#181 Helots (8-9)

avg: 572.86  •  sd: 63.14  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
112 Genny The Boys Loss 4-14 371.58 Jun 22nd Boston Invite 2019
206 Spring Break '93 Win 15-8 992 Jun 22nd Boston Invite 2019
189 Watch City Win 11-9 779.35 Jun 22nd Boston Invite 2019
143 Shrike Loss 6-13 207.08 Jun 22nd Boston Invite 2019
108 Somerville BAG Loss 13-15 783.79 Jun 23rd Boston Invite 2019
159 Ender's Outcasts Loss 7-12 216.01 Jun 23rd Boston Invite 2019
153 BUDA U20B Loss 9-11 512.41 Jun 23rd Boston Invite 2019
226 Fusion Win 13-7 791.42 Jul 13th Ow My Knee
220 Genny Lite Win 13-7 830.62 Jul 13th Ow My Knee
207 Sky Hook Win 13-9 838.19 Jul 13th Ow My Knee
148 Overcast Loss 8-10 514.55 Jul 14th Ow My Knee
134 Green Means Bro Loss 3-13 276.72 Sep 7th Founders Mens Club Sectional Championship 2019
236 Space Force Win 13-7 579.78 Sep 7th Founders Mens Club Sectional Championship 2019
132 JAWN Loss 9-12 541.2 Sep 7th Founders Mens Club Sectional Championship 2019
- Axial Tilt Win 13-3 600 Ignored Sep 7th Founders Mens Club Sectional Championship 2019
172 Hazard Loss 8-13 160.12 Sep 8th Founders Mens Club Sectional Championship 2019
216 Apollo 7 Win 13-5 941.66 Sep 8th Founders 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)