#149 Rawhide (12-14)

avg: 927.02  •  sd: 50.25  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
168 San Antonio Warhawks Win 11-8 1196.84 Jun 24th Texas 2 Finger 2023
153 Sprawl Loss 9-10 773.87 Jun 24th Texas 2 Finger 2023
69 Clutch Loss 2-13 810.39 Jun 24th Texas 2 Finger 2023
48 Alamode** Loss 3-13 956.94 Ignored Jun 25th Texas 2 Finger 2023
68 Brawl Loss 1-13 818.55 Jun 25th Texas 2 Finger 2023
122 Lil Heroes Loss 6-13 488.43 Jun 25th Texas 2 Finger 2023
165 Firefly TX Win 11-10 983.17 Jul 22nd Riverside Classic 2023
122 Lil Heroes Loss 8-12 647.27 Jul 22nd Riverside Classic 2023
153 Sprawl Win 11-7 1365.76 Jul 22nd Riverside Classic 2023
125 Cowtown Cannons Loss 5-12 456.61 Jul 22nd Riverside Classic 2023
168 San Antonio Warhawks Win 13-11 1060.07 Jul 23rd Riverside Classic 2023
197 Texas United Win 14-12 918 Jul 23rd Riverside Classic 2023
159 Choice City Hops Loss 14-15 759.65 Jul 23rd Riverside Classic 2023
116 Atlanta Arson Loss 6-10 647.73 Aug 26th Ragna Rock 2023
172 Memphis Pharaohs Win 13-6 1425.27 Aug 26th Ragna Rock 2023
208 Grit Win 13-11 819.33 Aug 26th Ragna Rock 2023
218 Supercell Win 13-7 1090.36 Aug 27th Ragna Rock 2023
89 Second Nature Loss 7-13 742.07 Aug 27th Ragna Rock 2023
103 Scythe Loss 10-13 874.94 Aug 27th Ragna Rock 2023
237 Arkansas Win 13-6 968.8 Aug 27th Ragna Rock 2023
77 BARNSTORM Loss 5-13 774.75 Sep 9th 2023 Mens Ozarks Sectional Championship
218 Supercell Win 13-5 1132.83 Sep 9th 2023 Mens Ozarks Sectional Championship
255 Dreadnought: Battleship [B]** Win 13-3 524.74 Ignored Sep 9th 2023 Mens Ozarks Sectional Championship
77 BARNSTORM Loss 8-10 1112.09 Sep 10th 2023 Mens Ozarks Sectional Championship
105 Dreadnought Win 10-9 1312.83 Sep 10th 2023 Mens Ozarks Sectional Championship
105 Dreadnought Loss 10-15 734.23 Sep 10th 2023 Mens Ozarks 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)