#267 SUNY-Geneseo (10-15)

avg: 809.64  •  sd: 59.81  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
335 Connecticut-B Win 9-7 838.24 Feb 22nd Bring The Huckus 2025
337 Delaware-B Win 8-7 669.57 Feb 22nd Bring The Huckus 2025
265 Drexel Loss 6-8 516.24 Feb 22nd Bring The Huckus 2025
214 MIT Loss 9-10 895.68 Feb 22nd Bring The Huckus 2025
383 West Chester-B Win 13-3 826.64 Feb 22nd Bring The Huckus 2025
335 Connecticut-B Win 11-5 1158.9 Feb 23rd Bring The Huckus 2025
265 Drexel Loss 4-7 320.58 Feb 23rd Bring The Huckus 2025
160 Ithaca Loss 5-9 701.64 Mar 29th Northeast Classic 2025
235 Skidmore Win 9-6 1345.57 Mar 29th Northeast Classic 2025
212 SUNY-Albany Loss 8-11 657.83 Mar 29th Northeast Classic 2025
192 Vassar Loss 7-10 703.4 Mar 29th Northeast Classic 2025
352 Army Win 13-6 1017.21 Mar 30th Northeast Classic 2025
197 Haverford Loss 7-12 557.96 Mar 30th Northeast Classic 2025
210 Penn State-B Loss 12-13 902.54 Mar 30th Northeast Classic 2025
212 SUNY-Albany Loss 8-11 657.83 Mar 30th Northeast Classic 2025
177 Hamilton Loss 11-14 856.55 Apr 12th Western NY D III Mens Conferences 2025
358 SUNY-Oneonta Loss 13-14 267.82 Apr 12th Western NY D III Mens Conferences 2025
254 Colgate Loss 9-15 346.77 Apr 13th Western NY D III Mens Conferences 2025
328 SUNY-Cortland Win 13-7 1139.34 Apr 13th Western NY D III Mens Conferences 2025
254 Colgate Win 10-5 1436.15 Apr 26th Metro East D III College Mens Regionals 2025
389 Stevens Tech** Win 13-1 787.46 Ignored Apr 26th Metro East D III College Mens Regionals 2025
328 SUNY-Cortland Win 12-5 1181.81 Apr 26th Metro East D III College Mens Regionals 2025
75 Wesleyan Loss 6-12 1020.05 Apr 26th Metro East D III College Mens Regionals 2025
160 Ithaca Loss 6-12 651.39 Apr 27th Metro East D III College Mens Regionals 2025
192 Vassar Loss 6-11 546.37 Apr 27th Metro East D III College Mens Regionals 2025
**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)