Social power, influence, and performance in the NBA, Part 1

# Explore valuation and attendance using data science and machine learning

Python, pandas, and a touch of R

### Content series:

## This content is part # of # in the series: Social power, influence, and performance in the NBA, Part 1

## This content is part of the series:Social power, influence, and performance in the NBA, Part 1

Stay tuned for additional content in this series.

## Getting started

In this tutorial series, learn how to analyze how social media affects the NBA using Python, pandas, Jupyter Notebooks, and a touch of R. Here in Part 1, learn the basics of data science and machine learning around the teams in the NBA. The players of the NBA are the subject of Part 2.

## What is data science, machine learning, and AI?

There is a lot of confusion around the terms data science, machine learning, and artificial intelligence. Often used interchangeably, but from a high level:

- Data science is a philosophy of thinking scientifically about data.
- Machine learning is a technique in which computers learn without explicitly being instructed to learn.
- Artificial intelligence is intelligence exhibited by machines. (Machine learning is one example of an AI technique; another example is optimization.)

### 80/20 machine learning in practice

An overlooked part of machine learning is the 80/20 rule in which approximately 80 percent of the time is spent getting and manipulating the data, and 20 percent is devoted to the fun stuff like analyzing data, modeling the data, and coming up with predictions.

##### Figure 1. 80/20 Machine learning in practice

A problem of data manipulation that isn't obvious is getting the data in the first place. It is one thing to experiment with a publicly available data set; it is another entirely to scrape the internet, call APIs, and get the data in usable shape. Even beyond those issues, a problem that can be even more challenging is getting the data into production.

##### Figure 2. Full-stack data science

Rightfully so, a lot of attention is paid to machine learning and the skills required to model: applied math, domain expertise, and knowledge of tooling. To get a production machine-learning system deployed is a whole other matter. This is covered at a high level in this tutorial series with the hope that it will inspire you to create machine-learning models and deploy them into production.

### What is machine learning?

Beyond a high-level description, there's a hierarchy to machine learning. At the top is supervised learning and unsupervised learning. There are two types of supervised learning techniques: classification problems and regression problems that have a training set with labeled data.

An example of a supervised regression machine-learning problem is predicting future housing prices from historical sales data. An example of a supervised classification problem is using a historical repository of images to classify objects in images: cars, houses, shapes, etc.

Unsupervised learning involves modeling where data is not labeled. The correct answer might not be known and needs to be discovered. A common example is clustering. An example of clustering is to find groups of NBA players with things in common and label those clusters manually — for example, top scorer, top rebounders, etc.

### Phrasing the problem: What is the relationship between social influence and the NBA?

With the basics out of the way, it is time to dig in:

- Does individual player performance affect a team's wins?
- Does on-the-court performance correlate with social media influence?
- Does engagement on social media correlate with popularity on Wikipedia?
- Is follower count or engagement a better predictor of popularity on Wikipedia?
- Does salary correlate with on-the-court performance?
- Does salary correlate with social media performance?
- Does winning bring more fans to games?
- What drives the valuation of teams: attendance, local real estate market?

To answer these questions and others, it is necessary to retrieve several categories of data:

- Wikipedia popularity
- Twitter engagement
- Arena attendance
- NBA performance data
- NBA salary data

##### Figure 3. NBA data sources

### Going deep into the 80-percent problem: Gathering data

Gathering this data is a nontrivial software engineering problem. The first step to collecting all of the data is figuring out where to start. For this tutorial, a good place to start is to collect all the players from the NBA 2016-17 season.

This brings up a helpful point about how to collect data: If it is easy to collect data manually — for example, download from a website and clean up the data manually in Excel — then this is a reasonable way to start with a data science problem. If collecting one data source and manually cleaning the data turns into more than a few hours, then it's probably best to write code to solve the problem.

Fortunately, collecting the first data source is as simple as downloading a CSV from Basketball Reference. Now that the first data collection is out of the way, it's time to quickly explore what it looks like using pandas and Jupyter Notebook. Before you can run some code, you need to:

- Create a virtual environment (based on Python 3.6)
- Install the packages used in this tutorial: pandas and Jupyter Notebook.

