not
have to worry much about is the power forward position because we observe a plethora of 3 star
power forwards on the roster in Elite Eight winners.
Attracting recruits. ​Here​ you can find the dashboard published to Tableau public.

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Figure. 4. Visualizing college recruits by how well the college basketball program did in the
NCAA tournament their year before graduation (Blue - 3 stars, Orange - 4 stars, Red - 5 stars)
Here we look at how well a recruit’s school did the year before they graduated - typically
their junior year (see figure. 4). The Round_Calculated feature captures a single record for a
recruit based on the highest round game their college won in the NCAA tournament. We will
exclude teams that did not win an NCAA tournament game, Round_Calculated = -1. We observe
a more equal ratio of 3, 4, and 5 star recruits in teams that made it to later rounds compared to
earlier rounds. Just winning the Round of 32 or higher appears to be a good indicator of your
likelihood to get more 4 and 5 star recruits than 3 star recruits.

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Figure. 5. Visualizing college recruits by their respective teams average adjusted efficiency
margin the year before the recruits’ graduations
The average adjusted efficiency margin matters for every position when you are trying to
recruit 3, 4, or 5 stars. It does seem like there is some more overlap between the distributions of 4
and 5 stars than 3 or 4 stars. It seems though that teams can take their own adjusted efficiency
margin and use that as guidance when going after recruits. There is some wiggle room especially
for teams trying to get to the next level. There is strong overlap in adjusted efficiency margins
between 4 and 5 star point guards, but there is less overlap when it comes between 3 and 4 star
point guards.
CONCLUSION
This paper presented two visualization dashboards created to improve the recruiting process for
college basketball programs at different levels. At the end of the day, the recruits will decide
where they want to go, but a few changes for a team can make a big difference in recruiting
efforts. The results suggest winning the Round of 32 or higher will bring in a higher ratio of 4
and 5 star recruits compared to 3 star recruits. Although to make it past the Round of 32, teams
will need to improve their strength of schedule adjusted efficiency margin but more importantly
their non-conference strength of schedule adjusted efficiency margin.
The scope of this study can be expanded to include more datasets, especially ones with
qualitative data. One aspect of the game that would be interesting to look at is a coaches impact
on the game and how that can subsequently impact recruiting efforts. A limitation of this project
was the absence of player level data, so incorporating a dataset that has those details could
further enhance this work.

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Works Cited
Bruton, Michelle. “NCAA Bracket 2019: The Secret Formula for Picking a Winner.” ​Bleacher
Report​, Bleacher Report, 19 Mar. 2019,
bleacherreport.com/articles/2826396-ncaa-bracket-2019-the-secret-formula-for-picking-
-winner.
Paruk, Sascha. “The 7 Attributes of a March Madness Champion.” ​Sports Betting Dime​,
www.sportsbettingdime.com/guides/strategy/7-attributes-of-march-madness-winners/.
Neudorfer, Ayala, and Saharon Rosset. "Predicting the NCAA basketball tournament using
isotonic least squares pairwise comparison model". Journal of Quantitative Analysis in
Sports 14.4: 173-183. https://doi.org/10.1515/jqas-2018-0039 Web.
Treme, J., et al. “The Impact of Recruiting on NCAA Basketball Success.” Applied Economics
Letters, vol. 18, no. 9, June 2011, pp. 795–798. EBSCOhost,
doi:10.1080/13504851.2010.507171.
Evans, Brent, and Joshua D. Pitts. “The Determinants of NCAA Basketball Recruiting
Outcomes.” Applied Economics Letters, vol. 24, no. 5, Mar. 2017, pp. 351–354.
EBSCOhost, doi:10.1080/13504851.2016.1192268.
Pappas, Lisa, and Lisa Whitman. “Riding the Technology Wave: Effective Dashboard Data
Visualization.” ​SpringerLink​, Springer, Berlin, Heidelberg, 9 July 2011,
doi.org/10.1007/978-3-642-21793-7_29.