Analyzing-TV-Data
Using data manipulation and visualization techniques, I explore one of two different television broadcast datasets: The Super Bowl and hit sitcom The Office!
TV, halftime shows, and the Big Game
After exploring and cleaning our data a little, I try to answer the following questions:
- What are the most extreme game outcomes?
- How does the game affect television viewership?
- How have viewership, TV ratings, and ad cost evolved over time?
- Who are the most prolific musicians in terms of halftime show performances?
# Load the CSV data into DataFrames
super_bowls = pd.read_csv('super_bowls.csv')
tv = pd.read_csv('tv.csv')
halftime_musicians = pd.read_csv('halftime_musicians.csv')| date | super_bowl | venue | city | state | attendance | team_winner | winning_pts | qb_winner_1 | qb_winner_2 | coach_winner | team_loser | losing_pts | qb_loser_1 | qb_loser_2 | coach_loser | combined_pts | difference_pts | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 2018-02-04 | 52 | U.S. Bank Stadium | Minneapolis | Minnesota | 67612 | Philadelphia Eagles | 41 | Nick Foles | NaN | Doug Pederson | New England Patriots | 33 | Tom Brady | NaN | Bill Belichick | 74 | 8 |
| 1 | 2017-02-05 | 51 | NRG Stadium | Houston | Texas | 70807 | New England Patriots | 34 | Tom Brady | NaN | Bill Belichick | Atlanta Falcons | 28 | Matt Ryan | NaN | Dan Quinn | 62 | 6 |
| 2 | 2016-02-07 | 50 | Levi's Stadium | Santa Clara | California | 71088 | Denver Broncos | 24 | Peyton Manning | NaN | Gary Kubiak | Carolina Panthers | 10 | Cam Newton | NaN | Ron Rivera | 34 | 14 |
| super_bowl | network | avg_us_viewers | total_us_viewers | rating_household | share_household | rating_18_49 | share_18_49 | ad_cost | |
|---|---|---|---|---|---|---|---|---|---|
| 0 | 52 | NBC | 103390000 | NaN | 43.1 | 68 | 33.4 | 78.0 | 5000000 |
| 1 | 51 | Fox | 111319000 | 172000000.0 | 45.3 | 73 | 37.1 | 79.0 | 5000000 |
| 2 | 50 | CBS | 111864000 | 167000000.0 | 46.6 | 72 | 37.7 | 79.0 | 5000000 |
| super_bowl | musician | num_songs | |
|---|---|---|---|
| 0 | 52 | Justin Timberlake | 11.0 |
| 1 | 52 | University of Minnesota Marching Band | 1.0 |
| 2 | 51 | Lady Gaga | 7.0 |
Let's start by looking at combined points for each Super Bowl by visualizing the distribution. Let's also pinpoint the Super Bowls with the highest and lowest scores.
plt.hist(super_bowls.combined_pts)
Most combined scores are around 40-50 points, with the extremes being roughly equal distance away in opposite directions. Going up to the highest combined scores at 74 and 75, we find two games featuring dominant quarterback performances.
Point difference distribution
Let's take a look at point difference now.
Do blowouts translate to lost viewers?
The vast majority of Super Bowls are close games. Makes sense. However, watching one team crushing the other would be boring, in my opinion. Do large point differences translate to lost viewers?
games_tv = pd.merge(tv[tv['super_bowl'] > 1], super_bowls, on='super_bowl')
sns.regplot(x=games_tv.difference_pts, y=games_tv.share_household, data=games_tv)
The downward sloping regression line and the 95% confidence interval for that regression suggest that bailing on the game if it is a blowout is common.
Viewership and the ad industry over time
Regardless of the score though, I bet most people stick it out for the halftime show, which is good news for the TV networks and advertisers. A 30-second spot costs a pretty $5 million now, but has it always been that way? And how have number of viewers and household ratings trended alongside ad cost? We can find out using line plots that share a "Super Bowl" x-axis.
plt.subplot(3, 1, 1)
plt.plot(tv.super_bowl,tv.avg_us_viewers, color='#648FFF')
plt.subplot(3, 1, 2)
plt.plot(tv.super_bowl, tv.rating_household, color='#DC267F')
plt.subplot(3, 1, 3)
plt.plot(tv.super_bowl,tv.ad_cost, color='#FFB000')
plt.tight_layout()
plt.show()
Who has the most halftime show appearances?
Let's see all of the musicians that have done more than one halftime show, including their performance counts.
# Count halftime show appearances for each musician and sort them from most to least
halftime_appearances = halftime_musicians.groupby('musician').count()['super_bowl'].reset_index()
halftime_appearances = halftime_appearances.sort_values('super_bowl', ascending=False)
# Display musicians with more than one halftime show appearance
display(halftime_appearances[halftime_appearances.super_bowl > 1])| musician | super_bowl | |
|---|---|---|
| 28 | Grambling State University Tiger Marching Band | 6 |
| 104 | Up with People | 4 |
| 1 | Al Hirt | 4 |
| 83 | The Human Jukebox | 3 |
| 76 | Spirit of Troy | 2 |
| 25 | Florida A&M University Marching 100 Band | 2 |
| 26 | Gloria Estefan | 2 |
| 102 | University of Minnesota Marching Band | 2 |
| 10 | Bruno Mars | 2 |
| 64 | Pete Fountain | 2 |
| 5 | Beyoncé | 2 |
| 36 | Justin Timberlake | 2 |
| 57 | Nelly | 2 |
| 44 | Los Angeles Unified School District All City H... | 2 |
Who performed the most songs in a halftime show?
Let's filter out marching bands by filtering out musicians with the word "Marching" in them and the word "Spirit" (a common naming convention for marching bands is "Spirit of something").
# Filter out most marching bands
no_bands = halftime_musicians[~halftime_musicians.musician.str.contains('Marching')]
no_bands = no_bands[~no_bands.musician.str.contains('Spirit')]
no_bands = no_bands.sort_values('num_songs', ascending=False)
# ...and display the top 15
display(no_bands.head(15))| super_bowl | musician | num_songs | |
|---|---|---|---|
| 0 | 52 | Justin Timberlake | 11.0 |
| 70 | 30 | Diana Ross | 10.0 |
| 10 | 49 | Katy Perry | 8.0 |
| 2 | 51 | Lady Gaga | 7.0 |
| 90 | 23 | Elvis Presto | 7.0 |
| 33 | 41 | Prince | 7.0 |
| 16 | 47 | Beyoncé | 7.0 |
| 14 | 48 | Bruno Mars | 6.0 |
| 3 | 50 | Coldplay | 6.0 |
| 25 | 45 | The Black Eyed Peas | 6.0 |
| 20 | 46 | Madonna | 5.0 |
| 30 | 44 | The Who | 5.0 |
| 80 | 27 | Michael Jackson | 5.0 |
| 64 | 32 | The Temptations | 4.0 |
| 36 | 39 | Paul McCartney | 4.0 |
Wow, 11 songs by Justin Timberlake, that's a lot.