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Load college football recruiting projections from the SportsDataverse data repo

Source: R/load_cfb_datasets.R
load_cfb_recruiting_proj.Rd

Loads recruiting-based team projections – one row per team-season with talent-derived projection inputs. Published to the cfb_recruiting_proj release tag on the sportsdataverse-data repo.

Usage

load_cfb_recruiting_proj(
  seasons = most_recent_cfb_season(),
  ...,
  dbConnection = NULL,
  tablename = NULL
)

Arguments

seasons

A vector of 4-digit years associated with given college football seasons. Published coverage runs 2016 through the most recent season. Pass seasons = TRUE for every published season. (Min: 2016)

...

Additional arguments passed to an underlying function that writes the season data into a database.

dbConnection

A DBIConnection object, as returned by DBI::dbConnect()

tablename

The name of the data table within the database

Value

Returns a cfbfastR_data tibble.

col_nametypesdescription
seasoninteger
team_idinteger
pred_winsdoubleRidge projection of the team's season win total, fit strictly on prior seasons from talent composite, blue-chip ratio, offensive and defensive returning production, and prior wins.
pred_margindoubleRidge projection of the team's average per-game scoring margin, from the same preseason-known feature set as pred_wins.
pred_net_epadoubleReserved slot for a projected adjusted net EPA; it ships all-null because the adjusted-EPA training target is not currently loadable.

Author

Saiem Gilani

Examples

# \donttest{
  try(load_cfb_recruiting_proj(2016))
#> ── college football recruiting projections from the SportsDataverse data repo ──
#> ℹ Data updated: 2026-08-27 04:22:18 UTC
#> # A tibble: 201 × 5
#>    season team_id pred_wins pred_margin pred_net_epa
#>     <int> <chr>       <dbl>       <dbl>        <dbl>
#>  1   2016 333         10.3        18.6            NA
#>  2   2016 99           9.10       13.4            NA
#>  3   2016 52           9.75       16.3            NA
#>  4   2016 194          9.46       15.0            NA
#>  5   2016 30           8.50       10.7            NA
#>  6   2016 145          8.95       13.0            NA
#>  7   2016 2            7.22        5.21           NA
#>  8   2016 61           9.07       13.4            NA
#>  9   2016 2633         8.71       12.0            NA
#> 10   2016 57           8.63       11.7            NA
#> # ℹ 191 more rows
# }