thejasonfisher / recruitr

A college football recruiting package

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

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Our Authors

• Saiem Gilani
  

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}
}