2025-03-30

Chosen Data

The dataset selected for this presentation is “NBA - Player Stats - Season 24/25” by Eduardo Palmieri, sourced from Kaggle https://www.kaggle.com/datasets/eduardopalmieri/nba-player-stats-season-2425. It contains detailed player statistics for every game of the 2024/25 NBA season up until February 7, 2025.

This data was chosen because it includes detailed and individualized data for every player in each gamed played this season allowing for in-depth statistical analysis and easy comparisons between players.

The data is stored in a CSV file with 16,513 entries, where each row represents a player’s performance in a single game. It includes 24 statistical metrics such as points scored, field goals attempted and made, assists, rebounds, steals, blocks, and more.

If you would like to check out the data or use it in a project of your own, please feel free to check it out at the link provided.

Data Manipulation

The following code was used to import required libraries and manipulate the data to append columns that will be used to create graphic representations later within this presentation.

library(dplyr)
library(ggplot2)
library(plotly)
NBA_data <- read.csv("database_24_25.csv", sep = ",", header = TRUE)

NBA_data <- NBA_data %>%
  group_by(Player) %>%
  mutate(
    points_by_3pt = X3P * 3,
    points_by_ft = FT * 1,
    points_by_2pt = (FG - X3P) * 2,
    Accumulated_PTS = cumsum(PTS)
  ) %>%
  ungroup()

Graph 1: Leading Scorers

The graph below shows the top 10 players’ total season points. The graph only shows since Janruary 1, 2025 to improve readability, but at the start of the season (October 24, 2024) their accumulated points were all 0.

Graph 1 Discussion

Interestingly the total points scored within the season fall within 3 clusters with Shai Gilgeous-Alexander pulling away from the rest.

This data also shows very constant growth for all of the players. This means that these players are consistently putting up high point games every time. Constant growth also indicates that the players are remaining healthy and able to perform every game.

Graph 2: Scoring Breakdown

The most interesting takeaway from this graph is Giannis Antetokoumpo’s ability to make it within the top 10 scorers with only scoring 18 points from beyond the three point line. He definitely makes up for the loss of 3 point scoring with his strong close and middle range game.

Another key takeaway from this graph is Shai has a whopping 385 points from free throws. This is 86 points higher than the next highest held by Devin Booker at 299. These 385 points account for around 24 percent of his total points.

Points Statistical Analysis

The points per game distribution of the top 3 scorers in the NBA:

## [1] "Shai Gilgeous-Alexander point distribution:"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   18.00   28.00   31.50   32.44   35.75   54.00
## [1] "Anthony Edwards point distribution:"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##    8.00   21.00   26.00   27.22   32.50   53.00
## [1] "Nikola Jokic point distribution:"
##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
##   10.00   24.25   28.00   29.70   35.00   56.00

Points Statistical Analysis Discussion

Minimum Values:

  • Anthony Edwards and Nikola Jokic have similar lowest scoring games at 8 and 10 points respectively. These fall well below their averages, but may have outside factors like an injury causing this.

  • Shai’s lowest scoring game is 18 points, meaning so far this season Shai has put up impressive performances every game.

Maximum Values:

  • All 3 players have put up spectacular scoring games putting up points in the mid-50s.

IQR and Median:

  • Shai’s IQR (7.75) is smaller than Edwards’ (11.5) and Jokic’s (10.75), meaning 50% of his games fall within an 8-point range
  • Shai Gilgeous-Alexander is having a remarkable season having the highest median and smallest IQR showing great and consistent results.

Graph 3: Well Rounded Players

Putting up high point games is not the only indicator of contribution to the team. Other stats like rebounds and assists can also tell impact on the game.

Nikola Jokic has impressively been able to make the top 10 scoring list while playing at the center position. Thus far in the season, he has also gotten 584 rebounds and 475 assists for the Nuggets. This graph highlights his strengths beyond scoring, showcasing his well-rounded impact on the court.

Graph 4: Team Stats - Win/Loss Heatmap

Looking at the rows shows the team’s win percentage against the column opponent. This means that the more green horizontally the higher the win percentage the team has. More green horizontally indicates a good season.

When looking at the columns, this is the win percentage against the team. This means more teams beat the team in that column. More green in a teams columns indicates more losses and a bad season.

Graph 4 Discussion and Conclusion

Graph 4 Discussion:

Looking at the rows we can tell that the Oklahoma City Thunder and Cleveland Cavaliers are having strong seasons from their high win percentages.

Alternatively, if we look at the columns we can conclude that the Washington Wizards and the Utah Jazz are struggling this season because they are getting beaten by their opponents at high rates.

Conclusion:

So far this season we can tell that Shai Gilgeous-Alexander has been a dominant scorer in the league leading to OKC Thunder to a very successful season and we should expect to see him continue to perform at a very high level. Other players like Nikola Jokic have also been able to put up impressive numbers in scoring and in other stats. It will be interesting to see how the MVP run will play out for the rest of the season and seeing if these players can continue to put up the numbers they have in this portion of the season.