#321 Carleton Hot Karls (6-13)

avg: 589.49  •  sd: 60.22  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
156 Minnesota-B Loss 3-13 536.27 Feb 9th Ugly Dome I 2019
364 Minnesota-C Win 10-8 673.93 Feb 9th Ugly Dome I 2019
306 Bethel Loss 8-10 378.45 Feb 9th Ugly Dome I 2019
186 Macalester Win 11-10 1156.62 Feb 9th Ugly Dome I 2019
177 Winona State Loss 6-13 462.04 Feb 9th Ugly Dome I 2019
94 Appalachian State** Loss 4-13 772.43 Ignored Mar 23rd College Southerns XVIII
56 California-San Diego** Loss 5-13 992.76 Ignored Mar 23rd College Southerns XVIII
173 Georgia College Loss 7-13 511.58 Mar 23rd College Southerns XVIII
165 Georgia Southern Loss 6-13 491.91 Mar 23rd College Southerns XVIII
257 Charleston Loss 9-15 314.85 Mar 24th College Southerns XVIII
207 North Florida Loss 9-11 716.31 Mar 24th College Southerns XVIII
246 Florida-B Loss 6-9 456.85 Mar 24th College Southerns XVIII
53 Indiana** Loss 2-13 1026.62 Ignored Mar 30th Old Capitol Open 2019
156 Minnesota-B Loss 5-15 536.27 Mar 30th Old Capitol Open 2019
419 Northwestern-St. Paul Win 11-4 677.48 Mar 30th Old Capitol Open 2019
313 Drake Loss 7-11 146.91 Mar 30th Old Capitol Open 2019
390 Creighton Win 12-7 795.09 Mar 31st Old Capitol Open 2019
424 Coe Win 15-1 635.35 Mar 31st Old Capitol Open 2019
249 Wisconsin- La Crosse Win 13-12 982.01 Mar 31st Old Capitol 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)