#247 Wisconsin-Whitewater (6-7)

avg: 748.21  •  sd: 54.98  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
117 Colorado Mines Loss 5-9 775.84 Feb 18th Snow Melt 2023
184 Denver Win 11-10 1132.98 Feb 18th Snow Melt 2023
184 Denver Loss 3-13 407.98 Feb 19th Snow Melt 2023
206 Colby Loss 7-10 529.74 Mar 4th Philly Special1
165 Penn State-B Loss 0-15 498.45 Mar 4th Philly Special1
220 Dickinson Loss 6-8 557.14 Mar 4th Philly Special1
165 Penn State-B Loss 11-15 717.29 Mar 5th Philly Special1
267 Vermont-C Win 10-9 776.42 Mar 5th Philly Special1
145 Carthage Loss 7-10 782.46 Mar 19th Meltdown College
277 Loyola-Chicago Win 11-8 958.03 Mar 19th Meltdown College
- Illinois Chicago Win 10-4 972.9 Mar 19th Meltdown College
294 Winona State Win 9-7 785.17 Mar 19th Meltdown College
309 Wisconsin-Stevens Point Win 10-7 778.53 Mar 19th Meltdown College
**Blowout Eligible


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)