‘Jeopardy!’ and ‘Wheel of Fortune’ Airtimes in Graphs and Maps

Category: ‘Game Shows’

Answer: Between ‘Jeopardy!’ and ‘Wheel of Fortune’, this program is shown first each day.

Question: What is…well, it depends on where you live.

I grew up in New Jersey, and every weeknight, ‘Jeopardy!’ started at 7:00pm and ‘Wheel of Fortune’ came on directly after at 7:30pm.  One year when I was visiting family in Virginia, and I entered a Bizarro world where ‘Wheel’ was on FIRST, and ‘Jeopardy!’ second.

Many years later, I had a thought.  What do most Americans see first?  ‘Jeopardy!’ or ‘Wheel’?

There are 210 different media markets in the United States.  From the research I’ve gathered from the ‘Jeopardy!’ and ‘Wheel’ websites, 206 of the 210 media markets have a local TV affiliate which airs the shows.  Now I will admit that the four missing markets may indeed get these shows.  Perhaps the listings on the show websites were not complete, or perhaps they receive these broadcasts from neighboring markets.  I’m not quite sure.

129 of the markets show ‘Jeopardy!’ first, and 77 of the markets show ‘Wheel’ first.  There’s also information available about the approximate number of televisions in a given media market, and to that end 74 million televisions get ‘Jeopardy!’ first, and 40 million get ‘Wheel’ first.

jeopardywheel_firstmarket_graphgraph

However, just seeing the numbers isn’t the full picture.  What does this information look like on a map?  Well, here you go.

jeopardywheel_firstmarket_geo_graphgraph

It’s interesting to take a geographic look at markets which show ‘Wheel’ first.  There’s a concentration on the east coast, and pockets across the nation.

For the majority of the nation, when you watch these shows, they come one right after the other.  That’s not the case across the entire nation, as this next set of graphs will show.

jeopardywheel_timeairedbar_graphgraph

‘Jeopardy!’ tends to get a much earlier start time overall.  A number of markets will choose to show ‘Jeopardy!’ early on in the day, especially those markets in the mid-west which tend to show ‘Jeopardy!’ before the local news, and ‘Wheel’ a few hours later right before Prime Time.  If you want to be the first in the nation to see the show, I recommend moving to the Montgomery-Selma, Alabama market.  They show new episodes at 9:30am local time.  The last market in the nation to air new episodes is the Lafayette, Louisiana market, which starts the show at 12:36 AM.  KATC-3 airs ABC Prime Time shows, Local News, Jimmy Kimmel, Nightline, Inside Edition, and then finally good ol’ ‘Jeopardy!’.

Wheel of Fortune is a much different story.  There are four time slots: 6:00, 6:30, 7:00, and 7:30.  That’s it.  No deviation.  No late nights or early mornings.

Here’s a look of the build over time, with respect to local time. You can see ‘Jeopardy!’ gradually building up through the day, and then in the ‘Power Hours’ between 6:00 and 8:00, ‘Wheel’ is shown for everyone.  And finally, our friends in the Lafayette market get to see ‘Jeopardy!’ in the late late evening.

jeopardywheel_timeaired_graphgraph

And here’s what this looks like on a map.  The lighter the color, the earlier in the day it’s shown.  For ‘Jeopardy!’, you’ll notice that most of the early showings happen in the Central time zone.  Interestingly, most of the largest markets in the US show ‘Jeopardy!’ closer to prime time.  However, Chicago shows it at 2:30pm local time on the station WLS.

jeopardywheel_timeaired_geo_jeop_graphgraph

‘Wheel’, as mentioned before, is much more uniform.  Earlier 6pm to 7pm times in the Central and Mountain time zones, with Eastern and Pacific tending to air in the 7pm to 8pm hour.

jeopardywheel_timeaired_geo_wheel_graphgraph

Now, what happens if we take a look at time as it relates to a single time zone?  A show may air at 7:00pm in the East, but when it’s shown at 7:00pm on the west coast, it’ll be 10:00pm back east.  These graphs show the build over time with time zone shifts applied as they relate to the Eastern time zone.  So, when something is shown at 7:30pm Eastern and 6:30pm Central, they’re actually on at the same time.

jeopardywheel_timeairedtimezonebar_graphgraph

Here’s that build over time, with Montgomery kicking things off at 10:30am and Lafayette shutting it down at 1:36am Eastern Tim the following morning.  ‘Wheel’ is more spread out in this case, with the final showing at 11:30pm Eastern Time in the Honolulu market.

jeopardywheel_timeairedtimezone_graphgraph

Here’s what those time shifts look like on a map, with the gradients scaled to show later times in a darker hue.  First, ‘Jeopardy!’.

