Boxer's Pay Analysis
Business Problem and Key Questions
Due to my interest in boxing, as an amateur boxer myself, I thought it would be interesting to analyze what factors most affect a boxers pay.
There were a few standard insights I wanted to know about each boxer:
Boxer's age
Boxers record
Boxers win %
Boxer's knockout %
To find out why some boxers earned more, I needed to answer the following key questions:
What weight class did boxers earn the most money?
What age range earned the most money?
What boxers earned the most?
What boxers earned the least?
Data Preparation
To prepare my data, I first had to connect the data to excel.
I downloaded a CSV file from a boxing organizations public dataset:
WBC_Match_Details_2009-20017
I then imported it into excel.
First I deleted the unnecessary columns, which left me with the following:
To find the boxers weight class, I created a new column with the following formula:
=
IF($G2<=105,"Minimumweight",
IF($G2<=108,"Jr Flyweight",
IF($G2<=112,"Flyweight",
IF($G2<=115,"Super Flyweight",
IF($G2<=118,"Bantamweight",
IF($G2<=122,"Super Bantamweight",
IF($G2<=126,"Featherweightweight",
IF($G2<=130,"Super Featherweight",
IF($G2<=135,"Lightweight",
IF($G2<=140,"Super Lightweight",
IF($G2<=147,"Welterweight",
IF($G2<=154,"Super Welterweight",
IF($G2<=160,"Middleweight",
IF($G2<=168,"Super Middleweight",
IF($G2<=175,"Light Heavyweight",
IF($G2<=200,"Cruiserweight",
IF($G2>200,"Heavyweight"
)))))))))))))))))
This gave me the weight class of each boxer
I then sorted the 'Purse' column from largest to smallest, and deleted duplicates within the 'boxer' column. This gave me each boxers highest pay for a single match.
I then saved the new CSV file and imported it into Power BI.
For the 4 standard insights, I used the following approach:
For the boxer's record I created 2 new measures to find the number of wins by decision and losses by decision
Wins by Decision = 'Boxer Information'[Wins] - 'Boxer Information'[KO]
Losses By Decision = 'Boxer Information'[Losses] - 'Boxer Information'[L-KO]
Created a measure for the boxer's Win%
Win % = 'Boxer Information'[Wins] / 'Boxer Information'[TFights]
Created a measure for the boxer's % Wins by Knockout
% Wins by Knockout = 'Boxer Information'[KO] / 'Boxer Information'[wins]
Used a SWITCH statement to find the boxer's Age Range
Age Range = SWITCH(TRUE(), 'Boxer Information'[Age] < 30 , "Under 30", 'Boxer Information'[Age] <36 , "30-35", 'Boxer Information'[Age] < 100, "35+")
Here's the approach I used to answer what weight class boxers earned the most money:
Created a bar chart to show the highest purses by weight class.
Here's the approach I used to answer what age range earned the most money:
Created a bar chart to show the average purse by age range
Here's the approach I used to answer what boxers earned the most & who earned the least:
Created an area chart to show the average purse by win %
Created a table which highlights specific boxer details
*Standard metrics can also be filtered by drilling through on a certain boxer of interest *
Within this table there are 2 key visualizations:
A highlighted column with a gradient to show what boxers are earning the most
A bar column showing each boxer's number of matches
Results & Key Takeaways
Weight Class Earnings
Boxers in the welterweight class earn the most money, with the highest earning at $25 million
Age Range Earnings
2. Boxers over 35 earned the most money on average, at $700,000
Boxer Earnings
3. Emmanuel Pacquiao earned the most money, at $25 million
4. Anesito Gesta earned the least money, at $1,000
