NCAA Selection Discussion - Containment Thread

[youthathletics]

I like it....Army does this every year, they know how the system works. They play to the system...book RU and someone else like Cuse or COrnell for the OOC matchups, then ride into the PL Tourney dialed in.

I wonder if they can get another notch in their belt if they booked a UVA. Terps, Yale, Duke, etc over having an NJIT, VMI, Sienna, Wagner. But I do undersand the the brutality of the PL 9 team log jam, with a locked in Bye week....certainly limits your OOC dates.

[runrussellrun]

I like it....Army does this every year, they know how the system works. They play to the system...book RU and someone else like Cuse or COrnell for the OOC matchups, then ride into the PL Tourney dialed in.

I wonder if they can get another notch in their belt if they booked a UVA. Terps, Yale, Duke, etc over having an NJIT, VMI, Sienna, Wagner. But I do undersand the the brutality of the PL 9 team log jam, with a locked in Bye week....certainly limits your OOC dates.

Marchand only had 4 shots........not a good nite

[laxreference]

If it doesn't consider which of those teams on your schedule you've beaten and which ones have beaten you, it isn't a "powerful metric." Looking only at aggregates isn't enough.

Strenth-of-Record does look at who you beat and who you lost to.

Step 1: Assign points for each win based on the RPI (or other metric) of the opponent in reverse order (i.e. 75 points for beating #1)
Step 1a: Assign weight to victories depending on how notable they are; each victory is weighted (say 20%) less than the one before it
Step 2: Assign points for each loss based on the RPI (or other metric) of the opponent (i.e. 75 points for losing to #75)
Step 2a: Assign weight to losses depending on how notable they are; each loss is weighted (say 30%) less than the one before it
Step 3: Sum the points from step 1a (total win points / quality wins factor) and subtract the points from step 2a (total loss points / bad loss factor)

This gives you the total Strength-of-Record (if you use RPI as the input, it's called RPI SOR); it is the simplest method, but there are some ways to adjust it if you want to emphasize a team's best wins vs punish teams for their worst losses. Here's the profile for Cornell's season.


Capture.PNG

I’m not sure about the “weight” aspect to this. Seems arbitrary. Why not just rank each win and loss straight up?

Let's say Cornell MLAX had beaten Harvard and lost to Yale instead of the reverse. Without steps 1a and 2a, it wouldn't affect their SOR. I agree with BigRed that any system used for Selection purposes should care who the wins and losses were against.

But with the decay factors, their bad loss score is improved because the Harvard game has become a victory and their most weighted loss is now Yale. Every team's worst loss is weighted the same, so having a better "worst loss" improves their score.

Their quality wins score would be reduced because you replaced their best win with a less impressive victory, but since the Harvard game is being compared against every other team's 5th best victory, it doesn't go down as much. Net effect = higher RPI SOR.

You can say my 20/30 weights are arbitrary, and you wouldn't be wrong. I've always thought a team's best performances should be more important in NCAA selections than their worst losses, which is why I chose to decay wins more slowly. Other people may think different weightings make more sense.

If you want to see how this works in practice with every team in DI MLAX, you can see the calculation for each team here. You can even set your own weights and see how it affects the rankings.

It seems that the decay parameter is about time, not about who the win or loss was against. If it’s tied to time, why not make that explicit and set a decay based on actually how long ago the win/loss was? One thing a good system should be is easily grokable by althe average fan.

It's not based on time. You sort the wins by how notable they are (i.e. best teams beaten are ordered first and given the most weight). So the least notable wins get the least weight and count the least toward your quality wins score.

In the same way, you sort the losses by how notable they are (so worst teams who beat you come first).

We are already used to comparing teams' best wins vs other teams' best wins and their worst losses against other teams' worst losses, so I assumed that the average fan would be able to grasp it.

Doesn’t the RPI already determine the ordering and weight of the notable wins/losses? I still don’t understand the need for the decay.

If you don't weight the most notable results higher, then if two teams have an 8-2 record against the same 10 opponents, but the losses are to different teams, they have the same RPI SOR. That may be fine, but I think that most people would want the system to distinguish whether a team's best wins are better than another teams' best wins. Or you may not, in which case you'd probably prefer no decay factor.

[rolldodge]

If it doesn't consider which of those teams on your schedule you've beaten and which ones have beaten you, it isn't a "powerful metric." Looking only at aggregates isn't enough.

