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/.
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")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>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"))
For more information on the package and function reference, please see
the recruitR documentation
website.
Follow the SportsDataverse on Twitter and star this repo
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}
}