PITCHf/x Homework

Due: Monday, October 17th

Please use R and dplyr to answer the questions below on a separate sheet of paper.

R Studio Server: http://rstudio.saintannsny.org:8787/

Useful Labs:

You may want to look over the following labs to see how you answered similar questions in the past.

Data Transformation: http://rpubs.com/jcross/data_transformation_lahman

More Data Transformation: http://rpubs.com/jcross/data_transformation2

Grouping and Summarizing: http://rpubs.com/jcross/nba_play_by_play

PITCHf/x Data

# Reading in data on pitches from MLB 2016 regular season
p <- read.csv('/home/rstudioshared/shared_files/data/pitchdata2016.csv')

# loading the dplyr library
library(dplyr)

# looking at the first six rows of data
head(p)

The code above loads pitch data from the 2016 MLB regular season. If you’re interested, you can find a good primer on PITCHf/x here https://fastballs.wordpress.com/2010/04/18/a-pitchfx-primer/ but here’s an even briefer description of several of the columns:

• batter, pitcher: These are player id’s used by MLBAM (Major League Baseball Advanced Media)
• stand, p_throws: Identify whether the batter and pitcher are batting/throwing left- or right-handed.
• type: B, S or X for balls, strikes and balls in play
• des2: A description of the pitch result
• start_speed: The estimated speed of the pitch when the pitcher releases it (pitches slow down on their way to the plate).
• sz_top, sz_bottom: The estimated top and bottom of the strike zone (based on the hitter’s stance) in feet from the ground.
• pfx_x, pfx_z: The horizontal and vertical movement of the pitch (relative to a pitch acting under gravity alone) in inches. Note that negatives numbers are down or (from the catcher’s perspective) to the left.
• px, pz: The horizontal (from the center of the plate) and vertical (from the ground) location of the pitch in feet. Negative px numbers indicate pitches that are to the left of the center of the plate from the catcher’s perspective.
• pitch_type: See a description of pitch classifications here.
• pitch_group: larger categories based on pitch_type, fastball, breaking ball or changeup
• called_pitch: 1 if the umpired called a ball/strike, 0 otherwise
• HomeTeamId, AwayTeamId: Ids for MLB teams used by Baseball Information Systems (BIS)
• StartBalls, StartStrikes: The ball/strike count prior to the pitch in question.
• InningHalf: 0 for the top of the inning, 1 for the bottom of the inning.

Questions

Please use dplyr functions to answer the following questions (filter, group_by, summarize, top_n etc.)

1. Find the median start speed for each pitch type (pitch_type). Write the top 3 and bottom 3 pitch types by median start speed.

2. Find the median start speed for every ball/strike count. What count has the highest median pitch speed? What count has the lowest median pitch speed?

3. Limiting the data to pitches in the fastball group (pitch_group==“fastball”), in what inning do pitchers throw the fastest?

BONUS: When looking for the innings in which pitchers throw the hardest and the softest in question #3, your results included some extra innings that are rarely reached. Try answering question #3 again but this time limiting your results to innings in which at least 500 fastballs were thrown.