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GitHub Repository: veeralakrishna/DataCamp-Project-Solutions-Python
 Path: blob/master/A New Era of Data Analysis in Baseball/notebook.ipynb
Views: 1229
Kernel: Python 3

1. The Statcast revolution

This is Aaron Judge. Judge is one of the physically largest players in Major League Baseball standing 6 feet 7 inches (2.01 m) tall and weighing 282 pounds (128 kg). He also hit the hardest home run ever recorded. How do we know this? Statcast.

Statcast is a state-of-the-art tracking system that uses high-resolution cameras and radar equipment to measure the precise location and movement of baseballs and baseball players. Introduced in 2015 to all 30 major league ballparks, Statcast data is revolutionizing the game. Teams are engaging in an "arms race" of data analysis, hiring analysts left and right in an attempt to gain an edge over their competition. This video describing the system is incredible.

In this notebook, we're going to wrangle, analyze, and visualize Statcast data to compare Mr. Judge and another (extremely large) teammate of his. Let's start by loading the data into our Notebook. There are two CSV files, judge.csv and stanton.csv, both of which contain Statcast data for 2015-2017. We'll use pandas DataFrames to store this data. Let's also load our data visualization libraries, matplotlib and seaborn.

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline

# Load Aaron Judge's Statcast data
judge = pd.read_csv('datasets/judge.csv')

# Load Giancarlo Stanton's Statcast data
stanton = pd.read_csv('datasets/stanton.csv')

2. What can Statcast measure?

The better question might be, what can't Statcast measure?

Starting with the pitcher, Statcast can measure simple data points such as velocity. At the same time, Statcast digs a whole lot deeper, also measuring the release point and spin rate of every pitch.

Moving on to hitters, Statcast is capable of measuring the exit velocity, launch angle and vector of the ball as it comes off the bat. From there, Statcast can also track the hang time and projected distance that a ball travels.

Let's inspect the last five rows of the judge DataFrame. You'll see that each row represents one pitch thrown to a batter. You'll also see that some columns have esoteric names. If these don't make sense now, don't worry. The relevant ones will be explained as necessary.

# Display all columns (pandas will collapse some columns if we don't set this option)
pd.set_option('display.max_columns', None)

