Monthly Archives: January 2016

Weekend PWR outlook (Jan 29-31)

If you missed yesterday’s article, you might want to start with Playoff cutline movers to get some perspective on big movers over the last few weeks and where teams are likely to end the regular season. This article will look at what movements are likely this weekend.

Teams in the top 20 tend to face significantly more downside potential each week than upside. It is pretty intuitive if you think about winning percentages—for a .500 team to climb to the top, it needs to net win a lot more games than the top teams do (which is especially difficult, given that the top teams’ past success is likely correlated with continued wins). However, a few losses can result in a precipitous drop. Teams in the 45-55 range often face similarly disproportionate upside potential, but we don’t notice that as much because it’s less interesting.

#19 Minnesota-Duluth exemplifies the imbalance—with a sweep the Bulldogs are most likely to climb to #18 (though could do a bit better, particularly with SCSU and Denver wins), but if swept is most likely to fall to about #24.

This is a good time for a warning about edge cases. Unexpected things can and do happen. When I talk about “likely” outcomes, those generally only cover 60-70% of the possibilities. So, you could infer that something other than what I call “likely” will occur every 3rd or 4th prediction. That’s why I usually also show you the entire possibilities curve in a graph. Using UMD’s outcomes for this weekend as an example, ranks 15-27 come up in over 1% of scenarios, while ranks 14-30 are mathematically possible (albeit extremely unlikely).

duluth

Having lost 5 of its last 6, #9 Nebraska-Omaha is at risk of falling to the bubble with two more losses.

uno

#10 Yale also faces significant downside facing Rensselaer and Union. A pair of losses could result in a drop to the 15-16 range.

yale

#12 Mass.-Lowell shows the risks of hosting a bottom-ranked team. A pair of wins over Arizona St could result in no movement at all, though a pair of losses would probably drop the River Hawks to 18-19.

masslowell

#26 St Lawrence has plummeted after losing 6 of its last 7. The hole is so deep now that it would take the rest of the regular season to climb back out, with a pair of wins this week most likely only resulting in a climb of about 2 rank positions.

stlawrence

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.

Resources

Playoff cutline movers

It’s been three weeks since my first look at the 2016 cutlines, in which I identified five different tranches of teams. With most teams having played about 6 games and having about 10 remaining, some have managed to noticeably shift their fates.

The article noted that no one was safe (which is still true), but that 1-11 would be fine as long as they didn’t slump with performances approaching .500.

#9 Nebraska-Omaha (then #3) is demonstrating just such a swing, with a 1-5 run since that article. They now need to win about 5 of the remaining 10 to stay on or above the bubble going into conference playoffs.

#14 Cornell (then #7) is also teetering on the edge following a 2-3-1 run. The Big Red need to take at least 6 of the remaining 10 for a chance to stay on the bubble at the end of the regular season.

#26 St. Lawrence (then #10) has plummeted with a 1-6 run, and now needs an improbable 9 out of 10 wins to get back into at-large position.

uno

cornell

stlawrence

The article also observed that the 12-19 teams were very much alive, and generally needed to win 60-80% of their games to stay positioned for an at-large bid. A 3-0-3 run has treated #7 Boston College well (rising from #16). An 0-3-2 run has treated #29 Union (formerly #18) poorly.

bc

Union

In the 20-26 block, which I noted is the lowest from which a team usually manages to break out for an at-large bid, #13 Denver has thus far delivered with a 5-0-1 run. The Pioneers need to keep up that success and win about 6 of the remaining 10 scheduled regular season games to go into the conference tournament on the bubble.

denver

I noted that 27-45 weren’t mathematically eliminated, but needed a near perfect season for a shot (and that those near the top were in much better shape than those near the bottom). Of that group, #22 Miami (then #28) has come the closest with a 3-1-1 performance that still leaves them needing near perfection for a shot at the bubble.

miami

From the 46-60 block, which I predicted needed to win the conference tournament for a bid, #27 Northeastern (formerly #49) has made the most noise with an unexpected 6-0 run (the Huskies were 3-12-4 until that run). However, even if they improbably maintain perfection over the remaining 9 scheduled games, the bubble still seems just on the edge of their reach.

northeastern

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.

Resources

How many teams will each conference put in the playoffs? (2016 edition)

This time of year always brings speculation about which teams are positioned for the NCAA tournament, which sometimes leads to discussions about each conference’s performance.

Looking at how many teams each conference has in the top 16 in PWR gives an interesting benchmark of performance to date. But, that occasionally raises questions of how the 2nd half schedules might reshape that field. Because we already know the rest of the regular season schedule, it’s pretty straightforward to simulate the rest of the regular season (assuming teams will continue to perform as they have to date) to see how each conference is likely to fare at the end of the regular season.

How many teams will each conference put in the playoffs?

Likelihood of each conference’s number of teams in the top 14 PWR at the end of the regular season
0 1 2 3 4 5
Atlantic Hockey 90% 10%
Big Ten 17% 70% 13%
ECAC 1% 19% 68% 12%
Hockey East 6% 51% 43%
NCHC 1% 44% 51% 5%
WCHA 96% 4%

Shaded cells represent the number of teams each conference currently has in the top 14. The Big Ten is most likely to make a gain (not surprisingly, since they hold positions 15 and 16), while Hockey East and the NCHC are  most likely to take a loss (holding positions 12 & 13, and 14, respectively).

How is each conference doing compared to its historical performance?

