This is a unique draft.
Adding three teams means adding a minimum 9 starters at attack and a minimum of 9 at middie. There will likely be at 3 potential starters at attack, and 3 at middie. In the event at least one starter or prime sub for every team. It doesn’t include the top 20 recruits. There is no perfect solution but this one seems as good as it can get,
2023 - New NCAA Teams that Count
Re: 2023 - New NCAA Teams that Count
I am fine adding teams, but what type of compensation will there be for lost players?
Ie. If we drop Hofstra...I lose Justin Sykes and Dylan Sheridan. Sykes had 16g and 3a last year as a middie and was slated to be moved to attack (I believe) for his senior season.
Just want to make sure compensation is equal to the losses. How will we choose this?
Ie. If we drop Hofstra...I lose Justin Sykes and Dylan Sheridan. Sykes had 16g and 3a last year as a middie and was slated to be moved to attack (I believe) for his senior season.
Just want to make sure compensation is equal to the losses. How will we choose this?
Re: 2023 - New NCAA Teams that Count
One idea, and I’m not sure it’s good/I’m not suggesting it, just throwing it out there:
We wait until the end of this season to see what Ford/Sykes/etc actually do, and award compensation picks in the 2024 draft based on that performance.
We wait until the end of this season to see what Ford/Sykes/etc actually do, and award compensation picks in the 2024 draft based on that performance.
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Re: 2023 - New NCAA Teams that Count
That is a great idea; similar to an NFL trade where future comp is based on traded players performance that season
Re: 2023 - New NCAA Teams that Count
I'm fine with this if Matt is. He understands the work he needs to put into this, so I defer to him.genghiskhanbluejay wrote: ↑Fri Sep 02, 2022 10:01 amThat is a great idea; similar to an NFL trade where future comp is based on traded players performance that season
Re: 2023 - New NCAA Teams that Count
I am also ok with it. I much prefer using hard data as opposed to using a meaningless projection that may or may not be within a deviation or two of reality.