HiveKueen
Two Pair
League Runners,
Do you try to have a "most improved" award in your league? Is so, how do you make the determination.
This will be my next project with chatGPT (aka Chatty).
Here's my start with this conversation (warning, AI babble coming
):
My kickoff question was:
Let's pivot a bit. what metric would be best for basing a most improved player on. I'm thinking avg pts per game but am wondering if there is an even better one?
Chatty's chatty response was...
This is an excellent pivot, because “Most Improved Player” is one of the hardest awards to get right — and one of the easiest to make feel unfair if the metric is wrong.
Let’s walk this carefully and land on something that is:
AvgPoints already:
Where:
So it already captures:
No new abstractions.
Using:
min(GPS1,GPS2)\sqrt{\min(GP_{S1}, GP_{S2})}min(GPS1,GPS2)
means:
This avoids:
Total points is attendance-biased
Encourages grinding, not improvement
Penalizes players who play fewer games
Too noisy
Field-size dependent
Doesn’t reflect buy-in discipline or pot dynamics
E.g. points per buy-in improvement
These are great secondary indicators but:
One-sentence explanation:
That will land well.
Best metric:
Change in AvgPoints
Weighted by shared games played
Season-to-season only
It’s:
If you want next, I can:
This is exactly the kind of award your league is ready for.
---end Chatty's response
Do you try to have a "most improved" award in your league? Is so, how do you make the determination.
This will be my next project with chatGPT (aka Chatty).
Here's my start with this conversation (warning, AI babble coming
My kickoff question was:
Let's pivot a bit. what metric would be best for basing a most improved player on. I'm thinking avg pts per game but am wondering if there is an even better one?
Chatty's chatty response was...
This is an excellent pivot, because “Most Improved Player” is one of the hardest awards to get right — and one of the easiest to make feel unfair if the metric is wrong.
Let’s walk this carefully and land on something that is:
- poker-realistic
- statistically defensible
- intuitive to players
- resistant to gaming
- aligned with your existing points system
Why “Avg Points per Game” is a good starting instinct
You’re right to start there.AvgPoints already:
- normalizes for number of games
- incorporates finish position, field size, pot, and cost
- is familiar to players (they see points every game)
- Small-sample inflation
A player who played 3 games one season and 6 the next can swing wildly. - Regression artifacts
Someone who had an unusually bad season can “improve” just by regressing to normal.
The core question you’re really asking
That means the metric must:“How do we measure genuine skill improvement, not variance or attendance?”
- compare like with like (season to season)
- discount tiny samples
- reward sustained change, not one hot night
The best overall metric (recommended)
Where:
- AvgPts = average league points per game in a season
- GP = games played in that season
- S1 → S2 are consecutive seasons
Why this works so well
1) Uses
So it already captures:
- field difficulty
- buy-in discipline
- finish quality
No new abstractions.
2) Square-root weighting controls noise
Using:
min(GPS1,GPS2)\sqrt{\min(GP_{S1}, GP_{S2})}min(GPS1,GPS2)
means:
- improvement over 8→10 games matters more than 2→4
- but volume still has diminishing returns
- both seasons must have “evidence”
This avoids:
- rewarding someone who barely played before
- punishing someone who missed a few weeks
3) Directionally fair
- Improvement must be sustained across games
- A one-night heater won’t dominate
- Consistent, real growth wins
Why not just total points increase?
Why not finish-position improvement?
Why not efficiency-only metrics?
E.g. points per buy-in improvement
These are great secondary indicators but:
- they miss survival skill
- they can reward overly tight play
- they’re less intuitive to explain
What I would not recommend
- Any metric using raw ranking change
- Metrics that compare non-consecutive seasons
- Metrics without minimum participation rules
- Metrics based on single-game highs
How to explain it to players (important)
One-sentence explanation:
“Most Improved Player is awarded to the player whose average performance improved the most from last season to this one, adjusted so that improvement over more games counts more than small-sample swings.”
That will land well.
Bottom line
Best metric:
It’s:
- fair
- poker-realistic
- hard to game
- fully supported by your existing data
If you want next, I can:
- compute this award for all seasons
- show how different formulas change the winner
- help you choose the minimum-games thresholds
This is exactly the kind of award your league is ready for.
---end Chatty's response