Strenth-of-Record does look at who you beat and who you lost to.

Step 1: Assign points for each win based on the RPI (or other metric) of the opponent in reverse order (i.e. 75 points for beating #1)
Step 1a: Assign weight to victories depending on how notable they are; each victory is weighted (say 20%) less than the one before it
Step 2: Assign points for each loss based on the RPI (or other metric) of the opponent (i.e. 75 points for losing to #75)
Step 2a: Assign weight to losses depending on how notable they are; each loss is weighted (say 30%) less than the one before it
Step 3: Sum the points from step 1a (total win points / quality wins factor) and subtract the points from step 2a (total loss points / bad loss factor)

This gives you the total Strength-of-Record (if you use RPI as the input, it's called RPI SOR); it is the simplest method, but there are some ways to adjust it if you want to emphasize a team's best wins vs punish teams for their worst losses. Here's the profile for Cornell's season.


Capture.PNG

I’m not sure about the “weight” aspect to this. Seems arbitrary. Why not just rank each win and loss straight up?

Let's say Cornell MLAX had beaten Harvard and lost to Yale instead of the reverse. Without steps 1a and 2a, it wouldn't affect their SOR. I agree with BigRed that any system used for Selection purposes should care who the wins and losses were against.

But with the decay factors, their bad loss score is improved because the Harvard game has become a victory and their most weighted loss is now Yale. Every team's worst loss is weighted the same, so having a better "worst loss" improves their score.

Their quality wins score would be reduced because you replaced their best win with a less impressive victory, but since the Harvard game is being compared against every other team's 5th best victory, it doesn't go down as much. Net effect = higher RPI SOR.

You can say my 20/30 weights are arbitrary, and you wouldn't be wrong. I've always thought a team's best performances should be more important in NCAA selections than their worst losses, which is why I chose to decay wins more slowly. Other people may think different weightings make more sense.

If you want to see how this works in practice with every team in DI MLAX, you can see the calculation for each team here. You can even set your own weights and see how it affects the rankings.

It seems that the decay parameter is about time, not about who the win or loss was against. If it’s tied to time, why not make that explicit and set a decay based on actually how long ago the win/loss was? One thing a good system should be is easily grokable by althe average fan.

It's not based on time. You sort the wins by how notable they are (i.e. best teams beaten are ordered first and given the most weight). So the least notable wins get the least weight and count the least toward your quality wins score.

In the same way, you sort the losses by how notable they are (so worst teams who beat you come first).

We are already used to comparing teams' best wins vs other teams' best wins and their worst losses against other teams' worst losses, so I assumed that the average fan would be able to grasp it.

Doesn’t the RPI already determine the ordering and weight of the notable wins/losses? I still don’t understand the need for the decay.

If you don't weight the most notable results higher, then if two teams have an 8-2 record against the same 10 opponents, but the losses are to different teams, they have the same RPI SOR. That may be fine, but I think that most people would want the system to distinguish whether a team's best wins are better than another teams' best wins. Or you may not, in which case you'd probably prefer no decay factor.

If the schedule is that same and the record is the same, but the wins and losses are to different teams, just use the RPI to determine the overall strength of the wins and the overall weakness of the losses. Bringing another arbitrary datapoint into the mix is unnecessary complication IMHO.

[Matnum PI]

If you don't weight the most notable results higher, then if two teams have an 8-2 record against the same 10 opponents, but the losses are to different teams, they have the same RPI SOR. That may be fine, but I think that most people would want the system to distinguish whether a team's best wins are better than another teams' best wins. Or you may not, in which case you'd probably prefer no decay factor.

This is a major flaw with RPI. It doesn't evaluate game by game, team by team. It paints with broad brushstrokes. I don't hate it much as I don't hate any/many ranking systems. But this flaw does bother me.

[laxreference]

If the schedule is that same, but the wins and losses are to different teams, just use the RPI to determine the overall strength of the wins and the overall weakness of the losses. Bringing another arbitrary datapoint into the mix is unnecessary complication IMHO.

If I'm understanding you correctly, I think I disagree that using the RPI would allow you to differentiate which resume is better.

