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4_comp_2019_main.R
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source("0_load_stuff.R")
##########################
### Plotting Functions ###
##########################
##################################################
######### pitcher vs. pitcher histograms #########
##################################################
pit_v_pit_hists <- function(pit_names, diff=TRUE, pitcher_exits=pitcher_exits, war_df=WAR_df_2019, facet_yr=F) {
# browser()
drl = pitcher_exits %>%
left_join(war_df) %>%
filter(PIT_NAME %in% pit_names)
# drl %>% group_by(PIT_NAME) %>% summarise(mr = mean(CUM_RUNS), sdr = sd(CUM_RUNS))
drl1 = left_join(drl, drl %>% group_by(PIT_NAME,CUM_RUNS) %>% summarise(mi = mean(INNING), .groups="drop"))
# dnf1 %>% select(PIT_NAME,GAME_ID,INNING,CUM_RUNS,mi) %>% arrange(PIT_NAME,CUM_RUNS)
if (facet_yr) {
drl2 = drl1 %>% select(PIT_NAME,GAME_ID,INNING,CUM_RUNS,mi,YEAR) %>%
arrange(PIT_NAME,YEAR,CUM_RUNS) %>%
group_by(PIT_NAME,YEAR,CUM_RUNS) %>%
summarise(Count = n(), mi = mi, YEAR, .groups="drop") %>%
distinct() %>%
mutate(mi = round(mi,1))
} else {
drl2 = drl1 %>% select(PIT_NAME,GAME_ID,INNING,CUM_RUNS,mi) %>%
arrange(PIT_NAME,CUM_RUNS) %>%
group_by(PIT_NAME,CUM_RUNS) %>%
summarise(Count = n(), mi = mi, .groups="drop") %>%
distinct() %>%
mutate(mi = round(mi,1))
}
drl3a = tibble()
for (pit_name in pit_names) {
drl1_name1 = drl2 %>% filter(PIT_NAME == pit_name)
if (diff) {
diff_df = merge(drl1_name1, drl1_name2, by="CUM_RUNS", all=T) %>%
mutate(Count.x = replace_na(Count.x,0),
Count.y = replace_na(Count.y,0)) %>%
mutate(Count = Count.x - Count.y) %>%
select(CUM_RUNS,Count) %>%
mutate(PIT_NAME = "Difference", mi=NA)
drl3 = bind_rows(drl2, diff_df)
drl3$PIT_NAME = factor(drl3$PIT_NAME, levels=c(name1,name2,"Difference"))
drl3 = drl3 %>% mutate(color =
ifelse(PIT_NAME=="Difference" & Count > 0, "darkgreen",
ifelse(PIT_NAME=="Difference" & Count < 0, "firebrick",
"black")))
} else {
drl3 = drl2
drl3$color = "black"
}
drl3a = bind_rows(drl3a, drl3)
}
# prl = drl3 %>%
# # ggplot(aes(x=CUM_RUNS,y=Count,label=mi)) +
# ggplot(aes(x=CUM_RUNS,y=Count)) +
# facet_wrap(~PIT_NAME) +
# # geom_histogram() +
# geom_col(fill=drl3$color) +
# # scale_y_continuous(name="Count",breaks=seq(-100,100,by=2)) +
# scale_x_continuous(name="Runs Allowed in a Game",breaks=seq(0,20,by=2))
# # labs(title="Distribution of Runs Allowed in a Game")
prl = drl3 %>% ggplot(aes(x=CUM_RUNS,y=Count))
if (facet_yr) {
prl = prl + facet_wrap(~PIT_NAME+YEAR)
} else {
prl = prl + facet_wrap(~PIT_NAME)
}
prl = prl +
geom_col(fill=drl3$color) +
scale_x_continuous(name="Runs Allowed in a Game",breaks=seq(0,20,by=2))
prl
# ggsave(paste0(output_folder,"plot_Lynn_Ryu_",year,".png"), prl)
}
pit1_vs_pit2_hists <- function(name1, name2, diff=TRUE, pitcher_exits=pitcher_exits, war_df=WAR_df_2019, facet_yr=F) {
# browser()
drl = pitcher_exits %>%
left_join(war_df) %>%
filter(PIT_NAME %in% c(name1, name2))
# drl %>% group_by(PIT_NAME) %>% summarise(mr = mean(CUM_RUNS), sdr = sd(CUM_RUNS))
drl1 = left_join(drl, drl %>% group_by(PIT_NAME,CUM_RUNS) %>% summarise(mi = mean(INNING), .groups="drop"))
