#251 Mishigami (4-15)

avg: 416.19  •  sd: 85.65  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
213 Mastodon Loss 8-9 520.52 Aug 3rd Heavyweights 2019
214 Stackcats Loss 9-11 388.05 Aug 3rd Heavyweights 2019
154 Melt Loss 10-13 585.66 Aug 3rd Heavyweights 2019
279 Identity Theft Win 13-8 706.09 Aug 4th Heavyweights 2019
192 Jabba Win 13-9 1126.93 Aug 4th Heavyweights 2019
171 Mousetrap Win 11-7 1266.44 Aug 4th Heavyweights 2019
73 Petey's Pirates Loss 6-13 674.68 Aug 24th Indy Invite Club 2019
263 SlipStream Loss 8-12 -105.61 Aug 24th Indy Invite Club 2019
170 Thunderpants the Magic Dragon Loss 4-13 207.18 Aug 24th Indy Invite Club 2019
132 Liquid Hustle Loss 7-13 466.29 Aug 25th Indy Invite Club 2019
187 Pixel Loss 6-12 161.65 Aug 25th Indy Invite Club 2019
263 SlipStream Loss 9-12 -9.82 Aug 25th Indy Invite Club 2019
73 Petey's Pirates** Loss 4-13 674.68 Ignored Sep 7th East Plains Mixed Club Sectional Championship 2019
249 Second Wind Win 13-10 750.96 Sep 7th East Plains Mixed Club Sectional Championship 2019
228 Midwestern Mediocrity Loss 9-13 107.37 Sep 7th East Plains Mixed Club Sectional Championship 2019
175 Moonshine Loss 8-13 285.67 Sep 7th East Plains Mixed Club Sectional Championship 2019
205 Pi+ Loss 12-15 375.72 Sep 8th East Plains Mixed Club Sectional Championship 2019
182 Rocket LawnChair Loss 13-15 555.52 Sep 8th East Plains Mixed Club Sectional Championship 2019
249 Second Wind Loss 6-13 -177.18 Sep 8th East Plains Mixed 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)