#94 Magma Bears (13-11)

avg: 1291.23  •  sd: 69.15  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
109 Ascension Win 12-9 1514.14 Jul 15th Boston Invite 2023
133 BAG Win 13-6 1634.3 Jul 15th Boston Invite 2023
154 Odyssey Win 12-4 1494.28 Jul 15th Boston Invite 2023
169 MBTA Win 13-6 1430.61 Jul 15th Boston Invite 2023
180 SUPA FC Win 13-8 1285.55 Aug 5th Philly Open 2023
157 Winc City Fog of War Win 13-7 1446.53 Aug 5th Philly Open 2023
252 Deepfake** Win 13-1 649.62 Ignored Aug 5th Philly Open 2023
133 BAG Loss 7-10 644.64 Aug 6th Philly Open 2023
115 Bomb Squad Loss 9-13 725.77 Aug 6th Philly Open 2023
72 Colt Loss 6-13 789.28 Aug 6th Philly Open 2023
83 Red Wolves Loss 10-12 1109.86 Aug 26th The Incident 2023
24 Blueprint Loss 10-11 1639.52 Aug 26th The Incident 2023
91 Helots Win 11-5 1894.88 Aug 26th The Incident 2023
52 Oakgrove Boys Loss 8-12 1086.53 Aug 26th The Incident 2023
24 Blueprint Loss 7-14 1181.63 Sep 9th 2023 Mens Metro New York Sectional Championship
252 Deepfake** Win 15-1 649.62 Ignored Sep 9th 2023 Mens Metro New York Sectional Championship
202 Spring Break '93** Win 15-5 1260.13 Ignored Sep 9th 2023 Mens Metro New York Sectional Championship
72 Colt Loss 12-15 1088.79 Sep 10th 2023 Mens Metro New York Sectional Championship
202 Spring Break '93** Win 15-4 1260.13 Ignored Sep 10th 2023 Mens Metro New York Sectional Championship
158 Alibi Win 15-5 1487.94 Sep 23rd 2023 Northeast Mens Regional Championship
71 Big Wrench Win 12-11 1520.01 Sep 23rd 2023 Northeast Mens Regional Championship
2 PoNY** Loss 2-13 1733.94 Ignored Sep 23rd 2023 Northeast Mens Regional Championship
26 Sprout Loss 8-15 1180.96 Sep 23rd 2023 Northeast Mens Regional Championship
23 Mephisto Loss 9-13 1364.4 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)