From efb692a2657840732671dd7f3f0aa6b9e7e9353f Mon Sep 17 00:00:00 2001 From: milowessel Date: Fri, 23 Jan 2026 14:35:25 -0500 Subject: [PATCH 1/3] Test Xaringan Presentation --- TestXaringan.Rmd | 28 ++++++++ TestXaringan.html | 180 ++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 208 insertions(+) create mode 100644 TestXaringan.Rmd create mode 100644 TestXaringan.html diff --git a/TestXaringan.Rmd b/TestXaringan.Rmd new file mode 100644 index 0000000..b03df61 --- /dev/null +++ b/TestXaringan.Rmd @@ -0,0 +1,28 @@ +--- +title: "Presentation Ninja" +subtitle: "⚔
with xaringan" +author: "Milo Wessel" +institute: "RStudio, PBC" +date: "2016/12/12 (updated: `r Sys.Date()`)" +output: + xaringan::moon_reader: + lib_dir: libs + nature: + highlightStyle: github + highlightLines: true + countIncrementalSlides: false +--- + +background-image: url(https://upload.wikimedia.org/wikipedia/commons/b/be/Sharingan_triple.svg) + +```{r setup, include=FALSE} +options(htmltools.dir.version = FALSE) +``` + +??? + +Image credit: [Wikimedia Commons](https://commons.wikimedia.org/wiki/File:Sharingan_triple.svg) + +--- + +# This is a test presentation diff --git a/TestXaringan.html b/TestXaringan.html new file mode 100644 index 0000000..a0d4cab --- /dev/null +++ b/TestXaringan.html @@ -0,0 +1,180 @@ + + + + Presentation Ninja + + + + + + + + + + + + + + + + + From cb8dc8b7b7b9de77ba8b56a1ec40ff327756a9ef Mon Sep 17 00:00:00 2001 From: milowessel Date: Fri, 23 Jan 2026 14:38:15 -0500 Subject: [PATCH 2/3] Moved to presentations folder --- .DS_Store | Bin 0 -> 6148 bytes .../TestXaringan.Rmd | 0 .../TestXaringan.html | 0 3 files changed, 0 insertions(+), 0 deletions(-) create mode 100644 .DS_Store rename TestXaringan.Rmd => presentations/TestXaringan.Rmd (100%) rename TestXaringan.html => presentations/TestXaringan.html (100%) diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000000000000000000000000000000000000..54663c274296c825b0645bb98d277ac76533abab GIT binary patch literal 6148 zcmeHKIc~#13?yw828;kMQ|=e?gN32IARkB^y9gjyMilp|@~%8B!=n+Dbao>Q0q*Q_ zITSR7D2j;gug52morrAUhVpI8-0a?bW~+=S5RMurIm?&4!9Ql%e+P{FAQ$-rcbM>d z)EbV%dw1yj&eCX9fC^9nDnJFOzzr10rn=eQpcYF|0V=R81?>A!;D$AE4D?S279RnC z4Z?2N`z!%0768`7F%S`$1{D}o%@IR`j(Ewunm7gqT{MRe%_nP4DC$o~{o?7OHIOS6 zpaN3`)^Xff|9^)6GyhLXJW&BEuqXwzX`u*gFM_w_~8U iV;rm Date: Fri, 30 Jan 2026 13:40:31 -0500 Subject: [PATCH 3/3] Mini Project Idea 1 --- .DS_Store | Bin 6148 -> 6148 bytes .../MiloWesselData400MiniProjectIdea1 (1).md | 15 +++++++++++++++ ...oWesselData400MiniProjectIdea1.md (1).html | 12 ++++++++++++ 3 files changed, 27 insertions(+) create mode 100644 ideas/MiloWesselData400MiniProjectIdea1 (1).md create mode 100644 ideas/MiloWesselData400MiniProjectIdea1.md (1).html diff --git a/.DS_Store b/.DS_Store index 54663c274296c825b0645bb98d277ac76533abab..2f3909a39dde89ca76ed50a4315beeca73ab53b3 100644 GIT binary patch delta 477 zcmZoMXfc=|#>B)qu~2NHo}w@Z0|Nsi1A_nqLvd1haY0f}e$vK+D;euS;;amr3@Hq$ z42cLipy;-wf}G6a5(9&4j7-cdtZeKY>>S)2vB4Sn<-sM1C8fnqiAB*MUPyj^P7;ir zm=ucQViBx9}JP<;N;+p7Z9ti z)-}*k&^4&lQK&YzFw{}7FgLBO<>U}m*0&Cd&(6us%kKa>4G0*4o&f`1C=H`Jfea)M zr4%RUBmo@=QjR2?lWrKCoS$1z57UM!oty9C0tzCaC`WsL7~jlk#~smCq!3V1kO8p* zyA5T*MR_^-d5q~3JG3?)3}M;K&cV+CjINCbzcWwf7ts{~X#+XB0YZblyE#N;1M|cN F766)%bIAYz delta 92 zcmZoMXfc=|#>CJzu~2NHo}wrt0|NsP3otOGGUPFoFeCzT=EOqv$vw>KjEtKPGP|*C ozQD}KyqTSYp9823DDs_oGQWr}CrIZ3AZD0s!y~;pMq~*y0MMiloWesselData400MiniProjectIdea1.md +

Milo Wessel
+Data 400
+30 January 2026

+

Data 400 Mini-Project Idea #1

+

My first idea for my mini project adresses the question: “Which draft-context and early-career performance factors best predict how much an NBA player will over- or under-perform the median career earnings for their draft position?” This relates to both my three-course sequence in economics as well as my deep interest in the front office world of professional sports, and more specifically, the NBA.

+

Data for this project is readily available on websites such as Basketball Reference, Spotrac, and others, but those two will be the primary sites I use data from. I plan to scrape those two (or more) websites to get draft-related (e.g. draft position, age, and year), salary (individual player career earnings for both the model and the median values, which I will adjust for inflation), and early-career performance-based data (e.g. BPM, VORP, PER, TS%, etc.).

+

I plan to use multiple regression because it allows me to isolate the impact of individual “predictors”, such as a player’s Year 2 VORP or their draft age, while holding other factors constant. By training the model using the “Draft ROI” (the difference between a player’s actual career earnings and the median for their draft slot) amid a combination of draft-context and performance variables, I can determine which specific factors carry the most weight in predicting financial over-performance.

+

This project primarily matters to NBA front offices and ownership groups, the people who are making decisions about player contracts. By identifying the specific early indicators of a high Draft ROI, General Managers can continue the modern trend of focusing less on subjective scouting and making more objective, data-driven decisions during contract extensions and trade negotiations. Additionally, the project can provide value to sports agents and players, as it will hopefully highlight which performance metrics (if improved early in a career) are most correlated with higher salaries over the course of a player’s career.

+

On a broader scale, this project can contribute to efficiency/transparency in the professional sports labor market. By identifying specific factors that predict financial over-performance, this research can help ensure that as a player’s career takes off, compensation is more accurately aligned with actual value production rather than their draft position. Ultimately, I believe that applying this approach to sports data can improve the industry’s ability to recognize and reward talent that might otherwise be overlooked by traditional, potentially biased evaluation methods.

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