Rvest Tables Returning Empty

I am trying to scrape the tables from the following link: https://www.mlbdraftleague.com/mahoning-valley/roster

library(rvest)
library(magrittr)

url <- "https://www.mlbdraftleague.com/mahoning-valley/roster"
page <- read_html(url) %>% 
  html_table(fill = T)

I tried that and it returned empty Dfs with the right amount of tables (5) and the right amount of columns, but the dataframes are empty. All help is appreciated.

library(tidyverse)
library(httr2)

"https://statsapi.mlb.com/api/v1/teams/545/roster?hydrate=person(rosterEntries,education,stats(type=season,season=2023,sportId=22,teamId=545))&rosterType=active&season=2023&sportId=22" %>% 
  request() %>% 
  req_perform() %>% 
  resp_body_json(simplifyVector = TRUE) %>% 
  pluck("roster") %>% 
  unnest(everything(), names_sep = "_") 

# A tibble: 41 × 47
   person_id person_full_name      person_link   person_first_name person_last_name person_birth_date person_current_age person_birth_city person_birth_state_p…¹
       <int> <chr>                 <chr>         <chr>             <chr>            <chr>                          <int> <chr>             <chr>                 
 1    813834 AJ Rausch             /api/v1/peop… AJ                Rausch           2002-03-19                        21 Powell            OH                    
 2    800677 Ahmad Harajli         /api/v1/peop… Ahmad             Harajli          2001-08-31                        21 Dearborn          MI                    
 3    701144 Alex Shea             /api/v1/peop… Brian             Shea             2001-05-04                        22 Union             KY                    
 4    701475 Andreaus Lewis        /api/v1/peop… Andreaus          Lewis            2002-12-10                        20 Atlanta           GA                    
 5    701499 Andrew Lucas          /api/v1/peop… Andrew            Lucas            2000-02-04                        23 Camarillo         CA                    
 6    813836 Braeden O'Shaughnessy /api/v1/peop… Braeden           O'Shaughnessy    2000-11-19                        22 Poland            OH                    
 7    681376 Brandon Hylton        /api/v1/peop… Brandon           Hylton           2000-02-01                        23 Livingston        NJ                    
 8    695480 Brennyn Abendroth     /api/v1/peop… Brennyn           Abendroth        2003-06-07                        20 Effingham         IL                    
 9    695746 Cale Lansville        /api/v1/peop… Cale              Lansville        2003-01-06                        20 Englewood         CO                    
10    809953 Cam Liss              /api/v1/peop… Cameron           Liss             2000-04-15                        23 Spokane           WA                    
# ℹ 31 more rows

One of the options is plucking relevant columns one by one from a nested list, though it makes more sense when there are just a few / fewer of those, like with coaches :

library(dplyr)
library(jsonlite)
library(purrr)
url_roster  <- "https://statsapi.mlb.com/api/v1/teams/545/roster?hydrate=person(rosterEntries,education,stats(type=season,season=2023,sportId=22,teamId=545))&rosterType=active&season=2023&sportId=22"
url_coaches <- "https://statsapi.mlb.com/api/v1/teams/545/coaches?hydrate=person&rosterType=active&sportId=22&season=2023"

fromJSON(url_roster, simplifyVector = FALSE)[["roster"]] %>% 
  map(~ list(pos    = pluck(.x, "person", "primaryPosition", "type"),
             name   = pluck(.x, "person", "fullName"),
             j_nr   = pluck(.x, "jerseyNumber"),
             wins   = pluck(.x, "person", "stats", 1, "splits", 1, "stat", "wins"),
             losses = pluck(.x, "person", "stats", 1, "splits", 1, "stat", "losses"),
             era    = pluck(.x, "person", "stats", 1, "splits", 1, "stat", "era"),
             b_side = pluck(.x, "person", "batSide", "code"),
             p_hand = pluck(.x, "person", "pitchHand", "code"),
             height = pluck(.x, "person", "height"),
             weight = pluck(.x, "person", "weight"),
             dob    = pluck(.x, "person", "birthDate"),
             school = pluck(.x, "person", "education", "colleges", 1, "name")
             )) %>% 
  bind_rows()
#> # A tibble: 41 × 12
#>    pos   name  j_nr  b_side p_hand height weight dob   school  wins losses era  
#>    <chr> <chr> <chr> <chr>  <chr>  <chr>   <int> <chr> <chr>  <int>  <int> <chr>
#>  1 Outf… AJ R… "17"  R      R      "5' 1…    195 2002… Ohio      NA     NA <NA> 
#>  2 Pitc… Ahma… "41"  R      R      "6' 4…    240 2001… Michi…     1      1 6.14 
#>  3 Pitc… Alex… "48"  L      L      "6' 4…    211 2001… Cinci…     0      0 12.60
#>  4 Catc… Andr… "9"   L      R      "5' 1…    195 2002… Pensa…    NA     NA <NA> 
#>  5 Pitc… Andr… "33"  R      R      "5' 1…    190 2000… Texas…     1      1 1.80 
#>  6 Infi… Brae… "4"   R      R      "6' 3…    200 2000… Young…    NA     NA <NA> 
#>  7 Outf… Bran… "32"  L      R      "6' 8…    255 2000… Stets…    NA     NA <NA> 
#>  8 Pitc… Bren… "52"  R      R      "6' 4…    195 2003… South…     0      0 6.00 
#>  9 Pitc… Cale… ""    R      R      "6' 1…    205 2003… San J…    NA     NA <NA> 
#> 10 Pitc… Cam … "46"  L      L      "6' 0…    202 2000… Washi…     0      0 0.00 
#> # ℹ 31 more rows

fromJSON(url_coaches, simplifyVector = FALSE)[["roster"]] %>% 
  map(~ list(title  = pluck(.x, "title"),
             name   = pluck(.x, "person", "fullName"))) %>% 
  bind_rows()
#> # A tibble: 4 × 2
#>   title           name         
#>   <chr>           <chr>        
#> 1 Manager         Dmitri Young 
#> 2 Hitting Coach   Bryant Nelson
#> 3 Pitching Coach  Ray King     
#> 4 Assistant Coach Craig Antush

^{Created on 2023-06-13 with reprex v2.0.2}

You might also want to check baseballr package.