Source: This exploratory analysis originated from a DataCamp project.

TV, Halftime shows, and the Big Game

Whether or not you like football, the Super Bowl is a spectacle. There's a little something for everyone at your Super Bowl party. Drama in the form of blowouts, comebacks, and controversy for the sports fan. There are the ridiculously expensive ads, some hilarious, others gut-wrenching, thought-provoking, and weird. The half-time shows with the biggest musicians in the world, sometimes riding giant mechanical tigers or leaping from the roof of the stadium. It's a show, baby. And in this notebook, we're going to find out how some of the elements of this show interact with each other. After exploring and cleaning our data a little, we're going to answer questions like:

  • 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?

Left Shark Steals The Show Left Shark Steals The Show. Katy Perry performing at halftime of Super Bowl XLIX. Photo by Huntley Paton. Attribution-ShareAlike 2.0 Generic (CC BY-SA 2.0).

The dataset we'll use was scraped and polished from Wikipedia. It is made up of three CSV files, one with game data, one with TV data, and one with halftime musician data for all 52 Super Bowls through 2018. Let's take a look, using display() instead of print() since its output is much prettier in Jupyter Notebooks.

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
3 2015-02-01 49 University of Phoenix Stadium Glendale Arizona 70288 New England Patriots 28 Tom Brady NaN Bill Belichick Seattle Seahawks 24 Russell Wilson NaN Pete Carroll 52 4
4 2014-02-02 48 MetLife Stadium East Rutherford New Jersey 82529 Seattle Seahawks 43 Russell Wilson NaN Pete Carroll Denver Broncos 8 Peyton Manning NaN John Fox 51 35
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
3 49 NBC 114442000 168000000.0 47.5 71 39.1 79.0 4500000
4 48 Fox 112191000 167000000.0 46.7 69 39.3 77.0 4000000
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
3 50 Coldplay 6.0
4 50 Beyoncé 3.0

2. Taking note of dataset issues

For the Super Bowl game data, we can see the dataset appears whole except for missing values in the backup quarterback columns (qb_winner_2 and qb_loser_2), which make sense given most starting QBs in the Super Bowl (qb_winner_1 and qb_loser_1) play the entire game.

From the visual inspection of TV and halftime musicians data, there is only one missing value displayed, but I've got a hunch there are more. The Super Bowl goes all the way back to 1967, and the more granular columns (e.g. the number of songs for halftime musicians) probably weren't tracked reliably over time. Wikipedia is great but not perfect.

An inspection of the .info() output for tv and halftime_musicians shows us that there are multiple columns with null values.

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 53 entries, 0 to 52
Data columns (total 9 columns):
super_bowl          53 non-null int64
network             53 non-null object
avg_us_viewers      53 non-null int64
total_us_viewers    15 non-null float64
rating_household    53 non-null float64
share_household     53 non-null int64
rating_18_49        15 non-null float64
share_18_49         6 non-null float64
ad_cost             53 non-null int64
dtypes: float64(4), int64(4), object(1)
memory usage: 3.8+ KB


<class 'pandas.core.frame.DataFrame'>
RangeIndex: 134 entries, 0 to 133
Data columns (total 3 columns):
super_bowl    134 non-null int64
musician      134 non-null object
num_songs     88 non-null float64
dtypes: float64(1), int64(1), object(1)
memory usage: 3.2+ KB

3. Combined points distribution

For the TV data, the following columns have missing values and a lot of them:

  • total_us_viewers (amount of U.S. viewers who watched at least some part of the broadcast)
  • rating_18_49 (average % of U.S. adults 18-49 who live in a household with a TV that were watching for the entire broadcast)
  • share_18_49 (average % of U.S. adults 18-49 who live in a household with a TV in use that were watching for the entire broadcast)

For the halftime musician data, there are missing numbers of songs performed (num_songs) for about a third of the performances.

There are a lot of potential reasons for these missing values. Was the data ever tracked? Was it lost in history? Is the research effort to make this data whole worth it? Maybe. Watching every Super Bowl halftime show to get song counts would be pretty fun. But we don't have the time to do that kind of stuff now! Let's take note of where the dataset isn't perfect and start uncovering some insights.

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.

