MLB Analysis

Introduction

The data I chose contains MLB pitch-level data for every pitch thrown between 2015-2018. It is broken into four separate data sets; one for at bat details, one for general game information, one for pitch level details, and one for player details.

The following table shows the dimensions of each table:

Table Dimensions
Table	Variables	Observations
atbats	11	740,389
games	19	9,699
pitches	39	2,852,973
players	3	2,218

For this project I chose to do some analytics on a specific pitcher, Brent Suter. In the real world, pitchers and coaches could both benefit from this data by learning how they can improve. For example, knowing where pitches are placed around the strike zone can improve strategy.

Further in the future, another good idea would be to use this data to run machine learning models to predict which pitch was coming next. If the coach could potentially signal to the batter which pitch he thinks is coming next, the batter can be better prepared to hit the ball.

Overall, there are definitely many different ways that baseball players and coaches can benefit from the insights gained from analyzing this data.

Click here to view the original source of data.

Packages Required

The following packages are required for this project:

library(dplyr)        # Used as a fast, consistent tool for working with data frame like objects
library(data.table)   # Used for a fast and friendly way to load the datasets
library(tidyr)        # Used specifically for data tidying
library(stringr)      # Used for string manipulation, such as splitting a string
library(ggplot2)      # Used for creating elegant data visualizations
library(knitr)        # Used for dynamic report generation
library(kableExtra)   # Additional features for knitr 'kable' function

Data Preparation

To begin, I have loaded the data files into R using the ‘fread’ function.

Next, I got a feel for the data by viewing the first 5 rows of each table.

At Bats Data
ab_id	batter_id	event	g_id	inning	o	p_throws	pitcher_id	stand	top
2.015e+09	572761	Groundout	201500001	1	1	L	452657	L	TRUE
2.015e+09	518792	Double	201500001	1	1	L	452657	L	TRUE
2.015e+09	407812	Single	201500001	1	1	L	452657	R	TRUE
2.015e+09	425509	Strikeout	201500001	1	2	L	452657	R	TRUE
2.015e+09	571431	Strikeout	201500001	1	3	L	452657	L	TRUE

Games Data
attendance	away_final_score	away_team	date	elapsed_time	g_id	home_final_score	home_team	start_time	umpire_1B	umpire_2B	umpire_3B	umpire_HP	venue_name	weather	wind	delay
35055	3	sln	2015-04-05	184	201500001	0	chn	7:17 PM	Mark Wegner	Marty Foster	Mike Muchlinski	Mike Winters	Wrigley Field	44 degrees, clear	7 mph, In from CF	0
45909	1	ana	2015-04-06	153	201500002	4	sea	1:12 PM	Ron Kulpa	Brian Knight	Vic Carapazza	Larry Vanover	Safeco Field	54 degrees, cloudy	1 mph, Varies	0
36969	2	atl	2015-04-06	156	201500003	1	mia	4:22 PM	Laz Diaz	Chris Guccione	Cory Blaser	Jeff Nelson	Marlins Park	80 degrees, partly cloudy	16 mph, In from CF	16
31042	6	bal	2015-04-06	181	201500004	2	tba	3:12 PM	Ed Hickox	Paul Nauert	Mike Estabrook	Dana DeMuth	Tropicana Field	72 degrees, dome	0 mph, None	0
45549	8	bos	2015-04-06	181	201500005	0	phi	3:08 PM	Phil Cuzzi	Tony Randazzo	Will Little	Gerry Davis	Citizens Bank Park	71 degrees, partly cloudy	11 mph, Out to RF	0