Because the pattern of installing packages and updating them, is so common
I put it into a `Makefile`

, as shown below:

#### Listing 1. Makefile contents

setup: mkdir -p ~/.socialpowernba && python3 -m venv ~/.socialpowernba install: pip install -r requirements.txt

To start working on a project, run ```
make setup && make
install
```

.

Another trick is to create an alias so that when you want to work on a
particular project, you automatically source the `virtualenv`

when you `cd`

into the project. The contents of the .zshrc file
with this alias inside look like:

alias nbatop="cd ~/src/socialpowernba && source ~/.socialpowernba/bin/activate"

To start the virtual environment, type `nbatop`

. You will
`cd`

into the checkout and start your virtualenv.

To inspect the data set you downloaded or used from the GitHub repo:

Start Jupyter Notebook: `Jupyter notebook`

. Running this launches a web browser in which you can explore existing
notebooks or create new ones.

If you are using the files in the GitHub repo, look for basketball_reference.ipynb, which is a simple notebook that looks at the data inside.

You can create your notebook using the menu on the web or load the notebook in the GitHub repo called basketball_reference. To perform an initial validation and exploration, load a CSV file into a pandas data frame. Loading a CSV file into pandas is easy, but there are two caveats:

- The columns in the CSV file must have names.
- The rows of each column are equal length.

Listing 2 shows how to load the file into pandas.

#### Listing 2. Jupyter Notebook basketball reference exploration

import pandas as pd nba = pd.read_csv("../data/nba_2017_br.csv") nba.describe()

The following image shows the result of the data loaded. The `describe`

function on a
pandas data frame provides descriptive statistics, including the number of
columns. In your data, and shown below, the number of columns
is 27, and the median (this is the 50-percent row) for each column. At this
point, it might be a good idea to play around with the Jupyter Notebook
you created and see what insight you can observe. To learn more about
what pandas can do, see the official pandas tutorial page.

##### Figure 4. NBA dataset load and describe

One thing this data set doesn't have is a clear way to rank offensive and defensive performance of a player in one statistic. There are a few ways to rank players in the NBA using just one statistic. The website FiveThirtyEight has a CARMELO ranking system. ESPN has Real Plus-Minus, which includes a handy output of wins attributed to each player. The NBA's single-number statistic is called PIE (Player Impact Estimate).

The difficulty level increases slightly when you get the data from both ESPN and the NBA websites. One approach is to scrape the website using a tool such as Scrapy. The approach used in this tutorial is a bit simpler than that, though. In this case, cutting and pasting from the website into Excel, manually cleaning up the data, then saving the data as a CSV is quicker than writing code to do it. Later, if this turns into a bigger project, this approach might not work as well. But for this tutorial, it's a great solution. A key takeaway for messy data science problems is to continue to make forward progress quickly without getting bogged into too much depth.

“It is possible to spend a lot of time perfecting a way to get a data source and clean it up, then realize the data isn't helpful to the model you are creating.”

The image below shows the NBA PIE dataset. The data also has a count of 486 or 486 rows. Getting the data from ESPN is a similar process to above. Other data sources to consider are salary and endorsements. ESPN has the salary information, and Forbes has a small subset of the endorsement data. Both of these data sources are in the GitHub project.

##### Figure 5. NBA PIE dataset

In Table 1, there is a listing of the data sources by name and location. In short order, we have many items from many different data sources.

##### Table 1. NBA data sources

Data source | Filename | Rows | Summary |
---|---|---|---|

Basketball-Reference | nba_2017_attendance.csv | 30 | Stadium attendance |

Forbes | nba_2017_endorsements.csv | 8 | Top players |

Forbes | nba_2017_team_valuations.csv | 30 | All teams |

ESPN | nba_2017_salary.csv | 450 | Most players |

NBA | nba_2017_pie.csv | 468 | All players |

ESPN | nba_2017_real_plus_minus.csv | 468 | All players |

Basketball-Reference | nba_2017_br.csv | 468 | All players |

FiveThirtyEight | nba_2017_elo.csv | 30 | Team rank |

There is still a lot of work to do to get all of the data downloaded and transformed into a unified data set. To make things even worse, collecting the data thus far was easy. There is still a big journey ahead. In looking at the shape of the data, a good place to start is to take the top eight players' endorsements and see if there is a pattern to tease out. Before that though, explore the valuation of teams in the NBA. From there, you can determine what impact a player has on the total value of an NBA franchise.

