#221 Surrilic Audovice (3-17)

avg: 272.69  •  sd: 78.88  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
76 Gamble** Loss 1-13 560.52 Ignored Jun 29th Texas 2 Finger Mens and Womens
147 Glycerine Win 11-10 904.12 Jun 29th Texas 2 Finger Mens and Womens
91 Harvey Cats** Loss 3-13 490.81 Ignored Jun 29th Texas 2 Finger Mens and Womens
116 Papa Bear** Loss 4-13 359.79 Ignored Jun 29th Texas 2 Finger Mens and Womens
154 Foxtrot Loss 10-15 308 Jun 30th Texas 2 Finger Mens and Womens
150 Louisiana Second Line Loss 7-13 211.03 Jun 30th Texas 2 Finger Mens and Womens
161 Supercell Loss 11-14 398.18 Jun 30th Texas 2 Finger Mens and Womens
154 Foxtrot Loss 11-17 283.45 Jul 13th Riverside Classic 2019
27 H.I.P** Loss 2-17 948.55 Ignored Jul 13th Riverside Classic 2019
218 Messengers-B Win 15-12 599.45 Jul 14th Riverside Classic 2019
76 Gamble Loss 9-15 645.04 Jul 14th Riverside Classic 2019
215 Quaze Win 14-12 565.82 Jul 14th Riverside Classic 2019
208 Alamode Loss 9-14 -68.17 Jul 14th Riverside Classic 2019
27 H.I.P** Loss 2-13 948.55 Ignored Jul 27th PBJ 2019
150 Louisiana Second Line Loss 6-13 168.57 Jul 27th PBJ 2019
215 Quaze Loss 11-13 116.02 Jul 27th PBJ 2019
219 Texas Heatwave Loss 4-13 -326.59 Jul 27th PBJ 2019
69 Riverside** Loss 5-13 606.25 Ignored Jul 28th PBJ 2019
208 Alamode Loss 12-14 184.74 Jul 28th PBJ 2019
219 Texas Heatwave Loss 10-12 35.29 Jul 28th PBJ 2019
**Blowout Eligible

FAQ

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
  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
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)