Pitches Data
ab_id	ax	ay	az	b_count	break_angle	break_length	break_y	code	end_speed	nasty	on_1b	on_2b	on_3b	pfx_x	pfx_z	pitch_num	pitch_type	px	pz	s_count	spin_dir	spin_rate	start_speed	sz_bot	sz_top	type	type_confidence	vx0	vy0	vz0	x	x0	y	y0	z0	zone
2.015e+09	7.665	34.685	-11.960	0	-25.0	3.2	23.7	C	84.1	55	FALSE	FALSE	FALSE	4.16	10.93	1	FF	0.416	2.963	0	159.235	2305.052	92.9	1.72	3.56	S	2	-6.409	-136.065	-3.995	101.1400	2.280	158.7800	50	5.302	3
2.015e+09	12.043	34.225	-10.085	0	-40.7	3.4	23.7	S	84.1	31	FALSE	FALSE	FALSE	6.57	12.00	2	FF	-0.191	2.347	1	151.402	2689.935	92.8	1.72	3.56	S	2	-8.411	-135.690	-5.980	124.2800	2.119	175.4100	50	5.307	5
2.015e+09	14.368	35.276	-11.560	0	-43.7	3.7	23.7	F	85.2	49	FALSE	FALSE	FALSE	7.61	10.88	3	FF	-0.518	3.284	2	145.125	2647.972	94.1	1.72	3.56	S	2	-9.802	-137.668	-3.337	136.7400	2.127	150.1100	50	5.313	1
2.015e+09	2.104	28.354	-20.540	0	-1.3	5.0	23.8	B	84.0	41	FALSE	FALSE	FALSE	1.17	6.45	4	FF	-0.641	1.221	2	169.751	1289.590	91.0	1.74	3.35	B	2	-8.071	-133.005	-6.567	109.6856	2.279	187.4635	50	5.210	13
2.015e+09	-10.280	21.774	-34.111	1	18.4	12.0	23.8	B	69.6	18	FALSE	FALSE	FALSE	-8.43	-1.65	5	CU	-1.821	2.083	2	280.671	1374.569	75.4	1.72	3.56	B	2	-6.309	-110.409	0.325	146.5275	2.179	177.2428	50	5.557	13

Players Data
id	first_name	last_name
452657	Jon	Lester
425794	Adam	Wainwright
457435	Phil	Coke
435400	Jason	Motte
519166	Neil	Ramirez

It is also a good idea to check for duplicates and missing values. In this case, there were no duplicates, however, there were some missing values that needed to be taken care of.

# Remove missing values in 'pitches'
colSums(is.na(pitches))

##           ab_id              ax              ay              az 
##               0           14189           14189           14189 
##         b_count         b_score     break_angle    break_length 
##               0               0           14189           14189 
##         break_y            code       end_speed           nasty 
##           14189               0           14114           14189 
##           on_1b           on_2b           on_3b            outs 
##               0               0               0               0 
##           pfx_x           pfx_z       pitch_num      pitch_type 
##           14142           14142               0               0 
##              px              pz         s_count        spin_dir 
##           14189           14189               0           14189 
##       spin_rate     start_speed          sz_bot          sz_top 
##           14189           14114            2083            2083 
##            type type_confidence             vx0             vy0 
##               0           14189           14189           14189 
##             vz0               x              x0               y 
##           14189               0           14189               0 
##              y0              z0            zone 
##           14189           14189           14189

pitches <- na.omit(pitches)

There is also some bad data when it comes to double header games. For some of the double headers, the first game shows an attendance of 0 or 1. These rows were removed.

I converted some variables as factor, including event, away team, home team, venue, code, pitch type, and play type, and spilt a few columns into two separate columns (weather split into temperature and forecast and wind split into wind speed and wind direction).

Updated Weather and Wind Columns
temp	forecast	wind_speed	wind_dir
44	clear	7	In from CF
54	cloudy	1	Varies
80	partly	16	In from CF
72	dome	0	None
71	partly	11	Out to RF

Additionally, I created boxplots to look for outliers. There was one variable, delay (in minutes), that had an outlier of 1860 minutes.

The following is a look at the specific outlier observation(s), which I removed from the data set.

