Using tabyls() function to explore pitch selection in the 2020 World Series

pitch_selection.Rmd

---
title: "tabyls"
author: "Jim Albert"
date: "10/27/2020"
output: html_document
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE,
                      warning = FALSE,
                      message = FALSE)
```

#### Introduction

- Much of baseball data consists of counts
- Creating tables (one-way, 2-way, 3-way)
- Graphing the counts or percentages
- Base ```table()``` function is incompatible with tidyverse
- Can use ```count()``` option with ```group_by()```
- Idea is to introduce ```tabyl()``` function which is part of the ```janitor``` package

#### Load five packages

```{r}
library(tidyverse)
library(janitor)
library(teamcolors)
library(ProbBayes)
library(gridExtra)
```

#### Overall pitch types for 2020 pitchers

Let's explore pitch type use among all pitchers
in 2020 season.

Read in data and combine "Curveball" and "Knuckle Curve" categories into a single "CurveBall".

```{r}
sc2020final <- read_csv("~/Dropbox/2020 WORK/statcast2020/sc2020final.csv")
sc2020final %>% 
  mutate(Pitch_Name = 
           ifelse(pitch_name %in% 
                    c("Curveball", "Knuckle Curve"),
                  "CurveBall", pitch_name)) ->
  sc2020final
```

Graph of all pitch types.

```{r}
sc2020final %>% 
  tabyl(Pitch_Name) %>% 
  ggplot(aes(Pitch_Name, percent)) +
  geom_col(fill = "red",
           color = "white") +
  coord_polar()
```

Alternative display not using polar coordinates.

```{r}
sc2020final %>% 
  tabyl(Pitch_Name) %>% 
  ggplot(aes(Pitch_Name, percent)) +
  geom_col(fill = "red",
           color = "white") 
```

Table of fractions of pitch type facing left and right-handed hitters.

```{r}
sc2020final %>% 
  tabyl(Pitch_Name, stand)  %>% 
  adorn_percentages("col") %>% 
  adorn_rounding(3)
```

Graph of pitch types faceted by side of batter.

```{r}
sc2020final %>% 
  tabyl(Pitch_Name, stand)  %>% 
  adorn_percentages("col") %>% 
  pivot_longer(
    cols = L:R,
    names_to = "Stand",
    values_to = "Percentage"
  ) %>% 
  ggplot(aes(Pitch_Name, Percentage)) +
  geom_col(fill = "yellow",
           color = "brown") +
  coord_polar() +
  facet_wrap(~ Stand, ncol = 2) +
  ggtitle("All Pitchers") +
  centertitle()
```

Now let's break down this comparison by arm of pitcher.

```{r}
sc2020final %>% 
  filter(p_throws == "L") %>% 
  tabyl(Pitch_Name, stand)  %>% 
  adorn_percentages("col") %>% 
  adorn_rounding(3)
```

```{r}
sc2020final %>% 
  filter(p_throws == "R") %>% 
  tabyl(Pitch_Name, stand)  %>% 
  adorn_percentages("col") %>% 
  adorn_rounding(3)
```

Graph for right-handed pitchers.

```{r}
sc2020final %>% 
  filter(p_throws == "R") %>% 
  tabyl(Pitch_Name, stand)  %>% 
  adorn_percentages("col") %>% 
  pivot_longer(
    cols = L:R,
    names_to = "Stand",
    values_to = "Percentage"
  ) %>% 
  ggplot(aes(Pitch_Name, Percentage)) +
  geom_col(fill = "yellow",
           color = "brown") +
  coord_polar() +
  facet_wrap(~ Stand, ncol = 2) +
  ggtitle("Right-Handed Pitchers") +
  centertitle()
```

Takeaways for RH pitchers:

- To lefthanded batters, for off speed pitches, tend to throw changeups (16%), sliders (13%) and curveballs (12%) with relatively equal frequencies, but changeups are the most common.  Throw four seam fastballs about 36% of time.

- To righthanded batters, for off speed pitches, tend to throw a lot of sliders (24%) and curveballs (11%) and changeups (6%) are not common.  Four seamers are thrown about 33% of time.

Graph for left-handed pitchers:

