Project Description¶
Major League Baseball (MLB) uses a technology called Statcast to collect detailed data about the exact location and movements of baseballs and players during games. In this project, we will use Statcast data to compare the home run performance of two of the biggest stars who played for the New York Yankees: Aaron Judge and Giancarlo Stanton. The data for this project comes from Baseball Savant.
Aaron Judge is one of the largest players in MLB. He stands 6 feet 7 inches (2.01 meters) tall and weighs 282 pounds (128 kilograms). He also hit one of the hardest home runs ever recorded, and we know this because of the precise measurements from Statcast.
Statcast is a cutting-edge tracking system introduced in 2015 at all 30 major league ballparks. It uses high-resolution cameras and radar to measure the exact position and movement of baseballs and players. This data has changed how baseball teams analyze the game, creating a new focus on data-driven decisions. Teams compete by hiring analysts to gain advantages over their opponents.
In this project, we will clean, analyze, and visualize Statcast data to compare Aaron Judge and his teammate Giancarlo Stanton. Both players are known for hitting many home runs. In 2017, Stanton hit 59 home runs and Judge hit 52, leading the league by a large margin (the third-place player had 45 home runs).
Although Stanton and Judge share some similarities, they are also different in many ways. This project will explore how their home run hitting styles and performance compare.
The Data¶
We will work with two CSV files: judge.csv and stanton.csv. These files contain Statcast data from 2015 to 2017. Each row in the files represents one pitch thrown to the batter.
Custom Functions¶
We have created two custom functions to help visualize home run zones:
assign_x_coord: This function assigns an x-coordinate based on Statcast’s strike zone numbering system.assign_y_coord: This function assigns a y-coordinate based on Statcast’s strike zone numbering system.
These functions will help us map pitches to specific locations in the strike zone, making it easier to compare how Judge and Stanton hit home runs.
In [1]:
# Import the necessary packages
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# Load Aaron Judge's Statcast data
judge = pd.read_csv('judge.csv')
# Load Giancarlo Stanton's Statcast data
stanton = pd.read_csv('stanton.csv')
# Display all columns (pandas will collapse some columns if we don't set this option)
pd.set_option('display.max_columns', None)
# Custom Functions
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
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
# Display the last five rows of the Aaron Judge file
judge.tail()
Out[1]:
| pitch_type | game_date | release_speed | release_pos_x | release_pos_z | player_name | batter | pitcher | events | description | spin_dir | spin_rate_deprecated | break_angle_deprecated | break_length_deprecated | zone | des | game_type | stand | p_throws | home_team | away_team | type | hit_location | bb_type | balls | strikes | game_year | pfx_x | pfx_z | plate_x | plate_z | on_3b | on_2b | on_1b | outs_when_up | inning | inning_topbot | hc_x | hc_y | tfs_deprecated | tfs_zulu_deprecated | pos2_person_id | umpire | sv_id | vx0 | vy0 | vz0 | ax | ay | az | sz_top | sz_bot | hit_distance_sc | launch_speed | launch_angle | effective_speed | release_spin_rate | release_extension | game_pk | pos1_person_id | pos2_person_id.1 | pos3_person_id | pos4_person_id | pos5_person_id | pos6_person_id | pos7_person_id | pos8_person_id | pos9_person_id | release_pos_y | estimated_ba_using_speedangle | estimated_woba_using_speedangle | woba_value | woba_denom | babip_value | iso_value | launch_speed_angle | at_bat_number | pitch_number | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 3431 | CH | 2016-08-13 | 85.6 | -1.9659 | 5.9113 | Aaron Judge | 592450 | 542882 | NaN | ball | NaN | NaN | NaN | NaN | 14.0 | NaN | R | R | R | NYY | TB | B | NaN | NaN | 0 | 0 | 2016 | -0.379108 | 0.370567 | 0.739 | 1.442 | NaN | NaN | NaN | 0 | 5 | Bot | NaN | NaN | NaN | NaN | 571912.0 | NaN | 160813_144259 | 6.960 | -124.371 | -4.756 | -2.821 | 23.634 | -30.220 | 3.93 | 1.82 | NaN | NaN | NaN | 84.459 | 1552.0 | 5.683 | 448611 | 542882.0 | 571912.0 | 543543.0 | 523253.0 | 446334.0 | 622110.0 | 545338.0 | 595281.0 | 543484.0 | 54.8144 | 0.00 | 0.000 | NaN | NaN | NaN | NaN | NaN | 36 | 1 |
