Kivan PolimisTriple Double Russ
Kivan Polimis, Sun 06 March 2022, Sports
Kivan Polimis, Sun 06 March 2022, Sports
In this post, we use webscraping to analyze Russell Westbrook's (Russ) team performance when he records a triple double. We will use multiple Python modules and focus on BeautifulSoup, pandas, matplotlib, and seaborn
Russell Westbrook is a lightning rod for discussion in the NBA because of his style of play and clashes with popular players like his beef with former teammate Kevin Durant. Westbrook's high energy, domineering style of play is best characterized by the the statistic he is known for, the triple double.
Traditional NBA box scores collect statistics on 5 individual player categories: points, rebounds, assists, steals, and blocks1. When a player has 10 or more instances of each category ("double") in 3 categories ("triple), the collective statisitcal feat is known as a triple double. Westbrook is the NBA leader in career triple doubles and recently broke Hall of Famer Oscar Robertson's 47 year-old record in the 2020-21 season. Robertson was the original "Mr. Triple Double" and some have used the moniker with Russ.
While there are many supporters of Westbrook in print and video, including videos praising Russ for his ability to create triple doubles, there is also considerable pushback of Russ' pursuit of a triple double. Check the plays compiled in this video for instance. At the time of the compiled plays, Westbrook is 2 assists shy of a triple double and declines to take several open shots in order to pass the ball to a teammate in the hopes of getting an assist. Several plays are awkward with Westbrook putting teammates in bad positions and some plays ending in turnovers. For me, the most egregious example of a player selfishly pursuing a triple double is Ricky Davis in 2003. With seconds remaining in the game his team was winning by 25, Davis attempted a shot on his team's basket in order to secure what he hoped would be his 10th rebound and a triple double (Davis was not credited with the rebound or triple double). That Westbrook video is nothing near the cynicism of Davis, but the discussion prompted by Russ' triple double ability is: "Do Westbrook's teams win more games when he creates a triple double?"
In order to investigate Russ' team performance when he creates a triple double we need to gather his individual statistics and team performance during his games. We can gather both sources of information from Basketball-Reference.com and create a function to tabulate triple doubles. Then we will compare the win percentage of Westbrook's teams when he does and does not have a triple double to assess if his pursuit of a triple doubles negatively impacts team performance. We will subset our analysis to only Westbrook's Regular Season games
from utils import *
import constants as c
Russell Westbrook's player data is available at the following link: https://www.basketball-reference.com/players/w/westbru01.html. We can use the webbrowser module to open this url with the code below
westbrook_basketball_reference_url = "https://www.basketball-reference.com/players/w/westbru01.html"
webbrowser.open(westbrook_basketball_reference_url)
True
In order to create a dataset that includes Westbrook's individual and team performance, we need to scrape his yearly game log data. An example yearly game log is, https://www.basketball-reference.com/players/w/westbru01/gamelog/2022, the game log for this current season. To accomplish this task, we need to
I've written a helper function, get_player_metadata that we can use to scrape a player's main Basketball-Reference.com page. get_player_metadata returns a dictionary of the years available for the player (in the form of a list) and the player's name
