#171 RIT (8-12)

avg: 1081.65  •  sd: 62.51  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
278 Christopher Newport Win 13-8 1260.79 Feb 23rd Oak Creek Challenge 2019
206 West Chester Win 11-10 1091.25 Feb 23rd Oak Creek Challenge 2019
188 East Carolina Win 13-9 1448.93 Feb 23rd Oak Creek Challenge 2019
166 Virginia Commonwealth Win 12-6 1671.14 Feb 23rd Oak Creek Challenge 2019
174 Cedarville Win 12-8 1508.61 Feb 24th Oak Creek Challenge 2019
114 Liberty Loss 10-11 1175.11 Feb 24th Oak Creek Challenge 2019
142 Princeton Loss 7-9 930.37 Feb 24th Oak Creek Challenge 2019
53 Indiana Loss 7-13 1069.09 Mar 23rd CWRUL Memorial 2019
132 Kentucky Win 11-10 1376.16 Mar 23rd CWRUL Memorial 2019
87 Case Western Reserve Loss 10-13 1094.42 Mar 23rd CWRUL Memorial 2019
148 Michigan-B Loss 14-15 1056.95 Mar 24th CWRUL Memorial 2019
210 Rochester Win 12-11 1078.29 Mar 24th CWRUL Memorial 2019
183 Oberlin Loss 7-11 575.06 Mar 24th CWRUL Memorial 2019
160 Vanderbilt Loss 9-14 650.51 Mar 24th CWRUL Memorial 2019
33 Johns Hopkins Loss 6-13 1131.17 Mar 30th Atlantic Coast Open 2019
242 Rowan Win 13-10 1214.6 Mar 30th Atlantic Coast Open 2019
197 George Mason Loss 9-11 752.19 Mar 30th Atlantic Coast Open 2019
115 Villanova Loss 10-13 968.25 Mar 30th Atlantic Coast Open 2019
114 Liberty Loss 5-13 700.11 Mar 31st Atlantic Coast Open 2019
83 Rutgers Loss 12-14 1212.01 Mar 31st Atlantic Coast Open 2019
**Blowout Eligible

FAQ

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
  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
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)