Team 1: RPI #15
Team 2: RPI # 25
Team 3: RPI #35
Team 4: RPI #45

Team A: 3-1

Wins: T1 (75 - 15 = +60), T2 (+50), T4 (+30) = 140 quality win points
Losses: T3 (- 35) = -35 quality loss points
Undecayed RPI SOR: 105

Team B: 3-1

Wins: T3 (75 - 35= +40), T2 (+50), T4 (+30) = 120 quality win points
Losses: T1 (-15) = -15 quality loss points
Undecayed RPI SOR: 105

So if you don't weight the most notable results higher, you end up with a tie in SOR. Again, you may be fine with that.

But by using decay factors, if you want to give Team A the edge because their best win is better, you would be able to do that. Or if you want to give Team B the edge because they have the better loss, you could do that as well.

[laxreference]

If you don't weight the most notable results higher, then if two teams have an 8-2 record against the same 10 opponents, but the losses are to different teams, they have the same RPI SOR. That may be fine, but I think that most people would want the system to distinguish whether a team's best wins are better than another teams' best wins. Or you may not, in which case you'd probably prefer no decay factor.

This is a major flaw with RPI. It doesn't evaluate game by game, team by team. It paints with broad brushstrokes. I don't hate it much as I don't hate any/many ranking systems. But this flaw does bother me.

It's a major flaw if RPI is the metric used to sort teams for bubble purposes. It's a major flaw if you use an SOR approach that uses opponent RPI as the input, but doesn't weight the most notable results higher. But adding a weight to the most notable results solves the issue.

[rolldodge]

If the schedule is that same, but the wins and losses are to different teams, just use the RPI to determine the overall strength of the wins and the overall weakness of the losses. Bringing another arbitrary datapoint into the mix is unnecessary complication IMHO.

If I'm understanding you correctly, I think I disagree that using the RPI would allow you to differentiate which resume is better.

Team 1: RPI #15
Team 2: RPI # 25
Team 3: RPI #35
Team 4: RPI #45

Team A: 3-1

Wins: T1 (75 - 15 = +60), T2 (+50), T4 (+30) = 140 quality win points
Losses: T3 (- 35) = -35 quality loss points
Undecayed RPI SOR: 105

Team B: 3-1

Wins: T3 (75 - 35= +40), T2 (+50), T4 (+30) = 120 quality win points
Losses: T1 (-15) = -15 quality loss points
Undecayed RPI SOR: 105

So if you don't weight the most notable results higher, you end up with a tie in SOR. Again, you may be fine with that.

But by using decay factors, if you want to give Team A the edge because their best win is better, you would be able to do that. Or if you want to give Team B the edge because they have the better loss, you could do that as well.

I see. That makes sense. I still think the scenario that this reconciles is uncommon enough that you don’t need the complication across the board. I can’t think of a case where teams have exactly the same schedules and on top of that exactly the same records against that schedule but different wins and losses.

I think in the off chance that this could possibly happen, you just go with the team that has stronger wins.

[laxreference]

I see. That makes sense. I still think the scenario that this reconciles is uncommon enough that you don’t need the complication across the board. I can’t think of a case where teams have exactly the same schedules and on top of that exactly the same records against that schedule but different wins and losses.

I think in the off chance that this could possibly happen, you just go with the team that has stronger wins.

The other feature of weighting the most notable results higher is more important IMO.

# of games played is always going to be different between different teams. Straight SOR struggles with this because if you are Duke, who plays a ton of games, you are getting credit added to your total by beating lower ranked teams. Compare that with an ND who plays very few softies. So you have to choose whether you want to divide by total games played or not. Either way, one of those two scheduling strategies gets penalized.

With decay rates, your least notable victory is going to give you very little boost in the SOR formula because a) it's by definition against a poor team and b) the weight is so low that you get almost no credit. So neither of those strategies necessarily is better under a decaying SOR formula.

But if you are Duke and you happen to lose one of those games, it probably becomes your most notable loss and gets the full weight that comes from being the most notable result. So a decaying RPI SOR formula gets the risk/reward right.

ND doesn't get the small boost from their 14th victory, and Duke does. But if Duke happened to lose that game, it tanks their RPI SOR score.

[rolldodge]

I see. That makes sense. I still think the scenario that this reconciles is uncommon enough that you don’t need the complication across the board. I can’t think of a case where teams have exactly the same schedules and on top of that exactly the same records against that schedule but different wins and losses.

I think in the off chance that this could possibly happen, you just go with the team that has stronger wins.

