# app.R library(shiny) #library(tidyverse) library(ggplot2) library(dplyr) library(patchwork) library(showtext) library(magick) library(grid) library(gridExtra) library(gtable) library(httr) showtext_opts(dpi = 300) # Match plot DPI showtext_auto(enable = TRUE) font_add_google("Roboto Condensed", "roboto") is_barrel <- function(df) { df$hit_speedr <- round(df$hit_speed) df <- df |> mutate(barrel = ifelse((hit_speedr >= 124) & (hit_angle >= 0 & hit_angle <= 50),1,0)) |> mutate(barrel = ifelse((hit_speedr == 123) & (hit_angle >= 1 & hit_angle <= 50),1,barrel)) |> mutate(barrel = ifelse((hit_speedr == 122) & (hit_angle >= 2 & hit_angle <= 50),1,barrel)) |> mutate(barrel = ifelse((hit_speedr == 121) & (hit_angle >= 3 & hit_angle <= 50),1,barrel)) |> mutate(barrel = ifelse((hit_speedr == 120) & (hit_angle >= 4 & hit_angle <= 50),1,barrel)) |> mutate(barrel = ifelse((hit_speedr == 119) & (hit_angle >= 5 & hit_angle <= 50),1,barrel)) |> mutate(barrel = ifelse((hit_speedr == 118) & (hit_angle >= 6 & hit_angle <= 50),1,barrel)) |> mutate(barrel = ifelse((hit_speedr == 117) & (hit_angle >= 7 & hit_angle <= 50),1,barrel)) |> mutate(barrel = ifelse((hit_speedr == 116) & (hit_angle >= 8 & hit_angle <= 50),1,barrel)) |> mutate(barrel = ifelse((hit_speedr == 115) & (hit_angle >= 9 & hit_angle <= 50),1,barrel)) |> mutate(barrel = ifelse((hit_speedr == 114) & (hit_angle >= 10 & hit_angle <= 50),1,barrel)) |> mutate(barrel = ifelse((hit_speedr == 113) & (hit_angle >= 11 & hit_angle <= 50),1,barrel)) |> mutate(barrel = ifelse((hit_speedr == 112) & (hit_angle >= 12 & hit_angle <= 50),1,barrel)) |> mutate(barrel = ifelse((hit_speedr == 111) & (hit_angle >= 13 & hit_angle <= 50),1,barrel)) |> mutate(barrel = ifelse((hit_speedr == 110) & (hit_angle >= 14 & hit_angle <= 48),1,barrel)) |> mutate(barrel = ifelse((hit_speedr == 109) & (hit_angle >= 15 & hit_angle <= 46),1,barrel)) |> mutate(barrel = ifelse((hit_speedr == 108) & (hit_angle >= 16 & hit_angle <= 45),1,barrel)) |> mutate(barrel = ifelse((hit_speedr == 107) & (hit_angle >= 17 & hit_angle <= 43),1,barrel)) |> mutate(barrel = ifelse((hit_speedr == 106) & (hit_angle >= 18 & hit_angle <= 42),1,barrel)) |> mutate(barrel = ifelse((hit_speedr == 105) & (hit_angle >= 19 & hit_angle <= 40),1,barrel)) |> mutate(barrel = ifelse((hit_speedr == 104) & (hit_angle >= 20 & hit_angle <= 39),1,barrel)) |> mutate(barrel = ifelse((hit_speedr == 103) & (hit_angle >= 21 & hit_angle <= 37),1,barrel)) |> mutate(barrel = ifelse((hit_speedr == 102) & (hit_angle >= 22 & hit_angle <= 36),1,barrel)) |> mutate(barrel = ifelse((hit_speedr == 101) & (hit_angle >= 23 & hit_angle <= 34),1,barrel)) |> mutate(barrel = ifelse((hit_speedr == 100) & (hit_angle >= 24 & hit_angle <= 33),1,barrel)) |> mutate(barrel = ifelse((hit_speedr == 99) & (hit_angle >= 25 & hit_angle <= 31),1,barrel)) |> mutate(barrel = ifelse((hit_speedr == 98) & (hit_angle >= 26 & hit_angle <= 30),1,barrel)) |> select(-hit_speedr) return(df) } apply_percentile_calcs <- function(data) { # List of columns to apply percent_rank percent_rank_cols <- c("Z-Con%", "Z-Swing%", "O-Con%", "Avg EV", "Max EV", "EV90", "Barrel%", "Swing%", "wOBA", "wOBACON","xwOBA","xDamage") # List of columns to