#194 Ohio (13-14)

avg: 1142.62  •  sd: 40.04  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
68 Franciscan Loss 3-13 1060.48 Feb 3rd Huckin in the Hills X
292 Kent State Win 13-9 1166.19 Feb 3rd Huckin in the Hills X
212 West Virginia Loss 7-8 954.5 Feb 3rd Huckin in the Hills X
387 Ohio-B Win 13-7 800.03 Feb 3rd Huckin in the Hills X
319 Edinboro Win 15-2 1254.16 Feb 4th Huckin in the Hills X
292 Kent State Win 13-9 1166.19 Feb 4th Huckin in the Hills X
126 Towson Loss 11-15 1007.92 Feb 4th Huckin in the Hills X
237 Carthage Win 13-4 1589.65 Mar 9th Spring Spook 2024
292 Kent State Win 13-6 1347.63 Mar 9th Spring Spook 2024
157 Miami (Ohio) Loss 9-10 1164.76 Mar 9th Spring Spook 2024
347 Wright State** Win 13-3 1127.36 Ignored Mar 9th Spring Spook 2024
268 Akron Win 13-9 1289.78 Mar 10th Spring Spook 2024
69 Central Florida Loss 7-13 1081 Mar 30th Huck Finn 2024
61 Chicago Loss 6-13 1094.33 Mar 30th Huck Finn 2024
129 Michigan Tech Loss 3-13 782.05 Mar 30th Huck Finn 2024
119 Colorado College Loss 9-12 1062.79 Mar 31st Huck Finn 2024
107 Iowa State Loss 2-13 876.13 Mar 31st Huck Finn 2024
233 Oklahoma Loss 8-9 880.7 Mar 31st Huck Finn 2024
74 Cincinnati Loss 2-13 1014 Apr 20th Ohio D I Mens Conferences 2024
282 Toledo Win 9-6 1237.12 Apr 20th Ohio D I Mens Conferences 2024
35 Ohio State** Loss 5-13 1303.25 Ignored Apr 20th Ohio D I Mens Conferences 2024
292 Kent State Win 15-4 1347.63 Apr 21st Ohio D I Mens Conferences 2024
347 Wright State** Win 13-4 1127.36 Ignored Apr 21st Ohio D I Mens Conferences 2024
77 Carnegie Mellon Loss 7-15 1007.47 May 4th Ohio Valley D I College Mens Regionals 2024
16 Penn State** Loss 1-15 1548.22 Ignored May 4th Ohio Valley D I College Mens Regionals 2024
292 Kent State Win 14-6 1347.63 May 5th Ohio Valley D I College Mens Regionals 2024
212 West Virginia Win 14-10 1478.2 May 5th Ohio Valley D I College Mens Regionals 2024
**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)