diff --git a/.DS_Store b/.DS_Store new file mode 100644 index 0000000..2f3909a Binary files /dev/null and b/.DS_Store differ diff --git a/ideas/MiloWesselData400MiniProjectIdea1 (1).md b/ideas/MiloWesselData400MiniProjectIdea1 (1).md new file mode 100644 index 0000000..dda13a0 --- /dev/null +++ b/ideas/MiloWesselData400MiniProjectIdea1 (1).md @@ -0,0 +1,15 @@ +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. \ No newline at end of file diff --git a/ideas/MiloWesselData400MiniProjectIdea1.md (1).html b/ideas/MiloWesselData400MiniProjectIdea1.md (1).html new file mode 100644 index 0000000..310dc96 --- /dev/null +++ b/ideas/MiloWesselData400MiniProjectIdea1.md (1).html @@ -0,0 +1,12 @@ +MiloWesselData400MiniProjectIdea1.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.

+ + \ No newline at end of file diff --git a/presentations/TestXaringan.Rmd b/presentations/TestXaringan.Rmd new file mode 100644 index 0000000..b03df61 --- /dev/null +++ b/presentations/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/presentations/TestXaringan.html b/presentations/TestXaringan.html new file mode 100644 index 0000000..a0d4cab --- /dev/null +++ b/presentations/TestXaringan.html @@ -0,0 +1,180 @@ + + + + Presentation Ninja + + + + + + + + + + + + + + + + +