#81 Bullet (14-12)

avg: 1126.43  •  sd: 61.36  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
33 Freaks Loss 5-12 899.59 Jun 15th ATL Classic 2019
120 Rush Hour ATL Win 10-9 1072.33 Jun 15th ATL Classic 2019
126 Rougaroux Win 13-9 1323.59 Jun 15th ATL Classic 2019
86 ATLiens Loss 8-12 673.78 Jun 15th ATL Classic 2019
33 Freaks Loss 10-11 1374.59 Jun 16th ATL Classic 2019
66 Ironmen Win 10-8 1490.83 Jun 16th ATL Classic 2019
86 ATLiens Win 10-7 1504.6 Jun 16th ATL Classic 2019
140 Space Coast Ultimate Win 13-5 1433.66 Jul 20th 2019 Club Terminus
151 Predator Win 13-2 1366.7 Jul 20th 2019 Club Terminus
202 War Machine** Win 13-3 1036.17 Ignored Jul 20th 2019 Club Terminus
104 Charleston Heat Stroke Win 12-8 1466.37 Jul 21st 2019 Club Terminus
139 Space Cowboys Win 13-6 1434.13 Jul 21st 2019 Club Terminus
115 baNC Win 13-8 1458.55 Jul 21st 2019 Club Terminus
29 Clutch Loss 5-13 938.44 Aug 17th Mudbowl 2019
66 Ironmen Loss 9-12 882.8 Aug 17th Mudbowl 2019
126 Rougaroux Win 13-8 1401.19 Aug 17th Mudbowl 2019
106 H.O.G. Ultimate Loss 7-12 486.61 Aug 18th Mudbowl 2019
124 Swamp Horse Win 13-8 1411.54 Aug 18th Mudbowl 2019
126 Rougaroux Loss 11-13 676.19 Aug 18th Mudbowl 2019
104 Charleston Heat Stroke Loss 10-11 900.22 Sep 7th East Coast Mens Club Sectional Championship 2019
106 H.O.G. Ultimate Loss 10-11 882.12 Sep 7th East Coast Mens Club Sectional Championship 2019
120 Rush Hour ATL Loss 11-12 822.33 Sep 7th East Coast Mens Club Sectional Championship 2019
35 Tanasi Loss 6-13 873.63 Sep 7th East Coast Mens Club Sectional Championship 2019
120 Rush Hour ATL Win 15-6 1547.33 Sep 8th East Coast Mens Club Sectional Championship 2019
37 Lost Boys Loss 8-13 964.93 Sep 8th East Coast Mens Club Sectional Championship 2019
139 Space Cowboys Win 13-5 1434.13 Sep 8th East 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)