{recruitR} for College Sports Recruiting
recruitR 
A college football recruiting package
recruitR is an R package for working with college sports recruiting
data. It is an R API wrapper around
collegefootballdata’s recruiting and
roster endpoints.
Note: For details on the data sources, please go the website linked above. Sometimes there are inconsistencies in the underlying data itself. Please report issues here or to https://collegefootballdata.com/.
Installation
You can install the released version of
recruitR from
GitHub with:
# You can install using the pacman package using the following code:
if (!requireNamespace('pacman', quietly = TRUE)){
install.packages('pacman')
}
pacman::p_load_current_gh("sportsdataverse/recruitR")# if you would prefer devtools installation
if (!requireNamespace('devtools', quietly = TRUE)){
install.packages('devtools')
}
# Alternatively, using the devtools package:
devtools::install_github(repo = "sportsdataverse/recruitR")Offensive Tackle Example
library(recruitR)
library(dplyr)
library(ggplot2)Let’s say that we are interested in seeing how many offensive tackles in the 2020 recruiting cycle were:
- located in Florida
- located in the states bordering Florida
- ranked inside the top 1000
FL_OTs <- cfbd_recruiting_player(2020, recruit_type = 'HighSchool', state='FL', position ='OT')
GA_OTs <- cfbd_recruiting_player(2020, recruit_type = 'HighSchool', state='GA', position ='OT')
AL_OTs <- cfbd_recruiting_player(2020, recruit_type = 'HighSchool', state='AL', position ='OT')
SE_OTs <- dplyr::bind_rows(FL_OTs, GA_OTs, AL_OTs)
SE_OTs_1k <- SE_OTs %>%
dplyr::filter(ranking < 1000) %>%
dplyr::arrange(ranking)
SE_OTs_1k %>%
dplyr::select(ranking, name, school, committed_to, position,
height, weight, stars, rating, city, state_province)
#> # A tibble: 20 × 11
#> ranking name school committed_to position height weight stars rating city
#> <int> <chr> <chr> <chr> <chr> <dbl> <int> <int> <dbl> <chr>
#> 1 11 Broder… Litho… Georgia OT 77 298 5 0.995 Lith…
#> 2 38 Tate R… Darli… Georgia OT 78 322 4 0.982 Rome
#> 3 74 Myles … Great… Stanford OT 78 308 4 0.966 Norc…
#> 4 110 Marcus… St. T… LSU OT 77 305 4 0.952 Fort…
#> 5 128 Jalen … Oakle… Miami OT 78 331 4 0.942 Oran…
#> 6 157 Issiah… Norla… Florida OT 76 309 4 0.931 Miami
#> 7 271 Joshua… Suwan… Florida OT 78 335 4 0.905 Live…
#> 8 318 Connor… Jesuit Stanford OT 79 260 4 0.897 Tampa
#> 9 333 Javion… Centr… Alabama OT 77 295 4 0.895 Phen…
#> 10 491 Cayden… Fort … North Carol… OT 78 260 3 0.879 Fort…
#> 11 530 Austin… South… Georgia OT 77 278 3 0.876 Guyt…
#> 12 538 Michae… Lenna… Georgia Tech OT 77 295 3 0.876 Rusk…
#> 13 562 Jordan… Gaine… Georgia Tech OT 78 310 3 0.874 Gain…
#> 14 577 Brady … St. P… Ole Miss OT 79 310 3 0.873 Mobi…
#> 15 614 Trey Z… Roswe… North Carol… OT 78 294 3 0.871 Rosw…
#> 16 658 Gerald… Cardi… Florida OT 77 320 3 0.868 Fort…
#> 17 752 Jake W… Marie… Colorado OT 77 300 3 0.864 Mari…
#> 18 934 Joshua… Centr… Kentucky OT 76.5 304 3 0.856 Phen…
#> 19 953 Wing G… Lee C… Georgia Tech OT 79 285 3 0.855 Lees…
#> 20 971 Kobe M… Herit… Cincinnati OT 78 275 3 0.855 Ring…
#> # … with 1 more variable: state_province <chr>Plotting the Offensive Tackles by State
You can also create a plot:
SE_OTs_1k$stars <- factor(SE_OTs_1k$stars,levels = c(5,4,3,2))
SE_OTs_1k_grp <- SE_OTs_1k %>%
dplyr::group_by(state_province, stars) %>%
dplyr::summarize(players = n()) %>%
dplyr::ungroup()
ggplot(SE_OTs_1k_grp ,aes(x = state_province, y = players, fill = factor(stars))) +
geom_bar(stat = "identity",colour='black') +
xlab("State") + ylab("Number of Players") +
labs(title="Top-1000 Offensive Tackles in FL, GA, and AL - Class of 2020",
subtitle="Figure: @SaiemGilani | Data: @CFB_data with #recruitR")+
geom_text(aes(label = players),size = 4, position = position_stack(vjust = 0.5))+
scale_fill_manual(values=c("dodgerblue2","lightskyblue","red3","ghostwhite"))+
theme(legend.title = element_blank(),
legend.text = element_text(size = 12, margin=margin(t=0.2,r=0,b=0.2,l=-1.2,unit=c("mm")),
family = "serif"),
legend.background = element_rect(fill = "grey99"),
legend.key.width = unit(.2,"cm"),
legend.key.size = unit(.3,"cm"),
legend.position = c(0.25, 0.84),
legend.margin=margin(t = 0.4,b = 0.4,l=-1.2,r=0.4,unit=c('mm')),
legend.direction = "horizontal",
legend.box.background = element_rect(colour = "#500f1b"),
axis.title.x = element_text(size = 12, margin = margin(0,0,1,0,unit=c("mm")),
family = "serif",face="bold"),
axis.text.x = element_text(size = 10, margin=margin(0,0,1,0,unit=c("mm")),
family = "serif"),
axis.title.y = element_text(size = 12, margin = margin(0,0,0,0,unit=c("mm")),
family = "serif",face="bold"),
axis.text.y = element_text(size = 12, margin = margin(1,1,1,1,unit=c("mm")),
family = "serif"),
plot.title = element_text(size = 14, margin = margin(t=0,r=0,b=1.5,l=0,unit=c("mm")),
lineheight=-0.5, family = "serif",face="bold"),
plot.subtitle = element_text(size = 12, margin = margin(t=0,r=0,b=2,l=0,unit=c("mm")),
lineheight=-0.5, family = "serif"),
plot.caption = element_text(size = 12, margin=margin(t=0,r=0,b=0,l=0,unit=c("mm")),
lineheight=-0.5, family = "serif"),
strip.text = element_text(size = 10, family = "serif",face="bold"),
panel.background = element_rect(fill = "grey95"),
plot.background = element_rect(fill = "grey85"),
plot.margin=unit(c(top=0.4,right=0.4,bottom=0.4,left=0.4),"cm"))
Documentation
For more information on the package and function reference, please see
the recruitR documentation
website.
Breaking Changes
Follow the SportsDataverse on Twitter and star this repo
Our Authors
Citations
To cite the cfbfastR R
package in publications, use:
BibTex Citation
@misc{gilani_2021_recruitr,
author = {Gilani, Saiem},
title = {recruitR: The SportsDataverse's R Package for College Sports Recruiting Data.},
url = {https://recruitR.sportsdataverse.org/},
year = {2021}
}