The other feature of weighting the most notable results higher is more important IMO.

# of games played is always going to be different between different teams. Straight SOR struggles with this because if you are Duke, who plays a ton of games, you are getting credit added to your total by beating lower ranked teams. Compare that with an ND who plays very few softies. So you have to choose whether you want to divide by total games played or not. Either way, one of those two scheduling strategies gets penalized.

With decay rates, your least notable victory is going to give you very little boost in the SOR formula because a) it's by definition against a poor team and b) the weight is so low that you get almost no credit. So neither of those strategies necessarily is better under a decaying SOR formula.

But if you are Duke and you happen to lose one of those games, it probably becomes your most notable loss and gets the full weight that comes from being the most notable result. So a decaying RPI SOR formula gets the risk/reward right.

ND doesn't get the small boost from their 14th victory, and Duke does. But if Duke happened to lose that game, it tanks their RPI SOR score.

I'm a User Experience designer, so I look these things from an ease of use/understandability viewpoint. So my opinions are going to naturally favor those qualities over mathematical completeness. That's why I use the divide by number of games to get a quality of win per game. I understand your point about Duke playing the softies helping them when you don't divide by # of games, but can you expand on how divide by games hurts the ND case?

[laxreference]

The other feature of weighting the most notable results higher is more important IMO.

# of games played is always going to be different between different teams. Straight SOR struggles with this because if you are Duke, who plays a ton of games, you are getting credit added to your total by beating lower ranked teams. Compare that with an ND who plays very few softies. So you have to choose whether you want to divide by total games played or not. Either way, one of those two scheduling strategies gets penalized.

With decay rates, your least notable victory is going to give you very little boost in the SOR formula because a) it's by definition against a poor team and b) the weight is so low that you get almost no credit. So neither of those strategies necessarily is better under a decaying SOR formula.

But if you are Duke and you happen to lose one of those games, it probably becomes your most notable loss and gets the full weight that comes from being the most notable result. So a decaying RPI SOR formula gets the risk/reward right.

ND doesn't get the small boost from their 14th victory, and Duke does. But if Duke happened to lose that game, it tanks their RPI SOR score.

I'm a User Experience designer, so I look these things from an ease of use/understandability viewpoint. So my opinions are going to naturally favor those qualities over mathematical completeness. That's why I use the divide by number of games to get a quality of win per game. I understand your point about Duke playing the softies helping them when you don't divide by # of games, but can you expand on how divide by games hurts the ND case?

I would argue that dividing by the # of games helps a team like ND that plays fewer games against great competition. From an SOR/game played perspective, if ND and Duke played the same schedule, with the same record, but Duke added two games against softies and won them both, they could easily have a lower RPI SOR if you divide by games played. Here's an example that I thought out in more detail:

Two teams (A and B) have both played 12 games against the same opponents and have won every game (so they both have 12-0 records). The average ranking of the opponents played is 27. Team B then plays one extra game against the 65th ranked team. At selection time, Team A has a 12-0 record against teams with an average ranking of 27. Team B has a 13-0 record with an average opponent ranking of 30. Does Team A or Team B have the more impressive resume?

The issue that I see with dividing by games played is that you are going to have to put Team B behind Team A even though I'm sure that many would argue that they have the better resume. Do we want to incentivize teams to schedule fewer games against better competition and avoid games against worse competition? I don't think so, but that's what dividing by games played would get you (IMO).

Smart use of a decaying mechanism doesn't penalize either scheduling strategy so coaches don't feel incentivized to go one way or the other. Whatever they think is the best strategy for building their program they can go with.

[rolldodge]

I would argue that dividing by the # of games helps a team like ND that plays fewer games against great competition. From an SOR/game played perspective, if ND and Duke played the same schedule, with the same record, but Duke added two games against softies and won them both, they could easily have a lower RPI SOR if you divide by games played. Here's an example that I thought out in more detail:

Two teams (A and B) have both played 12 games against the same opponents and have won every game (so they both have 12-0 records). The average ranking of the opponents played is 27. Team B then plays one extra game against the 65th ranked team. At selection time, Team A has a 12-0 record against teams with an average ranking of 27. Team B has a 13-0 record with an average opponent ranking of 30. Does Team A or Team B have the more impressive resume?

