Analysis 1
| USA |
123 |
| China |
80 |
| Poland |
25 |
| Australia |
22 |
| Canada |
19 |
| France |
16 |
| India |
14 |
| United Kingdom |
14 |
| Philippines |
13 |
| Germany |
10 |
| Korea |
10 |
| Vietnam |
10 |
| New Zealand |
8 |
| Sweden |
8 |
| Russia |
7 |
| Singapore |
7 |
| Taiwan |
7 |
| Thailand |
7 |
| Denmark |
6 |
| Netherlands |
6 |
| Czech Republic |
5 |
| Indonesia |
5 |
| Ireland |
5 |
| Ukraine |
5 |
| Argentina |
4 |
| Hungary |
4 |
| Malaysia |
4 |
| Mexico |
4 |
| Peru |
4 |
| Spain |
4 |
| Switzerland |
4 |
| Brazil |
3 |
| Finland |
3 |
| Hong Kong |
3 |
| Italy |
3 |
| Japan |
3 |
| Mongolia |
3 |
| Norway |
3 |
| Romania |
3 |
| Austria |
2 |
| Bangladesh |
2 |
| Chile |
2 |
| Georgia |
2 |
| Israel |
2 |
| Slovakia |
2 |
| Belgium |
1 |
| Bolivia |
1 |
| Bulgaria |
1 |
| Colombia |
1 |
| Dominican Republic |
1 |
| Estonia |
1 |
| Greece |
1 |
| Iran |
1 |
| Kazakhstan |
1 |
| Kosovo |
1 |
| Kyrgyzstan |
1 |
| Lithuania |
1 |
| Slovenia |
1 |
| South Africa |
1 |
| Turkey |
1 |
| Uzbekistan |
1 |
Analysis 2
| Male |
248.949 |
| Female |
266.650 |
Blog Post
The dataset we are working with is the World Cube Association (WCA) Results Export. This dataset includes information about WCA participants, their scores, competition results, and demographic information. Per the documentation, the goal of publishing and maintaining this data is to provide the cube enthusiast community with an easy way to perform analysis and statistics on data of interest. The data our analysis was performed on was accessed on March 12th, 2025. The guiding question for our analysis is what do the most successful WCA competitors have in common? We define “most successful” as participants within the top 500 rank based on best average result for the 3x3x3 cube event. We are curious about what attributes these competitors may share.
Our first code chunk determines what country is most popular for these most successful competitors to play for. To do this, we utilized the RanksAverage file to determine the top 500 competitors. We found that many participants were tied for ranks, so we opted to include those in the top 500 ranks, which ended up being 503 competitors. We then joined this to the Persons file to find what country each participant played for based on their identification number. We found here that some participants played for multiple countries. We opted to include all counties instead of choosing one, as they all do count as a country played for by a top competitor. Our findings are that the most common country that the most successful competitors play for is the USA with 123 competitors. China is not far behind with 80, and Poland, Australia, Canada, Frame, India, the United Kingdom, Philippines, and Germany have between 10-25 competitors. For the second analysis we followed the same procedure of selecting the top 500 competitors as before, this time joining the RanksAverage file with the Persons file by the “id” variable. We renamed the “personid” variable in RanksAverage to “id” in order for the variable names to match and for the join to be successful. We then selected a few variables to analyze with WorldRank and Gender being our focus. On average, male competitors ranked higher than female competitors. Additionally, we found that there were many more male competitors compared to female competitors, a difference of about 475 people. These differences are displayed in the “Top 500 World Ranks by Gender” bar chart.
Through our analysis we found that the top competitors (defined here as the top 500 in the world) were mainly from the USA and China. Additionally, most of the top competitors were male compared to female. In our analysis we joined tables in order to look at variables from different datasets together. A potential issue with this is that a dataset can provide context for a variable and separating variables from different data sets can erase some of that. In this case for example, only select variables from the Persons data frame were examined, leaving out other information that provide a more holistic view into the competitor’s identity and boiling down their attributes to a single component such as the country they play for.