jeopardywheel_timeaired_geo_jeop_timezone_graphgraph

Then, ‘Wheel’.

jeopardywheel_timeaired_geo_wheel_timezone_graphgraph

When we look at the difference in times between shows within the same market, the majority of airings have one show directly after the other.  When that is not the case, ‘Jeopardy!’ will often be shown first, then a gap, then ‘Wheel’ later on in the day.  In fact, there is only one market in the US which shows Wheel first and then doesn’t show ‘Jeopardy!’ right after, and that’s our friends in Lafayette who show ‘Wheel’ at 6:30pm and wait until 12:36am to show ‘Jeopardy!’.

jeopardywheel_timedifference_graphgraph

Here’s those time differences shown geographically as well.  Blue hues are ‘Jeopardy!’, and Red hues are ‘Wheel’.  Notice there are only two red hues, since ‘Wheel’ is always followed directly by ‘Jeopardy!’ in those markets, save for Lafayette.

jeopardywheel_timeaired_geo_timedifference_graphgraph

Finally, I wondered which networks aired the shows, as in ABC, CBS, FOX, NBC, MYTV, or Independents.  The results were actually quite surprising and extremely spread out, but skewed in favor for ABC, CBS, and NBC.  Here’s a look at the number of markets plus the number of televisions within those markets for both shows.

jeopardywheel_jeopardyaffiliates_graphgraph

jeopardywheel_wheelaffiliates_graphgraph

You’ll notice that in terms of number of markets, it’s fairly even between ABC, CBS, and NBC.  However, ABC affiliates have coverage in the top four markets (New York, Los Angeles, Chicago, and Philadelphia) and six of the top eight, which skews the number of TVs highly in their favor.

Fun Fact: In 23 of the 206 markets, the two shows are actually shown on DIFFERENT networks.  Most of these cases tend to be in the Central and Mountain time zones.

jeopardywheel_wheelaffiliates_geo_jeop_graphgraph

 

jeopardywheel_wheelaffiliates_wheel_jeop_graphgraph

Overall, I hope you enjoyed.  If you want the tl;dr version:

  • ‘Jeopardy!’ is shown first in more media markets in the US
  • ‘Jeopardy!’ times are more spread throughout the day
  • ‘Wheel of Fortune’ has ‘Power Hours’ from 6pm to 8pm where the entire nation sees the show
  • The number of ABC, CBS, and NBC affiliates is fairly even, although ABC has more of the higher-population markets

Update: May 6, 2014

Not surprisingly, I’ve gotten some of the data points wrong. Reddit user RAS310 asked me:
Just the other day I was thinking about which affiliate airs the shows the most. Do you know which market is the sole one that airs Wheel at 6:30 Eastern? I thought none of them aired the show before 7.

This caused me to look back into some of my original data points. Well, it seems that the question has revealed a problem with the Wheel of Fortune website and with the KML files I used to draw the maps.

The airtime at 6:30 Eastern came back as the ROCHESTER, MN-MASON CITY, IA-AUSTIN, MN. This is wrong for two reasons.

Wheel recently changed how you can look up airtimes. Before it was a clickable map of the US, and it showed you the TV Markets and what time they aired Wheel. They went to an newer version based on ZIP code look up. I looked up a sample ZIP code for Austin, MN (55912). When you plug it into the Wheel website, KXAN-TV, a station in Austin, TX showed up. I didn’t realize I was looking at a Texas station, so I picked up the wrong airtime.

That didn’t explain the time zone shift though, as TX and MN are both Central time. It also looks like there’s a mix-up in the KML file of the TV Markets I obtained that switched the labels for Rochester, MN and Rochester, NY. I did time-zone shifts based on the codes for those, so Rochester, MN is EST for the color shifts, and Rochester, NY is CST for their color shifts.

So, a number of errors on my part in gathering the data.

Happy to hear any additional thoughts in the comments.

My Halloween Candy in Graphs – 2012

Last year I decided the best way to have fun on Halloween was to make graphs.  It was so much fun, I decided to do it again this year.

When the night was over, we had a whole lot more leftover candy than last year.  Did we buy too much candy?  Did not enough Trick-or-Treaters visit this year?  Why didn’t we run out of candy like we did last year?

