Review #2 about basketball HOT 3 CLOSED

Thanks @seanjtaylor I appreciate the detailed review. I'll have time this weekend to address your concerns and push up an update.

from basketball.

Thanks @seanjtaylor for this review!

from basketball.

Update pushed up that incorporates most of the reviewer's suggestions. What is specifically addressed is below in italics.

Weaknesses

• The majority of the paper is code for data-munging. While I appreciate the pragmatism here, it seems brittle to provide so many implementation details here, rather than high level function signatures. I'd much prefer to see a brief description of the functions and example input/output dataframes shown as tables. It's very hard to slog through R code inline while reading and I'm sure the reader will likely just use the functions directly anyway. The implementations are much less interesting than what they conceptually do.

Removed code and expanded on talking about the functions at a higher level. Example input/output df's shown as tables is now included.

• In many cases the code is not idiomatic R code. I'd strongly prefer example code that fits the tidy data usage patterns -- it would be more representative of modern R code and more readable.

This is still being worked on; I pushed this aside to get the paper out ASAP and figured that since the code itself is decoupled from the paper that I could work on that on the side while this is AE reviewed

• The visualizations in the paper could use some work. Figure 1 would be clearer with a truncated x-scale. Figures >= 2 are really the star of the show and should be presented first thing in the paper.

Made the Figure (no longer Figure 1) showing the distribution of euclidean distances truncated as suggested. I kept the Figures at the end of the paper but it's not a problem to move them up if AE agrees with this as well.

• In general there should be more visualizations or summarization of the data in order to understand what the data look like. I get very little sense of the format of the data from code, but a few plots with less-heavy modeling could tell a story about what's going on. For instance just a heatmap of positions and velocities (before even measuring acceleration) would be useful to see.

With the other changes I think it is more clear what the data looks like, and what new data is being created in the pipeline to produce the end result. I was considering adding such a heatmap but I think it would distract away from the empirical eta visualizations.

• The paper doesn't have a clear objective for success of "modeling player movement". I don't actually know what a good player movement model is, and after reading the paper I'm still not sure. There's a conclusion that the plots based on one game pass the "smell test" but I would challenge the authors to provide a better criterion for the success of this. I understand basketball pretty well, but I struggled to understand that these plots were a successful effort. For instance you might consider it a prediction problem (can I predict where a player will be some time steps ahead?) or might describe some clusters of movement patterns as representing different types of play.

I think the added text from the last bulletpoint underneath Minor Points, which suggests more use and/or practical applications that can be obtained from this result, helps address this point.

Minor Points

• The list of column names should be presented as a table, not bulleted list.

Changed

• It would be useful to have a graphical overview of the munging process (e.g a flowchart) as part of the paper so I could see where it was going at a high level. You might include the function names here.

Added TiKZ flowcharts

• I suggest that in the conclusion the authors take some time to suggest what an analyst might use these visualizations for. Are they useful for evaluation players? Do they have strategic implications?

Added a section that goes into more detail about how the visualizations can provide practical use, in the last paragraph.

from basketball.