The issue that I see with dividing by games played is that you are going to have to put Team B behind Team A even though I'm sure that many would argue that they have the better resume. Do we want to incentivize teams to schedule fewer games against better competition and avoid games against worse competition? I don't think so, but that's what dividing by games played would get you (IMO).

Smart use of a decaying mechanism doesn't penalize either scheduling strategy so coaches don't feel incentivized to go one way or the other. Whatever they think is the best strategy for building their program they can go with.

I deal with this by bucketing the RPIs. Every win over a team less than #40 gets the same value. But you can definitely argue this is just as arbitrary.

[rolldodge]

The other feature of weighting the most notable results higher is more important IMO.

# of games played is always going to be different between different teams. Straight SOR struggles with this because if you are Duke, who plays a ton of games, you are getting credit added to your total by beating lower ranked teams. Compare that with an ND who plays very few softies. So you have to choose whether you want to divide by total games played or not. Either way, one of those two scheduling strategies gets penalized.

With decay rates, your least notable victory is going to give you very little boost in the SOR formula because a) it's by definition against a poor team and b) the weight is so low that you get almost no credit. So neither of those strategies necessarily is better under a decaying SOR formula.

But if you are Duke and you happen to lose one of those games, it probably becomes your most notable loss and gets the full weight that comes from being the most notable result. So a decaying RPI SOR formula gets the risk/reward right.

ND doesn't get the small boost from their 14th victory, and Duke does. But if Duke happened to lose that game, it tanks their RPI SOR score.

I'm a User Experience designer, so I look these things from an ease of use/understandability viewpoint. So my opinions are going to naturally favor those qualities over mathematical completeness. That's why I use the divide by number of games to get a quality of win per game. I understand your point about Duke playing the softies helping them when you don't divide by # of games, but can you expand on how divide by games hurts the ND case?

I would argue that dividing by the # of games helps a team like ND that plays fewer games against great competition. From an SOR/game played perspective, if ND and Duke played the same schedule, with the same record, but Duke added two games against softies and won them both, they could easily have a lower RPI SOR if you divide by games played. Here's an example that I thought out in more detail:

Two teams (A and B) have both played 12 games against the same opponents and have won every game (so they both have 12-0 records). The average ranking of the opponents played is 27. Team B then plays one extra game against the 65th ranked team. At selection time, Team A has a 12-0 record against teams with an average ranking of 27. Team B has a 13-0 record with an average opponent ranking of 30. Does Team A or Team B have the more impressive resume?

The issue that I see with dividing by games played is that you are going to have to put Team B behind Team A even though I'm sure that many would argue that they have the better resume. Do we want to incentivize teams to schedule fewer games against better competition and avoid games against worse competition? I don't think so, but that's what dividing by games played would get you (IMO).

Smart use of a decaying mechanism doesn't penalize either scheduling strategy so coaches don't feel incentivized to go one way or the other. Whatever they think is the best strategy for building their program they can go with.

I think this is why the committee caps the SOS at your top 10 RPI games? Maybe something similar? I like the idea behind the decay, it still feels a little hard to quickly grasp.

[laxreference]

Two bubble teams (A and B) have gone 12-3 against the same schedule.

They are independents, so they have no AQ; their losses were to the same 3 teams; they did not play each other and both ended the year on a WLWLW streak

The avg RPI of the 12 teams that they beat is 27. Team B then plays a game against the #65 team, who they beat. (The game was previously scheduled; not a last minute add).

Assuming they are the last two teams in consideration for one spot in the NCAA field, who should get the bid?

[HopFan16]

Two bubble teams (A and B) have gone 12-3 against the same schedule.

They are independents, so they have no AQ; their losses were to the same 3 teams; they did not play each other and both ended the year on a WLWLW streak

The avg RPI of the 12 teams that they beat is 27. Team B then plays a game against the #65 team, who they beat. (The game was previously scheduled; not a last minute add).

Assuming they are the last two teams in consideration for one spot in the NCAA field, who should get the bid?

Assuming that beating the #65 team drops Team B's RPI and avg RPI win below those of Team A, then Team A should get in. That's the way the cookie crumbles. Team A scheduled smarter.

Otherwise, this scenario sounds like where input from the regional advisory committee, which is technically part of the criteria, should come into play.

[rolldodge]

Two bubble teams (A and B) have gone 12-3 against the same schedule.

