#201 SUNY-Fredonia (4-6)

avg: 880.95  •  sd: 107.41  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
199 Salisbury Win 10-8 1149.48 Mar 3rd Atlantic City 7 2018
- Stockton Win 13-0 1121.38 Mar 3rd Atlantic City 7 2018
192 Ohio Wesleyan Loss 8-9 806.92 Mar 4th Atlantic City 7 2018
202 Shippensburg Loss 7-9 601.11 Mar 4th Atlantic City 7 2018
129 Colby Loss 2-13 692.61 Mar 4th Atlantic City 7 2018
224 Hofstra Win 7-4 1193.11 Mar 4th Atlantic City 7 2018
191 Wisconsin-Milwaukee Loss 5-7 604.82 Mar 17th Rip Tide 2018
191 Wisconsin-Milwaukee Loss 3-7 332.96 Mar 17th Rip Tide 2018
191 Wisconsin-Milwaukee Loss 5-6 807.96 Mar 17th Rip Tide 2018
191 Wisconsin-Milwaukee Win 7-3 1532.96 Mar 17th Rip Tide 2018
**Blowout Eligible

FAQ

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)