#222 Valparaiso (2-10)

avg: 384.15  •  sd: 100.58  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
267 Illinois-Chicago Win 7-3 486.17 Mar 23rd Meltdown 2019
126 North Park** Loss 2-7 412.13 Ignored Mar 23rd Meltdown 2019
177 Wisconsin-La Crosse Loss 2-11 69 Mar 23rd Meltdown 2019
125 St Benedict** Loss 3-11 412.69 Ignored Mar 23rd Meltdown 2019
218 Loyola-Chicago Loss 5-11 -176.76 Mar 24th Meltdown 2019
148 Marquette Loss 6-9 462.37 Mar 24th Meltdown 2019
35 Truman State** Loss 0-13 1084.45 Ignored Mar 24th Meltdown 2019
218 Loyola-Chicago Win 11-5 1023.24 Mar 30th Illinois Invite 8
148 Marquette Loss 1-10 280.94 Mar 30th Illinois Invite 8
137 Illinois Loss 1-6 336.64 Mar 31st Illinois Invite 8
156 Wisconsin-Milwaukee Loss 2-3 720.02 Mar 31st Illinois Invite 8
208 Wisconsin-Oshkosh Loss 8-9 369.94 Mar 31st Illinois Invite 8
**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)