#187 SUNY-Geneseo (5-5)

avg: 995.97  •  sd: 71.89  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
69 Maryland Loss 7-13 982.43 Mar 11th Oak Creek Invite 2023
106 Liberty Loss 4-8 778.1 Mar 11th Oak Creek Invite 2023
83 RIT Loss 8-12 1009.2 Mar 11th Oak Creek Invite 2023
76 Princeton Loss 10-13 1155.12 Mar 11th Oak Creek Invite 2023
204 Maine Win 7-6 1056.22 Mar 12th Oak Creek Invite 2023
357 SUNY-Albany-B** Win 7-0 486.36 Ignored Apr 2nd Northeast Salvage
290 SUNY-Oneonta Win 9-4 1128.83 Apr 2nd Northeast Salvage
316 Rensselaer Polytech** Win 11-3 940.76 Ignored Apr 2nd Northeast Salvage
352 Siena** Win 13-2 623.97 Ignored Apr 2nd Northeast Salvage
83 RIT Loss 5-12 850.36 Apr 2nd Northeast Salvage
**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)