#164 Ohio (14-11)

avg: 976.87  •  sd: 80.86  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
124 Denver Loss 4-7 650.2 Feb 15th 2025 Commonwealth Cup Weekend 1
45 Elon** Loss 4-10 1005.05 Ignored Feb 15th 2025 Commonwealth Cup Weekend 1
256 Illinois-B Win 10-5 1154.43 Feb 15th 2025 Commonwealth Cup Weekend 1
187 North Carolina-B Win 11-3 1464.39 Feb 15th 2025 Commonwealth Cup Weekend 1
241 Michigan-B Win 10-4 1242.3 Feb 16th 2025 Commonwealth Cup Weekend 1
255 Wake Forest Win 10-5 1160.38 Feb 16th 2025 Commonwealth Cup Weekend 1
391 Denison** Win 13-1 -49.72 Ignored Mar 1st Huckin in the Hills XI
259 Drexel Win 8-6 879.27 Mar 1st Huckin in the Hills XI
320 Penn State-Behrend Win 13-6 873.35 Mar 1st Huckin in the Hills XI
147 Butler Loss 11-12 929.95 Mar 2nd Huckin in the Hills XI
210 Kent State Win 13-1 1358.34 Mar 2nd Huckin in the Hills XI
249 Cedarville Win 15-10 1066.4 Mar 15th Grand Rapids Invite 2025
60 Michigan State Loss 9-12 1140.98 Mar 15th Grand Rapids Invite 2025
123 Wisconsin-Milwaukee Loss 9-13 728.43 Mar 15th Grand Rapids Invite 2025
221 Wisconsin-B Loss 5-10 136.81 Mar 15th Grand Rapids Invite 2025
212 Eastern Michigan Win 15-3 1350.33 Mar 16th Grand Rapids Invite 2025
292 Western Michigan Loss 8-12 -34.95 Mar 16th Grand Rapids Invite 2025
281 Wisconsin-Platteville Win 15-7 1050.87 Mar 16th Grand Rapids Invite 2025
59 Asbury Loss 5-12 894.24 Mar 29th Corny Classic College 2025
133 Lipscomb Win 12-11 1239.86 Mar 29th Corny Classic College 2025
143 Michigan Tech Win 9-8 1187.91 Mar 29th Corny Classic College 2025
146 Dayton Win 11-8 1421.39 Mar 30th Corny Classic College 2025
146 Dayton Loss 6-7 930.78 Mar 30th Corny Classic College 2025
133 Lipscomb Loss 8-9 989.86 Mar 30th Corny Classic College 2025
143 Michigan Tech Loss 7-10 673.24 Mar 30th Corny Classic College 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)