apply inverse percent_rank inverse_percent_rank_cols <- c("Chase%", "Whiff%", "stdev(LA)", "SwStr%") # Create an empty list to store results percentile_list <- list() # Calculate regular percentiles for(col in percent_rank_cols) { percentile_list[[col]] <- data.frame( `Batter Name` = data[["Batter Name"]], # Using [[ ]] to preserve exact column name `Batter ID` = data[["Batter ID"]], # Using [[ ]] to preserve exact column name metric = col, percentile = round(percent_rank(data[[col]]) * 100), value = data[[col]], stringsAsFactors = FALSE ) } # Calculate inverse percentiles for(col in inverse_percent_rank_cols) { percentile_list[[col]] <- data.frame( `Batter Name` = data[["Batter Name"]], # Using [[ ]] to preserve exact column name `Batter ID` = data[["Batter ID"]], # Using [[ ]] to preserve exact column name metric = col, percentile = round((1 - percent_rank(data[[col]])) * 100), value = data[[col]], stringsAsFactors = FALSE ) } # Combine all results into one data frame result <- do.call(rbind, percentile_list) # Reset row names rownames(result) <- NULL return(result) } get_player_image <- function(player_id) { # Try MLB silo image first silo_url <- sprintf("https://img.mlbstatic.com/mlb-photos/image/upload/w_200,q_auto:best/v1/people/%s/headshot/silo/current", player_id) # Check if silo works silo_result <- tryCatch({ response <- httr::HEAD(silo_url) httr::status_code(response) == 200 }, error = function(e) FALSE) # If silo fails, use MiLB with correct formatting if (!silo_result) { return(sprintf("https://img.mlbstatic.com/mlb-photos/image/upload/c_fill,g_auto,b_white,ar_1:1/w_180/v1/people/%s/headshot/milb/current", player_id)) } # Return silo if it worked return(silo_url) } get_player_info <- function(player_id, season, level = "MLB") { # Initialize return values team <- "MLB" position <- NA # If MLB level, use original endpoint if(level == "MLB") { url <- paste0("https://statsapi.mlb.com/api/v1/people/", player_id, "/stats?stats=season&season=", season, "&group=hitting") response <- httr::GET(url) data <- httr::content(response, "parsed") if(length(data$stats) > 0 && length(data$stats[[1]]$splits) > 0) { team <- data$stats[[1]]$splits[[length(data$stats[[1]]$splits)]]$team$name } } else { # For minor leagues, get player info from sports endpoint sport_code <- if(level == "AAA") "11" else "14" # 11 for AAA, 14 for FSL url <- paste0("https://statsapi.mlb.com/api/v1/sports/", sport_code, "/players?season=", season) response <- httr::GET(url) # Convert response to data frame players_df <- jsonlite::fromJSON(rawToChar(response$content), flatten = TRUE)$people # Find player directly found_player <- players_df[players_df$id == player_id, ] if(nrow(found_player) > 0) { team_id <- found_player$currentTeam.id # Get parent org using team id team_url <- paste0("https://statsapi.mlb.com/api/v1/teams/", team_id, "?season=", season) team_response <- httr::GET(team_url) team_data <- jsonlite::fromJSON(rawToChar(team_response$content)) team <- team_data$teams$parentOrgName } } # Get position info (same for all levels) url2 <- paste0("https://statsapi.mlb.com/api/v1/people/", player_id) response2 <- httr::GET(url2) data2 <- httr::content(response2, "parsed") if(length(data2$people) > 