Part one of an analytical study and future report analyzing how historical data can be used better project player performance based on age, position, draft placement and career length.
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.
The focal point of this paper is an attempt to examine the relationship between the percentage-based contractual salaries (log(salary)) [hereto referred throughout as “LSalary”] of qualifying 2013 Major League Baseball players and the following statistics: age, age squared, games, home-runs, slugs, hits, at bats, and on base percentage. As there are many factors that are contributory towards LSalary, those players who are designated as pitchers have been omitted from the data set as they have dissimilar qualities that obscure the intended data set.
With this restricted data set, the sample consists of four hundred forty seven observations across thirty teams within the 2013 season. Utilizing the aforementioned restricted data collection, we have regressed LSalary, age, age squared, games, home-runs, slugs, hits, at bats, on base percentage, strikeouts, times caught stolen, stolen bases and runs. The empirical results suggest that age, age squared, games, home-runs, slugs, hits, at bats, and on base percentage are arguably significant drivers in determining a player’s LSalary.
Mats André Zuccarello Aasen, commonly known as Mats Zuccarello, was born on September 1, 1987, in
Oslo, Norway. He grew up in the bustling neighborhood of Løren, where his passion for ice hockey began
at a young age. His mother, Anita Zuccarello, is of Italian descent, and his father, Glenn Aasen, is
Norwegian. This multicultural background played a significant role in shaping his identity and versatility
on and off the ice.
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.
The focal point of this paper is an attempt to examine the relationship between the percentage-based contractual salaries (log(salary)) [hereto referred throughout as “LSalary”] of qualifying 2013 Major League Baseball players and the following statistics: age, age squared, games, home-runs, slugs, hits, at bats, and on base percentage. As there are many factors that are contributory towards LSalary, those players who are designated as pitchers have been omitted from the data set as they have dissimilar qualities that obscure the intended data set.
With this restricted data set, the sample consists of four hundred forty seven observations across thirty teams within the 2013 season. Utilizing the aforementioned restricted data collection, we have regressed LSalary, age, age squared, games, home-runs, slugs, hits, at bats, on base percentage, strikeouts, times caught stolen, stolen bases and runs. The empirical results suggest that age, age squared, games, home-runs, slugs, hits, at bats, and on base percentage are arguably significant drivers in determining a player’s LSalary.
Mats André Zuccarello Aasen, commonly known as Mats Zuccarello, was born on September 1, 1987, in
Oslo, Norway. He grew up in the bustling neighborhood of Løren, where his passion for ice hockey began
at a young age. His mother, Anita Zuccarello, is of Italian descent, and his father, Glenn Aasen, is
Norwegian. This multicultural background played a significant role in shaping his identity and versatility
on and off the ice.
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Mock 2024 NHL Draft Experts Divided after Celebrini, Levshunov, Silayev go in...Ice Brek
After the NHL Draft Lottery on Monday, Adam Kimelman, NHL.com’s deputy managing editor,
and Mike G., senior draft writer, Morreale make their predictions for how the first 16 selections
of the 2024 Upper Deck NHL Draft could turn out.
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Project Jump - Part 1
1. “THE JUMP”
An analytical study and future report showcasing the effects
drafting individuals with varying collegiate experience levels has
on the on-court and future monetary value of each professional
NBA Player.
The purpose of this study is to understand the effects of selecting
specific individuals at certain points in the draft and how it equates
to logical levels of production. Using this information, it is my hope
that we can generate reliable projections and realistic expectations
for both the organization and the draftee.
Researched and Written by: Justin Ullestad
2. THE JUMP
About this document:
This document is the first part of a larger study diagramming the value of any
player based on the draftee’s age and ranking/selection number in the NBA Draft.
Purpose:
The purpose of this document is to: 1) illustrate the true value of drafting a player
at a given point in the draft 2) emphasize production trends from any given draft
location 3) showcase the flaws and dangers when drafting freshmen outside of the
top five 4) and show the commonalities between both salary and career longevity
with draft year and pick number.
Background:
Over the past decade, the game of basketball has changed tremendously. With
fluidity, spacing, and exceptional ball movement replacing “isolation basketball”
for the most part, the intangibles teams should be looking for and analyzing during
the NBA Draft process need to reflect both these ideas, as well as the
culture/outlook of the locker room and organization. However, it is my belief that
the model for selecting individuals is flawed in this aspect, in that teams are often
drafting based on a “players ceiling” and athleticism. Although these are both
important when picking a player, today’s NBA game doesn’t necessarily
guarantee that athleticism and potential equates to success.
