I'm a big fan of model #1:
After 7th season 20% TPE loss
After 8th season 25% TPE loss
After 9th season 35% TPE loss
After 10th season 35% TPE loss
After 11th season 35% TPE loss
After 12th season 35% TPE loss
After 13th season 50% TPE loss
After 14th season 60% TPE loss
After 15th season 75% TPE loss (irrelevant, retirement is forced regardless of TPE)
The raw regression chart is the one that sells it for me. On top of what you already mentioned, I think there are two key benefits for employing a model like this:
1. Players who are active and enjoy their current player and/or LR's have an opportunity to stick around longer.
2. I think we offer a necessary solution to two complex problems: a. drastic variation in quality of some draft classes, and b. smaller draft class sizes.
For #1, I like how we can continue to create a positive experience for a given user by extending the length of their current experience. I'm especially fond of the sentimental benefit for first-time creates like myself who are near-max earners and enjoy being on their current team.
For #2, when we run into situations like the past couple seasons where talent has been a much more shallow pool, having long-time players continue to be meaningful contributors offers teams a way to mitigate the damage done by regression. I'm not sure whether or not it would make the league more competitive, though at the very least I don't see any downside to any of this.
Additionally, I think having a regression system with a longer tail will more or less force teams to improve the quality of their draft scouting. Quality has varied by team and GM for what seems to be the entire history of the league; if there's additional incentive to draft players that could max earn (or at least appear like they'd stay active over time), I'd hypothesize that teams will have to step up scouting efforts. That then creates more competitive teams and a better experience for users during the draft process.
In summary, I think you should genuinely push for the adoption of that first model. Also, your analyses are amazing and I hope you continue to do this kind of work.
After 7th season 20% TPE loss
After 8th season 25% TPE loss
After 9th season 35% TPE loss
After 10th season 35% TPE loss
After 11th season 35% TPE loss
After 12th season 35% TPE loss
After 13th season 50% TPE loss
After 14th season 60% TPE loss
After 15th season 75% TPE loss (irrelevant, retirement is forced regardless of TPE)
The raw regression chart is the one that sells it for me. On top of what you already mentioned, I think there are two key benefits for employing a model like this:
1. Players who are active and enjoy their current player and/or LR's have an opportunity to stick around longer.
2. I think we offer a necessary solution to two complex problems: a. drastic variation in quality of some draft classes, and b. smaller draft class sizes.
For #1, I like how we can continue to create a positive experience for a given user by extending the length of their current experience. I'm especially fond of the sentimental benefit for first-time creates like myself who are near-max earners and enjoy being on their current team.
For #2, when we run into situations like the past couple seasons where talent has been a much more shallow pool, having long-time players continue to be meaningful contributors offers teams a way to mitigate the damage done by regression. I'm not sure whether or not it would make the league more competitive, though at the very least I don't see any downside to any of this.
Additionally, I think having a regression system with a longer tail will more or less force teams to improve the quality of their draft scouting. Quality has varied by team and GM for what seems to be the entire history of the league; if there's additional incentive to draft players that could max earn (or at least appear like they'd stay active over time), I'd hypothesize that teams will have to step up scouting efforts. That then creates more competitive teams and a better experience for users during the draft process.
In summary, I think you should genuinely push for the adoption of that first model. Also, your analyses are amazing and I hope you continue to do this kind of work.