Games Outlier
	attendance	away_final_score	away_team	date	elapsed_time	g_id	home_final_score	home_team	start_time	umpire_1B	umpire_2B	umpire_3B	umpire_HP	venue_name	temp	forecast	wind_speed	wind_dir	delay
9608	36508	6	mil	2018-09-24	229	201802339	4	sln	7:16 PM	Will Little	Ted Barrett	Mark Carlson	Lance Barksdale	Busch Stadium	78	cloudy	5	Out to LF	1860

Combine into One Table

Lastly, I decided to combine all four tables into one table.

The following table shows a summary (first 5 rows) of the “all” data set, which includes ~3 million records and 39 variables.

MLB All Data Set
ab_id	b_count	on_1b	on_2b	on_3b	pitch_num	pitch_type	s_count	batter_id	event	g_id	inning	o	p_throws	pitcher_id	stand	top	p_first_name	p_last_name	b_first_name	b_last_name	attendance	away_team	date	home_team	start_time	umpire_1B	umpire_2B	umpire_3B	umpire_HP	venue_name	temp	forecast	wind_speed	wind_dir
2.015e+09	0	FALSE	FALSE	FALSE	1	FF	0	572761	Groundout	201500001	1	1	L	452657	L	TRUE	Jon	Lester	Matt	Carpenter	35055	sln	2015-04-05	chn	7:17 PM	Mark Wegner	Marty Foster	Mike Muchlinski	Mike Winters	Wrigley Field	44	clear	7	In from CF
2.015e+09	0	FALSE	FALSE	FALSE	2	FF	1	572761	Groundout	201500001	1	1	L	452657	L	TRUE	Jon	Lester	Matt	Carpenter	35055	sln	2015-04-05	chn	7:17 PM	Mark Wegner	Marty Foster	Mike Muchlinski	Mike Winters	Wrigley Field	44	clear	7	In from CF
2.015e+09	0	FALSE	FALSE	FALSE	3	FF	2	572761	Groundout	201500001	1	1	L	452657	L	TRUE	Jon	Lester	Matt	Carpenter	35055	sln	2015-04-05	chn	7:17 PM	Mark Wegner	Marty Foster	Mike Muchlinski	Mike Winters	Wrigley Field	44	clear	7	In from CF
2.015e+09	0	FALSE	FALSE	FALSE	4	FF	2	572761	Groundout	201500001	1	1	L	452657	L	TRUE	Jon	Lester	Matt	Carpenter	35055	sln	2015-04-05	chn	7:17 PM	Mark Wegner	Marty Foster	Mike Muchlinski	Mike Winters	Wrigley Field	44	clear	7	In from CF
2.015e+09	1	FALSE	FALSE	FALSE	5	CU	2	572761	Groundout	201500001	1	1	L	452657	L	TRUE	Jon	Lester	Matt	Carpenter	35055	sln	2015-04-05	chn	7:17 PM	Mark Wegner	Marty Foster	Mike Muchlinski	Mike Winters	Wrigley Field	44	clear	7	In from CF

Exploratory Data Analysis

Brent Suter

For the exploratory data analysis, I am focusing on a specific pitcher, Brent Suter. First, I created a pie chart to show his most common pitch types.

From the graph above, we can see that Brent sticks to six different pitch types and mostly throws Four-seam Fastballs. Changeups and Sliders are his next popular choices. Below shows a frequency plot of how many of each type he throws specifically on the first pitch of each at bat.

The following graph shows the position of the ball as it crosses home plate for the pitcher, Brent. It is color coded by pitch type thrown. X = 0 means the ball went right down the middle of the plate and Z = 0 means the ball hit the ground.

Also, I created a similar plot except it is color coded by the pitch outcome (Strike, Ball, and In Play). Again, X = 0 means the ball went right down the middle of the plate and Z = 0 means the ball hit the ground.

Summary

In conclusion, this data can be very beneficial to baseball players and coaches. They can gain insights such as, where the pitches are landing based on the type of pitch thrown, in order to improve strategy for when a batter is facing a specific pitcher.

