#151 Riverside Messengers-B (5-15)

avg: 335.77  •  sd: 57.59  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
115 Rougaroux Loss 7-13 196.89 Aug 4th PBJ 2018
147 DUCS Win 13-10 739.85 Aug 4th PBJ 2018
69 Gamble** Loss 4-13 433.45 Ignored Aug 4th PBJ 2018
150 The Bayou Boys Win 13-9 759.81 Aug 4th PBJ 2018
82 Riverside** Loss 5-13 344.4 Ignored Aug 5th PBJ 2018
84 Gaucho Loss 8-15 374.48 Aug 5th PBJ 2018
141 DUPlex Win 15-13 731.2 Aug 5th PBJ 2018
150 The Bayou Boys Loss 10-13 13.11 Aug 18th Riverside Classic 2018
79 Papa Bear Loss 7-11 492.38 Aug 18th Riverside Classic 2018
69 Gamble** Loss 2-13 433.45 Ignored Aug 18th Riverside Classic 2018
166 Surrillic Audovice Win 13-7 640.29 Aug 18th Riverside Classic 2018
84 Gaucho** Loss 1-15 339.29 Ignored Aug 19th Riverside Classic 2018
147 DUCS Win 12-11 536.7 Aug 19th Riverside Classic 2018
141 DUPlex Loss 8-11 151.41 Sep 8th Texas Mens Sectional Championship 2018
84 Gaucho Loss 5-11 339.29 Sep 8th Texas Mens Sectional Championship 2018
- Rock Steady Loss 5-11 -246.06 Sep 8th Texas Mens Sectional Championship 2018
150 The Bayou Boys Loss 9-11 92.04 Sep 8th Texas Mens Sectional Championship 2018
79 Papa Bear Loss 8-11 593.66 Sep 9th Texas Mens Sectional Championship 2018
35 Nitro** Loss 1-11 702.25 Ignored Sep 9th Texas Mens Sectional Championship 2018
147 DUCS Loss 6-11 -134.99 Sep 9th Texas Mens Sectional Championship 2018
**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)