player_metadata = get_player_metadata(westbrook_basketball_reference_url)
years_available_list = player_metadata.get("years_available")
total_years_of_data = len(years_available_list)
player_metadata
{'years_available': ['2009',
'2010',
'2011',
'2012',
'2013',
'2014',
'2015',
'2016',
'2017',
'2018',
'2019',
'2020',
'2021',
'2022'],
'player_name': 'Russell Westbrook'}
westbrook_career_game_logs_df_list = []
errors_list = []
years_available_list and iterate through each year of game log data and extract player statistical information on the individual game basis with the function extract_player_game_logs. We can also get team results from the same function# The url template that we pass in year info
url_template = 'https://www.basketball-reference.com/players/w/westbru01/gamelog/{year}'
# for each year of data avaialable, gather game log data
for year in years_available_list:
# Use try/except block to catch and inspect any urls that cause an error
try:
print(f'getting game log data from {year}')
# get the formatted game log data url
formatted_url = url_template.format(year=year)
westbrook_yearly_game_logs = extract_player_game_logs(formatted_url)
# append the current dataframe to the list of dataframes
westbrook_career_game_logs_df_list.append(westbrook_yearly_game_logs)
except Exception as e:
# Store the url and the error it causes in a list
error =[formatted_url, e]
# then append it to the list of errors
errors_list.append(error)
getting game log data from 2009 getting game log data from 2010 getting game log data from 2011 getting game log data from 2012 getting game log data from 2013 getting game log data from 2014 getting game log data from 2015 getting game log data from 2016 getting game log data from 2017 getting game log data from 2018 getting game log data from 2019 getting game log data from 2020 getting game log data from 2021 getting game log data from 2022
errors_list
[]
westbrook_career_game_logs_df_list into a single data frame with the concat function from the pandas module. print(len(westbrook_career_game_logs_df_list)==len(years_available_list))
print(len(westbrook_career_game_logs_df_list))
True 14
westbrook_career_game_logs_df = pd.concat(westbrook_career_game_logs_df_list, axis=0)
total_career_games = westbrook_career_game_logs_df.shape[0]
print(f"collected data for {total_career_games} games for {player_metadata.get('player_name')}")
collected data for 1093 games for Russell Westbrook
westbrook_career_game_logs_df.head()
| player_name | basketball_ref_id | age | season | date | box_score_link | team | opponent | location | result | ... | three_pt_attempted | three_pt_pct | steals | blocks | turnovers | plus_minus | game_score | margin | result_b | active | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | Russell Westbrook | w/westbru01 | 19-352 | 2008-09 | 2008-10-29 | /boxscores/200810290OKC.html | OKC | MIL | 1 | L (-11) | ... | 2.0 | 0.5 | 0.0 | 1.0 | 2.0 | 5.0 | 11.0 | -11.0 | 0.0 | 1 |
| 1 | Russell Westbrook | w/westbru01 | 19-355 | 2008-09 | 2008-11-01 | /boxscores/200811010HOU.html | OKC | HOU | 0 | L (-12) | ... | 1.0 | 0.0 | 1.0 | 0.0 | 2.0 | -19.0 | 2.8 | -12.0 | 0.0 | 1 |
| 2 | Russell Westbrook | w/westbru01 | 19-356 | 2008-09 | 2008-11-02 | /boxscores/200811020OKC.html | OKC | MIN | 1 | W (+3) | ... | 1.0 | 0.0 | 2.0 | 0.0 | 3.0 | 7.0 | 7.6 | 3.0 | 1.0 | 1 |
| 3 | Russell Westbrook | w/westbru01 | 19-359 | 2008-09 | 2008-11-05 | /boxscores/200811050OKC.html | OKC | BOS | 1 | L (-13) | ... | 5.0 | 0.6 | 1.0 | 0.0 | 2.0 | -3.0 | 6.7 | -13.0 | 0.0 | 1 |