# Display the last five rows of the Aaron Judge file

print(judge.tail())
pitch_type game_date release_speed release_pos_x release_pos_z \ 3431 CH 2016-08-13 85.6 -1.9659 5.9113 3432 CH 2016-08-13 87.6 -1.9318 5.9349 3433 CH 2016-08-13 87.2 -2.0285 5.8656 3434 CU 2016-08-13 79.7 -1.7108 6.1926 3435 FF 2016-08-13 93.2 -1.8476 6.0063 player_name batter pitcher events description spin_dir \ 3431 Aaron Judge 592450 542882 NaN ball NaN 3432 Aaron Judge 592450 542882 home_run hit_into_play_score NaN 3433 Aaron Judge 592450 542882 NaN ball NaN 3434 Aaron Judge 592450 542882 NaN foul NaN 3435 Aaron Judge 592450 542882 NaN called_strike NaN spin_rate_deprecated break_angle_deprecated break_length_deprecated \ 3431 NaN NaN NaN 3432 NaN NaN NaN 3433 NaN NaN NaN 3434 NaN NaN NaN 3435 NaN NaN NaN zone des game_type stand \ 3431 14.0 NaN R R 3432 4.0 Aaron Judge homers (1) on a fly ball to center... R R 3433 14.0 NaN R R 3434 4.0 NaN R R 3435 8.0 NaN R R p_throws home_team away_team type hit_location bb_type balls \ 3431 R NYY TB B NaN NaN 0 3432 R NYY TB X NaN fly_ball 1 3433 R NYY TB B NaN NaN 0 3434 R NYY TB S NaN NaN 0 3435 R NYY TB S NaN NaN 0 strikes game_year pfx_x pfx_z plate_x plate_z on_3b on_2b \ 3431 0 2016 -0.379108 0.370567 0.739 1.442 NaN NaN 3432 2 2016 -0.295608 0.320400 -0.419 3.273 NaN NaN 3433 2 2016 -0.668575 0.198567 0.561 0.960 NaN NaN 3434 1 2016 0.397442 -0.614133 -0.803 2.742 NaN NaN 3435 0 2016 -0.823050 1.623300 -0.273 2.471 NaN NaN on_1b outs_when_up inning inning_topbot hc_x hc_y \ 3431 NaN 0 5 Bot NaN NaN 3432 NaN 2 2 Bot 130.45 14.58 3433 NaN 2 2 Bot NaN NaN 3434 NaN 2 2 Bot NaN NaN 3435 NaN 2 2 Bot NaN NaN tfs_deprecated tfs_zulu_deprecated pos2_person_id umpire \ 3431 NaN NaN 571912.0 NaN 3432 NaN NaN 571912.0 NaN 3433 NaN NaN 571912.0 NaN 3434 NaN NaN 571912.0 NaN 3435 NaN NaN 571912.0 NaN sv_id vx0 vy0 vz0 ax ay az sz_top \ 3431 160813_144259 6.960 -124.371 -4.756 -2.821 23.634 -30.220 3.93 3432 160813_135833 4.287 -127.452 -0.882 -1.972 24.694 -30.705 4.01 3433 160813_135815 7.491 -126.665 -5.862 -6.393 21.952 -32.121 4.01 3434 160813_135752 1.254 -116.062 0.439 5.184 21.328 -39.866 4.01 3435 160813_135736 5.994 -135.497 -6.736 -9.360 26.782 -13.446 4.01 sz_bot hit_distance_sc launch_speed launch_angle effective_speed \ 3431 1.82 NaN NaN NaN 84.459 3432 1.82 446.0 108.8 27.410 86.412 3433 1.82 NaN NaN NaN 86.368 3434 1.82 9.0 55.8 -24.973 77.723 3435 1.82 NaN NaN NaN 92.696 release_spin_rate release_extension game_pk pos1_person_id \ 3431 1552.0 5.683 448611 542882.0 3432 1947.0 5.691 448611 542882.0 3433 1761.0 5.721 448611 542882.0 3434 2640.0 5.022 448611 542882.0 3435 2271.0 6.068 448611 542882.0 pos2_person_id.1 pos3_person_id pos4_person_id pos5_person_id \ 3431 571912.0 543543.0 523253.0 446334.0 3432 571912.0 543543.0 523253.0 446334.0 3433 571912.0 543543.0 523253.0 446334.0 3434 571912.0 543543.0 523253.0 446334.0 3435 571912.0 543543.0 523253.0 446334.0 pos6_person_id pos7_person_id pos8_person_id pos9_person_id \ 3431 622110.0 545338.0 595281.0 543484.0 3432 622110.0 545338.0 595281.0 543484.0 3433 622110.0 545338.0 595281.0 543484.0 3434 622110.0 545338.0 595281.0 543484.0 3435 622110.0 545338.0 595281.0 543484.0 release_pos_y estimated_ba_using_speedangle \ 3431 54.8144 0.00 3432 54.8064 0.98 3433 54.7770 0.00 3434 55.4756 0.00 3435 54.4299 0.00 estimated_woba_using_speedangle woba_value woba_denom babip_value \ 3431 0.000 NaN NaN NaN 3432 1.937 2.0 1.0 0.0 3433 0.000 NaN NaN NaN 3434 0.000 NaN NaN NaN 3435 0.000 NaN NaN NaN iso_value launch_speed_angle at_bat_number pitch_number 3431 NaN NaN 36 1 3432 3.0 6.0 14 4 3433 NaN NaN 14 3 3434 NaN 1.0 14 2 3435 NaN NaN 14 1

3. Aaron Judge and Giancarlo Stanton, prolific sluggers

This is Giancarlo Stanton. He is also a very large human being, standing 6 feet 6 inches tall and weighing 245 pounds. Despite not wearing the same jersey as Judge in the pictures provided, in 2018 they will be teammates on the New York Yankees. They are similar in a lot of ways, one being that they hit a lot of home runs. Stanton and Judge led baseball in home runs in 2017, with 59 and 52, respectively. These are exceptional totals - the player in third "only" had 45 home runs.

Stanton and Judge are also different in many ways. One is batted ball events, which is any batted ball that produces a result. This includes outs, hits, and errors. Next, you'll find the counts of batted ball events for each player in 2017. The frequencies of other events are quite different.

# All of Aaron Judge's batted ball events in 2017
judge_events_2017 = judge.loc[judge['game_year'] == 2017].events
print("Aaron Judge batted ball event totals, 2017:")
print(judge_events_2017.value_counts())

# All of Giancarlo Stanton's batted ball events in 2017
stanton_events_2017 = stanton.loc[stanton['game_year'] == 2017].events
print("\nGiancarlo Stanton batted ball event totals, 2017:")
print(stanton_events_2017.value_counts())
Aaron Judge batted ball event totals, 2017: strikeout 207 field_out 146 walk 116 single 75 home_run 52 double 24 grounded_into_double_play 15 force_out 11 intent_walk 11 hit_by_pitch 5 fielders_choice_out 4 sac_fly 4 field_error 4 triple 3 strikeout_double_play 1 Name: events, dtype: int64 Giancarlo Stanton batted ball event totals, 2017: field_out 239 strikeout 161 single 77 walk 72 home_run 59 double 32 grounded_into_double_play 13 intent_walk 13 force_out 7 hit_by_pitch 7 field_error 5 sac_fly 3 fielders_choice_out 2 strikeout_double_play 2 pickoff_1b 1 Name: events, dtype: int64

4. Analyzing home runs with Statcast data

So Judge walks and strikes out more than Stanton. Stanton flies out more than Judge. But let's get into their hitting profiles in more detail. Two of the most groundbreaking Statcast metrics are launch angle and exit velocity:

  • Launch angle: the vertical angle at which the ball leaves a player's bat
  • Exit velocity: the speed of the baseball as it comes off the bat

This new data has changed the way teams value both hitters and pitchers. Why? As per the Washington Post:

Balls hit with a high launch angle are more likely to result in a hit. Hit fast enough and at the right angle, they become home runs.

Let's look at exit velocity vs. launch angle and let's focus on home runs only (2015-2017). The first two plots show data points. The second two show smoothed contours to represent density.

# Filter to include home runs only
judge_hr = judge[judge['events']=='home_run']
stanton_hr = stanton[stanton['events']=='home_run']

# Create a figure with two scatter plots of launch speed vs. launch angle, one for each player's home runs
fig1, axs1 = plt.subplots(ncols=2, sharex=True, sharey=True)
sns.regplot(x='launch_angle', y='launch_speed', fit_reg=False, color='tab:blue', data=judge_hr, ax=axs1[0]).set_title('Aaron Judge\nHome Runs, 2015-2017')
sns.regplot(x='launch_angle', y='launch_speed', fit_reg=False, color='tab:blue', data=stanton_hr, ax=axs1[1]).set_title('Giancarlo Stanton\nHome Runs, 2015-2017')

# Create a figure with two KDE plots of launch speed vs. launch angle, one for each player's home runs
fig2, axs2 = plt.subplots(ncols=2, sharex=True, sharey=True)
sns.kdeplot(judge_hr.launch_angle, judge_hr.launch_speed, cmap="Blues", shade=True, shade_lowest=False, ax=axs2[0]).set_title('Aaron Judge\nHome Runs, 2015-2017')
sns.kdeplot(stanton_hr.launch_angle, stanton_hr.launch_speed, cmap="Blues", shade=True, shade_lowest=False, ax=axs2[1]).set_title('Giancarlo Stanton\nHome Runs, 2015-2017')
C:\Aanaconda3\lib\site-packages\scipy\stats\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result. return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval
Text(0.5,1,'Giancarlo Stanton\nHome Runs, 2015-2017')
Image in a Jupyter notebookImage in a Jupyter notebook

5. Home runs by pitch velocity

It appears that Stanton hits his home runs slightly lower and slightly harder than Judge, though this needs to be taken with a grain of salt given the small sample size of home runs.

Not only does Statcast measure the velocity of the ball coming off of the bat, it measures the velocity of the ball coming out of the pitcher's hand and begins its journey towards the plate. We can use this data to compare Stanton and Judge's home runs in terms of pitch velocity. Next you'll find box plots displaying the five-number summaries for each player: minimum, first quartile, median, third quartile, and maximum.

# Combine the Judge and Stanton home run DataFrames for easy boxplot plotting
judge_stanton_hr = pd.concat([judge_hr, stanton_hr])

# Create a boxplot that describes the pitch velocity of each player's home runs
sns.boxplot(x='player_name',y='release_speed', color='tab:blue', data=judge_stanton_hr).set_title('Home Runs, 2015-2017')
Text(0.5,1,'Home Runs, 2015-2017')
Image in a Jupyter notebook

6. Home runs by pitch location (I)

So Judge appears to hit his home runs off of faster pitches than Stanton. We might call Judge a fastball hitter. Stanton appears agnostic to pitch speed and likely pitch movement since slower pitches (e.g. curveballs, sliders, and changeups) tend to have more break. Statcast does track pitch movement and type but let's move on to something else: pitch location. Statcast tracks the zone the pitch is in when it crosses the plate. The zone numbering looks like this (from the catcher's point of view):

