#203 War Machine (5-24)

avg: 439.03  •  sd: 55.17  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
108 H.O.G. Ultimate Loss 11-12 887.99 Jun 15th ATL Classic 2019
120 baNC Loss 7-12 415.71 Jun 15th ATL Classic 2019
52 El Niño** Loss 4-13 756.84 Ignored Jun 15th ATL Classic 2019
67 Ironmen** Loss 3-13 638.16 Ignored Jun 15th ATL Classic 2019
120 baNC Loss 4-13 336.22 Jun 16th ATL Classic 2019
117 Rush Hour ATL Loss 3-13 368.19 Jun 16th ATL Classic 2019
36 Freaks** Loss 3-11 875.4 Ignored Jul 6th Huntsville Huckfest 2019
210 Gentlemen's Club Win 11-4 991.85 Jul 6th Huntsville Huckfest 2019
192 Trent's Team Loss 6-11 -28.06 Jul 6th Huntsville Huckfest 2019
117 Rush Hour ATL Loss 6-11 421.49 Jul 6th Huntsville Huckfest 2019
38 Lost Boys** Loss 1-11 842.99 Ignored Jul 7th Huntsville Huckfest 2019
183 Battleship Loss 8-11 191.24 Jul 7th Huntsville Huckfest 2019
143 Space Cowboys Loss 4-11 227.93 Jul 7th Huntsville Huckfest 2019
86 Bullet** Loss 3-13 506.7 Ignored Jul 20th 2019 Club Terminus
142 Space Coast Ultimate Loss 7-11 372.55 Jul 20th 2019 Club Terminus
152 Predator Loss 6-12 187.31 Jul 20th 2019 Club Terminus
221 Traffic Loss 10-11 137.31 Jul 21st 2019 Club Terminus
117 Rush Hour ATL Loss 7-13 410.65 Jul 21st 2019 Club Terminus
199 Villains Loss 11-12 350.95 Jul 21st 2019 Club Terminus
210 Gentlemen's Club Loss 8-11 26.24 Aug 17th Mudbowl 2019
108 H.O.G. Ultimate Loss 3-13 412.99 Aug 17th Mudbowl 2019
221 Traffic Win 12-9 607.67 Aug 17th Mudbowl 2019
221 Traffic Win 13-9 680.88 Aug 18th Mudbowl 2019
192 Trent's Team Win 13-9 937.2 Aug 18th Mudbowl 2019
152 Predator Loss 10-11 641.62 Aug 18th Mudbowl 2019
36 Freaks** Loss 3-13 875.4 Ignored Sep 7th Gulf Coast Mens Club Sectional Championship 2019
221 Traffic Win 11-3 862.31 Sep 7th Gulf Coast Mens Club Sectional Championship 2019
183 Battleship Loss 9-12 211.48 Sep 7th Gulf Coast Mens Club Sectional Championship 2019
127 Rougaroux Loss 1-13 305.97 Sep 7th Gulf Coast 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)