# dnf1 %>% select(PIT_NAME,GAME_ID,INNING,CUM_RUNS,mi) %>% arrange(PIT_NAME,CUM_RUNS)
if (facet_yr) {
drl2 = drl1 %>% select(PIT_NAME,GAME_ID,INNING,CUM_RUNS,mi,YEAR) %>%
arrange(PIT_NAME,YEAR,CUM_RUNS) %>%
group_by(PIT_NAME,YEAR,CUM_RUNS) %>%
summarise(Count = n(), mi = mi, YEAR, .groups="drop") %>%
distinct() %>%
mutate(mi = round(mi,1))
} else {
drl2 = drl1 %>% select(PIT_NAME,GAME_ID,INNING,CUM_RUNS,mi) %>%
arrange(PIT_NAME,CUM_RUNS) %>%
group_by(PIT_NAME,CUM_RUNS) %>%
summarise(Count = n(), mi = mi, .groups="drop") %>%
distinct() %>%
mutate(mi = round(mi,1))
}
drl1_name1 = drl2 %>% filter(PIT_NAME == name1)
drl1_name2 = drl2 %>% filter(PIT_NAME == name2)
if (diff) {
diff_df = merge(drl1_name1, drl1_name2, by="CUM_RUNS", all=T) %>%
mutate(Count.x = replace_na(Count.x,0),
Count.y = replace_na(Count.y,0)) %>%
mutate(Count = Count.x - Count.y) %>%
select(CUM_RUNS,Count) %>%
mutate(PIT_NAME = "Difference", mi=NA)
drl3 = bind_rows(drl2, diff_df)
drl3$PIT_NAME = factor(drl3$PIT_NAME, levels=c(name1,name2,"Difference"))
drl3 = drl3 %>% mutate(color =
ifelse(PIT_NAME=="Difference" & Count > 0, "darkgreen",
ifelse(PIT_NAME=="Difference" & Count < 0, "firebrick",
"black")))
} else {
drl3 = drl2
drl3$color = "black"
}
# prl = drl3 %>%
# # ggplot(aes(x=CUM_RUNS,y=Count,label=mi)) +
# ggplot(aes(x=CUM_RUNS,y=Count)) +
# facet_wrap(~PIT_NAME) +
# # geom_histogram() +
# geom_col(fill=drl3$color) +
# # scale_y_continuous(name="Count",breaks=seq(-100,100,by=2)) +
# scale_x_continuous(name="Runs Allowed in a Game",breaks=seq(0,20,by=2))
# # labs(title="Distribution of Runs Allowed in a Game")
prl = drl3 %>% ggplot(aes(x=CUM_RUNS,y=Count))
if (facet_yr) {
prl = prl + facet_wrap(~PIT_NAME+YEAR)
} else {
prl = prl + facet_wrap(~PIT_NAME)
}
prl = prl +
geom_col(fill=drl3$color) +
scale_x_continuous(name="Runs Allowed in a Game",breaks=seq(0,20,by=2))
prl
# ggsave(paste0(output_folder,"plot_Lynn_Ryu_",year,".png"), prl)
}
#########################################
######### AGGREGATED HISTOGRAMS #########
#########################################
agg_pit1_vs_pit2_hists <- function(names_overvalued, names_undervalued, diff=TRUE) {
# browser()
drl = pitcher_exits %>%
left_join(WAR_df_2019) %>%
filter(PIT_NAME %in% c(names_overvalued, names_undervalued)) %>%
mutate(overvalued = PIT_NAME %in% names_overvalued)
drl1 = left_join(drl, drl %>%
group_by(overvalued,CUM_RUNS) %>% #group_by(PIT_NAME,CUM_RUNS) %>%
summarise(mi = mean(INNING),.groups="drop"))
drl2 = drl1 %>%
select(PIT_NAME,GAME_ID,INNING,CUM_RUNS,mi,overvalued) %>%
# select(PIT_NAME,GAME_ID,INNING,CUM_RUNS,mi,overvalued) %>%
arrange(overvalued,CUM_RUNS) %>%
group_by(overvalued,CUM_RUNS) %>%
summarise(Count = n(), mi = mi, .groups="drop") %>%
distinct() %>%
mutate(mi = round(mi,1))
drl1_names_overvalued = drl2 %>% filter(overvalued)
drl1_names_undervalued = drl2 %>% filter(!overvalued)
if (diff) {
diff_df = merge(drl1_names_undervalued, drl1_names_overvalued, by="CUM_RUNS", all=T) %>%
mutate(Count.x = replace_na(Count.x,0),
Count.y = replace_na(Count.y,0)) %>%
mutate(Count = Count.x - Count.y) %>%
