For those that don’t know, I used to be a major sports fan most of my life. I gave my time, money, energy, and emotion away to the NFL, NHL, NBA, and NCAA for decades, like everybody else in the blue collar city I was raised in. It was only over the last year or two did I suspect something wasn’t quite right. Since then, it’s been a bumpy ride from “the referees are fixing these games” to “everybody, including the players and coaches, are in on it”.

Nowadays when I turn on a game I laugh a little at how obvious the scripting is. When it’s a hometown team, it’s better if I don’t watch at all since when I see my hometown teams purposely throwing a game and my friends and family back home are in a bad mood, I get very angry at the deceit. For more on my work tying sports with Intelligence, please see the comments in these two posts, where I was posting as MH.

While watching the Buffalo Bills purposely allow touchdowns to the New England Patriots this past Sunday, I got angry and started looking for Youtube videos discussing some more about scripted NFL games. What I found was disinfo, which I will get into. But first, watch this 3 minute video which is the absolute best evidence I have ever seen of scripted sports.

Continue reading “The Obvious Nature of Scripted Sports” →

Note to Reader: This blog post has been put “under review” as we have had growing pains in developing the technology we use to identify twins, replicas and zombies. The eyes behind the technology are getting better, so as you read this piece note that if you are troubled by its conclusions that we will be looking at it in more depth and with better eyes. For the time being, it is speculation.

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This one might seem a little random, but it is very important because it exposes another department of Intelligence you might not think of: The Food Network.

I initially suspected Cass Elliott of the Mamas and Papas became Paula Deen on a really weird hunch. I was watching an interview of comedian Bill Burr talking about Paula Deen as a $100 million dollar whale shortly after her racism scandal. I found it curious she was worth so much.

After I sent the Elliott/Deen matchup over to Mark, he looked it over and concluded that they were not the same person. However, he realized that both Elliott and Deen were twins, something we are finding is all too common nowadays.

Continue reading “The Paula Deen Twins” →

Mark has famously stated that he believes the odds of aligning 7 facial features on two random people is approximately 1 in 10 million. His math is very simple. He estimates that there is a 1 in 10 chance that one facial feature will align, thus multiplying 1/10 to the 7th power, you get 1 in 10 million.

I decided to put those numbers to the test. I went ahead and downloaded the 2d facial recognition set called “Aberdeen” that is found here.

It contains photos of 90 unique college students from Aberdeen sometime in the late 80’s or early 90’s. What I did was take one photo of a gentleman named Adrian (seen below) and match him up to each of the 58 other white males in the data set.

In planning this experiment I made 7 checkpoints on Adrian’s face where I would compare alignment by aligning the pupils and resizing the eyeballs to match in order to incorporate head size. If alignment was exact or very close, I marked that checkpoint with a “Yes” on a spreadsheet (which I will share upon request). The checkpoints were the eyebrows, the tip of the nose, the subnasale, the crista philtri (top of the lips), the labiale interious (bottom of the lip), the gnathion (chin) and the bottom ear lobe.

Continue reading “The Real Math of Facial Alignment” →

We’ve been hearing a whole lot of this lately, both from real people and from disinfo. I admit, I have been a little curious myself as to what effect angles can affect ear alignment. I decided to try a little experiment using photos of Mark, and photos I found on the internet to help us measure as close as possible what percentages we should expect.

The concept is this: I will have a picture of the subject at a level position. I will measure the pixel distance from the person’s trichion (hairline) to the bottom of the chin. Then I will align that image with the same person angling their head up or down at a certain angle. I will measure the pixel distance of the top and bottom of the ears and divide that by their head size to get a percentage. That is the ballpark percentage we should then expect the same person’s ears to be misaligned at that angle degree. If they are misaligned to a much larger degree, it is likely not the same person (not considering all other variables, which I will get into).

Continue reading “Hey, Their Heads Are At Different Angles!” →