The dataset used in this research is a large NBA data set that gives statistics on players and their season. It gives common basketball statistics like points per game, assists per game, games played, and more.
Question: What are the most common ages in the NBA?
This graph shows count of ages of players recorded in the NBA seasons. Can find correlations about players and when they start to fall off in general.
Question: How does age and level of scoring points per game interact with each other?
A scatter plot that shows points per game by age. I find this interesting because you can see the general downward trend in the points per game as the age gets higher. Interpretation: You see the trend of how points per game goes down as age goes up.
How do we narrow down the outliers in visualization 2 to find them?
Similar to the last visualization, but breaks it down a season-by-season basis. Interpretation: You can now have a better idea of in which seasons older players specifically performed well, and have an easier time knowing where to look in the dataset.
Do age and assists show a similar relationship as points and age?
This visualization takes and builds on visualization 3 by giving easier ways to read the data with geom_smooth(). The smooth lines generally go down, which is easier to attain a concrete result from, but it also shows that age does not play as significant of a factor in assists per game as it does in points per game. It still does generally go down though.
How can I identify which players averaged a high amount of points AND assists per game compared to the rest of the league in each season?
This visualization shows points per game and assists per game on the axis’ along with the teams a player played for sorted by color. Interpretation: You can truly identify standout players of each season, and even potentially pin point them down because of the colors of the points that indicate which team the player played for.