#206 Spring Break '93 (5-14)

avg: 427.19  •  sd: 68.93  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
112 Genny The Boys Loss 5-13 371.58 Jun 22nd Boston Invite 2019
181 Helots Loss 8-15 8.05 Jun 22nd Boston Invite 2019
143 Shrike Loss 6-15 207.08 Jun 22nd Boston Invite 2019
189 Watch City Loss 5-11 -69.86 Jun 22nd Boston Invite 2019
188 Thunder Boys Win 13-8 1027.01 Jun 23rd Boston Invite 2019
159 Ender's Outcasts Loss 4-15 136.52 Jun 23rd Boston Invite 2019
50 Colt** Loss 3-13 744.87 Ignored Jul 20th Vacationland 2019
68 Deathsquad** Loss 5-13 609.27 Ignored Jul 20th Vacationland 2019
164 Rising Tide U20B Loss 10-11 573.52 Jul 20th Vacationland 2019
103 Burly Loss 6-11 481.69 Jul 20th Vacationland 2019
197 Madhouse Loss 8-11 110.54 Jul 21st Vacationland 2019
- Neap Tide Win 12-4 600 Ignored Jul 21st Vacationland 2019
163 One Night Loss 6-11 162.64 Jul 21st Vacationland 2019
112 Genny The Boys Loss 9-11 722.38 Sep 7th Metro New York Mens Club Sectional Championship 2019
77 Log Jam** Loss 3-13 554.43 Ignored Sep 7th Metro New York Mens Club Sectional Championship 2019
- White Sauce Hot Sauce Win 10-8 916.09 Sep 7th Metro New York Mens Club Sectional Championship 2019
241 defunCT Win 15-4 436.29 Sep 8th Metro New York Mens Club Sectional Championship 2019
226 Fusion Win 15-11 615.05 Sep 8th Metro New York Mens Club Sectional Championship 2019
207 Sky Hook Loss 10-12 181.5 Sep 8th Metro New York 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)