#96 Bench (13-6)

avg: 1067.59  •  sd: 75.42  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
207 Buffalo Brain Freeze Win 12-8 920.77 Jun 11th Rochester Round Robin
119 Mashed Loss 9-10 858.99 Jun 11th Rochester Round Robin
84 Buffalo Lake Effect Win 8-7 1255.22 Jun 11th Rochester Round Robin
40 UNION Win 9-8 1616.86 Jun 11th Rochester Round Robin
190 Rainbow Win 14-8 1101.29 Jul 8th AntlerLock
95 Scarecrow Loss 5-13 468.5 Jul 8th AntlerLock
163 Sunken Circus Win 14-7 1354.62 Jul 8th AntlerLock
245 Vintage Ultimate Club** Win 15-3 672.48 Ignored Jul 8th AntlerLock
130 Zero Strategy Loss 8-12 450.33 Jul 8th AntlerLock
84 Buffalo Lake Effect Loss 4-5 1005.22 Jul 9th AntlerLock
217 Compost Plates Win 13-6 1002.26 Aug 19th Ow My Knee 2023
130 Zero Strategy Win 10-7 1281.15 Aug 19th Ow My Knee 2023
48 Townies Win 11-10 1527.86 Aug 19th Ow My Knee 2023
48 Townies Win 11-10 1527.86 Aug 19th Ow My Knee 2023
119 Mashed Win 12-10 1222.11 Sep 9th 2023 Mixed Upstate New York Sectional Championship
40 UNION Loss 5-11 891.86 Sep 9th 2023 Mixed Upstate New York Sectional Championship
84 Buffalo Lake Effect Loss 7-10 740.55 Sep 9th 2023 Mixed Upstate New York Sectional Championship
217 Compost Plates** Win 13-4 1002.26 Ignored Sep 9th 2023 Mixed Upstate New York Sectional Championship
207 Buffalo Brain Freeze Win 13-9 898.19 Sep 10th 2023 Mixed Upstate New York Sectional Championship
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
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
What's the algorithm again?
  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
Is there a longer explanation of all this?
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)