#200 Rochester Open Club (10-8)

avg: 682.18  •  sd: 81.62  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
226 Buffalo Frostbite Win 13-4 1086.54 Jun 11th Rochester Round Robin
226 Buffalo Frostbite Win 13-5 1086.54 Jun 11th Rochester Round Robin
238 Mohawk Valley Wild Win 9-5 856.42 Jul 22nd Rochester Round Robin 2
226 Buffalo Frostbite Win 11-4 1086.54 Jul 22nd Rochester Round Robin 2
238 Mohawk Valley Wild Win 9-8 452.36 Jul 22nd Rochester Round Robin 2
226 Buffalo Frostbite Win 13-10 814.69 Jul 22nd Rochester Round Robin 2
121 John Doe Loss 7-12 568.84 Aug 5th Philly Open 2023
186 Town Hall Stars Loss 9-12 408.93 Aug 5th Philly Open 2023
91 Helots** Loss 4-13 694.88 Ignored Aug 5th Philly Open 2023
206 Rebels Loss 9-10 488.29 Aug 6th Philly Open 2023
252 Deepfake** Win 13-2 649.62 Ignored Aug 6th Philly Open 2023
209 Long Island Riff Raff Win 11-9 838.75 Aug 6th Philly Open 2023
226 Buffalo Frostbite Loss 12-13 361.54 Sep 9th 2023 Mens Upstate New York Regional Championship
189 Dirty Laundry Loss 4-9 148.17 Sep 9th 2023 Mens Upstate New York Regional Championship
21 Phoenix** Loss 3-13 1259.25 Ignored Sep 9th 2023 Mens Upstate New York Regional Championship
238 Mohawk Valley Wild Win 13-8 823.52 Sep 9th 2023 Mens Upstate New York Regional Championship
158 Alibi Loss 11-12 762.94 Sep 10th 2023 Mens Upstate New York Regional Championship
238 Mohawk Valley Wild Win 15-9 842.84 Sep 10th 2023 Mens Upstate New York Regional 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)