| Stat Category | Point Value |
|---|---|
| Passing Yards | 1 point for every 25 yards |
| Passing TDs | 6 points |
| Passing Interceptions | -2 points |
| Rushing Yeards | 1 point for every 10 yards |
| Rushing TDs | 6 points |
| Receiving Yards | 1 point for every 10 yards |
| Receiving TDs | 6 points |
| Fumbles Lost | -2 points |
Load the data from the from the qb_games.csv file.
NOTE: Games, a count of games for the current season and career_games have been added so that you can differentiate between regular season games (the games used to calculate fantasy stats) and post season or playoff games. Career Games are a count of how many games a player has played for their career. It was added as a dimension to consider when measuring player growth, plateau and decline.
In [21]:
%matplotlib inline
import pandas as pd
import matplotlib as mp
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
qb_games = pd.read_csv('qb_games.csv')
qb_games.columns.values
Out[21]:
In [31]:
qb_games['Fantasy Points'] = (qb_games['Pass Yds']/25) + (6 * qb_games['Pass TD']) - (2 * qb_games['Pass Int']) + (qb_games['Rush Yds'] /10) + (6 * qb_games['Rush TD'])
qb_fantasy = qb_games[['Name','Career Year', 'Year', 'Game Count', 'Career Games', 'Date', 'Pass Att', 'Pass Yds', 'Pass TD', 'Pass Int', 'Pass Rate', 'Rush Att', 'Rush Yds', 'Rush TD', 'Fantasy Points']]
qb_fantasy.head(10)
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In [32]:
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
print(len(qb_fantasy))
yearly_fantasy_points = qb_fantasy.groupby(['Career Year'], as_index=False).mean()
yearly_fantasy_points[['Career Year', 'Pass Att', 'Pass Rate', 'Fantasy Points']]
Out[32]:
In [26]:
color = ['red']
ax = sns.barplot(yearly_fantasy_points['Career Year'], (yearly_fantasy_points['Fantasy Points'] ), palette=color)
You can trace a sharp improvement in performance over years 1-6 where the fantasy total points increase from a yearly average of 10 to over 15 points. There is then a plateau through seasons 7 through 14, and a slight uptick at seasons 15-17, but the averages do not break above 20 points on average.
In [28]:
color = ['blue']
ax = sns.barplot(yearly_fantasy_points['Career Year'], (yearly_fantasy_points['Pass Att'] ), palette=color)
In [34]:
color = ['green']
ax = sns.barplot(yearly_fantasy_points['Career Year'], (yearly_fantasy_points['Pass Rate'] ), palette=color)
In [36]:
qb_means = qb_fantasy[['Pass Att', 'Pass Rate', 'Fantasy Points']].mean()
qb_means
Out[36]:
In [120]:
pass_att = qb_means['Pass Att']
qb_upper_pass_att = qb_fantasy.loc[qb_fantasy['Pass Att'] > pass_att]
qb_pass_att_mean = qb_upper_pass_att['Pass Att'].mean()
print('Shifting data to only include pass attempts when greater than %d average pass attempts' %(pass_att))
qb_att = qb_upper_pass_att.groupby(['Career Year'], as_index=False).mean()
color = ['blue']
ax = sns.barplot(qb_att['Career Year'], (qb_att['Pass Att'] ), palette=color)
In [124]:
pass_rate = qb_means['Pass Rate']
qb_upper_pass_rate = qb_fantasy.loc[qb_fantasy['Pass Rate'] > pass_rate]
qb_pass_rate_mean = qb_upper_pass_rate['Pass Rate'].mean()
print('Shifting data to only include pass attempts when greater than %d average pass attempts' %(pass_rate))
qb_rate = qb_upper_pass_rate.groupby(['Career Year'], as_index=False).mean()
color = ['green']
ax = sns.barplot(qb_rate['Career Year'], (qb_rate['Pass Rate'] ), palette=color)
In [126]:
qb_upper_fantasy_rate = qb_fantasy.loc[qb_fantasy['Pass Rate'] > pass_rate]
qb_name = qb_upper_fantasy_rate.groupby(['Name'], as_index=False)
print(len(qb_name))
qb_fantasy_rate_mean = qb_upper_fantasy_rate['Fantasy Points'].mean()
print(qb_fantasy_rate_mean)
qb_rate = qb_upper_fantasy_rate.groupby(['Career Year'], as_index=False).mean()
color = ['red']
ax = sns.barplot(qb_rate['Career Year'], (qb_rate['Fantasy Points'] ), palette=color)
In [91]:
qb_upper_pass_rate = qb_fantasy.loc[qb_fantasy['Pass Rate'] > pass_rate]
qb_fantasy_rate = qb_upper_pass_rate.mean()
print(qb_fantasy_rate['Fantasy Points'])
qb_rate = qb_upper_pass_rate.groupby(['Career Year'], as_index=False).mean()
color = ['green']
ax = sns.barplot(qb_rate['Career Year'], (qb_rate['Fantasy Points'] ), palette=color)