#232 Butler (12-14)

avg: 1010.21  •  sd: 60.04  •  top 16/20: 0%

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# Opponent Result Game Rating Status Date Event
274 Air Force Loss 11-13 621.67 Mar 2nd FCS D III Tune Up 2024
168 Kenyon Loss 10-12 1014.23 Mar 2nd FCS D III Tune Up 2024
198 Messiah Loss 11-13 900.15 Mar 2nd FCS D III Tune Up 2024
173 Xavier Loss 6-13 633.43 Mar 2nd FCS D III Tune Up 2024
176 Navy Loss 6-13 612.43 Mar 3rd FCS D III Tune Up 2024
84 Elon Loss 6-13 976.44 Mar 3rd FCS D III Tune Up 2024
52 Whitman** Loss 0-13 1156.72 Ignored Mar 3rd FCS D III Tune Up 2024
285 Penn State-Behrend Win 13-3 1400.45 Mar 23rd Butler Spring Fling
301 Rose-Hulman Win 11-6 1261 Mar 23rd Butler Spring Fling
173 Xavier Loss 10-12 995.31 Mar 23rd Butler Spring Fling
222 Ball State Win 9-6 1462.23 Mar 24th Butler Spring Fling
151 Grace Loss 7-13 754.1 Mar 24th Butler Spring Fling
236 Loyola-Chicago Win 10-4 1590.9 Mar 24th Butler Spring Fling
363 Indiana-B Win 10-7 827.75 Mar 24th Butler Spring Fling
349 Kalamazoo Win 12-4 1124.43 Apr 13th Eastern Great Lakes D III Mens Conferences 2024
316 Calvin University Win 15-6 1260.41 Apr 13th Eastern Great Lakes D III Mens Conferences 2024
110 Davenport Loss 9-15 952.09 Apr 13th Eastern Great Lakes D III Mens Conferences 2024
301 Rose-Hulman Loss 12-13 589.31 Apr 13th Eastern Great Lakes D III Mens Conferences 2024
349 Kalamazoo Win 12-11 649.43 Apr 14th Eastern Great Lakes D III Mens Conferences 2024
220 Hillsdale Loss 6-15 450.3 Apr 14th Eastern Great Lakes D III Mens Conferences 2024
301 Rose-Hulman Win 15-7 1314.31 Apr 14th Eastern Great Lakes D III Mens Conferences 2024
220 Hillsdale Win 15-10 1503.91 Apr 27th Great Lakes D III College Mens Regionals 2024
151 Grace Loss 10-12 1073.51 Apr 27th Great Lakes D III College Mens Regionals 2024
294 Knox Win 12-9 1083.18 Apr 27th Great Lakes D III College Mens Regionals 2024
333 North Park Win 15-1 1182.5 Apr 28th Great Lakes D III College Mens Regionals 2024
110 Davenport Loss 7-15 867.57 Apr 28th Great Lakes D III College Mens Regionals 2024
**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)