| 4 | Russell Westbrook | w/westbru01 | 19-361 | 2008-09 | 2008-11-07 | /boxscores/200811070UTA.html | OKC | UTA | 0 | L (-7) | ... | 0.0 | 0.0 | 0.0 | 0.0 | 2.0 | -5.0 | -0.2 | -7.0 | 0.0 | 1 |
5 rows × 37 columns
westbrook_career_game_logs_df.info()
<class 'pandas.core.frame.DataFrame'> Int64Index: 1093 entries, 0 to 62 Data columns (total 37 columns): # Column Non-Null Count Dtype --- ------ -------------- ----- 0 player_name 1093 non-null object 1 basketball_ref_id 1093 non-null object 2 age 1093 non-null object 3 season 1093 non-null object 4 date 1093 non-null datetime64[ns] 5 box_score_link 1093 non-null object 6 team 1093 non-null object 7 opponent 1093 non-null object 8 location 1093 non-null int64 9 result 1093 non-null object 10 game_number 1093 non-null int64 11 game 1093 non-null float64 12 started_game 1093 non-null float64 13 minutes_played 1093 non-null object 14 points 1093 non-null float64 15 field_goals_made 1093 non-null float64 16 field_goals_att 1093 non-null float64 17 field_goal_pct 1093 non-null float64 18 free_throws_made 1093 non-null float64 19 free_throw_att 1093 non-null float64 20 free_throw_pct 1093 non-null float64 21 personal_fouls 1093 non-null float64 22 assists 1093 non-null float64 23 total_rebs 1093 non-null float64 24 offensive_rebs 1093 non-null float64 25 defensive_rebs 1093 non-null float64 26 three_pt_made 1093 non-null float64 27 three_pt_attempted 1093 non-null float64 28 three_pt_pct 1093 non-null float64 29 steals 1093 non-null float64 30 blocks 1093 non-null float64 31 turnovers 1093 non-null float64 32 plus_minus 1093 non-null float64 33 game_score 1093 non-null float64 34 margin 1093 non-null float64 35 result_b 1093 non-null float64 36 active 1093 non-null int64 dtypes: datetime64[ns](1), float64(24), int64(3), object(9) memory usage: 324.5+ KB
westbrook_career_game_logs_df.iloc[0]
player_name Russell Westbrook basketball_ref_id w/westbru01 age 19-352 season 2008-09 date 2008-10-29 00:00:00 box_score_link /boxscores/200810290OKC.html team OKC opponent MIL location 1 result L (-11) game_number 1 game 1.0 started_game 0.0 minutes_played 21:56 points 13.0 field_goals_made 4.0 field_goals_att 9.0 field_goal_pct 0.444 free_throws_made 4.0 free_throw_att 6.0 free_throw_pct 0.667 personal_fouls 2.0 assists 4.0 total_rebs 4.0 offensive_rebs 4.0 defensive_rebs 0.0 three_pt_made 1.0 three_pt_attempted 2.0 three_pt_pct 0.5 steals 0.0 blocks 1.0 turnovers 2.0 plus_minus 5.0 game_score 11.0 margin -11.0 result_b 0.0 active 1 Name: 0, dtype: object
now that we have all of Westbrook's individual and team statistics, we need to
our triple double metric, triple_double, is a binary indicator with a value of 1 when a triple double is achieved through one of 7 scenarios:
def triple_double(row):
if row['points']>=10 and row['total_rebs']>=10 and row['assists']>=10:
return 1
if row['points']>=10 and row['total_rebs']>=10 and row['blocks']>=10:
return 1
if row['points']>=10 and row['total_rebs']>=10 and row['steals']>=10:
return 1
if row['points']>=10 and row['assists']>=10 and row['steals']>=10:
return 1
if row['points']>=10 and row['assists']>=10 and row['blocks']>=10:
return 1
if row['total_rebs']>=10 and row['assists']>=10 and row['blocks']>=10:
return 1
if row['total_rebs']>=10 and row['assists']>=10 and row['steals']>=10:
return 1
westbrook_career_game_logs_df["triple_double"] = westbrook_career_game_logs_df.apply(triple_double, axis=1)
westbrook_career_game_logs_df.loc[:, "triple_double"] = westbrook_career_game_logs_df.loc[:, "triple_double"].fillna(0)
westbrook_career_game_logs_df["triple_double"].astype(int)
0 0
1 0
2 0
3 0
4 0
..
