#130 Towson (12-8)

avg: 1116.83  •  sd: 60.19  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
104 Dayton Loss 10-11 1089.61 Feb 3rd Huckin in the Hills X
281 Edinboro** Win 13-3 1050.84 Ignored Feb 3rd Huckin in the Hills X
51 Franciscan Loss 2-12 896.28 Feb 3rd Huckin in the Hills X
361 Ohio-B** Win 13-4 553.02 Ignored Feb 3rd Huckin in the Hills X
51 Franciscan Loss 7-15 896.28 Feb 4th Huckin in the Hills X
204 Ohio Win 15-11 1190.76 Feb 4th Huckin in the Hills X
203 West Virginia Win 12-9 1164.74 Feb 4th Huckin in the Hills X
84 Appalachian State Loss 12-13 1201.8 Mar 2nd Oak Creek Challenge 2024
206 George Washington Win 13-3 1403.3 Mar 2nd Oak Creek Challenge 2024
165 RIT Win 11-8 1330.9 Mar 2nd Oak Creek Challenge 2024
101 Cornell Win 13-11 1453.41 Mar 3rd Oak Creek Challenge 2024
175 Maryland-Baltimore County Win 13-10 1256.14 Mar 3rd Oak Creek Challenge 2024
90 SUNY-Buffalo Loss 10-11 1150.01 Mar 3rd Oak Creek Challenge 2024
120 Army Loss 9-10 1035.57 Mar 16th Free Tournament
71 Penn State-B Loss 7-13 808.93 Mar 16th Free Tournament
352 Rensselaer Polytech Win 12-7 537.41 Mar 16th Free Tournament
214 Scranton Win 12-11 887.89 Mar 16th Free Tournament
71 Penn State-B Loss 8-12 925.31 Mar 17th Free Tournament
214 Scranton Win 15-7 1362.89 Mar 17th Free Tournament
166 Villanova Win 15-8 1523.35 Mar 17th Free Tournament
**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)