add cbd_net_archive endpoint

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: cbbdata
 Type: Package
 Title: API for College Basketball Data
-Version: 0.3.0
+Version: 0.3.0.9000
 Authors@R: person("Andrew", "Weatherman", email = "[email protected]", 
   role = c("aut", "cre"))
 Description: Provides direct access to clean and tidy college basketball data using the 
@@ -37,3 +37,5 @@ Roxygen: list(markdown = TRUE)
 URL: https://cbbdata.aweatherman.com/
 BugReports: https://github.com/andreweatherman/cbbdata
 Config/Needs/website: rmarkdown
+Depends: 
+    R (>= 2.10)

NAMESPACE

@@ -10,6 +10,7 @@ export(cbd_kenpom_ratings)
 export(cbd_kenpom_ratings_archive)
 export(cbd_login)
 export(cbd_match_teams)
+export(cbd_net_archive)
 export(cbd_teams)
 export(cbd_torvik_conf_factors)
 export(cbd_torvik_current_resume)
@@ -57,6 +58,7 @@ importFrom(httr2,request)
 importFrom(httr2,resp_status)
 importFrom(janitor,clean_names)
 importFrom(jsonlite,fromJSON)
+importFrom(lubridate,month)
 importFrom(lubridate,year)
 importFrom(magrittr,"%>%")
 importFrom(magrittr,extract2)

NEWS.md

@@ -1,3 +1,6 @@
+# cbbdata (development version, 2024-07-20)
+- Added [`cbd_net_archive`](https://cbbdata.aweatherman.com/reference/cbd_net_archive.html) to pull daily NET rankings, along with KenPom and Torvik ratings, from the 2023-24 season. This endpoint will update throughout the 2024-25 season. Prior data might be added in future versions.
+
 # cbbdata 0.3.0
 - Added [`cbd_bpi_ratings`](https://cbbdata.aweatherman.com/reference/cbd_bpi_ratings.html) to pull current ESPN BPI ratings + rankings, SOR/SOS, and tournament projections.
 - Added [`cbd_all_metrics`](https://cbbdata.aweatherman.com/reference/cbd_all_metrics.html) to source Torvik, KenPom, BPI, SOR/SOS, and NET in one unified tibble.

R/cbd_net_archive.R

@@ -0,0 +1,19 @@
+#' Daily NET Rankings
+#'
+#' Pulls daily NET rankings along with other metrics from the 2023-24 season.
+#'
+#' @param ... OPTIONAL. To load the complete data set, pass no arguments through
+#'   to the function. Else, you can filter on  `team`, `conf`, or `year`.
+#'
+#' @examples
+#' \donttest{try(cbd_net_archive(team = "Duke"))}
+#' @export
+cbd_net_archive <- function(...) {
+
+  base_url <- 'https://www.cbbdata.com/api/etc/archive-net?'
+
+  data <- cbbdata:::get_cbd_file(base_url, ...)
+
+  return(data)
+
+}

R/cbd_torvik_season_simulation.R

@@ -17,6 +17,7 @@
 #' @importFrom httr modify_url
 #' @importFrom jsonlite fromJSON
 #' @importFrom magrittr %>%
+#' @importFrom lubridate month
 #' @examples
 #' \donttest{try(cbd_torvik_season_simulation('Duke', 2023, date = '20230101'))}
 #'
@@ -26,7 +27,8 @@ cbd_torvik_season_simulation <- function(team, year, simulations = 10000, date =
 
   cbbdata:::check_key() # ensure user is logged-in
 
-  if(is.null(date)) { date = gsub('-', '', Sys.Date() - 1) }
+  if(is.null(date) & lubridate::month(Sys.Date()) %in% c(11:12, 1:4)) { date = gsub('-', '', Sys.Date() - 1) } else (date = cbbdata::last_sim_dates[as.character(year)])
+
 
   url <- modify_url(
     url = 'https://www.cbbdata.com/api/torvik/season/simulation?',