Using the 2015-2018 MLB data, various plots and graphs were created. For this project, we learned that Brent throws six pitches, mostly the Four-seam Fastball.

I did struggle with creating predictive models. Even after only using 10% of the data, I was still running into memory issues. In the future, I think it would be great to use this data to create machine learning predictive models.

Appendix

A1. Data

atbats data

ab_id - at-bat ID (first 4 digits are year)

batter_id - player ID of the batter (player names found in player_names.csv)

event - description of the result of the at-bat

g_id - game ID (first 4 digits are year)

inning - inning number

o - number of outs after this at-bat

p_score - score for the pitcher’s team

p_throws - which hand pitcher throws with (single character, R or L)

pitcher_id - player ID of the pitcher (player names found in player_names.csv)

stand - which side batter hits on (single character, R or L)

top - True if it’s the top of the inning / False if it’s the bottom

games data

attendance - number of fans who attended (NOTE: for first game of doubleheaders, value is often erroneously 1 or 0)

away_final_score - final score for the visiting team

away_team - three letter abbreviation for away team; third letter sometimes indicates league (national vs american)

date - date of game

elapsed_time - length of game in minutes

g_id game ID

home_final_score - final score for the home team

home_team - three letter abbreviation for home team; third letter sometimes indicates league (national vs american)

start_time - start time of game

umpire_1B - first and last name of the umpire at first base

umpire_2B - first and last name of the umpire at second base

umpire_3B - first and last name of the umpire at third base

umpire_HP - first and last name of the umpire at home plate

venue_name - name of stadium

weather - description of weather

wind - description of wind

delay - length of delay before game in minutes

pitches data

ab_id - at-bat ID

ax

ay

az

b_count - balls in the current count

b_score - score for the batter’s team

break_angle

break_length

break_y

code - records the result of the pitch (See A2)

end_speed - speed of the pitch when it reaches the plate

nasty

on_1b - True if there’s a runner on first, False if empty

on_2b - True if there’s a runner on second, False if empty

on_3b - True if there’s a runner on third, False if empty

outs - number of outs (before pitch is thrown)

pfx_x

pfx_z

pitch_num - pitch number (of at-bat)

pitch_type - type of pitch (See A3)

px - x-location as pitch crosses the plate (X=0 means right down the middle)

pz - z-location as pitch crosses the plate (Z=0 means the ground)

s_count - strikes in the current count

spin_dir - direction in which pitch is spinning, measured in degrees

spin_rate - the pitch’s spin rate, measured in RPM

start_speed - speed of the pitch just as it’s thrown

sz_bot

sz_top

type - simplified code: S (strike) B (ball) or X (in play)

type_confidence - confidence in pitch_type classification (unsure what 2 means)

vx0

vy0

vz0

x

x0

y

y0

z0

zone

players data

id - player ID (matches with batter_id and pitcher_id)

first_name - first name

last_name - last name

A2. Pitch Result Codes

B - Ball

*B - Ball in dirt

S - Swinging Strike

C - Called Strike

F - Foul

T - Foul Tip

L - Foul Bunt

I - Intentional Ball

W - Swinging Strike (Blocked)

M - Missed Bunt

P - Pitchout

Q - Swinging pitchout

R - Foul pitchout

Values that only occur on last pitch of at-bat:

X - In play, out(s)

D - In play, no out

E - In play, runs

H - Hit by pitch

Note: all codes, except for H, come directly from the XML files. All at-bats with code H were given no code in the XMLs.

A3. Pitch Types

CH - Changeup

CU - Curveball

EP - Eephus

FC - Cutter

FF - Four-seam Fastball

FO - Pitchout (also PO)

FS - Splitter

FT - Two-seam Fastball

IN - Intentional ball

KC - Knuckle curve

KN - Knuckeball

PO - Pitchout (also FO)

SC - Screwball

SI - Sinker

SL - Slider

UN - Unknown