58 0
59 0
60 0
61 0
62 0
Name: triple_double, Length: 1093, dtype: int64
np.mean(westbrook_career_game_logs_df["triple_double"])*100
17.657822506861848
westbrook_career_game_logs_df.shape[0]*np.mean(westbrook_career_game_logs_df["triple_double"])
193.0
westbrook_active_games_pct = np.mean(westbrook_career_game_logs_df["active"])*100
print(f"Russell Westbrook has been active for {westbrook_active_games_pct:.2f}% of his teams' games")
Russell Westbrook has been active for 91.95% of his teams' games
westbrook_active_games_df = westbrook_career_game_logs_df.loc[westbrook_career_game_logs_df["active"]==1]
triple_double_games_df = westbrook_active_games_df.loc[westbrook_active_games_df["triple_double"]==1]
non_triple_double_games_df = westbrook_active_games_df.loc[westbrook_active_games_df["triple_double"]!=1]
westbrook_active_games_pct = np.mean(westbrook_career_game_logs_df["active"])*100
westbrook_career_games = westbrook_career_game_logs_df.shape[0]
westbrook_active_games = westbrook_active_games_df.shape[0]
triple_double_games = triple_double_games_df.shape[0]
non_triple_double_games = non_triple_double_games_df.shape[0]
active_triple_double_pct = np.mean(westbrook_active_games_df["triple_double"])*100
print(f"Russell Westbrook has been active for {westbrook_active_games_pct:.2f}% of his teams' games")
print(f"He has played in {westbrook_active_games} of {westbrook_career_games} potential games")
print(f"Westbrook has recorded a triple double in {triple_double_games} games, {active_triple_double_pct:.2f}% of his active games")
print(f"Westbrook has {non_triple_double_games} games without a triple double")
Russell Westbrook has been active for 91.95% of his teams' games He has played in 1005 of 1093 potential games Westbrook has recorded a triple double in 193 games, 19.20% of his active games Westbrook has 812 games without a triple double
To complete our comparison, we need Westbrook's win percentage in 4 scenarios
We can use the result_b column, a binary indicator of results where 0 indicates a loss and 1 indicates a win to calculate these win percentages across our different data frames. The mean for result_b is the percentage (after multiplying by 100) of games won under the scenario
westbrook_career_win_pct = np.mean(westbrook_career_game_logs_df['result_b'])*100
print(f"Russell Westbrook's teams have won {westbrook_career_win_pct:.2f}% of their games")
Russell Westbrook's teams have won 54.44% of their games
westbrook_active_win_pct = np.mean(westbrook_active_games_df['result_b'])*100
print(f"Russell Westbrook's teams win {westbrook_active_win_pct:.2f}% of their games when he is active")
Russell Westbrook's teams win 59.20% of their games when he is active
triple_double_win_pct = np.mean(triple_double_games_df['result_b'])*100
print(f"Russell Westbrook's teams win {triple_double_win_pct:.2f}% of their games when he DOES record a triple double")
Russell Westbrook's teams win 73.58% of their games when he DOES record a triple double
non_triple_double_win_pct = np.mean(non_triple_double_games_df['result_b'])*100
print(f"Russell Westbrook's teams win {non_triple_double_win_pct:.2f}% of their games when he DOES NOT record a triple double")
Russell Westbrook's teams win 55.79% of their games when he DOES NOT record a triple double
import tabulate
data = [["Career Win Pct", f"{westbrook_career_win_pct:.2f}%"],
["Active Win Pct", f"{westbrook_active_win_pct:.2f}%"],
["Triple Double Win Pct", f"{triple_double_win_pct:.2f}%"],
["Non-Triple Double Win Pct", f"{non_triple_double_win_pct:.2f}%"]]
table = tabulate.tabulate(data, tablefmt='html')
table
| Career Win Pct | 54.44% |
| Active Win Pct | 59.20% |
| Triple Double Win Pct | 73.58% |
| Non-Triple Double Win Pct | 55.79% |
contingency_table = pd.crosstab(westbrook_active_games_df.result_b,westbrook_active_games_df.triple_double) #Contingency Table
contingency_table