README.Rmd

@@ -14,20 +14,18 @@ knitr::opts_chunk$set(
 library(tidyverse)
 ```
 
-# Welcome to CBBData: Your Gateway to In-Depth College Basketball Insights
+# CBBData, Your Gateway to In-Depth College Basketball Insights
 
 <!-- badges: start -->
 <!-- badges: end -->
 
-In the vast arena of college basketball data, finding comprehensive, easily accessible, and up-to-date statistics can be a daunting task. `cbbdata` emerges as your reliable partner in this quest, offering an unparalleled depth of college basketball insights, just a line of code away.
+Getting reliable, up-to-date college basketball data shouldn't be a hassle. With `cbbdata`, it isn't. This package makes it straightforward to access a wide range of stats with minimal effort.
 
-**Unmatched Access, Uncomplicated Process:** `cbbdata` is your key to unlocking a treasure trove of college basketball statistics. Say goodbye to the hurdles of data scraping. Our package is designed for efficiency and simplicity, providing a straightforward pathway to the data you need.
+**Easy Access:** `cbbdata` simplifies the process of fetching college basketball statistics. No more scraping or searching through multiple sources: just clean, easy access to the data you need.
 
-**Powered by Precision and Speed:** At the heart of `cbbdata` lies a robust architecture, powered by Flask and Python. Leveraging SQL queries and direct file transfers, we ensure you receive the most accurate and recent data. Our commitment to speed and reliability means you get the data you need, when you need it, without the wait.
+**Fast and Accurate:** Built with Flask and Python, `cbbdata` uses SQL queries and direct file transfers to ensure you get accurate and current data quickly. Our database is updated every 15 minutes during the season.
 
-**Diverse Data at Your Fingertips:** Whether you're looking for detailed player stats, team analytics, game outcomes, or advanced metrics, `cbbdata` has it all. Our comprehensive database is constantly evolving, bringing you the latest and most detailed insights into the college basketball world.
-
-**Getting Started is Easy:** Begin your journey with `cbbdata` by signing up for a free API key. With this key, a world of college basketball data awaits you. Experience the ease and power of `cbbdata` and elevate your analysis to the next level.
+**Comprehensive Data:** Whether you need player stats, team analytics, game results, or advanced metrics, `cbbdata` has you covered with nearly 30 endpoints.
 
 ## Installation
 
@@ -101,7 +99,7 @@ cbbdata::cbd_torvik_team_factors(year = 2024, no_bias = TRUE) %>%
 E.g., if you want to see which ACC player averages the most points at home:
 ```{r, message=FALSE, warning=FALSE}
 cbbdata::cbd_torvik_player_split(year = 2024, conf = 'ACC', split = 'location') %>%
-  dplyr::filter(games >= 3) %>%
+  dplyr::filter(games >= 10) %>%
   dplyr::slice_max(pts, n = 1) %>%
   dplyr::select(team, player, pts, games)
 ```
@@ -151,6 +149,7 @@ cbbdata::cbd_torvik_season_simulation('Duke', 2024) %>%
 
 #### Tournament Results + Resumes
 -   Daily NET rankings and quadrant records (`cbd_torvik_current_resume`)
+-   Historical NET rankings and per-day metrics (`cbd_net_archive`)
 -   Tournament performance (`cbd_torvik_ncaa_results`)
 -   Tournament "committee sheets" (`cbd_torvik_ncaa_sheets`)
 -   Resume database (`cbd_torvik_resume_database`)

README.md

@@ -1,39 +1,27 @@
 
 <!-- README.md is generated from README.Rmd. Please edit that file -->
 
-# Welcome to CBBData: Your Gateway to In-Depth College Basketball Insights
+# CBBData, Your Gateway to In-Depth College Basketball Insights
 
