#121 John Doe (12-8)

avg: 1089.35  •  sd: 56.11  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
114 Bloom Loss 10-12 911.19 Jul 22nd Filling the Void 2023
40 Cash Crop 2 Loss 7-13 1062.84 Jul 22nd Filling the Void 2023
210 Oakay Win 13-7 1140.71 Jul 22nd Filling the Void 2023
138 Queen City Kings Loss 11-12 870.15 Jul 22nd Filling the Void 2023
93 Charleston Heat Stroke Loss 7-12 770.85 Jul 23rd Filling the Void 2023
248 Power Point Ultimate** Win 15-3 737.63 Ignored Jul 23rd Filling the Void 2023
210 Oakay Win 12-9 928.54 Jul 23rd Filling the Void 2023
200 Rochester Open Club Win 12-7 1202.69 Aug 5th Philly Open 2023
186 Town Hall Stars Win 9-7 1033.63 Aug 5th Philly Open 2023
91 Helots Loss 7-11 827.99 Aug 5th Philly Open 2023
180 SUPA FC Win 13-5 1389.39 Aug 6th Philly Open 2023
157 Winc City Fog of War Win 10-6 1385.16 Aug 6th Philly Open 2023
117 Chimney Loss 7-12 599.06 Aug 6th Philly Open 2023
223 Beef Depot Win 13-8 988.64 Sep 9th 2023 Mens Capital Sectional Championship
183 Bearfax Win 13-7 1340.53 Sep 9th 2023 Mens Capital Sectional Championship
157 Winc City Fog of War Win 10-9 1014 Sep 9th 2023 Mens Capital Sectional Championship
123 Bryce Whitney Fan Club Win 9-6 1479.68 Sep 10th 2023 Mens Capital Sectional Championship
52 Oakgrove Boys Loss 8-10 1265.02 Sep 10th 2023 Mens Capital Sectional Championship
95 Puzzles Loss 8-12 847.95 Sep 10th 2023 Mens Capital Sectional Championship
157 Winc City Fog of War Win 12-7 1409.51 Sep 10th 2023 Mens Capital 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)