#225 Forge (4-15)

avg: 488.24  •  sd: 63.84  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
98 Riverside** Loss 3-13 678.58 Ignored Jul 22nd Riverside Classic 2023
229 Riverside Messengers-B Win 7-2 1051.74 Jul 22nd Riverside Classic 2023
168 San Antonio Warhawks Loss 6-8 530.74 Jul 22nd Riverside Classic 2023
77 BARNSTORM** Loss 5-13 774.75 Ignored Jul 22nd Riverside Classic 2023
229 Riverside Messengers-B Win 15-5 1051.74 Jul 23rd Riverside Classic 2023
159 Choice City Hops Loss 11-15 503.48 Jul 23rd Riverside Classic 2023
153 Sprawl Loss 6-8 598.38 Jul 23rd Riverside Classic 2023
254 Adventure Time Win 13-1 566.76 Aug 5th Dog Days of Summer 23
165 Firefly TX Loss 6-8 557.68 Aug 5th Dog Days of Summer 23
69 Clutch** Loss 3-13 810.39 Ignored Aug 5th Dog Days of Summer 23
102 Harvey Cats** Loss 5-13 607.19 Ignored Aug 6th Dog Days of Summer 23
50 H.I.P** Loss 4-13 953.51 Ignored Aug 6th Dog Days of Summer 23
203 Shrimp Discs Loss 13-15 437.85 Aug 6th Dog Days of Summer 23
98 Riverside** Loss 6-15 678.58 Ignored Sep 9th 2023 Mens Texas Sectional Championship
229 Riverside Messengers-B Loss 10-11 326.74 Sep 9th 2023 Mens Texas Sectional Championship
97 Texas Duffy** Loss 6-15 682.15 Ignored Sep 9th 2023 Mens Texas Sectional Championship
229 Riverside Messengers-B Loss 10-14 53.04 Sep 10th 2023 Mens Texas Sectional Championship
168 San Antonio Warhawks Loss 6-15 231.23 Sep 10th 2023 Mens Texas Sectional Championship
251 Throwing Shade Win 15-10 504.5 Sep 10th 2023 Mens Texas 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)