Update app.R

app.R

@@ -21,16 +21,66 @@ library(ggplot2)
 #   phand = c("R", "R", "L", "L")
 # )
 
-calculate_timstuff <- function(data) {
-  data_matrix <- as.matrix(data)
-  mod <- xgb.load('TimStuff2.model')
-  prediction <- predict(mod, data_matrix)
-  timstuff <- (100 - (prediction - -0.002620635) / 0.006021368 * 10)
-  return(timstuff)
+calculate_EAA <- function(extension) {
+  extension / 6.3
+}
+
+calculate_SADiff <- function(pfxX, pfxZ, spinDirection) {
+  inSA <- atan2(pfxZ, pfxX) * 180/pi + 90
+  inSA <- ifelse(inSA < 0, inSA + 360, inSA)
+  SADiff <- spinDirection - inSA
+  SADiff <- ifelse(SADiff > 180, SADiff - 360, SADiff)
+  SADiff <- ifelse(SADiff < -180, SADiff + 360, SADiff)
+  return(SADiff)
 }
+
+calculate_VAA <- function(vz0, ay, az, vy0, y0) {
+  -atan((vz0+(az*(-sqrt((vy0*vy0)-(2*ay*(y0-(17/12))))-vy0)/
+                ay))/(-sqrt((vy0*vy0)-(2*ay*(y0-(17/12))))))*(180/pi)
+}
+
+scale_TimStuff <- function(raw_score, model_mean, model_sd) {
+  scaled_score <- (raw_score - model_mean) / model_sd
+  result <- 100 - (scaled_score * 10)
+  return(result)
+}
+
+timstuff <- function(game) {
+  # game <- calculate_primary(game)
+  game <- game %>%
+    mutate(VAA = calculate_VAA(vz0, ay, az, vy0, y0),
+           SADiff = calculate_SADiff(pfxX, pfxZ, spinDirection),
+           EAA = calculate_EAA(extension),
+           #SADiff = calculate_SADiff(pfxX, pfxZ, spinDirection),
+           team_fielding_id = ifelse(description %in% c("Called Strike", "Swinging Strike", "Swinging Strike (Blocked)"), 1, 0),
+           swing = ifelse(description %in% c("Foul", "Foul Pitchout", "In play, no out", "In play, out(s)", "In play, run(s)", "Swinging Strike", "Swinging Strike (Blocked)", "Foul Tip"), 1, 0),
+           is_strike_swinging = ifelse(is_strike_swinging, 1, 0),
+           Pitch = pitch_name,
+           ishandL = ifelse(phand == "L",1,0))
+  #mutate(SADiff = calculate_SADiff(pfxX, pfxZ,spinDirection))
+  # game <- calculate_primary(game)
+  
+  feature_vars <- c("ishandL","start_speed", "IVB", "HB", "EAA", "x0", "z0", "spin_rate","SADiff")
+  complete_rows <- complete.cases(game[, feature_vars])
+  game_complete <- game[complete_rows, ]
+  game_na <- game[!complete_rows,]
+  game_na$TimStuff <- NA
+  
+  rhp <- game_complete
+  
+  # rhp <- game_complete[game_complete$ishandL == 0]
+  # 
+  # lhp$TimStuff <- scale_TimStuff(predict(model, as.matrix(cbind(lhp$ishandL,lhp$start_speed, lhp$IVB, lhp$HB, lhp$EAA, lhp$x0, lhp$z0, lhp$spin_rate, lhp$SADiff,lhp$primary_speed,lhp$primary_IVB,lhp$primary_HB))), -0.00249975,  0.007566558)
+  
+  rhp$TimStuff <- scale_TimStuff(predict(model, as.matrix(cbind(rhp$ishandL,rhp$start_speed, rhp$IVB, rhp$HB, rhp$EAA, rhp$x0, rhp$z0, rhp$spin_rate, rhp$SADiff))),   -0.002620635,   0.006021368)
+  
+  game_complete <- rbind(rhp,game_na)
+  return(game_complete)
+}
+
 MLB24T <- read.csv("MLB24T.csv", header = TRUE, check.names = FALSE, fileEncoding = "UTF-8")
 
-MLB24T <- calculate_timstuff(MLB24T)
+MLB24T <- timstuff(MLB24T)
 
 pitcher_data <- MLB24T %>%
   group_by(`Pitcher Name`,pitch_name,phand) %>%
@@ -46,6 +96,15 @@ pitcher_data <- MLB24T %>%
   )
 
 
+
+calculate_timstuff <- function(data) {
+  data_matrix <- as.matrix(data)
+  mod <- xgb.load('TimStuff2.model')
+  prediction <- predict(mod, data_matrix)
+  timstuff <- (100 - (prediction - -0.002620635) / 0.006021368 * 10)
+  return(timstuff)
+}
+
 ui <- fluidPage(
   theme = shinytheme("flatly"),