They are independents, so they have no AQ; their losses were to the same 3 teams; they did not play each other and both ended the year on a WLWLW streak

The avg RPI of the 12 teams that they beat is 27. Team B then plays a game against the #65 team, who they beat. (The game was previously scheduled; not a last minute add).

Assuming they are the last two teams in consideration for one spot in the NCAA field, who should get the bid?

Isn't this one of the reasons the committee looks at wins via RPI tiers? The avg RPI could be the same, but one team could have more top 5 or top 10 wins. My hypothesis is that (in some years) they particularly like to take teams with top 5 wins because it indicates an ability for an upset.

[NOVALax2015]

Interesting discussion, especially the zero-sum nature of 2 teams with the same schedule: one with better wins, the other with better losses. It all gets to what you want in the tourney. Do you reward consistency, which would point to the team with better losses? IMO, no. For bubble teams, I'd prefer the team with the higher "standard deviation" of performance - the team with better wins (and worse losses) - as that team is more likely to upset a highly ranked team in the tourney.

[rolldodge]

Interesting discussion, especially the zero-sum nature of 2 teams with the same schedule: one with better wins, the other with better losses. It all gets to what you want in the tourney. Do you reward consistency, which would point to the team with better losses? IMO, no. For bubble teams, I'd prefer the team with the higher "standard deviation" of performance - the team with better wins (and worse losses) - as that team is more likely to upset a highly ranked team in the tourney.

I agree. Teams should be judged mainly by their wins. Losses should be accounted for but carry less weight. Wins should always improve a teams standing, losses should always hurt.

[laxreference]

Interesting discussion, especially the zero-sum nature of 2 teams with the same schedule: one with better wins, the other with better losses. It all gets to what you want in the tourney. Do you reward consistency, which would point to the team with better losses? IMO, no. For bubble teams, I'd prefer the team with the higher "standard deviation" of performance - the team with better wins (and worse losses) - as that team is more likely to upset a highly ranked team in the tourney.

I agree. Teams should be judged mainly by their wins. Losses should be accounted for but carry less weight. Wins should always improve a teams standing, losses should always hurt.

I was really surprised that this was not the dominant opinion actually. I did a twitter poll asking the question above about the same record + a win vs a lowly ranked team. Not that it's scientific and who knows how many of the answers were trolling, but 57% said that the team with the extra win should be left out.

I would be fine with the boost from a terrible win being very close to zero, but I feel like it has to be positive certainly.

[runrussellrun]

Interesting discussion, especially the zero-sum nature of 2 teams with the same schedule: one with better wins, the other with better losses. It all gets to what you want in the tourney. Do you reward consistency, which would point to the team with better losses? IMO, no. For bubble teams, I'd prefer the team with the higher "standard deviation" of performance - the team with better wins (and worse losses) - as that team is more likely to upset a highly ranked team in the tourney.

I agree. Teams should be judged mainly by their wins. Losses should be accounted for but carry less weight. Wins should always improve a teams standing, losses should always hurt.

Wins.....such a strange idea. Some, call it silly. Top 16, not a bad field, but somewhere, it IS written, that only certain "types" of friends can go to beach day. American East with multiple teams !! insanity

https://www.ncaa.com/stats/lacrosse-men ... t/team/233

but.....golly gee.....that UPenn sure does know how to lose themselves some games. Matters NOT.....top 10 in the magic dust formula......will get the Quakers in. Winning doesn't mean much and the sporting world thinks lacrosse is strange.

Penn lost to Brown

Brown lost to Quinnipiac

Quinnipiac lost to Wagner
----------------

And, apologize to all the Wahoo fans. University of Virginia HAS.......played a regular season AWAY game against a New England opponent: (east of the Hudson river )

Manhattan 2018 (Uva squeeked out the win 8-5 )
Brown Lockdown year. (Brown won) any connection or reason as to why this game happened?

and....apparently, played Dartmouth, at Dartmouth....at some point in history. Maybe the 1938 game, when FDR was president ?

Why does THIS matter ?



https://static.virginiasports.com/pdfs/ ... 1682083176

Vermont
Umass
Sacred Heart
Yale
Harvard
Bryant
Fairfield
Providence
Merrimack
Dartmouth
Quinnipiac
Lowell
Brown
Holy Cross
Boston U.



UVA is an independent team. NO .....reason to play other ACC teams, at all. Cool they are playing in Easton, PA. Getting the boys introduced to hotel living for the playoffs.