Data
Below is a showcase of the data chosen and how it was visualized.
Introduction
For the data gathered I chose to use a bar graph as it clearly identifies the differences between the different variable age groups within the data set as compared to other forms of graphs. The two factors are the age of the subjects and the amount of time they have devoted to the game.
Data Visualisation
Filtering through the data I only acquired the subjects' ages, ranking, amount of hours played per week, and total hours in-game. Placing them in a separate spreadsheet I created two bar charts one showing how many people of differing ages managed to achieve the ranks as well as how much time was played between the number of people of differing ages
Findings
My findings reveal that people which were younger near the ages of 16-20 managed to achieve a higher rank with those who were older more likely to acquire a lower rank than other age groups. The findings also showed that people which put in more time tended to acquire a higher rank.
Insights
One can understand from the data that younger people which play the game tend to acquire a higher rank than those of older ages. Due to the fact that they play the game more per week. One can ascertain the average amount of time needed to acquire a rank by taking the number of players within the rank and dividing it by the total amount of hours played. Rank 1: 264hrs Rank 2: 331hrs Rank 3: 494hrs Rank 4: 588hrs Rank 5: 2019hrs Rank 6: 988hrs Rank 7: 1581hrs
There is a discrepancy between the steady increase in hours when at rank 5 where it takes more time to acquire rank 5 than rank 6 or 7. This is probably due to a skill ceiling within the game where the majority of players who do not have adequate skill find themselves plateauing at rank 5 despite more hours played than the other ranks.
Conclusion
Based on the information one can conclude that younger players tend to have a better chance of achieving a higher rank. From Rank 5 onwards, superior skill will also be required compared to hours played in order to reach the higher ranks as the majority of players are spending their time within rank 5.
References
- UCI Machine Learning Repository: Skillcraft1 Master Table Dataset Data Set, https://archive.ics.uci.edu/ml/datasets/SkillCraft1+Master+Table+Dataset.
