#75 Grinnell (10-3)

avg: 1486.77  •  sd: 81.33  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
146 Kansas Win 9-6 1587.45 Mar 4th Midwest Throwdown 2023
341 Iowa State-B** Win 13-2 770.62 Ignored Mar 4th Midwest Throwdown 2023
246 Northern Iowa Win 11-5 1348.85 Mar 4th Midwest Throwdown 2023
35 Missouri Win 8-7 1911.83 Mar 5th Midwest Throwdown 2023
48 Iowa State Loss 6-9 1227.78 Mar 5th Midwest Throwdown 2023
210 Wisconsin-Eau Claire Win 11-6 1442.99 Mar 5th Midwest Throwdown 2023
90 Chicago Win 5-4 1558.78 Apr 1st Huck Finn1
116 John Brown Win 6-5 1430.98 Apr 1st Huck Finn1
104 Florida State Win 5-3 1763.57 Apr 1st Huck Finn1
68 Wisconsin-Milwaukee Loss 4-7 1053.77 Apr 1st Huck Finn1
118 Marquette Win 8-7 1425.78 Apr 2nd Huck Finn1
61 Emory Win 9-8 1701.98 Apr 2nd Huck Finn1
48 Iowa State Loss 12-14 1425.39 Apr 2nd Huck Finn1
**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)