## Exploring team valuation for the NBA

The first order of business is to create a new Jupyter Notebook. Luckily for you, the Jupyter Notebook is already created. You'll find it in the GitHub repo: exploring_team_valuation_nba.

Next, import a common set of libraries that are typically used to explore data in a Jupyter Notebook.

### Listing 3. Common Jupyter Notebook initial imports

import pandas as pd import statsmodels.api as sm import statsmodels.formula.api as smf import matplotlib.pyplot as plt import seaborn as sns color = sns.color_palette() %matplotlib inline

Now you need to create a pandas data frame for each source.

#### Listing 4. Create data frame for sources

attendance_df = pd.read_csv("../data/nba_2017_attendance.csv") endorsement_df = pd.read_csv("../data/nba_2017_endorsements.csv") valuations_df = pd.read_csv("../data/nba_2017_team_valuations.csv") salary_df = pd.read_csv("../data/nba_2017_salary.csv") pie_df = pd.read_csv("../data/nba_2017_pie.csv") plus_minus_df = pd.read_csv("../data/nba_2017_real_plus_minus.csv") br_stats_df = pd.read_csv("../data/nba_2017_br.csv") elo_df = pd.read_csv("../data/nba_2017_elo.csv")

A neat trick when you're working with a lot of data sources is to show the first few lines of each data frame. You can see what this looks like in the images below.

##### Figure 6. Endorsement data frames section A

##### Figure 7. Endorsement data frames section B

Now, merge the team valuation data with attendance data and create a plot. Listing 5 provides the code for merging the pandas data frames.

#### Listing 5. Merging pandas data frames

attendance_valuation_df = attendance_df.merge(valuations_df, how="inner", on="TEAM") attendance_valuation_df.head()

The image below shows the output of the merge.

##### Figure 8. Attendance valuation data frame merge head

To get a better feel for the data you just merged, do a couple of quick visualizations. The first step is to tell the notebook to display wider graphs, then to do a Seaborne pairplot, as shown below.

#### Listing 6. Seaborn pairplot

from IPython.core.display import display, HTML display(HTML("<style>.container { width:100% !important; }</style>")) sns.pairplot(attendance_valuation_df, hue="TEAM")

Looking at the plots, notice the relationship between average attendance and player valuation. There is a strong linear relationship between the two features, as represented by the almost straight line formed by the points.

##### Figure 9. Seaborn pairplot NBA attendance versus valuation

Another way to look at this data is a correlation plot. To create a correlation plot, use the code provided below and the following image shows the output.

#### Listing 7. Seaborn correlation plot

corr = attendance_valuation_df.corr() sns.heatmap(corr, xticklabels=corr.columns.values, yticklabels=corr.columns.values)

##### Figure 10. Seaborn correlation plot NBA attendance versus valuation

The correlation plot shows a relationship to value in millions of dollars (of an NBA team), percentage of average capacity of the stadium that is filled (PCT), and average attendance. A heatmap showing average attendance numbers versus valuation for every team in the NBA will help you dive into this a bit more. To generate a heatmap in Seaborn, it is necessary to reshape the data into a pivot table (much like what is available in Excel). A pivot table allows the Seaborn charting to pivot, among three values and shows how each of the three columns relates to the other two. The code below shows how to reshape the data into a pivot shape.

#### Listing 8. Seaborn heatmap plot

valuations = attendance_valuation_df.pivot("TEAM", "AVG", "VALUE_MILLIONS") plt.subplots(figsize=(20,15)) ax = plt.axes() ax.set_title("NBA Team AVG Attendance vs Valuation in Millions: 2016-2017 Season") sns.heatmap(valuations,linewidths=.5, annot=True, fmt='g')

Figure 11 shows some interesting outliers, for example, the Brooklyn Nets are valued at $1.8 billion, yet they have one of the lowest average attendance rates in the NBA. This is worth a look.

##### Figure 11. Seaborn correlation plot NBA attendance versus valuation

One way to investigate further is to perform a linear regression using the Statsmodels package. According to Statsmodels.org, the Statsmodels package "is a Python module that provides classes and functions for the estimation of many different statistical models, as well as for conducting statistical tests, and statistical data exploration. An extensive list of result statistics are available for each estimator."

You can install the Statsmodels package by using ```
pip install
Statsmodel
```