 <!-- badges: start -->
 <!-- badges: end -->
 
-In the vast arena of college basketball data, finding comprehensive,
-easily accessible, and up-to-date statistics can be a daunting task.
-`cbbdata` emerges as your reliable partner in this quest, offering an
-unparalleled depth of college basketball insights, just a line of code
-away.
-
-**Unmatched Access, Uncomplicated Process:** `cbbdata` is your key to
-unlocking a treasure trove of college basketball statistics. Say goodbye
-to the hurdles of data scraping. Our package is designed for efficiency
-and simplicity, providing a straightforward pathway to the data you
-need.
-
-**Powered by Precision and Speed:** At the heart of `cbbdata` lies a
-robust architecture, powered by Flask and Python. Leveraging SQL queries
-and direct file transfers, we ensure you receive the most accurate and
-recent data. Our commitment to speed and reliability means you get the
-data you need, when you need it, without the wait.
-
-**Diverse Data at Your Fingertips:** Whether you’re looking for detailed
-player stats, team analytics, game outcomes, or advanced metrics,
-`cbbdata` has it all. Our comprehensive database is constantly evolving,
-bringing you the latest and most detailed insights into the college
-basketball world.
-
-**Getting Started is Easy:** Begin your journey with `cbbdata` by
-signing up for a free API key. With this key, a world of college
-basketball data awaits you. Experience the ease and power of `cbbdata`
-and elevate your analysis to the next level.
+Getting reliable, up-to-date college basketball data shouldn’t be a
+hassle. With `cbbdata`, it isn’t. This package makes it straightforward
+to access a wide range of stats with minimal effort.
+
+**Easy Access:** `cbbdata` simplifies the process of fetching college
+basketball statistics. No more scraping or searching through multiple
+sources: just clean, easy access to the data you need.
+
+**Fast and Accurate:** Built with Flask and Python, `cbbdata` uses SQL
+queries and direct file transfers to ensure you get accurate and current
+data quickly. Our database is updated every 15 minutes during the
+season.
+
+**Comprehensive Data:** Whether you need player stats, team analytics,
+game results, or advanced metrics, `cbbdata` has you covered with nearly
+30 endpoints.
 
 ## Installation
 
@@ -118,20 +106,19 @@ E.g., if you want to see what the no-bias T-Rank top 10 looks like:
 cbbdata::cbd_torvik_team_factors(year = 2024, no_bias = TRUE) %>%
   dplyr::slice(1:10) %>%
   dplyr::select(team, barthag, adj_o, adj_d)
-#> API Key set!
 #> # A tibble: 10 × 4
-#>    team        barthag adj_o adj_d
-#>    <chr>         <dbl> <dbl> <dbl>
-#>  1 Houston       0.979  117.  83.9
-#>  2 Purdue        0.974  126.  92.3
-#>  3 Auburn        0.955  120.  91.7
-#>  4 Connecticut   0.955  123.  94.4
-#>  5 Arizona       0.949  120.  92.9
-#>  6 Tennessee     0.944  118.  91.9
-#>  7 Alabama       0.941  126.  98.9
-#>  8 Iowa St.      0.940  115.  90.2
-#>  9 BYU           0.934  120.  95.3
-#> 10 Marquette     0.930  117.  93.1
+#>    team           barthag adj_o adj_d
+#>    <chr>            <dbl> <dbl> <dbl>
+#>  1 Houston          0.978  119.  85.7
+#>  2 Connecticut      0.977  127.  92.0
+#>  3 Purdue           0.967  126.  93.8
+#>  4 Auburn           0.953  120.  92.6
+#>  5 Arizona          0.952  121.  93.0
+#>  6 Iowa St.         0.950  114.  88.0
+#>  7 Tennessee        0.945  116.  90.5
+#>  8 North Carolina   0.937  119.  93.9
+#>  9 Duke             0.935  121.  95.7
+#> 10 Marquette        0.928  118.  94.4
 ```
 