We can plot this using a 2D histogram. For simplicity, let's only look at strikes, which gives us a 9x9 grid. We can view each zone as coordinates on a 2D plot, the bottom left corner being (1,1) and the top right corner being (3,3). Let's set up a function to assign x-coordinates to each pitch.

def assign_x_coord(row):
    """
    Assigns an x-coordinate to Statcast's strike zone numbers. Zones 11, 12, 13,
    and 14 are ignored for plotting simplicity.
    """
    # Left third of strike zone
    if row.zone in [1, 4, 7]:
        return 1
    # Middle third of strike zone
    if row.zone in [2, 5, 8]:
        return 2
    # Right third of strike zone
    if row.zone in [3, 6, 9]:
        return 3

7. Home runs by pitch location (II)

And let's do the same but for y-coordinates.

def assign_y_coord(row):
    """
    Assigns a y-coordinate to Statcast's strike zone numbers. Zones 11, 12, 13,
    and 14 are ignored for plotting simplicity.
    """
    # Upper third of strike zone
    if row.zone in [1, 2, 3]:
        return 3
    # Middle third of strike zone
    if row.zone in [4, 5, 6]:
        return 2
    # Lower third of strike zone
    if row.zone in [7, 8, 9]:
        return 1

8. Aaron Judge's home run zone

Now we can apply the functions we've created then construct our 2D histograms. First, for Aaron Judge (again, for pitches in the strike zone that resulted in home runs).

# Zones 11, 12, 13, and 14 are to be ignored for plotting simplicity
judge_strike_hr = judge_hr.copy().loc[judge_hr.zone <= 9]

# Assign Cartesian coordinates to pitches in the strike zone for Judge home runs
judge_strike_hr['zone_x'] = judge_strike_hr.apply(assign_x_coord, axis=1)
judge_strike_hr['zone_y'] = judge_strike_hr.apply(assign_y_coord, axis=1)

# Plot Judge's home run zone as a 2D histogram with a colorbar
plt.hist2d(judge_strike_hr['zone_x'], judge_strike_hr['zone_y'], bins = 3, cmap='Blues')
plt.title('Aaron Judge Home Runs on\n Pitches in the Strike Zone, 2015-2017')
plt.gca().get_xaxis().set_visible(False)
plt.gca().get_yaxis().set_visible(False)
cb = plt.colorbar()
cb.set_label('Counts in Bin')
Image in a Jupyter notebook

9. Giancarlo Stanton's home run zone

And now for Giancarlo Stanton.

# Zones 11, 12, 13, and 14 are to be ignored for plotting simplicity
stanton_strike_hr = stanton_hr.copy().loc[stanton_hr.zone <= 9]

# Assign Cartesian coordinates to pitches in the strike zone for Stanton home runs
stanton_strike_hr['zone_x'] = stanton_strike_hr.apply(assign_x_coord, axis=1)
stanton_strike_hr['zone_y'] = stanton_strike_hr.apply(assign_y_coord, axis=1)

# Plot Stanton's home run zone as a 2D histogram with a colorbar
plt.hist2d(stanton_strike_hr['zone_x'], stanton_strike_hr['zone_y'], bins = 3, cmap='Blues')
plt.title('Giancarlo Stanton Home Runs on\n Pitches in the Strike Zone, 2015-2017')
plt.gca().get_xaxis().set_visible(False)
plt.gca().get_yaxis().set_visible(False)
cb = plt.colorbar()
cb.set_label('Counts in Bin')
Image in a Jupyter notebook

10. Should opposing pitchers be scared?

A few takeaways:

  • Stanton does not hit many home runs on pitches in the upper third of the strike zone.
  • Like pretty much every hitter ever, both players love pitches in the horizontal and vertical middle of the plate.
  • Judge's least favorite home run pitch appears to be high-away while Stanton's appears to be low-away.
  • If we were to describe Stanton's home run zone, it'd be middle-inside. Judge's home run zone is much more spread out.

The grand takeaway from this whole exercise: Aaron Judge and Giancarlo Stanton are not identical despite their superficial similarities. In terms of home runs, their launch profiles, as well as their pitch speed and location preferences, are different.

Should opposing pitchers still be scared?

# Should opposing pitchers be wary of Aaron Judge and Giancarlo Stanton
should_pitchers_be_scared = True