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Wrestleknownothing

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Everything posted by Wrestleknownothing

  1. yeah, unseeded is always tough. Is that the 9ish seed, the 13ish seed, the 17ish seed? In his case they seeded to 12, but regardless going from unseeded to first is pretty damn impressive. Maybe the HOF needs to add a wing.
  2. Everyone seems to think it is wrestle know nothing, but I'm Irish on my father in law's side so it is actually wrestle known o'thing.
  3. Dark Horse HOF Wyatt "Chip and a Chair" Sheets - After an injury riddled season, comes in as the 33 seed at 157 in 2021 only after another wrestler had to withdraw (he was essentially the 34 seed), and takes 8th for a 25 slot improvement. Yahya "Yeah Yeah" Thomas - Also in 2021, Thomas entered 149 as the 25 seed after a shaky B1G tournie, only to catch a heater and finish 3rd for a 22 slot jump. Killian "So Nice I Will Do It Twice" Cardinale - There was something in the water in 2021. Cardinale entered that tournament as the 17 seed, but rallied to finish 7th at 125. Then, 2023 Cardinale told 2021 Cardinale to hold his beer. After having to take a med forfeit and an inj default at the ACC tournie, he snuck in as the 28 seed, before making the podium in 8th place.
  4. But how is he a dark horse? How is any undefeated wrestler with 6 top 10 wins a dark horse?
  5. Kinda depends on if the field contains a PSU wrestler. As a #1 seed, PSU wins 82% of the time. As a #2 seed it is 47%. And as a #3 seed it is 38%. So don't take the field if PSU is #1. Definitely take the field if they are #2 or #3. Everything else is a close to a push, so it depends on the odds you can get.
  6. Amen. In the four years since we expanded to seeding all 33 there have been three #26 seeds to AA (a huge outlier). 2019 - Dakota Greer OK ST 184 finished 7th. 2021 - Jackson Turley RUT 174 finished 8th. 2021 - Jake Woodley OK 197 finished 6th. So the question is which #26 seed will AA this year? Or if you like, seeds 16 - 33 have less than a 10% chance of AAing. So, who will AA from the bottom half of seeds?
  7. I created the raw data matrix first. By definition every row/seed sums to 100%. And there are patterns for each seed, but they can be a bit noisy. We are also not dealing with a huge amount of data here. Seeding to 33 is only 4 years old, for example, so seeds below 16 are sparse. So I used a polynomial regression with five degrees to fit a line through the data. I chose polynomial because it is a classification problem and there are essentially five classes to fit wrestlers into (AA, 9-12, 13-16, 17-24, 25-33). Using the resulting equation leads to a horizontal sum that is never 100%, so I had to refactor to force a 100% sum constraint. Then I did the polynomial fit vertically as there are distinct patterns there too, with some noise. There is no sum constraint vertically, but it messes with the horizontal sums. So, then I re-ran the horizontal fit with the 100% sum constraint. You can rinse and repeat the process as many times as you like to make small tweaks and smooth out the resulting lines, but there is not a lot of incremental improvement. At the end of the day, you are taking a data set that is limited (but not too limited) and using it to estimate what will happen as more years pass and more data is collected to fill in the gaps. That is all built on an assumption that the seeds are generally accurate (i.e. have a stable average result) with a mathematical distribution around the average. I think this is true, but homers tend to think their team is special and different and this is surely the year.
  8. If the guy who won the third place match lost in the semi to the guy who lost in the final, then we have true 2 and 3. If the guy who won the third place match lost to the eventually champ in the semi, then we do not have true 2 or 3. So, call it true second or true third. Its the same thing. We always have true 4 and 1.
  9. Same thing. Whenever there is a true third there is also a true second, and whenever there is not a true third there is also not a true second. They are always paired.
  10. I put together a table of the probability of placement by seed using placement data from 2010 - 2023. After getting the raw numbers, I fitted in two directions to come up with these approximations. It could probably benefit from a couple more iterations of fits, but I grew tired, and this is good enough for government work. How to read: The left column is the seed The second through twelfth columns are the exact placement. 9 represents the blood round losers, 9-12. 13 represents the prior round losers, 13-16, etc. The percentages represent the probability of the exact placement. For example, a #2 seed has a 23% of winning and a 32.3% of finishing second. The probabilities are additive left to right, but not top to bottom. For example, if you want to know the probability that a #1 seed makes the final, simply sum the second and third columns (52.3% + 22.5% + 74.8%). None of this is all that precise even though I fake precision by giving you tenths. But, again, good enough for government work.
  11. Who says he could remember the other two? He had to read their names off a card.
  12. Or a senile dad. https://www.google.com/amp/s/www.businessinsider.com/trump-forgot-name-kid-south-carolina-victory-speech-2024-2%3famp
  13. I think Nick Lee gets Austin Gomez and Destribats gets Tobier (if I am reading things correctly).
  14. Having recently heard about PSU's illnesses surely you do not think Missouri is ducking after what Smith said.
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