Quote:
Originally Posted by Lawnmower Man
There are basically three ways to approach it: (a) analytically, (b) simulation-based, and (c) empirically.
(a) An analytical model is an actual proof with a closed-form solution. You have to make a bunch of assumptions about scoring distributions, the joint distributions between players, the payout structure, etc. Then you can backsolve for optimal strategies either by hand or with a computer. You probably shouldn't try this if you aren't well-versed in theoretical statistics / mathematics.
(b) Simulation is likely your best bet. I'd start with a really simple model like a 3-man contest with a simplified game format, e.g., everyone drafts 1 player. Set the strategies, then run the contest a million times and see what the equities are. When you are satisfied with that, add one level of complexity (e.g., draft 2 players) and see what changes.
(c) You could download a bunch of DraftKings CSV files and try to estimate it empirically, but I wouldn't recommend this approach because I'm not convinced it would produce anything useful.
You're gonna need some coding chops to pull off (b) or (c).
I did (b) for NBA. In the first step i created a tournament with 10 000 different teams all within a salary near the max and with real ownership of the players.
Than in the second step my program simulated 10000 different outcomes using a gaussian distribution of projected points for each player.
Third step i looked which teams were in the top 1% of the field most often. It happened that ownership % was completely irrelevant. Teams with best value players perfomed best, even if their ownership was high.
But iam not a programer and my model has flaws. In reallife there might be more outliers in score which could favour low ownership. Also 10k simulations are not enough but the program was so slow lol.