#67 UpRoar (9-9)

avg: 1212.32  •  sd: 62.76  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
29 Clutch Loss 4-15 938.44 Jul 13th TCT Select Flight Invite West 2019
41 MKE Loss 13-14 1285.84 Jul 13th TCT Select Flight Invite West 2019
51 Turbine Loss 8-15 767.88 Jul 13th TCT Select Flight Invite West 2019
176 Daybreak Win 13-10 964.73 Jul 14th TCT Select Flight Invite West 2019
72 Sawtooth Win 12-10 1426.79 Jul 14th TCT Select Flight Invite West 2019
73 ISO Atmo Loss 8-13 691.54 Jul 14th TCT Select Flight Invite West 2019
23 CLE Smokestack Loss 10-13 1307.52 Jul 27th TCT Select Flight Invite East 2019
25 General Strike Loss 7-13 1007.36 Jul 27th TCT Select Flight Invite East 2019
59 Big Wrench Win 13-8 1767.61 Jul 27th TCT Select Flight Invite East 2019
36 Nitro Loss 8-11 1097.8 Jul 27th TCT Select Flight Invite East 2019
43 CITYWIDE Special Loss 11-13 1173.11 Jul 28th TCT Select Flight Invite East 2019
34 Mad Men Loss 8-13 978.25 Jul 28th TCT Select Flight Invite East 2019
142 ScooberDivers Win 13-9 1245.95 Sep 7th Florida Mens Club Sectional Championship 2019
203 Scootlyfe** Win 13-5 1035.85 Ignored Sep 7th Florida Mens Club Sectional Championship 2019
124 Swamp Horse Win 13-9 1333.94 Sep 7th Florida Mens Club Sectional Championship 2019
133 Vicious Cycle Win 13-5 1481.97 Sep 7th Florida Mens Club Sectional Championship 2019
49 El Niño Win 15-14 1472.98 Sep 8th Florida Mens Club Sectional Championship 2019
113 Omen Win 15-12 1267.32 Sep 8th Florida Mens Club Sectional Championship 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)