This primary goal of this project is to calculate Regularized Adjusted
Plus-Minus
(RAPM)—an “advanced statistic”—for NBA players. The calculated values
can be found in the set of metrics_join CSVs in the project’s
repository.
I plan to write about this project in more detail on my blog. so I encourage the reader to read more about it there.
If you were to fork this project and try to run it from scratch, below shows the required order of function calls.
First, download all of the data needed.
# pre-process ----
# Note that `overwrite = FALSE` is the default, but setting it explciitly here to remind
# the user that it is an option.
# This goes to the
download_pbp_raw_files(overwrite = FALSE)
download_nbastatr(overwrite = FALSE)
download_rpm_espn(overwrite = FALSE)
download_rapm_sz(overwrite = FALSE)Next, run the “main” function. This is what is run with the command-line interface (CLI) that also comes with the project.
# This reads from the config.yml files.
auto_main()Below is a visual comparison of various RAPM-related metrics, either
calculated in this project (i.e. calc) or retrieved from an external
source.
The data behind this visual
| y | apm_calc | bpm_nbastatr | dbpm_nbastatr | drapm_calc | drapm_sz | drpm_espn | obpm_nbastatr | orapm_calc | orapm_sz | orpm_espn | pm_nbastatr | rapm_both_calc | rapm_calc | rapm_sz | rpm_espn |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| apm_calc | NA | 0.0857897 | 0.0068202 | 0.3504192 | 0.1118109 | 0.0059843 | 0.0863602 | 0.4191907 | 0.0972412 | 0.0633819 | 0.1194202 | 0.5170582 | 0.6750558 | 0.2135919 | 0.0727325 |
| bpm_nbastatr | 0.0857897 | NA | 0.2852047 | 0.0273031 | 0.0027746 | 0.0471514 | 0.8274349 | 0.1482843 | 0.0453781 | 0.1901469 | 0.0437862 | 0.1743086 | 0.1600988 | 0.0418785 | 0.2560962 |
| dbpm_nbastatr | 0.0068202 | 0.2852047 | NA | 0.0912274 | 0.0503027 | 0.2727653 | 0.0168123 | -0.0024059 | -0.0017948 | -0.0018229 | 0.0191676 | 0.0321596 | 0.0191713 | 0.0189409 | 0.1234330 |
| drapm_calc | 0.3504192 | 0.0273031 | 0.0912274 | NA | 0.3322217 | 0.0765407 | -0.0020656 | 0.0098078 | -0.0003777 | -0.0027973 | 0.1413987 | 0.4035084 | 0.3356299 | 0.1240432 | 0.0358574 |
| drapm_sz | 0.1118109 | 0.0027746 | 0.0503027 | 0.3322217 | NA | 0.1041948 | -0.0009822 | -0.0023232 | -0.0017017 | -0.0006295 | 0.1791320 | 0.1442801 | 0.0756311 | 0.4384602 | 0.0256227 |
| drpm_espn | 0.0059843 | 0.0471514 | 0.2727653 | 0.0765407 | 0.1041948 | NA | -0.0008774 | -0.0029342 | -0.0026649 | 0.0064440 | 0.0491640 | 0.0298905 | 0.0175993 | 0.0421981 | 0.3028806 |
| obpm_nbastatr | 0.0863602 | 0.8274349 | 0.0168123 | -0.0020656 | -0.0009822 | -0.0008774 | NA | 0.2368059 | 0.0650393 | 0.2843836 | 0.0300633 | 0.1443483 | 0.1512988 | 0.0278430 | 0.1757929 |
| orapm_calc | 0.4191907 | 0.1482843 | -0.0024059 | 0.0098078 | -0.0023232 | -0.0029342 | 0.2368059 | NA | 0.3812504 | 0.1694076 | 0.2074359 | 0.4420484 | 0.7619807 | 0.2037157 | 0.1178648 |