```{r}
sc2020final %>% 
  filter(p_throws == "L") %>% 
  tabyl(Pitch_Name, stand)  %>% 
  adorn_percentages("col") %>% 
  pivot_longer(
    cols = L:R,
    names_to = "Stand",
    values_to = "Percentage"
  ) %>% 
  ggplot(aes(Pitch_Name, Percentage)) +
  geom_col(fill = "yellow",
           color = "brown") +
  coord_polar() +
  facet_wrap(~ Stand, ncol = 2) +
  ggtitle("Left-Handed Pitchers") +
  centertitle() 
```

Takeaways for LH pitchers:

- To lefthanded batters, for off speed pitches, tend to throw sliders (22%), followed by curveballs (12%) and changeups (5%).  For fastballs, four seam fastballs and sinkers are common thrown respectively 34% and 20% of time.

- To righthanded batters, for off speed pitches, tend to throw curveballs (11%), sliders (12%) and changeups (19%).  Four seamers are thrown about 35% of time.

#### World Series Data

- Read in the 2020 World Series data
- Obtained by ```baseballr``` package

```{r}
d <- read_csv("../2020 playoffs/ws2020.csv")
```

Find Team colors using ```teamcolors``` package.

```{r}
LA <- teamcolors %>% 
  filter(name == "Los Angeles Dodgers") %>% 
  select(primary) %>% pull()
TB <- teamcolors %>% 
  filter(name == "Tampa Bay Rays") %>% 
  select(tertiary) %>% pull()
```

#### Basic Use of tably() Function - One Categorical Variable

```{r}
d %>% 
  tabyl(pitch_name)
```

#### Some Recoding

Let's recode the pitch name variable, so Curveball and Knuckle Curve are both curve balls.

```{r}
d %>% 
  mutate(Pitch_Name = 
           ifelse(pitch_name %in% 
                    c("Curveball", "Knuckle Curve"),
                  "CurveBall", pitch_name)) -> d
```

Redo earlier table:

```{r}
d %>% 
  tabyl(Pitch_Name)
```
#### Graph output:

```{r}
d %>% 
  tabyl(Pitch_Name) %>% 
  ggplot(aes(Pitch_Name, percent)) +
  geom_col()
```

#### Two Categorical Variables

Identify team that is pitching:

```{r}
d %>% 
  mutate(pitch_team = ifelse(inning_topbot == "Top",
         home_team, away_team)) -> d
```

Tabulate by pitch name and pitching team:

```{r}
(d %>% 
  tabyl(Pitch_Name, pitch_team) -> d1)
```

#### Graph

- Basic barplot, facetted by Team variable.

```{r}
d1 %>% 
  pivot_longer(
    cols = LAD:TB,
    names_to = "Team",
    values_to = "Count"
  ) %>% 
  ggplot(aes(Pitch_Name, Count)) + 
  geom_col() +
  facet_wrap(~ Team, ncol = 1) +
  coord_flip()
```

- Better to use "dodge" positioning:

```{r}
d1 %>% 
  pivot_longer(
    cols = LAD:TB,
    names_to = "Team",
    values_to = "Count"
  ) %>% 
  ggplot(aes(Pitch_Name, Count,
             fill = Team)) + 
  geom_col(position = "dodge") +
  coord_flip() +
  scale_fill_manual(values = c(LA, TB))
```

- Pie graphs?

```{r}
d1 %>% 
  pivot_longer(
    cols = LAD:TB,
    names_to = "Team",
    values_to = "Count"
  ) %>% 
  ggplot(aes(Pitch_Name, Count,
             fill = Team)) + 
  geom_col(position = "dodge") +
  coord_polar() +
  scale_fill_manual(values = c(LA, TB))
```


#### Three Way tables

Categorize by pitching team, pitch type and stand of batter.

