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Top 9 maps and charts that explain Super Bowl

Each year, on a Sunday at the end of January or beginning of February, tens of millions of Americans declare their unofficial holiday. More than half the adult population in the US watch The Super Bowl, which determines the championship of American football.

The maps and charts below try to capture an essence of this fantastic event.

1. Super Bowl host cities 1967-2015

source: nolagraphy

2. Map of NFL Fandom

Each county is color-coded based on which official team Facebook fan page has the most ‘Likes’  from people who live in that county.

source: The Atlantic

3. Number of Super Bowl championships per NFL teams

source: Business Insider

4. List of Super Bowl Winners 1967-2017

Year No Winner Loser Score Venue
2017 51 New England Patriots Atlanta Falcons 34-28 Houston
2016 50 Denver Broncos Carolina Panthers 24–10 Santa Clara
2015 XLIX New England Patriots Seattle Seahawks 28-24 Arizona
2014 XLVIII Seattle Seahawks Denver Broncos 43-8 New jersey
2013 XLVII Baltimore Ravens San Francisco 49ers 34-31 New Orleans
2012 XLVI New York Giants New England Patriots 21-17 Indianapolis
2011 XLV Green Bay Packers Pittsburgh Steelers 31-25 Texas
2010 XLIV New Orleans Saints Indianapolis Colts 31-17 Miami
2009 XLIII Pittsburgh Steelers Arizona Cardinals 27-23 Tampa
2008  XLII  New York Giants New England Patriots 17-14 Arizona
2007  XLI Indianapolis Colts Chicago Bears 29-17 Miami
2006  XL  Pittsburgh Seattle 21-10 Detroit
2005  XXXIX  New England Philadelphia 24-21 Jacksonville
2004 XXXVIII New England Carolina 32-29 Houston
2003 XXXVII Tampa Bay Oakland 48-21 San Diego
2002 XXXVI New England St Louis 20-17 New Orleans
2001 XXXV Baltimore NY Giants 34-7 Tampa
2000 XXXIV St Louis Tennessee 23-16 Atlanta
1999 XXXIII Denver Atlanta 34-19 Miami
1998 XXXII Denver Green Bay 31-24 San Diego
1997 XXXI Green Bay New England 35-21 N Orleans
1996 XXX Dallas Pittsburgh 27-17 Tempe
1995 XXIX San Francisco San Diego 49-26 Miami
1994 XXVIII Dallas Buffalo 30-13 Atlanta
1993 XXVII Dallas Buffalo 53-17 Pasadena
1992 XXVI Washington Buffalo 37-24 Minneapolis
1991 XXV NY Giants Buffalo 20-19 Tampa
1990 XXIV San Francisco Denver 55-10 N Orleans
1989 XXIII San Francisco Cincinnati 20-16 Miami
1988 XXII Washington Denver 42-10 San Diego
1987 XXI NY Giants Denver 39-20 Pasadena
1986 XX Chicago New England 46-10 N Orleans
1985 XIX San Francisco Miami 38-16 Stanford
1984 XVIII LA Raiders Washington 38-9 Tampa
1983 XVII Washington Miami 27-17 Pasadena
1982 XVI San Francisco Cincinnati 26-21 Pontiac
1981 XV Oakland Philadelphia 27-10 N Orleans
1980 XIV Pittsburgh LA Rams 31-19 Pasadena
1979 XIII Pittsburgh Dallas 35-31 Miami
1978 XII Dallas Denver 27-10 N Orleans
1977 XI Oakland Minnesota 32-14 Pasadena
1976 X Pittsburgh Dallas 21-17 Miami
1975 IX Pittsburgh Minnesota 16-6 N Orleans
1974 VIII Miami Minnesota 24-7 Houston
1973 VII Miami Washington 14-7 Los Angeles
1972 VI Dallas Miami 24-3 N Orleans
1971 V Baltimore Dallas 16-13 Miami
1970 IV Kansas City Minnesota 23-7 N Orleans
1969 III NY Jets Baltimore 16-7 Miami
1968 II Green Bay Oakland 33-14 Miami
1967 I Green Bay Kansas City 35-10  Los Angeles

5. History of football in the US

source: Bill sports maps

6. The Top-Searched Super Bowl Recipes by State

source: Eater

7. Cost of a 30 second Super Bowl ad 1967-2015

source: USA Today

8. What Types of Superbowl Ads Get Shared the Most?

source: Visually

9. How big is Super Bowl

source: TvTome

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Google’s algos can now forecast flight delays even before airlines do

What causes flight delays? The reason could be anything from extreme weather to air traffic congestion to aircraft maintenance issues or something else entirely. The bottom line is, flight delays are extremely frustrating for weary travelers stuck at airports. So, Google is updating its Flights service with an awesome new feature – using machine learning algorithms to predict delays in departure times.

The Flights app is normally populated with the information Google pulls in from the airlines directly. But for this feature, Google has turned to historical flight status data and artificial intelligence technologies to predict when a flight will be delayed.

The algos will comb the data to see what parameters are common between delayed flights – location, weather conditions, aircraft arriving late from the previous destination, etc. Once Google is at least 80% sure that these factors are conspiring in a manner that an airplane will be delayed, it will flag that information to the Flights app, and also specify the likely reason for the holdup – without waiting for or depending on the information coming in from the airlines.

To be on the safe side, Google still recommends getting to the airport with enough time to spare.  But it hopes to give travelers enough information to manage expectations and prevent any unpleasant surprises.

Now, Google is not the first or only one trying to make the travel sector less complex with machine learning. Back in 2013, researchers at Singapore’s Institute for Infocomm Research used machine learning techniques to beat the industry benchmark for the estimation of the arrival time of domestic flights by 40%. Last year, Eastern Macedonia and Thrace Institute of Technology in Greece showcased how it was feasible to predict prices for flights based on historical fare data. But Google’s application of machine learning is certainly taking the technology mainstream like none other.

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