The basic premise was the same:

  • Buy candy
  • Count the candy before the night begins
  • Count the number of kids that trick-or-treat
  • Count the candy midway through the night
  • Note the time when the last piece of a specific candy is taken
  • Count the candy at the end of the night
  • Make graphs

Last Year’s Stats – 2011

  • 419 Treats, 379 Treats taken by Trick-or-Treaters
  • 189 Trick-or-Treaters
  • 2 Hours of Trick-or-Treating
  • 2.01 Treats taken per Trick-or-Treater

This Year’s Stats – 2012

  • 430 Treats, 307 Treats taken by Trick-or-Treaters
  • 216 Trick-or-Treaters
  • 1:45 Hours of Trick-of-Treating
  • 1.42 Treats taken per Trick-or-Treater

What a difference!  We bought about the same about of Treats as the year prior, yet we had a LOT more leftover candy, even though there were more Trick-or-Treaters.

Because of this, we only ran out of two types of candy: M&M’s Peanut and Skittles, and we ran out of those types in the final 15 minutes.

Here’s a graph showing the starting and ending percentages of the different candies:

Purple marks if it was taken LESS relative to other candies.
Orange marks if it was taken MORE relative to other candies.

Let’s also group the candy types together and see if there’s a trend:


Candy that was in Bar form (for example, Hershey’s and Snickers) was less popular than candy in Bit form (for example, M&Ms and Starburst).

Sugar-based candies (Skittles and Starburst) were more popular than Chocolate and Nut candies.  This is a departure from last year, when we had a bunch of Starburst left over!

In last year’s post I noted that trying to put all candies individually on a line chart would make it messy, and very hard to get any information out of the chart.  This year, I decided to use a Trellis chart to help alleviate that problem.

In this chart, each brand gets its own view.  However, for the candy types where I didn’t have a large starting amount, it’s hard to discern differences.  If we start each brand at 100% and work downwards from there, we can see trends of how quickly (or slowly) a particular type of candy was taken, and since we counted at the mid-point, we can see which types went faster earlier or later in the evening.


Trick-or-Treaters

Here is where this gets REALLY geeky.

Last year I put together a basic line chart highlighting the inverse relationship between number of Trick-or-Treaters and the amount of pieces they took.  As it turns out, my formula for calculating that number was flawed.

Here’s last year’s chart:

What was flawed about it was the way I calculated the number of pieces per Trick-or-Treater.  Last year I took a full count of the candy at specific times:

  • At the start
  • During the middle
  • When a brand of candy ran out
  • At the end

Here’s a screenshot of my Excel sheet from last year.  Any cell with a gray background means an actual observation, and white cells represent a formula to approximate what the best-guess of the remaining amount was.

The problem was with the old formula.  Last year I had assumed that the amount between observation points should have been:

S = Second Observation Point
F = First Observation Point
RR = Number of 15 Minute Intervals Remaining until Second Observation Point
TR = Total Number of 15 Minute Intervals between First and Second Observation Point

S + ((F-S) * RR/TR) = Candy Remaining for a Given Interval

This is pretty similar to a standard depreciation formula as you move from Date A to Date B.

However, that assumes the same amount of Trick-or-Treaters in each 15-minute interval, which was NOT the case.  Given that I knew how many kids visited within each 15-minute interval, I could better refine the formula to approximate the number of pieces remaining within each time block.

S = Second Observation Point
F = First Observation Point
RTT = Number of Trick-or-Treaters remaining until Second Observation Point
TTT = Total Number of Trick-or-Treaters between First and Second Observation Point

S + ((F-S) * RTT/TTT) = Candy Remaining for a Given Interval.

This leads to a much more refined formula.  Here’s last year’s chart again, with the old and new formulas:

So now that we’ve established a new (and hopefully better) formula, we can compare this year to last year using the same methodology:

Interesting things to note:

  • For the most part, the amount of Trick-or-Treaters visiting in each 15 minute block was about the same, with the exception of 7:30.  We had a rush of kids at that point!
  • Because we didn’t run out of very many types of candy this year, it was much more difficult to get more specific numbers behind how many pieces each kid took.  However, it can be generally observed that on average, each kid took less than they took last year, especially towards the end of the night.
  • From this it’s easier to explain why we had so much more leftover candy than last year; each Trick-or-Treater took less on average.

Multi-Packs

This year we picked up seven different types of multi-pack bags.  In every instance, we received more candy than what was promised on the bag, which was a nice plus.


Favorites and Non-Favorites

Last year we saw that sugar-based candies where the least likely to be taken by the Trick-or-Treaters.  What about this year?

This is a relatively boring graph, in that there’s very little movement.  That in itself tells a story, though, in the fact that for the most part, kids were taking candy in roughly equal proportions.  Was this because we broke the three types out into three separate buckets?  Were kids just evenly grabbing from each?