 #### Player Data:
@@ -145,12 +132,11 @@ home:
 
 ``` r
 cbbdata::cbd_torvik_player_split(year = 2024, conf = 'ACC', split = 'location') %>%
-  dplyr::filter(games >= 3) %>%
+  dplyr::filter(games >= 10) %>%
   dplyr::slice_max(pts, n = 1) %>%
   dplyr::select(team, player, pts, games)
-#>              team   player      pts games
-#>            <char>   <char>    <num> <int>
-#> 1: North Carolina RJ Davis 23.66667     6
+#>              team   player   pts games
+#> 1: North Carolina RJ Davis 22.75    12
 ```
 
 #### Team + Conference Data:
@@ -167,14 +153,15 @@ cbbdata::cbd_torvik_conf_factors(2024, venue = 'home', top = 100) %>%
   dplyr::filter(games >= 5) %>%
   dplyr::slice_max(efg, n = 5) %>%
   dplyr::select(conf, games, efg)
-#> # A tibble: 5 × 3
+#> # A tibble: 6 × 3
 #>   conf  games   efg
 #>   <chr> <dbl> <dbl>
-#> 1 P12      39  55.3
-#> 2 MWC      36  52.4
-#> 3 B10      64  52  
-#> 4 SEC      62  51.1
-#> 5 B12      69  51.1
+#> 1 P12      78  52.8
+#> 2 B10     125  51.6
+#> 3 B12     121  51.3
+#> 4 MWC      70  51.2
+#> 5 BE      108  51.1
+#> 6 ACC     112  51.1
 ```
 
 #### Game Data:
@@ -219,6 +206,7 @@ cbbdata::cbd_torvik_season_simulation('Duke', 2024) %>%
 #### Tournament Results + Resumes
 
 - Daily NET rankings and quadrant records (`cbd_torvik_current_resume`)
+- Historical NET rankings and per-day metrics (`cbd_net_archive`)
 - Tournament performance (`cbd_torvik_ncaa_results`)
 - Tournament “committee sheets” (`cbd_torvik_ncaa_sheets`)
 - Resume database (`cbd_torvik_resume_database`)
@@ -231,20 +219,14 @@ cbbdata::cbd_torvik_current_resume() %>%
   dplyr::mutate(q1_wins = readr::parse_number(quad1)) %>%
   dplyr::slice_max(q1_wins, n = 5) %>%
   dplyr::select(team, conf, q1_wins, net)
-#> # A tibble: 11 × 4
-#>    team           conf  q1_wins   net
-#>    <chr>          <chr>   <dbl> <int>
-#>  1 Purdue         B10         8     2
-#>  2 Connecticut    BE          8     3
-#>  3 Houston        B12         6     1
-#>  4 Wisconsin      B10         6    15
-#>  5 Arizona        P12         5     4
-#>  6 Kansas         B12         5    12
-#>  7 North Carolina ACC         5    10
-#>  8 Marquette      BE          5    11
-#>  9 Baylor         B12         5    14
-#> 10 Duke           ACC         5    20
-#> 11 Boise St.      MWC         5    40
+#> # A tibble: 5 × 4
+#>   team        conf  q1_wins   net
+#>   <chr>       <chr>   <dbl> <int>
+#> 1 Connecticut BE         19    12
+#> 2 Purdue      B10        18    16
+#> 3 Houston     B12        17     1
+#> 4 Tennessee   SEC        14     5
+#> 5 Texas A&M   SEC        13    21
 ```
 
 ### KenPom

_pkgdown.yml

@@ -27,9 +27,15 @@ home:
   - text: Andrew's Twitter
     href: https://twitter.com/andreweatherman
     target: _blank
+  - text: cbbplotR Package
+    href: https://cbbplotr.aweatherman.com/
+    target: _blank
   - text: Andrew's Personal Site
     href: https://www.aweatherman.com/
     target: _blank
+  - text: Andrew's Viz Site
+    href: https://www.viz.aweatherman.com/
+    target: _blank
 reference:
 - title: Metric Ratings
   desc: Functions for grabbing metric ratings from various sources
@@ -41,6 +47,7 @@ reference:
   - cbd_kenpom_ratings_archive
   - cbd_bpi_ratings
   - cbd_all_metrics
+  - cbd_net_archive
 - title: Player Data
   desc: Functions for grabbing player-level data
   contents: