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contract_comp_viz.py
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contract_comp_viz.py
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"""Summary
Attributes:
contract_ranking (list): Description
df (TYPE): Description
per_cols (list): Description
salary_cols (list): Description
standardize (TYPE): Description
"""
import os
import re
#import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.offline as pltly
import plotly.graph_objs as go
from data import SALARY_DATA_20_21, PER_DATA_20_21, TM_DATA_20_21, TM_DATA_17_18
SALARY_DATA = SALARY_DATA_20_21
PER_DATA = PER_DATA_20_21
TM_DATA = TM_DATA_20_21
# create mapping for team names to team abbreviations
team = TM_DATA_17_18['Team']
tm = TM_DATA_17_18['Tm']
team_tm_map = {x.replace('*', ''): y for x, y in zip(team, tm)}
"""
Resources:
https://www.basketball-reference.com/contracts/players.html
https://www.reddit.com/r/nba/comments/6dvmr1/best_advanced_stat_to_measure_how_good_a_player_is/
https://fansided.com/2017/01/31/nylon-calculus-reinventing-per/
Looks like best all in one stats are
PER (Player Efficiency Rating)
WS (Win Shares)
RPM (Real Plus Minus)
BPM (Boxscore Plus Minus)
"""
# get rid of the stuff to the right of \ i.e. LeBron James\jamesle01
standardize = lambda f: f.rsplit('\\')[0].lower()
# function for getting team abbreviation
get_tm_abbreviation = lambda f: team_tm_map[f.replace("*", "")]
# make money a float not a string
def monify(m):
"""Summary
Args:
m (string): money string
Returns:
float: money value
"""
try:
return float(re.sub('[$,]', '', m))
except:
return m
SALARY_DATA['Player'] = SALARY_DATA['Player'].map(standardize)
SALARY_DATA['Guaranteed'] = SALARY_DATA['Guaranteed'].map(monify)
# Make sure you only include players that have played at least 30 minutes in the season
PER_DATA = PER_DATA.loc[PER_DATA.MP > 30]
PER_DATA['Player'] = PER_DATA['Player'].map(standardize)
TM_DATA['Win Percentage'] = TM_DATA['W'] / (TM_DATA['W'] + TM_DATA['L'])
# add Team abbreviations (they stopped being available in later data pulls)
TM_DATA['Tm'] = TM_DATA['Team'].map(get_tm_abbreviation)
salary_cols = ['Player', 'Tm', 'Signed Using', 'Guaranteed']
per_cols = ['Player', 'PER', 'BPM']
tm_cols = ['Tm', 'Win Percentage']
df = TM_DATA[tm_cols].merge(
SALARY_DATA[salary_cols].merge(
PER_DATA[per_cols],
on='Player'),
on='Tm')
df.drop_duplicates(subset=['Player', 'Tm'], inplace=True)
print(df)
"""
Visualization
"""
sns.set(style="whitegrid")
f, ax = plt.subplots(figsize=(6.5, 6.5))
sns.scatterplot(x="PER", y="Guaranteed",
hue="Tm",
sizes=(1, 8), linewidth=0,
data=df, ax=ax)
ax.get_yaxis().get_major_formatter().set_scientific(False)
plt.suptitle("Player Efficiency vs. Guaranteed $\$ by Team")
# plt.show()
tracePER = go.Scatter(x=df['PER'], y=df['Guaranteed'], mode='markers', name='PER',
text=df['Player'] + " (" + df['Tm'] + ")", # string formatting didnt work here
marker=dict(
size=16,
color=df['Win Percentage'], # set color equal to a variable
colorbar=dict(title='Team Win Percentage (%)'),
colorscale='Viridis',
showscale=True
))
layout = go.Layout(
title='Good Contract, Bad Contract',
xaxis=dict(
title='Player Efficiency Rating (PER)',
titlefont=dict(
family='Courier New, monospace',
size=18,
color='#7f7f7f'
)
),
yaxis=dict(
title='Contract Guarantee ($)',
titlefont=dict(
family='Courier New, monospace',
size=18,
color='#7f7f7f'
)
)
)
fig = go.Figure(data=[tracePER], layout=layout)
# data = [tracePER]
pltly.plot(fig)
# pltly.plot([traceBPM])