0) { full_position <- data2$people[[1]]$primaryPosition$name position <- case_when( full_position == "First Base" ~ "1B", full_position == "Second Base" ~ "2B", full_position == "Third Base" ~ "3B", full_position == "Shortstop" ~ "SS", full_position == "Catcher" ~ "C", full_position == "Left Field" ~ "LF", full_position == "Center Field" ~ "CF", full_position == "Right Field" ~ "RF", full_position == "Outfielder" ~ "OF", full_position == "Outfield" ~ "OF", full_position == "Designated Hitter" ~ "DH", full_position == "Pitcher" ~ "P", full_position == "Two-Way Player" ~ "TWP", TRUE ~ as.character(full_position) ) } return(list( team = team, position = position )) } download_private_csv <- function(repo_id, filename) { url <- paste0("https://huggingface.co/datasets/", repo_id, "/resolve/main/", filename) response <- GET(url, add_headers(Authorization = paste("Bearer", Sys.getenv("GETCSV")))) if (status_code(response) == 200) { content <- content(response, "text") con <- textConnection(content) # Try different read options data <- read.csv(con, header = TRUE, check.names = FALSE, # This prevents R from modifying column names fileEncoding = "UTF-8", stringsAsFactors = FALSE) close(con) return(data) } else { stop("Failed to download dataset") } } MLB <- download_private_csv("TimStats/StatcastDataAll", "MLB.csv") AAA <- download_private_csv("TimStats/StatcastDataAll", "AAA.csv") FSLAll <- download_private_csv("TimStats/StatcastDataAll", "FSL.csv") temp_players <- MLB %>% filter(season == 2024) MLBC <- rbind(MLB,AAA,FSLAll) data <- is_barrel(MLBC) %>% mutate( BBE = case_when(description %in% c('In play, run(s)','In play, out(s)','In play, no out') ~ TRUE, TRUE ~ FALSE), Swing = case_when(description %in% c('Foul','Foul Bunt','Foul Pitchout','Foul Tip', 'In play, run(s)','In play, out(s)','In play, no out', 'Swinging Strike','Swinging Strike (Blocked)', 'Missed Bunt') ~ TRUE, TRUE ~ FALSE), Contact = case_when(description %in% c('In play, run(s)','In play, out(s)','In play, no out', 'Foul','Foul Bunt','Foul Pitchout') ~ TRUE, TRUE ~ FALSE), Whiff = case_when(description %in% c('Swinging Strike','Swinging Strike (Blocked)', 'Missed Bunt','Foul Tip') ~ TRUE, TRUE ~ FALSE), IZ = ifelse(zone <= 9, TRUE, FALSE), Single = case_when(result == "Single" & BBE == TRUE ~ TRUE, TRUE ~ FALSE), Double = case_when(result == "Double" & BBE == TRUE ~ TRUE, TRUE ~ FALSE), Triple = case_when(result == "Triple" & BBE == TRUE ~ TRUE, TRUE ~ FALSE), `Home Run` = case_when(result == "Home Run" & BBE == TRUE ~ TRUE, TRUE ~ FALSE), Walk = case_when(balls >= 4 & result == "Walk" ~ TRUE, TRUE ~ FALSE), HBP = case_when(description == "Hit By Pitch" & result == "Hit By Pitch" ~ TRUE, TRUE ~ FALSE), Strikeout = case_when(strikes >= 3 & result %in% c("Strikeout",'Stikeout Double Play') ~ TRUE, TRUE ~ FALSE), Sac = case_when(BBE == TRUE & result %in% c('Sac Fly','Sac Bunt', 'Sac Fly Double Play','Sac Bunt Double Play') ~ TRUE, TRUE ~ FALSE), IBB = case_when(pitchNum == 1 & result == "Intent Walk" ~ TRUE, TRUE ~ FALSE), AB = Strikeout + BBE - Sac, PA = AB + Walk + HBP + IBB ) %>% group_by(`Batter Name`,`Batter ID`,season,level) %>% summarise( BIP = sum(BBE,na.rm = TRUE), wOBA = round((sum(Single,na.rm = TRUE) * .882 + sum(Double, na.rm = TRUE) * 1.254 + sum(Triple,na.rm = TRUE) * 1.59 + sum(`Home Run`,na.rm = TRUE) * 2.05 + sum(Walk,na.rm = TRUE) * .689 + sum(HBP,na.rm = TRUE) * .720) / (sum(PA,na.rm = TRUE) - sum(IBB,na.rm = TRUE)), 3), wOBACON = round((sum(Single,na.rm = TRUE) * .882 + sum(Double, na.rm = TRUE) * 1.254 + sum(Triple,na.rm = TRUE) * 1.59 + sum(`Home Run`,na.rm = TRUE) * 2.05 )/ sum(BBE,na.rm = TRUE), 3), xwOBA = round(mean(expected_woba,na.rm = TRUE), 3), xDamage = round(mean(expected_woba[BBE == TRUE],na.rm = TRUE), 3), `Avg EV` = round(mean(hit_speed,na.rm = TRUE), 1), EV90 = round(quantile(hit_speed,0.9,na.rm = TRUE), 1), `Max EV` = round(max(hit_speed,na.rm = TRUE), 1), 'stdev(LA)' = round(sd(hit_angle,na.rm = TRUE), 1), 'Barrel%' = round(100 * mean(barrel[Swing == TRUE],na.rm = TRUE), 1), "Z-Con%" = round(100 * mean(Contact[IZ == TRUE & Swing == TRUE],na.rm = TRUE), 1), "Z-Swing%" = round(100 * mean(Swing[IZ == TRUE],na.rm = TRUE), 1), "O-Con%" = round(100 * mean(Contact[IZ == FALSE & Swing == TRUE],na.rm = TRUE), 1), "Chase%" = round(100 * mean(Swing[IZ == FALSE],na.rm = TRUE), 1), "Whiff%" = round(100 * mean(Whiff[Swing == TRUE],na.rm = TRUE), 1), "Swing%" = round(100 * mean(Swing,na.rm = TRUE), 1), "SwStr%" = round(100 * mean(Whiff,na.rm = TRUE), 1) ) # UI definition ui <- fluidPage( tags$head( tags$style(HTML(" .plot-container { width: 1000px !important; height: 1000px !important; } ")) ), titlePanel(NULL, windowTitle = "Baseball Stats Visualization"), sidebarLayout( sidebarPanel( selectInput("szn", "Season:", c(2024, 2023, 2022, 2021, 2021)), selectInput("level", "Level:", c("MLB", "AAA", "FSL")), selectInput("type", "Player Type:", c("Batter", "Pitcher")), selectInput("player", "Player:", choices = unique(temp_players$`Batter Name`)), # Add toggle for custom team checkboxInput("use_custom_team", "Use Custom Team", FALSE), # Conditional panel for team selection conditionalPanel( condition = "input.use_custom_team == true", selectInput( inputId = "team", label = "Select Team", choices = c( # Regular teams (sorted alphabetically) "Angels" = "LAA", "Astros" = "HOU", "Athletics" = "OAK", "Blue Jays" = "TOR", "Braves" = "ATL", "Brewers" = "MIL", "Cardinals" = "STL", "Cubs" = "CHC", "D-backs" = "ARI", "Dodgers" = "LAD", "Giants" = "SF", "Guardians" = "CLE", "Mariners" = "SEA", "Marlins" = "MIA", "Mets" = "NYM", "Nationals" = "WSH", "Orioles" = "BAL", "Padres" = "SD", "Phillies" = "PHI", "Pirates" = "PIT", "Rangers" = "TEX", "Rays" = "TB", "Red Sox" = "BOS", "Reds" = "CIN", "Rockies" = "COL", "Royals" = "KC", "Tigers" = "DET", "Twins" = "MIN", "White Sox" = "CHW", "Yankees" = "NYY", # MLB option at the top "MLB" = "MLB" ), selected = "MLB" ) ), ), mainPanel( div(class = "plot-container", plotOutput("statsPlot") ) ) ) ) # Server logic server <- function(input, output,session) { observeEvent(c(input$szn,input$level), { # Filter data based on selected season filtered_data <- MLBC[MLBC$season == input$szn & MLBC$level == input$level,] updateSelectInput(session, inputId = "player", choices = unique(filtered_data$`Batter Name`)) }) # Create reactive value to store team team_value <- reactiveVal("MLB") position_value <- reactiveVal("") # Watch for player or season changes to update team observeEvent(c(input$player, input$szn), { if (!input$use_custom_team && !is.null(input$player)) { player_id <- MLBC %>% filter(`Batter Name` == input$player) %>% pull(`Batter ID`) %>% unique() %>% first() if (!is.null(player_id)) { player_info <- get_player_info(player_id, input$szn, input$level) team_abb <- switch(player_info$team, "Los Angeles Angels" = "LAA", "Houston Astros" = "HOU", "Oakland Athletics" = "OAK", "Toronto Blue Jays" = "TOR", "Atlanta Braves" = "ATL", "Milwaukee Brewers" = "MIL", "St. Louis Cardinals" = "STL", "Chicago Cubs" = "CHC", "Arizona Diamondbacks" = "ARI", "Los Angeles Dodgers" = "LAD", "San Francisco Giants" = "SF", "Cleveland Guardians" = "CLE", "Seattle Mariners" = "SEA", "Miami Marlins" = "MIA", "New York Mets" = "NYM", "Washington Nationals" = "WSH", "Baltimore Orioles" = "BAL", "San Diego Padres" = "SD", "Philadelphia Phillies" = "PHI", "Pittsburgh Pirates" = "PIT", "Texas Rangers" = "TEX", "Tampa Bay Rays" = "TB", "Boston Red Sox" = "BOS", "Cincinnati Reds" = "CIN", "Colorado Rockies" = "COL", "Kansas City Royals" = "KC", "Detroit Tigers" = "DET", "Minnesota Twins" = "MIN", "Chicago White Sox" = "CHW", "New York Yankees" = "NYY", "MLB") if(is.na(team_abb)){ team_abb <- "MLB" } team_value(team_abb) position_value(player_info$position) } } }) current_team <- reactive({ if (input$use_custom_team) { return(input$team) } else { return(team_value()) } }) output$statsPlot <- renderPlot({ req(position_value()) # showtext::showtext_begin() # on.exit(showtext::showtext_end()) data <- data %>% filter(season == input$szn,level == input$level) BBE <- MLBC %>% filter(season == input$szn) %>% filter(level == input$level) %>% filter(`Batter Name` == input$player) %>% mutate( BBE = case_when(description %in% c('In play, run(s)','In play, out(s)','In play, no out') ~ TRUE, TRUE ~ FALSE) ) indv <- data %>% filter(`Batter Name` == input$player,level == input$level) #if(indv[1,5] >= 149){ qual <- data %>% filter(BIP > 249) data <- rbind(indv,qual) data <- unique(data) #} current_data <- apply_percentile_calcs(data %>% select(-BIP)) %>% filter(`Batter.Name` == input$player) %>% mutate(metric = factor(metric, levels = c( "wOBA", "wOBACON", "xwOBA", "xDamage", "Avg EV", "EV90", "Max EV", "stdev(LA)", "Barrel%", "Z-Con%", "Z-Swing%", "O-Con%", "Chase%", "Whiff%", "Swing%", "SwStr%" ))) %>% arrange(metric) #current_data <- data pos <- position_value() BBE <- sum(BBE$BBE,na.rm = TRUE) # Add Roboto Condensed font #font_add_google("Roboto Condensed", "roboto") #showtext_auto() # Color function current_data$color <- scales::gradient_n_pal(c("#325aa1","#90A4AE", "#D82129"))(current_data$percentile/100) # Labels plot labels_plot <- ggplot() + annotate("text", x = c(10, 50, 90), y = 1.2, label = c("Poor", "Average", "Great"), color = c("#3661ad", "#90A4AE", "#DC3545"), family = "roboto", size = 6) + annotate("text", x = c(10, 50, 90), y = .5, label = "▲", color = c("#3661ad", "#90A4AE", "#DC3545"), size = 12) + scale_x_continuous(limits = c(-16, 113), expand = c(0, 0)) + scale_y_continuous(limits = c(0.5, 1.5)) + theme_void() # Main plot main_plot <- ggplot(current_data, aes(y = factor(metric, levels = rev(metric)))) + geom_tile(aes(x = 50, width = 100), fill = "#c7dcdc", alpha = 0.3, height = 0.25) + geom_tile(aes(x = percentile/2, width = percentile, fill = color), height = 0.7) + annotate("segment", x = c(10, 50, 90), xend = c(10, 50, 90), y = 0, yend = 16.35, color = c("white"), linewidth = 1.5, alpha = 0.5) + geom_segment(aes(x = -2, xend = -16, y = as.numeric(factor(metric, levels = rev(metric))) - 0.3, yend = as.numeric(factor(metric, levels = rev(metric))) - 0.3), linetype = "longdash", color = "#399098", size = 1) + geom_segment(aes(x = 102, xend = 113, y = as.numeric(factor(metric, levels = rev(metric))) - 0.3, yend = as.numeric(factor(metric, levels = rev(metric))) - 0.3), linetype = "longdash", color = "#399098", size = 1) + geom_text(aes(x = -3, label = metric), hjust = 1, size = 5, family = "roboto") + geom_text(aes(x = 103, label = value), hjust = 0, size = 5, family = "roboto") + geom_point(aes(x = percentile, color = "white", fill = color), size = 12, shape = 21, stroke = 3) + geom_text(aes(x = percentile, label = percentile), size = 5, color = "white", fontface = "bold", family = "roboto") + scale_x_continuous(limits = c(-16, 113), expand = c(0, 0)) + scale_fill_identity() + scale_color_identity() + theme_minimal() + theme( axis.text = element_blank(), axis.title = element_blank(), panel.grid = element_blank(), plot.margin = margin(t = 0, r = 0, b = -20, l = 0), # Reduced bottom margin text = element_text(family = "roboto") ) # Load and process team logo if(current_team() == "MLB"){ logo_url <- "https://a.espncdn.com/combiner/i?img=/i/teamlogos/leagues/500/mlb.png?w=400&h=400&transparent=true" } else { logo_url <- sprintf("https://a.espncdn.com/combiner/i?img=/i/teamlogos/mlb/500/%s.png&h=200&w=200", current_team()) } logo_img <- image_read(logo_url) logo_raster <- as.raster(logo_img) #player_url <- sprintf(paste0("https://img.mlbstatic.com/mlb-photos/image/upload/d_headshot_silo_generic.png,ar_1:1,b_auto:border,c_pad,q_auto:best/w_60/v1/people/427012/headshot/milb/current")) player_url <- get_player_image(current_data[1,2]) player_img <- image_read(player_url) player_raster <- as.raster(player_img) # Create title with logo title_grob <- textGrob(paste0(input$player, " - ", pos, "\n BBE - ", BBE, "\n",input$level, " Percentile Rankings - ",input$szn), gp = gpar(fontsize = 25, fontface = "bold", fontfamily = "roboto")) logo_grob <- rasterGrob(logo_raster, x = 0, width = unit(.5, "npc"),hjust = 0) player_grob <- rasterGrob(player_raster, x = 0.5, width = unit(.5, "npc"),hjust = 0) title_with_logo <- arrangeGrob(logo_grob, title_grob,player_grob, ncol = 3, widths = c(.25,.5,.25)) caption_grob <- textGrob("Viz by: @TimStats | tim-stats.com | Data: MLB",gp = gpar(fontsize = 15, fontface = "bold", fontfamily = "roboto")) # Final arrangement with logo in title final_plot <- grid.arrange( title_with_logo, labels_plot, main_plot, caption_grob, heights = c(0.15, 0.05, 0.75, 0.05) ) grid.arrange( gtable_add_padding( final_plot, padding = unit(c(20, 20, 20, 20), "points") # top, right, bottom, left margins ) ) }, height = 1000, width = 1000, res = 97,pointsize = 12) } # Run the app shinyApp(ui = ui, server = server)