#114 Bloom (13-12)

avg: 1149.31  •  sd: 45.38  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
210 Oakay Win 13-7 1140.71 Jul 22nd Filling the Void 2023
121 John Doe Win 12-10 1327.47 Jul 22nd Filling the Void 2023
93 Charleston Heat Stroke Loss 10-11 1166.36 Jul 22nd Filling the Void 2023
40 Cash Crop 2 Loss 8-13 1124.21 Jul 22nd Filling the Void 2023
138 Queen City Kings Win 13-7 1552.68 Jul 23rd Filling the Void 2023
213 Stag Win 15-3 1158.46 Jul 23rd Filling the Void 2023
85 Space Cowboys Win 10-10 1312.3 Jul 23rd Filling the Void 2023
213 Stag Win 15-4 1158.46 Aug 5th Swan Boat 2023
132 Vicious Cycle Win 13-9 1456.28 Aug 5th Swan Boat 2023
42 UpRoar Loss 6-13 1005.32 Aug 5th Swan Boat 2023
217 Psychedelic Win 11-6 1079.66 Aug 5th Swan Boat 2023
120 El Niño Loss 8-9 966.7 Aug 6th Swan Boat 2023
118 Raptor Loss 7-11 644.7 Aug 6th Swan Boat 2023
120 El Niño Win 10-9 1216.7 Sep 9th 2023 Mens Florida Sectional Championship
247 Bloomin' Reptars Win 13-6 755.4 Sep 9th 2023 Mens Florida Sectional Championship
213 Stag Win 13-5 1158.46 Sep 9th 2023 Mens Florida Sectional Championship
42 UpRoar Loss 9-13 1186.75 Sep 9th 2023 Mens Florida Sectional Championship
42 UpRoar Loss 9-12 1259.95 Sep 10th 2023 Mens Florida Sectional Championship
118 Raptor Win 11-7 1578.48 Sep 10th 2023 Mens Florida Sectional Championship
42 UpRoar Loss 4-13 1005.32 Sep 23rd 2023 Southeast Mens Regional Championship
28 Tanasi Loss 7-13 1174.92 Sep 23rd 2023 Southeast Mens Regional Championship
85 Space Cowboys Loss 6-13 712.3 Sep 23rd 2023 Southeast Mens Regional Championship
89 Second Nature Loss 11-15 918.43 Sep 23rd 2023 Southeast Mens Regional Championship
61 Lost Boys Loss 14-15 1340.93 Sep 24th 2023 Southeast Mens Regional Championship
138 Queen City Kings Win 15-10 1448.75 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)