# data[].attributes.headline
Data Set of Ages of Players on Manchester United F.C. Roster 2020-2021

# data[].attributes.subject
Data Set of Ages of Players on Manchester United F.C. Roster 2020-2021

# data[].attributes.body
David de Gea  | 30
Lee Grant  | 38
Sergio Romero  | 34
Dean Henderson  | 24
Nathan Bishop  | 21
Victor  Lindelöf  | 26
Eric Bailly  | 26
Phil Jones  | 29
Harry Maguire  | 29
Luke Shaw  | 25
Alex Telles  | 28
Aaron Wan-Bissaka  | 23
Brandon Williams  | 20
Axel Tuanzebe  | 23
Pual Pogba  | 28
Juan Mata  | 32
Fred  | 28
Bruno Fernandes  | 26
Daniel James  | 23
Nemanja Matic  | 32
Donny van de Beek  | 23
Scott McTominay  | 24
Ethan Galbraith  | 19
Edinson Cavani  | 34
Anthony Martial  | 25
Marcus Rashford  | 23
Mason Greenwood  | 19
Amad Diallo  | 18
Shola Shoretire  | 17
Matej Kovar  | 20
Pual Woolston  | 22
Jacob Carney  | 19
Ondrej Mastny  | 18
Ethan Laird  | 19
Max Taylor  | 21
Di'shon Bernard  | 20
Harvey Neville  | 18
Reece Devine  | 19
Arnau Puigmal  | 20
Dylan Levitt  | 20
Mark Helm  | 19
Connor Stanley  | 19
Charlie McCann  | 18
Aliou Traore  | 20
D'mani Mellor  | 20
Anthony Elanga  | 18

# data[].attributes.body
Mean  | 23.413043
Median  | 22.5
Mode  | 20
Midrange  | 27.5
Standard Deviation  | 5.1621532
Range  | 21

# data[].attributes.body
I thought that it would be interesting to compare the ages of the players on the Manchester United F.C. It is crazy to believe that some of these players on the main roster are still in their teenage years, such as Ethan Galbraith. On the opposing side, Lee Grant is still playing at age 38 and is putting in great work for the team, 20 years older than the youngest players on the roster. Most of the other players fall around their early twenties.

To continue the talk on Lee Grant, his data is represented as the outlier of the team. He is the only player to fall within the 36-40 age group. The other players show a strong resemblance around their early twenties with the mean being approximately 23 and the median being 22.5. It is also crazy to see the range as high as 21 when they are all playing soccer on the same field and at the same skill.

I wanted to group these players into 5 different groups each group representing 5 consecutive years of age. This was a great number because it left plenty of room for me to add data to the chart without creating a cluster of information. It also fits perfectly with the youngest player being 17 and the oldest being 38.

Although I did not have data that could be presented over time, I was able to create three different charts representing the data groups. It is evident that in each chart as the age increases the frequency decreases. It seems obvious that these clubs want fresh and young talent on their team considering that the peak of this data is in the 16-20 age group. If I had to select one chart to display this data I would probably choose the histogram because it shows the most data or the line graph because it shows the steady decline in frequency as the players get older. Overall, it was interesting to see how age correlates to frequency when playing professional soccer.