#165 RIT (5-13)

avg: 965.29  •  sd: 61.7  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
85 Carnegie Mellon Loss 8-11 952.72 Jan 27th Mid Atlantic Warm Up
156 Johns Hopkins Loss 9-11 769.83 Jan 27th Mid Atlantic Warm Up
116 Liberty Loss 9-13 764.39 Jan 27th Mid Atlantic Warm Up
156 Johns Hopkins Loss 10-15 565.43 Jan 28th Mid Atlantic Warm Up
123 Pennsylvania Loss 10-15 693.88 Jan 28th Mid Atlantic Warm Up
298 Mary Washington Win 11-7 830.67 Jan 28th Mid Atlantic Warm Up
84 Appalachian State Loss 10-13 998.66 Mar 2nd Oak Creek Challenge 2024
206 George Washington Win 13-7 1360.83 Mar 2nd Oak Creek Challenge 2024
130 Towson Loss 8-11 751.22 Mar 2nd Oak Creek Challenge 2024
175 Maryland-Baltimore County Loss 10-11 803 Mar 3rd Oak Creek Challenge 2024
90 SUNY-Buffalo Loss 10-12 1036.89 Mar 3rd Oak Creek Challenge 2024
169 Rutgers Win 13-6 1551.64 Mar 3rd Oak Creek Challenge 2024
38 Duke Loss 8-15 1025.95 Mar 30th Atlantic Coast Open 2024
97 Florida State Loss 14-15 1122.77 Mar 30th Atlantic Coast Open 2024
156 Johns Hopkins Win 14-13 1144.03 Mar 30th Atlantic Coast Open 2024
256 Virginia Tech-B Win 15-7 1176.62 Mar 30th Atlantic Coast Open 2024
144 Pittsburgh-B Loss 8-15 497.21 Mar 31st Atlantic Coast Open 2024
60 Temple Loss 10-15 981.42 Mar 31st Atlantic Coast Open 2024
**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)