#37 Lost Boys (20-6)

avg: 1461.09  •  sd: 75.05  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
33 Freaks Win 11-4 2099.59 Jul 6th Huntsville Huckfest 2019
212 Gentlemen's Club** Win 13-3 984.5 Ignored Jul 6th Huntsville Huckfest 2019
120 Rush Hour ATL Win 11-5 1547.33 Jul 6th Huntsville Huckfest 2019
139 Space Cowboys** Win 11-4 1434.13 Ignored Jul 6th Huntsville Huckfest 2019
192 Trent's Team** Win 11-1 1118.85 Ignored Jul 7th Huntsville Huckfest 2019
202 War Machine** Win 11-1 1036.17 Ignored Jul 7th Huntsville Huckfest 2019
35 Tanasi Win 15-13 1687.81 Jul 7th Huntsville Huckfest 2019
27 H.I.P Win 13-8 2044.71 Aug 10th Chesapeake Open 2019
49 El Niño Win 13-9 1766.55 Aug 10th Chesapeake Open 2019
26 Blueprint Loss 9-12 1204.7 Aug 10th Chesapeake Open 2019
75 Richmond Floodwall Win 13-6 1765.17 Aug 10th Chesapeake Open 2019
43 CITYWIDE Special Loss 8-13 905.79 Aug 11th Chesapeake Open 2019
24 Brickhouse Loss 8-13 1072.04 Aug 11th Chesapeake Open 2019
115 baNC Win 13-8 1458.55 Aug 24th FCS Invite 2019
139 Space Cowboys** Win 13-3 1434.13 Ignored Aug 24th FCS Invite 2019
22 Vault Win 10-6 2162.08 Aug 24th FCS Invite 2019
79 Bash Bros Win 13-11 1378.56 Aug 24th FCS Invite 2019
111 Fathom Win 13-6 1573.92 Aug 25th FCS Invite 2019
51 Turbine Win 15-10 1786.29 Aug 25th FCS Invite 2019
22 Vault Loss 11-15 1284.75 Aug 25th FCS Invite 2019
104 Charleston Heat Stroke Win 13-8 1521.38 Sep 7th East Coast Mens Club Sectional Championship 2019
120 Rush Hour ATL Win 13-8 1443.49 Sep 7th East Coast Mens Club Sectional Championship 2019
35 Tanasi Loss 9-13 1055.06 Sep 7th East Coast Mens Club Sectional Championship 2019
139 Space Cowboys Win 13-8 1330.29 Sep 7th East Coast Mens Club Sectional Championship 2019
106 H.O.G. Ultimate Loss 8-13 510.96 Sep 8th East Coast Mens Club Sectional Championship 2019
81 Bullet Win 13-8 1622.59 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)