select(CUM_RUNS,Count) %>%
mutate(facet = "difference", mi=NA)
drl3 = bind_rows(drl2 %>% mutate(facet = ifelse(overvalued, "overvalued", "undervalued")) %>% select(-overvalued),
diff_df)
drl3$facet = factor(drl3$facet, levels=c("undervalued","overvalued","difference"))
drl3 = drl3 %>% mutate(color =
ifelse(facet=="difference" & Count > 0, "darkgreen",
ifelse(facet=="difference" & Count < 0, "firebrick",
"black")))
} else {
drl3 = drl2
drl3$color = "black"
}
prl = drl3 %>%
# ggplot(aes(x=CUM_RUNS,y=Count,label=mi)) +
ggplot(aes(x=CUM_RUNS,y=Count)) +
facet_wrap(~facet) +
# geom_histogram() +
geom_col(fill=drl3$color) +
# geom_text(vjust = 1.5,color="white") +
# geom_text(data=dnf2, aes( label = mi), vjust = -0.2)
scale_x_continuous(name="Runs Allowed in a Game",breaks=seq(0,20,by=2)) +
scale_y_continuous(name="Count",breaks=seq(-50,50,by=5))
# labs(title="Distribution of Runs Allowed in a Game")
prl
# ggsave(paste0(output_folder,"plot_Lynn_Ryu_",year,".png"), prl)
}
#########################################
####### GERRIT COLE'S 2019 SEASON #######
#########################################
plot_pit_szn <- function(pitname = "Gerrit Cole") {
cole19 = pitcher_exits %>%
left_join(WAR_df_2019) %>%
filter(PIT_NAME == pitname) %>%
select(DATE,HOME_TEAM_ID,AWAY_TEAM_ID,CUM_RUNS,INNING,exit_at_end_of_inning,
BASE_STATE,OUTS_CT, GWAR) %>%
mutate(
BASE_STATE = as.character(BASE_STATE),
OUTS_CT = as.character(OUTS_CT),
BASE_STATE = ifelse(exit_at_end_of_inning, "", BASE_STATE),
OUTS_CT = ifelse(exit_at_end_of_inning, "", OUTS_CT),
) %>%
arrange(DATE) %>%
rename(GWAR = GWAR,
`HOME TEAM` = HOME_TEAM_ID,
`AWAY TEAM` = AWAY_TEAM_ID,
`RUNS ALLOWED` = CUM_RUNS,
`EXIT INNING` = INNING,
`EXIT BASE STATE` = BASE_STATE,
`EXIT OUTS` = OUTS_CT) %>%
mutate(GWAR = round(GWAR,3)) %>%
select(-exit_at_end_of_inning)
data.frame(cole19)
WAR_df_2019 %>% filter(PIT_NAME == "Gerrit Cole") %>% select(GWAR)
plot_cole19 = cole19 %>% gt() %>%
data_color(
columns = GWAR,
colors = scales::col_numeric(
# palette = c("red", "green"),
# domain = NULL
# palette = c("#f8696b","#63be7b"), #"#ffeb84",
palette = c("#f8696b","#ffeb84", "#63be7b"),
# palette = c("#b22222","#228B22"), #"#ffeb84",
domain = c(min(cole19$GWAR), max(cole19$GWAR))
# domain = c(-0.5, 0.5)
)
# colors = scales::col_numeric(
# palette = paletteer::paletteer_d(
# palette = "ggsci::green_material"
# ) %>% as.character(),
# domain = NULL
# )
) %>%
cols_align(align = "center")
plot_cole19
}
# ################################################
# ####### GERRIT COLE'S 2019 GWAR RANKINGS #######
# ################################################
#
# gwar_2019_rankings = WAR_df_2019 %>%
# drop_na() %>%
# select(PIT_NAME, GWAR) %>%
# arrange(-GWAR) %>%
# ggplot() +
# geom_point(aes(x = GWAR, y = fct_reorder(PIT_NAME, GWAR)),
# size=2, color="black") + #dodgerblue2
# scale_x_continuous(breaks=seq(0,20,by=1), name="Grid WAR") +
# ylab("pitcher")
# # gwar_2019_rankings
# ggsave(paste0(output_folder,"plot_gwar_rankings_",year,".png"), width=12, height=17)
#
# data.frame(WAR_df_2019 %>% select(PIT_NAME, GWAR) %>% arrange(-GWAR))
#