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
23 1995-01-29 29 Joe Robbie Stadium Miami Gardens Florida 74107 San Francisco 49ers 49 Steve Young NaN George Seifert San Diego Chargers 26 Stan Humphreys NaN Bobby Ross 75 23
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
43 1975-01-12 9 Tulane Stadium New Orleans Louisiana 80997 Pittsburgh Steelers 16 Terry Bradshaw NaN Chuck Noll Minnesota Vikings 6 Fran Tarkenton NaN Bud Grant 22 10
45 1973-01-14 7 Memorial Coliseum Los Angeles California 90182 Miami Dolphins 14 Bob Griese NaN Don Shula Washington Redskins 7 Bill Kilmer NaN George Allen 21 7
49 1969-01-12 3 Orange Bowl Miami Florida 75389 New York Jets 16 Joe Namath NaN Weeb Ewbank Baltimore Colts 7 Earl Morrall Johnny Unitas Don Shula 23 9

4. Point difference distribution

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. One even happened recently in 2018's Super Bowl LII where Tom Brady's Patriots lost to Nick Foles' underdog Eagles 41-33 for a combined score of 74.

Going down to the lowest combined scores, we have Super Bowl III and VII, which featured tough defenses that dominated. We also have Super Bowl IX in New Orleans in 1975, whose 16-6 score can be attributed to inclement weather. The field was slick from overnight rain, and it was cold at 46 °F (8 °C), making it hard for the Steelers and Vikings to do much offensively. This was the second-coldest Super Bowl ever and the last to be played in inclement weather for over 30 years. The NFL realized people like points, I guess.

UPDATE: In Super Bowl LIII in 2019, the Patriots and Rams broke the record for the lowest-scoring Super Bowl with a combined score of 16 points (13-3 for the Patriots).

Let's take a look at point difference now.

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
27 1991-01-27 25 Tampa Stadium Tampa Florida 73813 New York Giants 20 Jeff Hostetler NaN Bill Parcells Buffalo Bills 19 Jim Kelly NaN Marv Levy 39 1
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
4 2014-02-02 48 MetLife Stadium East Rutherford New Jersey 82529 Seattle Seahawks 43 Russell Wilson NaN Pete Carroll Denver Broncos 8 Peyton Manning NaN John Fox 51 35
25 1993-01-31 27 Rose Bowl Pasadena California 98374 Dallas Cowboys 52 Troy Aikman NaN Jimmy Johnson Buffalo Bills 17 Jim Kelly Frank Reich Marv Levy 69 35
28 1990-01-28 24 Louisiana Superdome New Orleans Louisiana 72919 San Francisco 49ers 55 Joe Montana NaN George Seifert Denver Broncos 10 John Elway NaN Dan Reeves 65 45
32 1986-01-26 20 Louisiana Superdome New Orleans Louisiana 73818 Chicago Bears 46 Jim McMahon NaN Mike Ditka New England Patriots 10 Tony Eason Steve Grogan Raymond Berry 56 36

5. Do blowouts translate to lost viewers?

The vast majority of Super Bowls are close games. Makes sense. Both teams are likely to be deserving if they've made it this far. The closest game ever was when the Buffalo Bills lost to the New York Giants by 1 point in 1991, which was best remembered for Scott Norwood's last-second missed field goal attempt that went wide right, kicking off four Bills Super Bowl losses in a row. Poor Scott. The biggest point discrepancy ever was 45 points (!) where Hall of Famer Joe Montana's led the San Francisco 49ers to victory in 1990, one year before the closest game ever.

I remember watching the Seahawks crush the Broncos by 35 points (43-8) in 2014, which was a boring experience in my opinion. The game was never really close. I'm pretty sure we changed the channel at the end of the third quarter. Let's combine our game data and TV to see if this is a universal phenomenon. Do large point differences translate to lost viewers? We can plot household share (average percentage of U.S. households with a TV in use that were watching for the entire broadcast) vs. point difference to find out.

<matplotlib.axes._subplots.AxesSubplot at 0x7f830b53b390>

6. Viewership and the ad industry over time

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. Though it matches our intuition, we must take it with a grain of salt because the linear relationship in the data is weak due to our small sample size of 52 games.

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.

7. Halftime shows weren’t always this great

We can see viewers increased before ad costs did. Maybe the networks weren't very data savvy and were slow to react?

Another hypothesis: maybe halftime shows weren't that good in the earlier years? The modern spectacle of the Super Bowl has a lot to do with the cultural prestige of big halftime acts. I went down a YouTube rabbit hole and it turns out the old ones weren't up to today's standards. Some offenders:

  • Super Bowl XXVI in 1992: A Frosty The Snowman rap performed by children.
  • Super Bowl XXIII in 1989: An Elvis impersonator that did magic tricks and didn't even sing one Elvis song.
  • Super Bowl XXI in 1987: Tap dancing ponies. (Okay, that's pretty awesome actually.)