| orapm_sz | 0.0972412 | 0.0453781 | -0.0017948 | -0.0003777 | -0.0017017 | -0.0026649 | 0.0650393 | 0.3812504 | NA | 0.1361238 | 0.3027395 | 0.1785147 | 0.2277643 | 0.5434344 | 0.0906094 |
| orpm_espn | 0.0633819 | 0.1901469 | -0.0018229 | -0.0027973 | -0.0006295 | 0.0064440 | 0.2843836 | 0.1694076 | 0.1361238 | NA | 0.1209535 | 0.1041428 | 0.1121011 | 0.0596036 | 0.6037412 |
| pm_nbastatr | 0.1194202 | 0.0437862 | 0.0191676 | 0.1413987 | 0.1791320 | 0.0491640 | 0.0300633 | 0.2074359 | 0.3027395 | 0.1209535 | NA | 0.3326310 | 0.3124833 | 0.4888706 | 0.1893420 |
| rapm_both_calc | 0.5170582 | 0.1743086 | 0.0321596 | 0.4035084 | 0.1442801 | 0.0298905 | 0.1443483 | 0.4420484 | 0.1785147 | 0.1041428 | 0.3326310 | NA | 0.7345316 | 0.3308535 | 0.1489425 |
| rapm_calc | 0.6750558 | 0.1600988 | 0.0191713 | 0.3356299 | 0.0756311 | 0.0175993 | 0.1512988 | 0.7619807 | 0.2277643 | 0.1121011 | 0.3124833 | 0.7345316 | NA | 0.2980069 | 0.1374234 |
| rapm_sz | 0.2135919 | 0.0418785 | 0.0189409 | 0.1240432 | 0.4384602 | 0.0421981 | 0.0278430 | 0.2037157 | 0.5434344 | 0.0596036 | 0.4888706 | 0.3308535 | 0.2980069 | NA | 0.1136859 |
| rpm_espn | 0.0727325 | 0.2560962 | 0.1234330 | 0.0358574 | 0.0256227 | 0.3028806 | 0.1757929 | 0.1178648 | 0.0906094 | 0.6037412 | 0.1893420 | 0.1489425 | 0.1374234 | 0.1136859 | NA |
Top 20 RAPM players for 2017 (according to my calculations, which are probably off 😆)
| name | slug | rank | drapm | orapm | rapm |
|---|---|---|---|---|---|
| Dante Exum | UTA | 1 | 2.69 | 3.43 | 6.12 |
| Stephen Curry | GSW | 2 | -0.15 | 5.04 | 4.90 |
| Marcus Georges-Hunt | 3 | -1.07 | 5.79 | 4.71 | |
| Jordan Bell | GSW | 4 | 1.00 | 3.39 | 4.38 |
| Brandan Wright | 5 | 2.66 | 0.99 | 3.66 | |
| OG Anunoby | TOR | 6 | 1.30 | 2.33 | 3.63 |
| Iman Shumpert | SAC | 7 | 2.78 | 0.79 | 3.57 |
| Chris Paul | HOU | 8 | 0.32 | 3.05 | 3.37 |
| Nene | HOU | 9 | 1.04 | 2.16 | 3.20 |
| Mike Conley | MEM | 10 | 2.19 | 0.94 | 3.13 |
| Eric Gordon | HOU | 11 | 1.04 | 2.07 | 3.11 |
| Torrey Craig | DEN | 12 | 0.83 | 2.26 | 3.09 |
| Lucas Nogueira | 13 | 1.14 | 1.90 | 3.04 | |
| PJ Tucker | HOU | 14 | 0.76 | 2.22 | 2.99 |
| Robert Covington | MIN | 15 | 1.09 | 1.88 | 2.97 |
| Zaza Pachulia | DET | 16 | 0.31 | 2.57 | 2.88 |
| Thabo Sefolosha | UTA | 17 | 1.02 | 1.83 | 2.85 |
| Ersan Ilyasova | MIL | 18 | 1.02 | 1.57 | 2.59 |
| Joel Embiid | PHI | 19 | 0.97 | 1.49 | 2.46 |
| Ekpe Udoh | UTA | 20 | 1.00 | 1.46 | 2.46 |
2017 offensive RAPM coefficients for top 10 players as a function of cross-validated (CV) log-lambda values
2017 defensive RAPM coefficients for top 10 players
Ridge regression CV lambda penalties for 2017 offensive RAPM
Ridge regression CV lambda penalties for 2017 defensive RAPM
React
Typescript
Django
Laravel
Facebook
Microsoft
Google
Alibaba
D3
Tencent