Pitcher fastballs adjusted spin rate analysis

This player look up is part of The Washington Post Story: How baseball’s war on sticky stuff is already changing the game

More of the methodology of the analysis can be found on GitHub.

The table lists the average adjusted spin rate (rotations per minute divided by miles per hour) for each time period.

Only players who’ve thrown at least 20 pitches since June 3 were included.

Team represents player’s current team as of June 24, 2021.

knitr::opts_chunk$set(warning = F)
knitr::opts_chunk$set(message = F)

packages <- c("tidyverse", "readxl", "janitor", "sparkline",
              "geofacet", "forcats", "lubridate", "DT", "knitr")
if (length(setdiff(packages, rownames(installed.packages()))) > 0) {
  install.packages(setdiff(packages, rownames(installed.packages())), repos = "http://cran.us.r-project.org")  
}
library(tidyverse)
library(readxl)
library(janitor)
library(lubridate)
library(knitr)
library(sparkline)
library(DT)

options(knitr.kable.NA = '')

pitches <- read_csv("../../data/clean_data/combined_2017_2021.csv") %>%
  mutate(pitch_type=case_when(
    pi_pitch_type=="CH" ~ "changeup",
    pi_pitch_type=="SI" ~ "sinker",
    pi_pitch_type=="SL" ~ "slide",
    pi_pitch_type=="CU" ~ "knuckle curve",
    pi_pitch_type=="FA" ~ "4-seam fastball",
    pi_pitch_type=="FC" ~ "cutter",
    pi_pitch_type=="KN" ~ "knuckleball",
    pi_pitch_type=="SB" ~ "screwball",
    pi_pitch_type=="FS" ~ "sinking fastball/splitter",
    pi_pitch_type=="CS" ~ "curve, slow",
  ),
  pitch_results=case_when(
    pitch_result=="B" ~ "ball",
    pitch_result=="F" ~ "foul",
    pitch_result=="FB" ~ "fly ball/popup",
    pitch_result=="GB" ~ "ground ball",
    pitch_result=="LD" ~ "line drive",
    pitch_result=="PU" ~ "fly ball/popup",
    pitch_result=="S" ~ "strike"
  ),
  pitch_outcomes=case_when(
    pitch_outcome=="1B" ~ "first base",
    pitch_outcome=="2B" ~ "second base",
    pitch_outcome=="3B" ~ "third base",
    pitch_outcome=="B" ~ "ball",
    pitch_outcome=="F" ~ "foul",
    pitch_outcome=="HR" ~ "homerun",
    pitch_outcome=="OUT" ~ "out",
    pitch_outcome=="S" ~ "strike",
    pitch_outcome=="HBP" ~ "hit by pitch",
    pitch_outcome=="NIP" ~ "not in play",
    pitch_outcome=="RBOE" ~ "reached base on error"
  )) %>% 
  mutate(bauer_units=rpm/mph)

# 
# salaries <- read_csv("../../data/raw_data/salary_lut.csv") %>% 
#   mutate(salary=gsub("\\$", "", salary),
#          salary=as.numeric(gsub(",", "", salary))) %>% 
#   mutate(rank=rank(desc(salary)))
# 
# 
# salaries30 <- salaries %>% 
#   arrange(desc(salary)) 
# 
# salaries30 <- head(salaries, 30)
# 
# sel_names <- as.character(salaries30$mlbid)

team_names_df <- read_csv("../../data/clean_data/team_names.csv") %>% 
  select(-n)



year_over_year <- pitches %>% 
  filter(pitch_type=="4-seam fastball") %>% 
  mutate(when=case_when(
    year(game_date)==2017 ~ "2017",
    year(game_date)==2018 ~ "2018",
    year(game_date)==2019 ~ "2019",
    year(game_date)==2020 ~ "2020",
    year(game_date)==2021 & game_date < ymd("2021-06-03") ~ "2021",
    game_date >= ymd("2021-06-03") ~ "after June 3",
    TRUE ~ "missing"
  )) %>% 
  group_by(when, pitcher_name) %>% 
  summarize(total=n(),
            avg_bu=round(mean(bauer_units, na.rm=T),2))

y2021 <- pitches %>% 
  filter(game_date>=ymd("2021-06-03")) %>% 
  group_by(pitcher_team, pitcher_name) %>% 
  summarize(total=n()) %>% 
  filter(total>=20) %>% 
  ungroup() %>% 
  unique() %>% 
  select(pitcher_name, pitcher_team) %>% 
  left_join(team_names_df) 

year_over_year <- year_over_year %>% 
  filter(pitcher_name %in% y2021$pitcher_name) %>% 
  ungroup() %>% 
  select(-total) %>% 
  pivot_wider(names_from="when", values_from="avg_bu") %>% 
  left_join(y2021) %>% 
  filter(!is.na(`2020`) | !is.na(`2021`)) %>% 
  select(Team=pitcher_team_name,
         Pitcher=pitcher_name,
         `2017`, `2018`, `2019`, `2020`, `2021`, `After June 3`=`after June 3`) %>%
  mutate(`Change 2021`=round((`After June 3`-`2021`)/`2021`*100,2)) %>% 
  arrange(Team, Pitcher)

tidyyear <- year_over_year %>% 
  select(-`Change 2021`) %>% 
  pivot_longer(cols=`2017`:`After June 3`, names_to="time", values_to="rate")
  
prices_sparkline_data <- tidyyear %>%
  group_by(Team, Pitcher) %>%
  summarize(
    `2017 - After June 3` = spk_chr(
      rate, type ="line",
      chartRangeMin = 100, chartRangeMax = max(rate)
    )
  )

year_over_year <- left_join(year_over_year, prices_sparkline_data)

year_over_year %>% 
  select(Team, Pitcher, `2017 - After June 3`, 
         `2019`, `2020`, `2021`, `After June 3`, 
         `Change 2021`) %>% 
  
datatable(escape=FALSE, filter = 'top', 
          options = list(paging = FALSE, fnDrawCallback = htmlwidgets::JS(
  '
function(){
  HTMLWidgets.staticRender();
}
'
)
       )) %>% 
  spk_add_deps()

#write_csv(year_over_year, "../summarized_data/pitchers_focus.csv", na="")