#138 RIT (10-10)

avg: 1092.92  •  sd: 58.75  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
122 Boston University Win 11-7 1634.68 Jan 25th Mid Atlantic Warm Up 2025
60 Michigan State Loss 3-13 886.35 Jan 25th Mid Atlantic Warm Up 2025
78 Richmond Loss 5-13 748.03 Jan 25th Mid Atlantic Warm Up 2025
272 Virginia Commonwealth Win 10-3 1111.33 Jan 25th Mid Atlantic Warm Up 2025
206 Christopher Newport Win 13-10 1106.4 Jan 26th Mid Atlantic Warm Up 2025
66 Dartmouth Loss 4-15 852.25 Jan 26th Mid Atlantic Warm Up 2025
101 Yale Loss 8-11 896.87 Jan 26th Mid Atlantic Warm Up 2025
71 Case Western Reserve Loss 9-11 1155.86 Mar 1st Oak Creek Challenge 2025
177 Towson Loss 7-9 646.23 Mar 1st Oak Creek Challenge 2025
336 SUNY-Cortland** Win 10-3 791.65 Ignored Mar 1st Oak Creek Challenge 2025
56 Cornell Loss 7-13 966.41 Mar 2nd Oak Creek Challenge 2025
177 Towson Win 10-5 1499.46 Mar 2nd Oak Creek Challenge 2025
116 West Chester Loss 10-11 1067.17 Mar 2nd Oak Creek Challenge 2025
247 George Washington Win 15-4 1225.89 Mar 22nd Atlantic Coast Open 2025
113 Lehigh Loss 7-13 646.71 Mar 22nd Atlantic Coast Open 2025
43 Virginia Tech Loss 9-13 1193.64 Mar 22nd Atlantic Coast Open 2025
255 Wake Forest Win 15-3 1186.48 Mar 22nd Atlantic Coast Open 2025
180 American Win 15-12 1206.23 Mar 23rd Atlantic Coast Open 2025
184 East Carolina Win 15-9 1405.19 Mar 23rd Atlantic Coast Open 2025
170 Massachusetts -B Win 15-13 1174.04 Mar 23rd Atlantic Coast Open 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)