Weekend’s Pairwise Ranking outlook

Last week I wrote an update on the break lines for NCAA tournament chances. Because it targeted the end of the regular season, its predictions still hold. So, this week I’ll take a shorter term look at what’s likely to happen this weekend.

What the forecasts really mean (there is some math)

These forecasts are not yet to the precision of completely mathematically eliminating outcomes. The number of possible outcomes is still so large that it wouldn’t be as useful to (and I can’t!) go into the detail of what’s mathematically possible and not until the conference tournaments.

Instead, I’ll refer to outcomes with at least a 10% probability as “likely”, and outcomes with a 1% probability as something that “could happen”. That suggests that one time out of ten, you’re going to see a “could happen” outcome instead of a “likely” one. It also suggests that one time out of a hundred you’re going to see an outcome outside what I even declared “could happen”.

The probabilities of possible outcomes do have a bell-shaped distribution, as you’ll see in the graphs, so when I say “a range of #10-#13 is likely” it’s usually most likely (over a 50% chance) of being in the middle of the range, #11-#12 in this case.

Who could fall to the bubble

#7 Quinnipiac and #8 Mass.-Lowell seem relatively safe, neither being likely to drop below #13 even if swept (though drops as low as #14 could happen for each).

quinnipiac

masslowell

#9 Northeastern is the highest ranked team with an obvious chance to fall to the bubble, with a drop to #13-#16 likely if swept.

northeastern

#10 UND (#12-#14 likely and #16 could happen), #11 Cornell (#17-#19 likely and #21 could happen), and #12 Vermont (#16-#19 likely and #20 could happen) face similar downsides to the bubble if swept.

northdakota

cornell

vermont

What will the bubble teams do?

Now it gets interesting. While the bubble teams can all fall off the bubble if swept or shore up their position with a sweep, those on the upper part of the bubble seem to face more downside while those on the lower part of the bubble seem to face more upside.

#13 Colgate is likely to fall to #18-#20 if swept (as low as #21 could happen), rise to #10-#13 with a sweep (as high as #9 could happen), or stay about the same or fall slightly with a split.

colgate

Similarly, #14 Michigan is likely to fall to #18-#20 if swept (as low as #22 could happen), rise to #10-#12 with a sweep (as high as #9 could happen), or stay about the same or fall slightly with a split.

michigan

#15 Notre Dame only plays one game this weekend. A win would result in a likely climb to #10-#13 (as high as #9 could happen), while a loss would result in a likely fall to #15-#17 (as low as #18 could happen).

notredame

#16 Providence would likely climb to #10-#13 with a sweep (as high as #9 could happen), but is also likely to rise modestly to #13-#15 with a split.

providence

Teams that could climb onto the bubble

#17 Maine would likely climb to #11-#14 with a sweep (as high as #10 could happen).

maine

#18 Yale would likely climb to #13-#16 with a sweep (as high as #12 could happen). But, for Yale a split is likely to result in a modest drop to #18-#20.

yale

#19 Minnesota State and #20 New Hampshire can just reach the bottom of the bubble, with rankings ranging from #15-#17 and #16-#18 respectively likely with a sweep (#13 and #14 could happen for each respectively).

minnesotastate

newhampshire

Methodology

Forecasts include the results of games played through Sunday of this week, unless otherwise noted.

Each forecast is based on at least one million monte carlo simulations of the games in the described period. For each simulation, the PairWise Ranking (PWR) is calculated and the results tallied. The probabilities presented in the forecasts are the share of simulations in which a particular outcome occurred.

The outcome of each game in each simulation is determined by random draw, with the probability of victory for each team set by their relative KRACH ratings. So, if the simulation set included a contest between team A with KRACH 300 and team B with KRACH 100, team A will win the game in very close to 75% of the simulations. I don’t simulate ties or home ice advantage.

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