| triple_double | 0.0 | 1.0 |
|---|---|---|
| result_b | ||
| 0.0 | 359 | 51 |
| 1.0 | 453 | 142 |
z_stat, p_value = create_proportions_ztest(contingency_table)
z_stat: -4.520, p_value: 0.000 Reject the null hypothesis - suggest the alternative hypothesis is true
westbrook_active_games_df = copy.deepcopy(westbrook_active_games_df)
westbrook_active_games_df["year"] = westbrook_active_games_df["season"].apply(lambda x: x[0:4])
westbrook_active_games_df["year"] = pd.DatetimeIndex(westbrook_active_games_df["year"])
yearly_triple_double_counts = westbrook_active_games_df.resample(rule='Y', on='year')['triple_double'].sum()
yearly_triple_double_counts
year 2008-12-31 1.0 2009-12-31 1.0 2010-12-31 3.0 2011-12-31 0.0 2012-12-31 1.0 2013-12-31 2.0 2014-12-31 11.0 2015-12-31 18.0 2016-12-31 42.0 2017-12-31 25.0 2018-12-31 34.0 2019-12-31 8.0 2020-12-31 38.0 2021-12-31 9.0 Freq: A-DEC, Name: triple_double, dtype: float64
sns.set_style("white")
sns.set_color_codes()
# Create figure and plot space
fig, ax = plt.subplots(figsize=(12, 12))
# Add x-axis and y-axis
ax.bar(yearly_triple_double_counts.index.year,
yearly_triple_double_counts.values,
color='Blue')
# Set title and labels for axes
ax.set(xlabel="Season",
ylabel="Games with a Triple Double",
title= "Seasonal Counts of Triple Doubles by Russell Westbrook")
# add '2008' to years_available_list so that we have the Seasons shown on x-axis
years_available_list = ['2008'] + years_available_list
ax.xaxis.set_ticks([int(year) for year in years_available_list])
# Call add values function
add_value_labels(ax)
plt.show()
year variable from the date variable# westbrook_active_games_df = copy.deepcopy(westbrook_active_games_df)
# westbrook_active_games_df["year"] = pd.DatetimeIndex(westbrook_active_games_df["date"]).year
triple_double_games_df = copy.deepcopy(triple_double_games_df)
triple_double_games_df["year"] = triple_double_games_df["season"].apply(lambda x: x[0:4])
triple_double_games_df["year"] = pd.DatetimeIndex(triple_double_games_df["year"]).year
triple_double_games_gb_df = triple_double_games_df.groupby(['triple_double', 'location', 'year']).size().unstack(level=2).fillna(0).T
triple_double_games_gb_df
| triple_double | 1.0 | |
|---|---|---|
| location | 0 | 1 |
| year | ||
| 2008 | 0.0 | 1.0 |
| 2009 | 1.0 | 0.0 |
| 2010 | 0.0 | 3.0 |
| 2012 | 1.0 | 0.0 |
| 2013 | 1.0 | 1.0 |
| 2014 | 2.0 | 9.0 |
| 2015 | 7.0 | 11.0 |
| 2016 | 17.0 | 25.0 |
| 2017 | 16.0 | 9.0 |
| 2018 | 15.0 | 19.0 |
| 2019 | 3.0 | 5.0 |
| 2020 | 17.0 | 21.0 |
| 2021 | 4.0 | 5.0 |
# triple_double_games_gb_df.plot(kind = 'bar')
sns.set_style("white")
sns.set_color_codes()
fig, ax = plt.subplots(figsize=(16, 6))
fig.subplots_adjust(hspace=0.4)
plot_title = 'Seasonal Counts of Triple Doubles by Russell Westbrook \n Home vs Away'
triple_double_games_gb_df.iloc[:,].plot(ax = ax, title = plot_title,
ylabel = "Games with a Triple Double",
xlabel= "Season")
plt.axvline(x=2015.5, color='k', linestyle='--')
plt.xticks([int(year) for year in years_available_list])
plt.legend(["Away", "Home", "Kevin Durant Leaves OKC"], loc ="lower right")
plt.show()
Let's look at two scenarios that add to the Russ/triple double debate.
Since teammate Kevin Durant left in Free Agency before the 2016 season, Westbrook won his only MVP and started to define his play with triple doubles
triple_double_games_gb_df2 = triple_double_games_gb_df[1].reset_index(level="year")
triple_double_games_gb_df2.keys().name = ''
triple_double_games_gb_df2.rename(columns={0:"Away", 1:"Home"}, inplace=True)
triple_double_games_gb_df2
| year | Away | Home | |
|---|---|---|---|
| 0 | 2008 | 0.0 | 1.0 |
| 1 | 2009 | 1.0 | 0.0 |
| 2 | 2010 | 0.0 | 3.0 |
| 3 | 2012 | 1.0 | 0.0 |
| 4 | 2013 | 1.0 | 1.0 |
| 5 | 2014 | 2.0 | 9.0 |
| 6 | 2015 | 7.0 | 11.0 |
| 7 | 2016 | 17.0 | 25.0 |
| 8 | 2017 | 16.0 | 9.0 |
| 9 | 2018 | 15.0 | 19.0 |
| 10 | 2019 | 3.0 | 5.0 |