```{r}
(d %>% 
  tabyl(Pitch_Name, stand, pitch_team) -> d2)
```


#### adorn Functions

- ```adorn_totals()``` - add row and/or col totals

```{r}
d %>% 
  tabyl(Pitch_Name, pitch_team) %>%
  adorn_totals(c("row", "col"))
```

- ```adorn_percentages()``` - change to row or col percentages

```{r}
d %>% 
  tabyl(Pitch_Name, pitch_team) %>%
  adorn_percentages("col")
```

- ```adorn_pct_formatting()``` - add formatting to percentages

```{r}
d %>% 
  tabyl(Pitch_Name, pitch_team) %>%
  adorn_percentages("col") %>% 
  adorn_pct_formatting()
```

- ```adorn_rounding()``` - round tabular output

```{r}
d %>% 
  tabyl(Pitch_Name, pitch_team) %>%
  adorn_percentages("col") %>% 
  adorn_rounding(2)
```

- ```adorn_ns()``` -- add counts to percentages

```{r}
d %>% 
  tabyl(Pitch_Name, pitch_team) %>%
  adorn_percentages("col") %>% 
  adorn_rounding(3) %>% 
  adorn_ns()
```

- ```adorn_title()``` - add title

```{r}
d %>% 
  tabyl(Pitch_Name, pitch_team) %>%
  adorn_percentages("col") %>% 
  adorn_rounding(3) %>% 
  adorn_ns() %>% 
  adorn_title("top", "PITCH TYPE")
```

#### adorn() function on 3-way table

```{r}
(d2 %>% 
  adorn_percentages("col") %>% 
  adorn_rounding(3) -> d3)
```

#### Graph of a three-way table?

- facet by side of batter
- pie chart comparing percentages


```{r}
d3[[1]] %>% 
  mutate(Team = "LAD") -> d4a
d3[[2]] %>% 
  mutate(Team = "TB") -> d4b
(d4 <- rbind(d4a, d4b))
```

```{r}
d4 %>% 
  pivot_longer(
    cols = L:R,
    names_to = "Stand",
    values_to = "Percentage"
  ) %>% 
  ggplot(aes(Pitch_Name, Percentage,
             fill = Team)) + 
  geom_col(position = "dodge") +
  coord_polar() +
  facet_wrap(~ Stand, ncol = 2) +
  scale_fill_manual(values = c(LA, TB))
```

#### Breakdown by Side of Pitching Arm

Left-handers:

```{r}
d %>% 
  filter(p_throws == "L") %>% 
  tabyl(Pitch_Name, stand, pitch_team) -> d2
d2 %>% 
  adorn_percentages("col") %>% 
  adorn_rounding(3) -> d3
d3[[1]] %>% 
  mutate(Team = "LAD") -> d4a
d3[[2]] %>% 
  mutate(Team = "TB") -> d4b
d4 <- rbind(d4a, d4b)
d4 %>% 
  pivot_longer(
    cols = L:R,
    names_to = "Stand",
    values_to = "Percentage"
  ) %>% 
  ggplot(aes(Pitch_Name, Percentage,
             fill = Team)) + 
  geom_col(position = "dodge") +
  coord_polar() +
  facet_wrap(~ Stand, ncol = 2) +
  scale_fill_manual(values = c(LA, TB)) +
  ggtitle("Left-Handed Pitchers") +
  centertitle() 
```

Right-handers:

```{r}
d %>% 
  filter(p_throws == "R") %>% 
  tabyl(Pitch_Name, stand, pitch_team) -> d2
d2 %>% 
  adorn_percentages("col") %>% 
  adorn_rounding(3) -> d3
d3[[1]] %>% 
  mutate(Team = "LAD") -> d4a
d3[[2]] %>% 
  mutate(Team = "TB") -> d4b
d4 <- rbind(d4a, d4b)
d4 %>% 
  pivot_longer(
    cols = L:R,
    names_to = "Stand",
    values_to = "Percentage"
  ) %>% 
  ggplot(aes(Pitch_Name, Percentage,
             fill = Team)) + 
  geom_col(position = "dodge") +
  coord_polar() +
  facet_wrap(~ Stand, ncol = 2) +
  scale_fill_manual(values = c(LA, TB)) +
  ggtitle("Right-Handed Pitchers") +
  centertitle() 
```

Here are the associated tables:

Pitch types of left-handed pitchers:

```{r}
d %>% 
  filter(p_throws == "L") %>% 
  tabyl(Pitch_Name, stand, pitch_team)  %>% 
  adorn_percentages("col") %>% 
  adorn_rounding(3)
```

Pitch types of right-handed pitchers:

```{r}
d %>% 
  filter(p_throws == "R") %>% 
  tabyl(Pitch_Name, stand, pitch_team)  %>% 
  adorn_percentages("col") %>% 
  adorn_rounding(3)
```