Column
Visualization 1
Column
Visualiztion 2
Visualization 3 & 4 + Questions
1.
Our data is a csv file with data of the top 50 scorers in the NBA during the 2022-23 season. The data was received from the book “Visualize This” by Nathan Yau https://learning.oreilly.com/library/view/visualize-this-2nd/9781394214860/c06.xhtml
| Variable | Type |
|---|---|
| Rk | Quantitative (integer) |
| Player | Categorical |
| Pos | Categorical |
| Age | Quantitative (integer) |
| Tm | Categorical |
| G | Quantitative (integer) |
| GS | Quantitative (integer) |
| MP | Quantitative (float) |
| FG | Quantitative (float) |
| FGA | Quantitative (float) |
| FGpct | Quantitative (float) |
| 3P | Quantitative (float) |
| 3PA | Quantitative (float) |
| 3Ppct | Quantitative (float) |
| 2P | Quantitative (float) |
| 2PA | Quantitative (float) |
| 2Ppct | Quantitative (float) |
| eFGpct | Quantitative (float) |
| FT | Quantitative (float) |
| FTA | Quantitative (float) |
| FTpct | Quantitative (float) |
| ORB | Quantitative (float) |
| DRB | Quantitative (float) |
| TRB | Quantitative (float) |
| AST | Quantitative (float) |
| STL | Quantitative (float) |
| BLK | Quantitative (float) |
| TOV | Quantitative (float) |
| PF | Quantitative (float) |
| PTS | Quantitative (float) |
| Player-additional | Categorical |
2. I did not reuse any other visualizations as far as I know.
3. (they are above)
4.
The dataset I’m working with contains a set of basketball player performance attributes. Each observation represents a single player, and the variables describe measurable, quantitative aspects of their on-court contributions.
Visualization 1: The bubble plot shows how scoring and assisting relate while also incorporating rebounding through bubble size. By coloring the bubbles by position, the plot makes it clear that guards tend to produce more assists, while forwards and centers contribute more heavily to rebounding. The spread of the bubbles also shows that high scorers appear across multiple positions, highlighting that offensive contributions come from a variety of roles in today’s NBA.
Visualization 2: The heatmap summarizes average points per game for every position across all teams. This heatmap allows us to quickly compare which teams rely more heavily on certain positions for scoring. Some teams show strong scoring from guards, while others lean on their forwards or centers, revealing differences in team strategy and roster construction.
Visualization 3: The density plot examines how scoring varies by age group within each position. It shows that players in their prime years (25–30) typically have the highest scoring distributions, while younger players under 25 tend to show more variability across positions. Older players (30+) display different scoring patterns depending on their role, suggesting that age affects positions differently in terms of offensive output.
Visualization 4: The radar chart helps communicate not just raw performance numbers, but deeper insights into player roles, strengths, and comparative value. It translates complex, multidimensional statistical data into an interpretable visual story about how different athletes contribute to their teams.