-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathREADME.Rmd
106 lines (77 loc) · 2.8 KB
/
README.Rmd
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
---
output: github_document
---
```{r setup, include = FALSE}
knitr::opts_chunk$set(
error = FALSE,
warning = FALSE,
echo = TRUE
)
```
<!-- README.md is generated from README.Rmd. Please edit that file -->
# Introduction
This primary goal of this project is to calculate
[Regularized Adjusted Plus-Minus](https://www.nbastuffer.com/analytics101/regularized-adjusted-plus-minus-rapm/)
(RAPM)---an "advanced statistic"---for NBA players.
The calculated values can be found in the set of `metrics_join`
CSVs in the project's repository.
I plan to write about this project in more detail [on my blog](https://tonyelhabr.rbind.io).
so I encourage the reader to read more about it there.
# Usage
If you were to fork this project and try to run it from scratch, below
shows the required order of function calls.
First, download all of the data needed.
```{r usage_all, eval = FALSE}
# pre-process ----
# Note that `overwrite = FALSE` is the default, but setting it explciitly here to remind
# the user that it is an option.
# This goes to the
download_pbp_raw_files(overwrite = FALSE)
download_nbastatr(overwrite = FALSE)
download_rpm_espn(overwrite = FALSE)
download_rapm_sz(overwrite = FALSE)
```
Next, run the "main" function. This is what is run with the command-line interface (CLI)
that also comes with the project.
```{r usage_main, eval = FALSE}
# This reads from the config.yml files.
auto_main()
```
# Highlights
Below is a visual comparison of various RAPM-related metrics,
either calculated in this project (i.e. `calc`) or retrieved from an external source.
The data behind this visual
```{r metrics_join_summary, echo = FALSE}
metrics_join_summary <-
.import_data_from_path(
verbose = FALSE,
season = .SEASON,
path = config$path_metrics_join_summary
)
metrics_join_summary %>%
spread(x, adj_r_squared) %>%
knitr::kable()
```
Top 20 RAPM players for 2017 (according to my calculations, which are probably off `r emo::ji('laugh')`)
```{r rapm_coefs_calc, echo = FALSE}
rapm_coefs_calc <-
.import_data_from_path(
verbose = FALSE,
season = .SEASON,
path = config$path_rapm_coefs_calc
)
rapm_coefs_calc %>%
mutate_at(vars(matches('rapm$')), ~scales::number(., accuracy = .01)) %>%
select(-id) %>%
head(20) %>%
knitr::kable()
```
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
