#145 Wisconsin-La Crosse (9-14)

avg: 724.19  •  sd: 79.75  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
130 Iowa Loss 5-6 701.07 Mar 1st Midwest Throwdown 2025
215 Washington University-B Win 11-2 862.07 Mar 1st Midwest Throwdown 2025
140 Saint Louis Loss 9-10 612.53 Mar 1st Midwest Throwdown 2025
55 St Olaf** Loss 2-13 841.22 Ignored Mar 1st Midwest Throwdown 2025
140 Saint Louis Loss 6-7 612.53 Mar 2nd Midwest Throwdown 2025
63 Chicago** Loss 4-11 744.44 Ignored Mar 29th Old Capitol Open 2025
92 Iowa State Loss 6-11 565.04 Mar 29th Old Capitol Open 2025
128 Winona State Loss 6-8 544.11 Mar 29th Old Capitol Open 2025
173 Grinnell Win 6-3 1139.32 Mar 30th Old Capitol Open 2025
130 Iowa Win 7-1 1426.07 Mar 30th Old Capitol Open 2025
214 Minnesota-Duluth Win 11-7 735.07 Mar 30th Old Capitol Open 2025
245 Wisconsin-B Win 9-4 626.43 Apr 12th Lake Superior D I Womens Conferences 2025
98 Wisconsin-Eau Claire Loss 8-13 589.62 Apr 12th Lake Superior D I Womens Conferences 2025
237 Wisconsin-Stevens Point Win 11-7 546.83 Apr 12th Lake Superior D I Womens Conferences 2025
95 Marquette Loss 6-7 969.02 Apr 13th Lake Superior D I Womens Conferences 2025
216 Wisconsin-Milwaukee Win 13-4 860.38 Apr 13th Lake Superior D I Womens Conferences 2025
130 Iowa Loss 4-9 226.07 Apr 26th North Central D I College Womens Regionals 2025
24 Minnesota** Loss 3-15 1223.12 Ignored Apr 26th North Central D I College Womens Regionals 2025
98 Wisconsin-Eau Claire Loss 4-15 485.78 Apr 26th North Central D I College Womens Regionals 2025
216 Wisconsin-Milwaukee Win 11-4 860.38 Apr 26th North Central D I College Womens Regionals 2025
2 Carleton College** Loss 0-15 1968.81 Ignored Apr 27th North Central D I College Womens Regionals 2025
130 Iowa Win 9-6 1244.63 Apr 27th North Central D I College Womens Regionals 2025
95 Marquette Loss 2-13 494.02 Apr 27th North Central D I College Womens Regionals 2025
**Blowout Eligible

FAQ

What is the uncertainty of the mean?
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
What's the algorithm again?
  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
Is there a longer explanation of all this?
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)