. Following are the three lines necessary to run the regression.

#### Listing 9. Linear regression VALUE ~ AVG

results = smf.ols('VALUE_MILLIONS ~AVG', data=attendance_valuation_df).fit() print(results.summary())

The image below shows the output of the regression. The R-squared shows that
approximately 28 percent of the valuation can be explained by attendance, and the
*P* value of 0.044 falls within the range of being statistically significant.
One potential issue with the data is the plot of the residual
values doesn't look completely random. This is a good start of trying to
develop a model to explain what creates the valuation of an NBA
franchise.

##### Figure 12. Regression with residual plot

One way to potentially add more to the model is to add in the ELO numbers of each team. According to Wikipedia, "The ELO rating system is a method for calculating the relative skill levels of players in competitor-versus-competitor games such as chess." The ELO rating system is also used in sports.

ELO numbers have more information than a win/loss record because they rank according to the strength of the opponent played against. It seems like a good idea to investigate whether how good a team is affects the valuation.

To do that, merge the ELO data into this data as shown below.

#### Listing 10. Plotting ELO

attendance_valuation_elo_df = attendance_df.merge(elo_df, how="inner", on="TEAM") attendance_valuation_elo_df.head() attendance_valuation_elo_df.to_csv("../data/nba_2017_att_val_elo.csv") corr_elo = attendance_valuation_elo_df.corr() plt.subplots(figsize=(20,15)) ax = plt.axes() ax.set_title("NBA Team Correlation Heatmap: 2016-2017 Season (ELO, AVG Attendance, VALUATION IN MILLIONS)") sns.heatmap(corr_elo, xticklabels=corr_elo.columns.values, yticklabels=corr_elo.columns.values) corr_elo ax = sns.lmplot(x="ELO", y="AVG", data=attendance_valuation_elo_df, hue="CONF", size=12) ax.set(xlabel='ELO Score', ylabel='Average Attendance Per Game', title="NBA Team AVG Attendance vs ELO Ranking: 2016-2017 Season") attendance_valuation_elo_df.groupby("CONF")["ELO"].median() attendance_valuation_elo_df.groupby("CONF")["AVG"].median()

After the merge, there are two charts to create. The first, shown in Figure 13, is a new correlation heatmap. There are some positive correlations to examine more closely. In particular, attendance and ELO seem worth plotting out. In the heatmap below, the lighter the color, the more highly correlated two columns are. If the matrix shows the same value compared against itself, then the correlation is 1, and the square is beige. In the case of TOTAL and ELO, there appears to be a 0.5 correlation.

##### Figure 13. ELO correlation heatmap

Figure 14 plots ELO versus attendance. There does appear to be a weak
linear relationship between how good a team is (ELO RANK) versus the
attendance. The plot below colors the east and west scatter plots
separately, along with a confidence interval. The weak linear relationship
is represented by the straight line going through the points in the
*X,Y* space.

##### Figure 14. ELO versus attendance

A linear regression will help further examine this relationship shown in the plot.

#### Listing 11. Linear regression AVG ~ ELO

results = smf.ols('AVG ~ELO', data=attendance_valuation_elo_df).fit() print(results.summary())

The output of the regression (see Figure 15) shows an R-squared of 8
percent and a *P*-value of 0.027, so there is a statistically significant signal here as
well, but it is very weak.

##### Figure 15. ELO versus attendance regression

### Unsupervised machine learning: K-means cluster

One final item to tackle is to use k-means clustering to create three clusters based on AVG, ELO, and VALUE_MILLIONS.

#### Listing 12. K-means cluster

from sklearn.cluster import KMeans from sklearn.preprocessing import MinMaxScaler #Only cluster on these values numerical_df = val_housing_win_df.loc[:,["TOTAL_ATTENDANCE_MILLIONS", "ELO", "VALUE_MILLIONS", "MEDIAN_HOME_PRICE_COUNTY_MILLONS"]] #Scale to between 0 and 1 from sklearn.preprocessing import MinMaxScaler scaler = MinMaxScaler() print(scaler.fit(numerical_df)) print(scaler.transform(numerical_df)) #Add back to DataFrame from sklearn.cluster import KMeans k_means = KMeans(n_clusters=3) kmeans = k_means.fit(scaler.transform(numerical_df)) val_housing_win_df['cluster'] = kmeans.labels_ val_housing_win_df.head()

You can see by the plot shown below that there are three distinct groups, and the centers of the clusters represent different labels. A note to pay attention to is that sklearn MinMaxScaler is used to scale all of the columns to a value between 0 and 1, to normalize the difference between scales.

##### Figure 16. Team clusters

The image below shows the membership of cluster 1. The main takeaways from this cluster are that they are both the best teams in the NBA and teams that have the highest average attendance. Where things break apart is on total valuation. For example, the Utah Jazz is a very good team according to the ELO, and they have very good attendance, but they are not valued as high as other members of the cluster. This may mean there is an opportunity for the Utah Jazz to make small changes that significantly raise the valuation of the team.

##### Figure 15. Cluster membership

## Conclusion

In Part 1 of this two-part series, you learned the basics of data science and machine learning, and started to explore the relationship of valuation, attendance, and winning NBA teams. The tutorial's code was kept in a Jupyter Notebook you can reference here. Part 2 leaves the teams and explores individual athletes in the NBA. Endorsement data, true on-the-court performance, and social power with Twitter and Wikipedia is explored.

The lessons learned so far from the data exploration are:

- Valuation of an NBA team is affected by average attendance.
- ELO ranking (strength of team's record) is related to attendance. Generally speaking, the better a team is, the more fans attend games.
- The Eastern Conference has lower median attendance and ELO ratings.

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