| 11 | 2020 | 17.0 | 21.0 |
| 12 | 2021 | 4.0 | 5.0 |
with_kd_mask = triple_double_games_gb_df2["year"]<2016
without_kd_mask = triple_double_games_gb_df2["year"]>=2016
triple_double_games_gb_df2.loc[without_kd_mask]
| year | Away | Home | |
|---|---|---|---|
| 7 | 2016 | 17.0 | 25.0 |
| 8 | 2017 | 16.0 | 9.0 |
| 9 | 2018 | 15.0 | 19.0 |
| 10 | 2019 | 3.0 | 5.0 |
| 11 | 2020 | 17.0 | 21.0 |
| 12 | 2021 | 4.0 | 5.0 |
# triple_double_games_gb_df2.loc[with_kd_mask]
triple_doubles_without_kd = sum(triple_double_games_gb_df2.loc[without_kd_mask]["Away"])+ sum(triple_double_games_gb_df2.loc[without_kd_mask]["Home"])
triple_doubles_with_kd = sum(triple_double_games_gb_df2.loc[with_kd_mask]["Away"])+ sum(triple_double_games_gb_df2.loc[with_kd_mask]["Home"])
print(f"Westbrook recorded {triple_doubles_with_kd} triple doubles in {triple_double_games_gb_df2.loc[with_kd_mask].shape[0]} seasons with Kevin Durant as a teammate")
print(f"Westbrook has created {triple_doubles_without_kd} triple doubles in {triple_double_games_gb_df2.loc[without_kd_mask].shape[0]}+ seasons without Kevin Durant as a teammate")
Westbrook recorded 37.0 triple doubles in 7 seasons with Kevin Durant as a teammate Westbrook has created 156.0 triple doubles in 6+ seasons without Kevin Durant as a teammate
Lastly, the game margin during triple double games can provide a naive comparison about how Russ' pursuit of a triple double affects his team. For instance, if Russ is selfishly pursuing triple doubles at the expense of team success, we could expect that the margin of loss is greater in loses where he records a triple double than in loses where a triple double is not achieved. Similarly, smaller win margins in games with a triple double than without a triple double could suggest a negative team effect when Russ creates a triple double.
triple_double_win_margin = np.mean(triple_double_games_df.loc[triple_double_games_df["result_b"]==1]["margin"])
triple_double_loss_margin = np.mean(triple_double_games_df.loc[triple_double_games_df["result_b"]==0]["margin"])
non_triple_double_win_margin = np.mean(non_triple_double_games_df.loc[non_triple_double_games_df["result_b"]==1]["margin"])
non_triple_double_loss_margin = np.mean(non_triple_double_games_df.loc[non_triple_double_games_df["result_b"]==0]["margin"])
margin_data = [["Triple Double Win Margin", f"{triple_double_win_margin:.2f}"],
["Non-Triple Double Win Margin", f"{non_triple_double_win_margin:.2f}"],
["Triple Double Loss Margin", f"{triple_double_loss_margin:.2f}"],
["Non-Triple Double Loss Margin", f"{non_triple_double_loss_margin:.2f}"]]
margin_table = tabulate.tabulate(margin_data, tablefmt='html')
margin_table
| Triple Double Win Margin | 11.55 |
| Non-Triple Double Win Margin | 11.59 |
| Triple Double Loss Margin | -7.27 |
| Non-Triple Double Loss Margin | -9.96 |
In all honesty, these results are surprising (to me) and speak well for Russ and his supporters
1steals and blocks were added to the box score almost a decade after points, rebounds, and assists. This blazersedge.com article has excellent documentation of the NBA box score's evolution
import sys
import IPython
import matplotlib as mpl
from datetime import datetime
print('originally published 2022-03-06 11:43')
print(f'last updated: {datetime.now().strftime("%Y-%m-%d %H:%M")} \n')
print(f'Python version: {sys.version_info}')
print(f'matplotlib version: {mpl.__version__}')
print(f'iPython version: {IPython.__version__}')
print(f'urllib version: {urllib.request.__version__}')
print(f'seaborn version: {sns.__version__}')
print(f'pandas version: {pd.__version__}')
originally published 2022-03-06 11:43 last updated: 2022-03-06 19:22 Python version: sys.version_info(major=3, minor=9, micro=7, releaselevel='final', serial=0) matplotlib version: 3.4.3 iPython version: 7.29.0 urllib version: 3.9 seaborn version: 0.11.2 pandas version: 1.4.1