#213 Quaze (2-10)

avg: 381.04  •  sd: 73.98  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
138 Glycerine Loss 12-15 553.95 Jul 13th Riverside Classic 2019
106 Papa Bear Loss 7-15 419.96 Jul 13th Riverside Classic 2019
79 Riverside Loss 8-15 586.36 Jul 13th Riverside Classic 2019
217 Surrilic Audovice Loss 12-14 91.2 Jul 14th Riverside Classic 2019
179 E.V.I.L. Loss 9-15 87.84 Jul 14th Riverside Classic 2019
147 Louisiana Second Line Loss 7-11 329.25 Jul 27th PBJ 2019
26 H.I.P** Loss 2-13 983.07 Ignored Jul 27th PBJ 2019
217 Surrilic Audovice Win 13-11 541 Jul 27th PBJ 2019
79 Riverside** Loss 2-13 551.17 Ignored Jul 27th PBJ 2019
218 Texas Heatwave Win 13-11 539.23 Jul 28th PBJ 2019
106 Papa Bear Loss 7-15 419.96 Jul 28th PBJ 2019
179 E.V.I.L. Loss 11-15 222.16 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)