Update app.R

app.R

@@ -156,6 +156,11 @@ IF_OAA_25 <- download_private_parquet("CoastalBaseball/DefenseAppDataset", "IF_O
         Date = as.Date(Date),
         Inning = as.numeric(Inning))
 
+if obs_player_bearing_diff %in% names(IF_OAA_25) { 
+  IF_OAA_25$obs_player_direction_diff <- IF_OAA_25$obs_player_bearing_diff
+  IF_OAA_25 <- IF_OAA_25 %>% dplyr::select(-obs_player_bearing_diff)
+}
+
 OF_OAA_25 <- as.data.frame(download_private_parquet("CoastalBaseball/DefenseAppDataset", "CC_OAA25.parquet")) %>%
   mutate(obs_player = str_replace_all(obs_player, "(\\w+), (\\w+)", "\\2 \\1"),
         pred_catch = as.numeric(as.character(pred_catch)),
@@ -353,8 +358,6 @@ if_oaa_setup <- function(data) {
       player_angle_rad = atan2(obs_player_x, obs_player_z)) %>%
     filter(!is.na(obs_player))
   
-  # For IF model, success_ind needs to be factor
-  
   processed <- bake(IF_OAA_recipe, new_data = d) %>%
     dplyr::select(-any_of("success_ind")) %>%
     mutate(across(where(is.factor), as.numeric), across(where(is.character), as.numeric))
@@ -369,7 +372,7 @@ if_oaa_setup <- function(data) {
     Inning = as.numeric(Inning),
     play_prob = 1 - play_prob,
     play_pred = ifelse(play_prob >= .5, 1, 0),
-    OAA = (as.numeric(success_ind) - 1) - play_prob) %>%
+    OAA = (as.numeric(success_ind)) - play_prob) %>%
     dplyr::select(obs_player_name, obs_player, success_ind, play_prob, play_pred, OAA,
                   obs_player_direction_diff, obs_player_bearing, obs_player_x, obs_player_z,
                   HangTime, Distance, Bearing, Direction, ExitSpeed, Angle,
@@ -585,8 +588,7 @@ infield_star_graph <- function(player, position, IF_OAA) {
   pd <- pd %>% mutate(
     star_group = case_when(play_prob>=0.90~"1 Star",play_prob>=0.70~"2 Star",
                            play_prob>=0.50~"3 Star",play_prob>=0.25~"4 Star",
-                           play_prob>=0.00000001~"5 Star"),
-    obs_player_bearing_diff = obs_player_direction_diff)
+                           play_prob>=0.00000001~"5 Star"))
   
   OAA <- round(sum(pd$OAA,na.rm=T),1)
   n_plays <- nrow(pd)
@@ -596,7 +598,7 @@ infield_star_graph <- function(player, position, IF_OAA) {
   points_data <- pd %>% filter(play_prob > 0) %>%
     filter(play_prob <= 0.5 | between(obs_player_bearing_diff, -12.5, 12.5))
   
-  p <- ggplot(IF_OAA %>% mutate(obs_player_bearing_diff = obs_player_direction_diff),
+  p <- ggplot(IF_OAA,
               aes(obs_player_bearing_diff, play_prob)) +
     stat_summary_2d(aes(z = play_prob), fun = mean, bins = 15) +
     scale_fill_gradientn(