Looking back at 2014’s How many teams will each conference put in the playoffs?, ExileOnDaytonStreet on the USCHO forum had counted each conference’s current members’ average tournament appearances per year over the previous ten years:

Number of members that made that tournament per year
Atlantic Hockey 1.3
Big Ten 3
ECAC 2.3
Hockey East 4.1
NCHC 4.3
WCHA 1

Compared to their members’ historical performances–

  • Atlantic Hockey is on track, likely to just get its autobid.
  • The Big Ten continues to underperform, as it has since its inception. Of course, some of that can be attributed to its historically strong members now having to play each other instead of other teams.
  • ECAC is dramatically outperforming, with 4 at-large teams the most likely outcome based on performance to date.
  • Hockey East is right on track, likely getting 4-5 teams in at-large.
  • The NCHC is on-track to slightly behind, with 4 most likely but 3 much more likely than 5.
  • The WCHA, like Atlantic Hockey, is on track and likely to just get its autobid.

Keep in mind that the historical numbers are total tournament participants, whereas for forecasting purposes we just look at top 14 in PWR.

Is inter-conference play the key?

People sometimes speculate that inter-conference play is the key to a good PWR rating (though my own attempts to test that hypothesis have proven inconclusive at best).

Here is each conference’s current non-conference record (courtesy of CHN).

Inter-conference records (from CHN)
Atlantic Hockey .250
Big Ten .494
ECAC .606
Hockey East .545
NCHC .628
WCHA .458

The conferences likely to send the most teams to the tournament are indeed those with the best inter-conference records.

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.

Resources

A first look at the 2016 at-large bid cutlines

If you’re new here, you might want to start with Welcome to collegehockeyranked.com. While anything related to college hockey rankings is fair game for this site, in most articles I try to provide insight as to where teams are likely to end up in the PairWise Rankings (PWR) that mimic the NCAA’s men’s ice hockey tournament selection process (and, thus, which teams are likely to be selected for the tournament).

In last year’s When to start looking at PWR, I noted that the early January PWR does give us some useful information as to what each team needs to do to make the tournament at-large. Top teams can still fall out of contention (though it takes a notable collapse for the top few), and it’s pretty unusual for a team ranked much lower than 25 at this time of year to climb to an at-large bid.

To test those larger trends against this year’s schedule and results, I ran simulations for the remaining scheduled regular season games to see where each team is likely to end up. The full methodology is described at the bottom of this article.

Before we jump into the data, I do want to remind you that starting simulations now (with over 450 scheduled games remaining) makes it pretty likely that some of the 1% events will happen. So, just telling you the average outcome for each team wouldn’t be particularly useful, because it would include an assumption about the team’s future performance that will prove wrong for some teams. Instead, I tell you where a team is likely to end up conditional on how many games they win (or, how many games a team needs to win to achieve an outcome such as making the NCAA tournament at-large).

Which teams are likely to get an at-large bid?

Around this time last year, I asked, “Is anyone safe?”, and answered,

Not completely. Even #1 Harvard could slip to the bubble if it wins only 6-7 of its remaining 14 scheduled games. That’s not particularly likely

Harvard went 5-10-1 in its next 16 games to fall to #22 in the PWR at the end of the regular season. The Crimson were still very much on the bubble until they secured a bid by winning the ECAC tournament. Though the assumption that Harvard would keep performing as it had to date (and thus win far more than 6-7 more games) proved wrong, the simulated prediction proved correct that Harvard would be on the bubble if that happened.

#1 Quinnipiac’s KRACH is so strong relative to its scheduled competitors that none of my simulations (which weight likely outcomes by KRACH) had them winning fewer than 6 games! However, knowing that past results aren’t a perfect predictor of future results, we can look at the positioning of the “win 6” and guess that they could get into trouble if they win just 2-4 of their remaining scheduled games.

quinnipiac

If you’re feeling deja vu, let me add that #2 Harvard could find itself in trouble with only 6 wins in its remaining 16 scheduled games.

harvard

Down to about #11 Penn State, teams just need avoid a slump that approaches (or goes beneath) .500 to stay positioned for the at-large field.

1 Quinnipiac
2 Harvard
3 Nebraska-Omaha
4 St Cloud St
5 North Dakota
6 Providence
7 Cornell
8 Michigan
9 Yale
10 St. Lawrence
11 Penn State

pennstate

From about #12 Boston University to about #19 Minnesota, teams need to win 60-80% of their remaining games.

12 Boston University
13 Notre Dame
14 Mass.-Lowell
15 Rensselaer
16 Boston College
17 Minnesota State
18 Union
19 Minnesota

bostonuniversity

minnesota

The lowest rank at this time of year from which a team usually climbs to an at-large bid is in the mid-20s. It takes a hot streak, but someone usually does it.

20 Dartmouth
21 Denver
22 Bowling Green
23 Holy Cross
24 Robert Morris
25 Minnesota Duluth
26 Western Michigan

dartmouth westernmichigan

Is anyone out of contention?

#27 Michigan Tech to #45 Mercyhurst aren’t mathematically eliminated, but need something approaching a perfect remaining season to get an at-large bid. It’s a bit easier for teams near the top of the list (2-3 losses for most) than those at the bottom (almost no losses and a bit of a luck).

27 Michigan Tech
28 Miami
29 New Hampshire
30 Alaska Anchorage
31 Merrimack
32 Clarkson
33 Massachusetts
34 Wisconsin
35 Ferris State
36 Northern Michigan
37 Brown
38 Vermont
39 Princeton
40 Bentley
41 Bemidji State
42 Air Force
43 Ohio State
44 Connecticut
45 Mercyhurst

mtech

mercyhurst

For #46 Lake Superior State and below it looks like the only path to the NCAA tournament is through the conference tournaments. Those include:

46 Lake Superior State
47 Colgate
48 RIT
49 Northeastern
50 Sacred Heart
51 Alaska
52 Maine
53 Michigan State
54 Army
55 Arizona
56 Canisius
57 Colorado College
58 Alabama-Huntsville
59 Niagara
60 American International

LakeState

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.

Resources