Compared to last year, the increase of sugar candies compared to the average is the most interesting. What was the difference?  Did more kids take a liking to Skittles and Starburst?  Did we do a better job preventing the smaller Starburst packages from falling to the bottom of the bucket?  These are the mysteries of life that allude us all.
Just for kicks, here’s the graph comparing Bar candies to Bit candies:

Again, very little movement!

Intervals for Trick-or-Treating

We had 216 total Trick-or-Treaters.  I was able to track two things with each group:

  • How many kids in a group
  • What time that group arrived

There were 60 total groups of Trick-or-Treaters, with an average size of 3.6.

Here’s the distribution of groups:


This makes for interesting visualizations when you decide the time interval to split it by:





Planning for Next Halloween

  • Do we have too many types of candy?  I think that we do.  Next year, I want to simplify it by having only two or three choices.  This will also allow me to get better counts at more frequent intervals, resulting in more accurate calculations.  It will also create an actual choice for the Trick-or-Treater.  “Do I want Candy A or Candy B?”
  • Right now, with so many choices, most kids don’t think about it.  They take some candy, say thank you, and move onto the next house.  However, if you declare that they have a choice, it makes them think about what they might actually want to take.
  • What types of candy should we offer next year?
  • I wanted to also note the type of costumes Trick-or-Treaters had, and note trends, however with so many kids it was difficult to make effective observations and take notes.  I wonder what I can do next year to make capturing information easier?

Now, who wants to help me eat this leftover candy?

GraphGraph.com Month In Review – 2011-11

Every month we’re going to attempt to do a meta-review of the site, using graphs of course.

Visitors

We had a “soft launch” towards the end of October with a few hits, but we really took off in November, starting with a post about my Halloween Candy experiences this year, which was shared out by Geekadelphia.

Red bars represent days with new posts, and Blue bars are days without new posts.

We thought an interesting stat to look at would be the number of pageviews per visitor.  On days where the diamond is above 1, it represents where on average people clicked on something else within the site beyond just their initial entry page.  The highest day for that was 11/10/2011, when Drew had his post on Scrabble.

All in all, we’re very happy that in our first month we had 737 visits and 1,049 pageviews.  Thank you!

Visits by Post

Posts:

(266) – my halloween candy in graphs
(222) – site index
(161) – most valuable cities in ticket to ride
(85) – what word should i play
(62) – top 10 most popular cities in north america
(50) – most populous city by state
(33) – better than watching the detroit lions
(23) – geotagging visualization of philadelphia
(18) – twitter on 11-11-11
(12) – graphs we love: espn’s stats info twitter
(9) – graphlink: invisible bread
(4) – nfl championships per year
(1) – stanley cup 2011 sparklines
(1) – welcome to graphgraph
(102) – category, tag, & author pages (we kept this separate to keep the focus on individual posts or the site index)

Twitter, Facebook & Google Reader

Right now that chart is pretty barren.  However in future months we’ll see a line chart (with hopefully a lot of growth).

Facebook – 5 Likes -> Click here to like us
Twitter – 6 Followers > Click here to follow us
Google Reader – 7 Subscribers -> Click here to access the RSS feed and add us to your favorite reader

Thanks again to everyone for making this first month a success!

Four Degrees of Separation on Facebook

Facebook has a great post up from their Data team about relationships between people, and more specifically concluding that the majority of Facebook users are only four degrees of separation away from anyone else on the social networking site.

From the article:

We found that six degrees actually overstates the number of links between typical pairs of users: While 99.6% of all pairs of users are connected by paths with 5 degrees (6 hops), 92% are connected by only four degrees (5 hops). And as Facebook has grown over the years, representing an ever larger fraction of the global population, it has become steadily more connected. The average distance in 2008 was 5.28 hops, while now it is 4.74.

Very fascinating findings!  Read the full article for more information and some additional charts that tie everything together.  And while you’re there, how about you go ahead and “Like” us on Facebook?  Awesome!  Thanks.

My Halloween Candy In Graphs

Halloween usually means candy.  Less commonly, Halloween means making geeky graphs on the distribution of candy you give out to Trick-or-Treaters.

This is the first Halloween at my new house, so we didn’t know what to expect.  So, why not graph out everything?

The Stats

  • 419 Treats, 379 Treats taken by Trick-or-Treaters
  • 189 Trick-or-Treaters
  • 2 Hours of Trick-or-Treating
  • 2.01 Treats taken per Trick-or-Treater

Total Candy

Yes, I counted all the candy.  Geekier still, I graphed the starting percentages and ending percentages.

What does it all mean?  Are sugary candies less popular in my neighborhood than straight-up chocolate?

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