It turns out Michael Jackson's Super Bowl XXVII performance, one of the most watched events in American TV history, was when the NFL realized the value of Super Bowl airtime and decided they needed to sign big name acts from then on out. The halftime shows before MJ indeed weren't that impressive, which we can see by filtering our halftime_musician data.

super_bowl musician num_songs
80 27 Michael Jackson 5.0
81 26 Gloria Estefan 2.0
82 26 University of Minnesota Marching Band NaN
83 25 New Kids on the Block 2.0
84 24 Pete Fountain 1.0
85 24 Doug Kershaw 1.0
86 24 Irma Thomas 1.0
87 24 Pride of Nicholls Marching Band NaN
88 24 The Human Jukebox NaN
89 24 Pride of Acadiana NaN
90 23 Elvis Presto 7.0
91 22 Chubby Checker 2.0
92 22 San Diego State University Marching Aztecs NaN
93 22 Spirit of Troy NaN
94 21 Grambling State University Tiger Marching Band 8.0
95 21 Spirit of Troy 8.0
96 20 Up with People NaN
97 19 Tops In Blue NaN
98 18 The University of Florida Fightin' Gator March... 7.0
99 18 The Florida State University Marching Chiefs 7.0
100 17 Los Angeles Unified School District All City H... NaN
101 16 Up with People NaN
102 15 The Human Jukebox NaN
103 15 Helen O'Connell NaN
104 14 Up with People NaN
105 14 Grambling State University Tiger Marching Band NaN
106 13 Ken Hamilton NaN
107 13 Gramacks NaN
108 12 Tyler Junior College Apache Band NaN
109 12 Pete Fountain NaN
110 12 Al Hirt NaN
111 11 Los Angeles Unified School District All City H... NaN
112 10 Up with People NaN
113 9 Mercer Ellington NaN
114 9 Grambling State University Tiger Marching Band NaN
115 8 University of Texas Longhorn Band NaN
116 8 Judy Mallett NaN
117 7 University of Michigan Marching Band NaN
118 7 Woody Herman NaN
119 7 Andy Williams NaN
120 6 Ella Fitzgerald NaN
121 6 Carol Channing NaN
122 6 Al Hirt NaN
123 6 United States Air Force Academy Cadet Chorale NaN
124 5 Southeast Missouri State Marching Band NaN
125 4 Marguerite Piazza NaN
126 4 Doc Severinsen NaN
127 4 Al Hirt NaN
128 4 The Human Jukebox NaN
129 3 Florida A&M University Marching 100 Band NaN
130 2 Grambling State University Tiger Marching Band NaN
131 1 University of Arizona Symphonic Marching Band NaN
132 1 Grambling State University Tiger Marching Band NaN
133 1 Al Hirt NaN

8. Who has the most halftime show appearances?

Lots of marching bands. American jazz clarinetist Pete Fountain. Miss Texas 1973 playing a violin. Nothing against those performers, they're just simply not Beyoncé. To be fair, no one is.

Let's see all of the musicians that have done more than one halftime show, including their performance counts.

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

9. Who performed the most songs in a halftime show?

The world famous Grambling State University Tiger Marching Band takes the crown with six appearances. Beyoncé, Justin Timberlake, Nelly, and Bruno Mars are the only post-Y2K musicians with multiple appearances (two each).

From our previous inspections, the num_songs column has lots of missing values:

  • A lot of the marching bands don't have num_songs entries.
  • For non-marching bands, missing data starts occurring at Super Bowl XX.

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]"). Then we'll filter for Super Bowls after Super Bowl XX to address the missing data issue, then let's see who has the most number of songs.

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

10. Conclusion

So most non-band musicians do 1-3 songs per halftime show. It's important to note that the duration of the halftime show is fixed (roughly 12 minutes) so songs per performance is more a measure of how many hit songs you have. JT went off in 2018, wow. 11 songs! Diana Ross comes in second with 10 in her medley in 1996.

In this notebook, we loaded, cleaned, then explored Super Bowl game, television, and halftime show data. We visualized the distributions of combined points, point differences, and halftime show performances using histograms. We used line plots to see how ad cost increases lagged behind viewership increases. And we discovered that blowouts do appear to lead to a drop in viewers.

This year's Big Game will be here before you know it. Who do you think will win Super Bowl LIII?

The winner of Super Bowl LIII will be the New England Patriots

UPDATE: True Prediction.

References

  • DataCamp
  • Wikipedia

For more detail, code and interactive graphs Visit Notebook

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