#51 TireBizFriz (11-13)

avg: 1552.5  •  sd: 75.59  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
189 Dirty Laundry Win 15-7 1348.17 Jul 8th AntlerLock
226 Buffalo Frostbite** Win 15-2 1086.54 Ignored Jul 8th AntlerLock
79 Red Tide Loss 8-12 930.77 Jul 8th AntlerLock
9 Doublewide Loss 10-13 1778.67 Jul 15th TCT Pro Elite Challenge East 2023
12 Raleigh-Durham United Loss 10-13 1678.48 Jul 15th TCT Pro Elite Challenge East 2023
34 Trident I Loss 7-12 1141.3 Jul 15th TCT Pro Elite Challenge East 2023
19 Sub Zero Loss 9-14 1391.71 Jul 16th TCT Pro Elite Challenge East 2023
32 Scoop Loss 6-8 1375.08 Aug 5th Vacationland
231 Madhouse** Win 15-2 1023.21 Ignored Aug 5th Vacationland
62 Shade Win 15-10 1903.24 Aug 5th Vacationland
32 Scoop Loss 12-15 1375.08 Aug 6th Vacationland
71 Big Wrench Win 12-10 1633.13 Aug 6th Vacationland
92 Club M - Manic Win 13-11 1520.26 Aug 6th Vacationland
109 Ascension Win 13-5 1768.78 Sep 9th 2023 Mens West New England Sectional Championship
23 Mephisto Loss 9-13 1364.4 Sep 9th 2023 Mens West New England Sectional Championship
- ClubM - MESA** Win 13-5 600 Ignored Sep 9th 2023 Mens West New England Sectional Championship
26 Sprout Win 13-12 1870.77 Sep 10th 2023 Mens West New England Sectional Championship
23 Mephisto Loss 11-12 1657.97 Sep 10th 2023 Mens West New England Sectional Championship
71 Big Wrench Win 13-8 1891.17 Sep 23rd 2023 Northeast Mens Regional Championship
71 Big Wrench Win 15-7 1995.01 Sep 23rd 2023 Northeast Mens Regional Championship
24 Blueprint Loss 11-15 1383.35 Sep 23rd 2023 Northeast Mens Regional Championship
2 PoNY Loss 6-13 1733.94 Sep 23rd 2023 Northeast Mens Regional Championship
23 Mephisto Loss 11-13 1554.13 Sep 24th 2023 Northeast Mens Regional Championship
62 Shade Loss 7-13 892.11 Sep 24th 2023 Northeast 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)