#101 Liberty (7-4)

avg: 1189.56  •  sd: 111.95  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
203 North Carolina-B Win 13-6 1325.25 Feb 18th Commonwealth Cup Weekend1 2023
144 Franciscan Win 12-8 1422.16 Feb 18th Commonwealth Cup Weekend1 2023
77 Wisconsin-Milwaukee Loss 6-13 705.41 Feb 18th Commonwealth Cup Weekend1 2023
155 Cedarville Win 13-7 1495.72 Feb 19th Commonwealth Cup Weekend1 2023
126 Elon Loss 8-13 566.38 Feb 19th Commonwealth Cup Weekend1 2023
133 Davidson Win 12-6 1616.78 Feb 19th Commonwealth Cup Weekend1 2023
183 Maine Win 10-9 939.14 Mar 11th Oak Creek Invite 2023
67 Maryland Win 11-8 1735.64 Mar 11th Oak Creek Invite 2023
65 Princeton Loss 7-11 907.43 Mar 11th Oak Creek Invite 2023
173 SUNY-Geneseo Win 8-4 1439.13 Mar 11th Oak Creek Invite 2023
72 RIT Loss 8-11 984.54 Mar 12th Oak Creek Invite 2023
**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)