| 3432 | CH | 2016-08-13 | 87.6 | -1.9318 | 5.9349 | Aaron Judge | 592450 | 542882 | home_run | hit_into_play_score | NaN | NaN | NaN | NaN | 4.0 | Aaron Judge homers (1) on a fly ball to center... | R | R | R | NYY | TB | X | NaN | fly_ball | 1 | 2 | 2016 | -0.295608 | 0.320400 | -0.419 | 3.273 | NaN | NaN | NaN | 2 | 2 | Bot | 130.45 | 14.58 | NaN | NaN | 571912.0 | NaN | 160813_135833 | 4.287 | -127.452 | -0.882 | -1.972 | 24.694 | -30.705 | 4.01 | 1.82 | 446.0 | 108.8 | 27.410 | 86.412 | 1947.0 | 5.691 | 448611 | 542882.0 | 571912.0 | 543543.0 | 523253.0 | 446334.0 | 622110.0 | 545338.0 | 595281.0 | 543484.0 | 54.8064 | 0.98 | 1.937 | 2.0 | 1.0 | 0.0 | 3.0 | 6.0 | 14 | 4 |
| 3433 | CH | 2016-08-13 | 87.2 | -2.0285 | 5.8656 | Aaron Judge | 592450 | 542882 | NaN | ball | NaN | NaN | NaN | NaN | 14.0 | NaN | R | R | R | NYY | TB | B | NaN | NaN | 0 | 2 | 2016 | -0.668575 | 0.198567 | 0.561 | 0.960 | NaN | NaN | NaN | 2 | 2 | Bot | NaN | NaN | NaN | NaN | 571912.0 | NaN | 160813_135815 | 7.491 | -126.665 | -5.862 | -6.393 | 21.952 | -32.121 | 4.01 | 1.82 | NaN | NaN | NaN | 86.368 | 1761.0 | 5.721 | 448611 | 542882.0 | 571912.0 | 543543.0 | 523253.0 | 446334.0 | 622110.0 | 545338.0 | 595281.0 | 543484.0 | 54.7770 | 0.00 | 0.000 | NaN | NaN | NaN | NaN | NaN | 14 | 3 |
| 3434 | CU | 2016-08-13 | 79.7 | -1.7108 | 6.1926 | Aaron Judge | 592450 | 542882 | NaN | foul | NaN | NaN | NaN | NaN | 4.0 | NaN | R | R | R | NYY | TB | S | NaN | NaN | 0 | 1 | 2016 | 0.397442 | -0.614133 | -0.803 | 2.742 | NaN | NaN | NaN | 2 | 2 | Bot | NaN | NaN | NaN | NaN | 571912.0 | NaN | 160813_135752 | 1.254 | -116.062 | 0.439 | 5.184 | 21.328 | -39.866 | 4.01 | 1.82 | 9.0 | 55.8 | -24.973 | 77.723 | 2640.0 | 5.022 | 448611 | 542882.0 | 571912.0 | 543543.0 | 523253.0 | 446334.0 | 622110.0 | 545338.0 | 595281.0 | 543484.0 | 55.4756 | 0.00 | 0.000 | NaN | NaN | NaN | NaN | 1.0 | 14 | 2 |
| 3435 | FF | 2016-08-13 | 93.2 | -1.8476 | 6.0063 | Aaron Judge | 592450 | 542882 | NaN | called_strike | NaN | NaN | NaN | NaN | 8.0 | NaN | R | R | R | NYY | TB | S | NaN | NaN | 0 | 0 | 2016 | -0.823050 | 1.623300 | -0.273 | 2.471 | NaN | NaN | NaN | 2 | 2 | Bot | NaN | NaN | NaN | NaN | 571912.0 | NaN | 160813_135736 | 5.994 | -135.497 | -6.736 | -9.360 | 26.782 | -13.446 | 4.01 | 1.82 | NaN | NaN | NaN | 92.696 | 2271.0 | 6.068 | 448611 | 542882.0 | 571912.0 | 543543.0 | 523253.0 | 446334.0 | 622110.0 | 545338.0 | 595281.0 | 543484.0 | 54.4299 | 0.00 | 0.000 | NaN | NaN | NaN | NaN | NaN | 14 | 1 |
How many of each event did Judge and Stanton have in 2017?¶
In [2]:
# All of Aaron Judge's batted ball events in 2017
judge_events_2017 = judge.loc[judge['game_year'] == 2017].events.value_counts()
print("Aaron Judge batted ball event totals, 2017:")
print(judge_events_2017)
# All of Giancarlo Stanton's batted ball events in 2017
stanton_events_2017 = stanton.loc[stanton['game_year'] == 2017].events.value_counts()
print("\nGiancarlo Stanton batted ball event totals, 2017:")
print(stanton_events_2017)
Aaron Judge batted ball event totals, 2017: events 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 sac_fly 4 fielders_choice_out 4 field_error 4 triple 3 strikeout_double_play 1 Name: count, dtype: int64 Giancarlo Stanton batted ball event totals, 2017: events field_out 239 strikeout 161 single 77 walk 72 home_run 59 double 32 intent_walk 13 grounded_into_double_play 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: count, dtype: int64
Which player hits home runs slightly lower and slightly harder?¶
In [5]:
import warnings
warnings.filterwarnings("ignore", category=FutureWarning)
In [6]:
# Filter to include home runs only
judge_hr = judge.loc[judge['events'] == 'home_run']
stanton_hr = stanton.loc[stanton['events'] == 'home_run']
# Create a figure with two KDE plots
fig1, ax1 = plt.subplots(ncols=2, sharex=True, sharey=True, figsize=(14, 6))
sns.kdeplot(
x=judge_hr.launch_angle,
y=judge_hr.launch_speed,
cmap="Blues",
fill=True,
thresh=0.05,
ax=ax1[0]
).set_title('Aaron Judge\nHome Runs, 2015-2017')
sns.kdeplot(
x=stanton_hr.launch_angle,
y=stanton_hr.launch_speed,
cmap="Blues",
fill=True,
thresh=0.05,
ax=ax1[1]
).set_title('Giancarlo Stanton\nHome Runs, 2015-2017')
plt.tight_layout()
plt.show()
In [7]:
player_hr = "Stanton"
Compare both players' pitch velocity, or release_speed. Which player has the highest median?¶
In [8]:
# Combine the Judge and Stanton home run DataFrames
judge_stanton_hr = pd.concat([judge_hr, stanton_hr])
# Create a boxplot
sns.boxplot(x='player_name', y='release_speed', color='tab:blue', data=judge_stanton_hr).set_title('Home Runs, 2015-2017')
Out[8]:
Text(0.5, 1.0, 'Home Runs, 2015-2017')
In [9]:
player_fast = "Judge"
Create a 2D histogram for each player that visualizes the home run strike zones, ignoring zones 11, 12, 13, and 14 for simplicity.¶
In [11]:
# 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 and save the figure to a variable
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')
In [12]:
# 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')
