#28 Tanasi (13-10)

avg: 1732.45  •  sd: 52.41  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
83 Red Wolves Win 12-7 1868.49 Jul 15th TCT Pro Elite Challenge East 2023
5 Chicago Machine Loss 3-15 1589.71 Jul 15th TCT Pro Elite Challenge East 2023
39 Pittsburgh Temper Win 14-7 2207.6 Jul 15th TCT Pro Elite Challenge East 2023
7 DiG Loss 10-13 1839.37 Jul 16th TCT Pro Elite Challenge East 2023
27 Omen Win 10-9 1864.79 Jul 16th TCT Pro Elite Challenge East 2023
19 Sub Zero Loss 10-15 1411.98 Aug 19th TCT Elite Select Challenge 2023
50 H.I.P Win 14-11 1866.85 Aug 19th TCT Elite Select Challenge 2023
14 Sockeye Loss 10-14 1601.17 Aug 19th TCT Elite Select Challenge 2023
39 Pittsburgh Temper Loss 11-12 1499.71 Aug 20th TCT Elite Select Challenge 2023
31 Garden State Ultimate Loss 11-14 1362.78 Aug 20th TCT Elite Select Challenge 2023
34 Trident I Loss 9-12 1316.44 Aug 20th TCT Elite Select Challenge 2023
116 Atlanta Arson Win 13-4 1743.89 Sep 9th 2023 Mens East Coast Sectional Championship
119 Tennessee Folklore** Win 13-5 1700.59 Ignored Sep 9th 2023 Mens East Coast Sectional Championship
85 Space Cowboys Win 13-9 1730.87 Sep 9th 2023 Mens East Coast Sectional Championship
56 Little Red Wagon Win 13-8 1994.07 Sep 9th 2023 Mens East Coast Sectional Championship
30 Delirium Win 14-10 2077.38 Sep 10th 2023 Mens East Coast Sectional Championship
114 Bloom Win 13-7 1706.84 Sep 23rd 2023 Southeast Mens Regional Championship
4 Chain Lightning Loss 7-15 1611.89 Sep 23rd 2023 Southeast Mens Regional Championship
61 Lost Boys Loss 8-9 1340.93 Sep 23rd 2023 Southeast Mens Regional Championship
64 Hooch Win 11-8 1814.18 Sep 23rd 2023 Southeast Mens Regional Championship
42 UpRoar Win 15-5 2205.32 Sep 24th 2023 Southeast Mens Regional Championship
12 Raleigh-Durham United Loss 10-15 1553.02 Sep 24th 2023 Southeast Mens Regional Championship
30 Delirium Win 11-9 1927.88 Sep 24th 2023 Southeast Mens Regional Championship
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
The uncertainty of the mean is equal to the standard deviation of the set of game ratings, divided by the square root of the number of games. We treated a team’s ranking as a normally distributed random variable, with the USAU ranking as the mean and the uncertainty of the ranking as the standard deviation
What's the algorithm again?
  1. Calculate uncertainy for USAU ranking averge
  2. Model ranking as a normal distribution around USAU averge with standard deviation equal to uncertainty
  3. Simulate seasons by drawing a rank for each team from their distribution. Note the teams in the top 16 (club) or top 20 (college)
  4. Sum the fractions for each region for how often each of it's teams appeared in the top 16 (club) or top 20 (college)
  5. Subtract one from each fraction for "autobids"
  6. Award remainings bids to the regions with the highest remaining fraction, subtracting one from the fraction each time a bid is awarded
Is there a longer explanation of all this?
There is an article on Ulitworld written by Scott Dunham and I that gives a little more context (though it probably was the thing that linked you here)