Fit Event Prior Ridge — fit_event

Given an event key, selects an optimal lambda using LOOCV and fits the prior ridge model using pre-event EPA from statbotics as the prior.

Usage

fit_event_pridge(
  event_key,
  response_name = "score",
  grid = exp(seq(log(0.01), log(20), length.out = 100)),
  n_cores = NULL
)

Arguments

event_key: (char) TBA-legal event key (ex. "2025mdsev")
response_name: name of the desired response vector (typically "score")
grid: (vector) all possible lambda values to consider. Defaults to starting at just above zero to reduce matrix singularity in fits (guarantees that X^tX + (lambda)I is positive definite.)
n_cores: (int) number of cores to parallelize over. If NULL, will select (max - 1) cores

Details

Relies on statbotics API to establish priors

Examples

fit_event_pridge("2025mdsev")
#>   frc339   frc404   frc449   frc623   frc888  frc1111  frc1727  frc1811 
#>    19.29    39.69    60.89    20.90    44.62    21.73    45.04    25.41 
#>  frc1885  frc2106  frc2199  frc2377  frc2421  frc2537  frc3714  frc3748 
#>    28.72    54.23    39.44    12.07    22.12    19.09    12.30    35.97 
#>  frc3793  frc4464  frc4541  frc5587  frc7770  frc7886  frc8622  frc9403 
#>    20.28     7.22    25.33    14.67    24.88    18.12    13.82    21.08 
#>  frc9709 frc10224 frc10449 frc10679 
#>    14.70    34.00    10.87    21.93 
fit_event_pridge("2023new", n_cores = 3)
#>   frc11  frc177  frc179  frc195  frc494  frc503  frc857  frc900  frc955 frc1023 
#>   52.48   58.75   56.83   64.12   59.66   50.16   53.05   53.29   52.33   53.23 
#> frc1123 frc1156 frc1466 frc1468 frc1501 frc1538 frc1629 frc1746 frc1757 frc1816 
#>   52.05   45.27   48.01   51.52   53.95   63.64   50.35   57.08   56.66   43.85 
#> frc1836 frc2642 frc2960 frc2992 frc3003 frc3039 frc3161 frc3184 frc3218 frc3478 
#>   36.15   52.73   41.69   55.42   41.34   64.00   45.81   61.08   60.10   53.37 
#> frc3538 frc3572 frc3767 frc3932 frc3940 frc4069 frc4099 frc4112 frc4143 frc4145 
#>   72.78   45.51   50.01   43.97   52.28   50.04   48.44   30.45   63.21   49.94 
#> frc4329 frc4419 frc4522 frc4663 frc4905 frc4909 frc4944 frc5006 frc5135 frc5172 
#>   51.52   46.99   68.24   42.75   39.65   57.38   45.79   48.54   51.10   35.37 
#> frc5274 frc5338 frc5553 frc5665 frc5804 frc5990 frc6431 frc6606 frc6657 frc6817 
#>   38.27   40.46   28.43   39.79   57.20   54.54    7.43   19.86   30.17   18.90 
#> frc6909 frc7072 frc7285 frc7428 frc7617 frc8015 frc8016 frc8592 frc8717 frc8808 
#>   17.52   37.43   52.41   27.91   52.78   16.74   29.97   54.86   25.62   35.71 
#> frc8847 frc9023 frc9030 frc9062 frc9084 frc9126 frc9140 
#>   38.90   27.02   23.29   20.01   50.76   23.84   46.75 
fit_event_pridge("2026mdsev", response_name = "totalAutoPoints")
#>   frc339   frc614   frc623   frc686   frc836  frc1111  frc1389  frc1418 
#>     5.52    22.40    13.77    24.58    24.23     3.43    11.23     2.65 
#>  frc1446  frc1629  frc1719  frc1727  frc1915  frc2199  frc2377  frc2534 
#>     4.83    29.08     6.87    13.12     4.60    32.82     6.87     3.37 
#>  frc2849  frc2912  frc2914  frc2963  frc3714  frc3748  frc4505  frc4541 
#>    -0.18     5.12     4.81     5.99     7.11    17.76     3.38    14.27 
#>  frc6863  frc7770  frc8592  frc8622  frc8726  frc9033 frc10449 frc10679 
#>     9.25     5.67    23.11     2.68     8.42    12.58     3.31     2.73 
#> frc11211 frc11318 frc11350 
#>     5.10    15.05     2.49