#180 Wisconsin-La Crosse (7-5)

avg: 898.16  •  sd: 71.01  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
351 Kansas State** Win 11-4 623.87 Ignored Mar 23rd Free State Classic
179 Missouri S&T Loss 7-11 437.21 Mar 23rd Free State Classic
254 Oklahoma State Win 10-6 1095.69 Mar 23rd Free State Classic
335 Wichita State** Win 13-4 721.02 Ignored Mar 23rd Free State Classic
260 Illinois State Win 13-4 1158.62 Mar 24th Free State Classic
49 St Olaf** Loss 4-15 903.16 Ignored Mar 24th Free State Classic
93 Colorado-B Loss 10-11 1141.1 Mar 30th Old Capitol Open 2024
359 Iowa State-B** Win 11-4 569.92 Ignored Mar 30th Old Capitol Open 2024
124 Macalester Loss 3-10 546.58 Mar 30th Old Capitol Open 2024
195 Grinnell Win 6-5 971.06 Mar 31st Old Capitol Open 2024
260 Illinois State Win 8-6 859.11 Mar 31st Old Capitol Open 2024
140 Minnesota-B Loss 7-8 953.04 Mar 31st Old Capitol Open 2024
**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)