#95 Bates College (16-3)

avg: 1369.77  •  sd: 89.47  •  top 16/20: 0%

Click on a column to sort  • 
# Opponent Result Game Rating Status Date Event
301 Salisbury** Win 13-3 1253.13 Ignored Mar 9th Atlantic City 9
245 Stevens Tech Win 13-2 1476.22 Mar 9th Atlantic City 9
153 SUNY-Albany Win 13-10 1479.15 Mar 9th Atlantic City 9
252 SUNY-Cortland Win 13-0 1445.28 Mar 10th Atlantic City 9
122 Yale Win 11-9 1528.73 Mar 10th Atlantic City 9
77 Colby Loss 8-11 1107.08 Mar 10th Atlantic City 9
392 Emerson** Win 13-3 871.58 Ignored Mar 17th LolaPaLweiston
- Colby-B** Win 13-0 122.52 Ignored Mar 17th LolaPaLweiston
77 Colby Win 12-7 1993.2 Mar 17th LolaPaLweiston
288 Massachusetts-Lowell Win 13-6 1312.56 Mar 24th Mill City Throwdown 2019
217 Amherst College Win 13-7 1484.11 Mar 24th Mill City Throwdown 2019
176 Bentley Win 13-6 1665.27 Mar 24th Mill City Throwdown 2019
317 Worcester Polytech** Win 13-1 1196.85 Ignored Mar 24th Mill City Throwdown 2019
315 Muhlenberg Win 13-6 1206.97 Mar 30th Layout Pigout 2019
84 Brandeis Loss 6-12 852.58 Mar 30th Layout Pigout 2019
178 Army Win 12-6 1639.04 Mar 30th Layout Pigout 2019
228 Swarthmore Win 13-8 1411.02 Mar 30th Layout Pigout 2019
96 Bowdoin Loss 5-13 767.81 Mar 31st Layout Pigout 2019
178 Army Win 13-11 1288.57 Mar 31st Layout Pigout 2019
**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)