#163 Crossfire (9-12)

avg: 859.38  •  sd: 41.59  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
158 Alibi Loss 9-10 762.94 Jul 15th Boston Invite 2023
137 Expendables Loss 8-11 632.79 Jul 15th Boston Invite 2023
72 Colt Loss 6-13 789.28 Jul 15th Boston Invite 2023
62 Shade Loss 4-13 849.64 Jul 15th Boston Invite 2023
158 Alibi Win 11-10 1012.94 Aug 19th Ow My Knee 2023
215 EZ Win 13-3 1148.4 Aug 19th Ow My Knee 2023
226 Buffalo Frostbite Win 13-6 1086.54 Aug 19th Ow My Knee 2023
158 Alibi Loss 8-13 391.78 Aug 20th Ow My Knee 2023
215 EZ Win 12-11 673.4 Aug 20th Ow My Knee 2023
238 Mohawk Valley Wild Win 13-8 823.52 Aug 20th Ow My Knee 2023
235 Adelphos Win 13-3 974.59 Aug 26th The Incident 2023
189 Dirty Laundry Win 10-8 1010.83 Aug 26th The Incident 2023
115 Bomb Squad Loss 8-13 648.18 Aug 26th The Incident 2023
252 Deepfake** Win 13-3 649.62 Ignored Aug 26th The Incident 2023
115 Bomb Squad Loss 12-13 1019.34 Aug 27th The Incident 2023
70 OAT Loss 8-15 834.65 Aug 27th The Incident 2023
32 Scoop** Loss 2-15 1075.57 Ignored Sep 9th 2023 Mens East New England Sectional Championship
133 BAG Loss 11-14 720.96 Sep 9th 2023 Mens East New England Sectional Championship
181 Jerk Factory Win 15-11 1169.61 Sep 9th 2023 Mens East New England Sectional Championship
133 BAG Loss 10-11 909.3 Sep 10th 2023 Mens East New England Sectional Championship
82 Lantern Loss 5-15 749.71 Sep 10th 2023 Mens East New England Sectional 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)