Peer Review – NBA Player Performance Analysis by Sneha Agrawal
As someone who is not familiar with NBA players or basketball statistics, I found some parts of the analysis a bit hard to follow without more background.
This project explores two simple questions using NBA stats: does being in the playoffs affect a player’s performance (measured by Game Score), and is there a link between total rebounds and points scored? The structure is clear, with a logical flow from the purpose to the results. The visuals look good, and the author uses the right statistical tools. The questions are meaningful and the results are interesting.
However, to help readers who may not know basketball stats, a few things should be added.(GmSc), (TRB), and (PTS) are used in the project, but I didn’t know what they meant. It would help a lot to explain these early in the report so readers like me can follow along more easily. in the beginning so the reader doesn’t get confused.
The project mentions different audiences such as coaches, analysts, fantasy players, and fans, but doesn’t explain how each can use the results. A few quick examples would help.
The dataset is loaded correctly using R, but the project doesn’t say how many players are included, which season the data is from, or where it came from. Adding this info would make the project more complete and easier to understand.
One of the visuals, a boxplot comparing Game Score between playoff and non-playoff players, is missing because the code was commented out. Turning the boxplot back on and explaining what it shows would add value. However, without explanation, it is hard to understand what this plot means, especially for someone not familiar with the sport or the metric.
The scatter plot between rebounds and points scored includes a regression line, which is good, but there’s no explanation. It would also be clearer if the correlation value (r = 0.091) or R² was shown directly on the plot using annotate () in ggplot.
Also, pointing out some special cases or patterns, like players who score a lot but don’t rebound much, could help.
The statistical test results are explained, but some details are missing about what test was used (e.g. t-test), the average Game Scores for each group, standard deviations, how many players were analyzed, and what the size of the effect was. These details help readers trust the results. Also, it’s important to check assumptions to be sure the test is valid.
The report mentions that the rebounds and points scored are weakly related, but this could be explained better. Just because a relationship is statistically significant doesn’t mean it’s useful for predicting something. It would be helpful to say this directly. If possible, you could also explain why Pearson’s r was used and show r or R² visually on the plot. Adding confidence intervals would also make the results more helpful.
The insights are a good start but would be stronger if you clearly explained how the results can be used. For example, analysts could combine Game Score and rebounds to get a better picture of performance. Without this, some readers might miss the real value of the findings.
Finally, a summary slide or section with key points would help the reader remember the most important insights. It would also be helpful if the conclusion gave a bit more explanation of what the findings mean and how they can be useful for different types of audiences. This would make the conclusion feel more complete and clearly connect the results to practical use. As someone unfamiliar with basketball stats, I think the project has useful ideas, but it was sometimes hard to fully understand the meaning of the visuals and results without more explanation. With a few added definitions and clearer conclusions, it could be much more helpful for readers like me.
Overall, this project uses the right tools, asks meaningful questions, and has clear visuals. But it needs a few more details to be fully complete: define key terms, describe the dataset, show the stats behind the analysis, and explain how different readers can use the results. With these changes, the project will be much stronger and more useful for everyone.
Thank you for sharing your research. I hope my feedback helps you improve your Methods section and strengthens your overall paper. Best of luck with your final submission!