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Sign up[PRE REVIEW]: pyHoops: A Python package for advanced basketball data analytics #1784
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Hello human, I'm @whedon, a robot that can help you with some common editorial tasks. For a list of things I can do to help you, just type:
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What happens now? This submission is currently in a You can help the editor by looking at this list of potential reviewers to identify individuals who might be able to review your submission (please start at the bottom of the list). Also, feel free to suggest individuals who are not on this list by mentioning their GitHub handles here. |
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Failed to discover a valid open source license. |
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PDF failed to compile for issue #1784 with the following error: /app/vendor/bundle/ruby/2.4.0/bundler/gems/whedon-efe915e61673/lib/whedon.rb:135:in |
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kyleniemeyer
commented
Oct 4, 2019
Hello @alessandroBombelli, thanks for your interest in JOSS. Before we proceed, during my review of your submission, I could not find the research application of your software. While useful for folks in basketball, I don't quite see where someone would cite this in the academic literature (not an explicit requirement, but one way we think about whether software has a research use). Can you clarify this, per one of our submission requirements?
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danielskatz
commented
Oct 4, 2019
alessandroBombelli/pyHoops#46 should fix the immediate paper building process |
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alessandroBombelli
commented
Oct 4, 2019
Dear @kyleniemeyer, apologies if the research application is not clear. I agree that, at a first glance, the obvious research application might not be as straight-forward as in other cases. On the other hand, a strong interest in data analytics applied to sports has been growing in recent years, as testified by ad-hoc conferences (MIT Sloan Sports Analytics Conference) and journals (Journal of Sports Sciences). To this avail, the research application of this work is the possibility to compute a set of team performance indices (per player and per lineup) to be used, as example, to infer lineup performances in future games. Knowing how different lineups perform against different opposing lineups (e.g., "tall", or "fast" lineups), can be used as part of a decision-making process to improve performances of a team. In addition, a sufficiently comprehensive database retrieved via pyHoops (e.g., a full season), can be used as the input for a prediction model (e.g., random forest) to assess what are the features (lineup- or player-specific) more crucial for a team's success. I hope this answers your question. I am willing to elaborate more if the answer is not satisfactory. Thank you! |
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danielskatz
commented
Oct 4, 2019
I can take |
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alessandroBombelli
commented
Oct 4, 2019
Dear @danielskatz, sorry for the issue. but it is my first time here and, as such, I am not familiar with the formatting. I followed the example provided in the guidelines but thought that, being a single author, the distinction between affiliation and affiliations was not necessary. I fixed the issue now. Please let me know otherwise. |
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kyleniemeyer
commented
Oct 4, 2019
@danielskatz thanks for volunteering—did you mean to close the issue? |
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kyleniemeyer
commented
Oct 4, 2019
@alessandroBombelli ok, so I think it would help to revise your article to better describe the research applications of the software, so this is clear to reviewers (and readers), focusing specifically on the types of research questions/problems this helps answer. What you are describing to me sounds more like what a basketball team might use, but not an academic researcher writing a publication (for example)—but I may still be misunderstanding. |
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danielskatz
commented
Oct 4, 2019
@whedon generate pdf |
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danielskatz
commented
Oct 4, 2019
I've suggested more changes in alessandroBombelli/pyHoops#48 - after merging this, you can enter |
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kyleniemeyer
commented
Oct 4, 2019
@whedon assign @danielskatz as editor |
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OK, the editor is @danielskatz |
whedon commentedOct 4, 2019
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edited
Submitting author: @alessandroBombelli (Alessandro Bombelli)
Repository: https://github.com/alessandroBombelli/pyHoops
Version: v1.5
Editor: @danielskatz
Reviewer: Pending
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