As recent NBA Draft history illustrates (as you will see in this study), teams are
more likely to take a college freshman in the top five than any other class. Since
2004, more freshmen have been taken in the top five than all other college classes
combined. However, only 12 of those players rank in the top 100 PER over their
first five years in the NBA. Anthony Davis (who ranks #1 in PER in the data
sample) is one of the most valuable young players in the league today, but even he
has admitted that it took him a couple years to build the necessary strength to
compete on a nightly basis in the NBA after playing just one year at Kentucky.
With the advancement and increased use of metrics altering the way teams and
players approach the game; the next step should be to use this data to make more
educated decisions during the draft selection process. Using measurable metrics
that take into consideration not only players skill set and intangibles, but also
player comparisons from past like-scenarios would not only benefit the
organizations participating in the draft, but also the players deciding whether or
not to enter the draft, the draftees, and the overall health of the NBA game.
3. THE JUMP
Data Selection:
For this segment of the project, I chose to use the first five years of any given
player’s career between the 2004-05 and 2014-15 seasons. In order to garner an
effective sample to cross the last decade basketball, it was important to sample a
similar set of data for each player.
Given the first five years (or less) of a player’s time in the League is considered
the most important years of his career – for both monetary and on-court value
reasons – this data gives teams a baseline of what to expect/desire from draftees at
various levels of selections during the draft process. Generally, the players who
had a PER ranked in the top 100 in this model have had/are having/on their way to
having solid careers in the NBA.
Next Steps in this Project:
This project was originally intended to be a thesis paper to conclude my MBA at
Florida Atlantic University. At this moment, my desire is to continue my research
and publish my findings in either paper or book form. The current set of data you
are about to be immersed in is a small sample size of what the final project is to
cover. Other topics to be included in the final document will include:
• Pros and cons of changing the NBA age limit and effective ways to
implement such a rule
• Effects early entry into the NBA draft is having on college basketball
• Disappearance of “true post players” and how early exits from college
could play a factor
• How a NCAA/NBA relationship could increase revenue, player heath and
safety, and viewership
Thank you:
I want to thank you for taking the time to read through this analytical study. There
is still more research to be done before the project will be completed, but I feel
this information is relevant to not only the NBA Draft process, but to the current
landscape of the League in general. I am incredibly passionate about the sport
basketball and the future of the game as a whole.
If you have any comments or questions about this particular study, the overall
project or any of the following data, please feel free to contact me directly and I
would be happy to discuss it with you in more detail.
4. THE JUMP
Draft Totals by Class (2004-2015)
Over the past decade, the high school and freshman
classes have dominated the headlines during NBA
Draft Weekend. With 29 of the possible 60 top 5
selections including either of these classes (1 out of
every lottery picks), the league has seen an influx of
young talent at every position.
Although underclassmen are characteristically taken
early on, it is interesting note that the senior class
dominates the last finals 45 selections in the draft
within this data set. Additionally, teams have
been able to find quality international players
outside the lottery over the past decade with
16 of the 20 highest paid international players
being selected after pick 14.
Although there is tremendous surface value
for taking talented underclassmen early in the
draft, the remainder of this document will
hopefully shed light on the true value of
where and when players are selected during the draft process.
6. THE JUMP
Average Career Length by Draft Class
(2004-2015)
One of the major selling
points projected by teams
and the media for selecting a
younger player early in the
NBA Draft is this idea that
youth equates to a longer
career/tenure with the
organization. If an
organization can grab a
player at 19 years, he is still
in the prime of his career
after 10 seasons.
Although that theory makes
sense on the surface, the
average career length of
NBA player isn’t nearly that
long. In fact, the only area
that theory remotely reaches
a “players prime” in this data
set is when considering the
average career of a High
School player drafted in the
first round (small sample
size).
The reality is that lottery selected players average a longer career length than those
who are drafted later or go undrafted. However, there isn’t a strong correlation that
age plays a dominant factor in forecasting a players career length. As shown in the
above graph, the variation between those players who attend college and decide
enter the draft early (Freshman – Junior) is just over 8%. Additionally, the data is
weighted heavily towards the underclassmen (as many of the top players in college
leave for the NBA early) which illustrates that seniors taken post lottery have a
longer career average than that of an underclassman taken at the same pick
number.