The following is a polar chart setup for player scouting reports. I believe it is best for scouts to focus on the areas that we can not yet accurately measure using data. This template utilizes the Basketball Athleticism model created by Ben Taylor and explained in detail on his podcast, link to podcast.
So the scout will grade the player on a 1-100 scale for each category, than a data analyst will store results than aggregate scores updating the charts throughout the season. This way decision makers have a clear visualization of the scouting departments assessment of players in these hard to define, often arbitrary categories. This would be a more valuable addition to scouting reports for prospective NBA players as well as current ones.
It would also be a valuable tool in better understanding your own team’s scouting department. Once a scout has submitted enough reports, you could analyze those reports to find strengths and biases that said scout may hold.
## ── Attaching packages ─────────────────────────────────────── tidyverse 1.3.1 ──
## ✓ ggplot2 3.3.5 ✓ purrr 0.3.4
## ✓ tibble 3.1.4 ✓ dplyr 1.0.7
## ✓ tidyr 1.2.0 ✓ stringr 1.4.0
## ✓ readr 2.0.1 ✓ forcats 0.5.1
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
## Loading required package: usethis