Post descriptions of your losing models

[HUY]

I understand that nobody wants to share their winning models because then they won't be winning any more. So I think there will be people who will have no problem to post their losing models, just to get some discussion going. A system qualifies as "losing" if it has been bet unsuccessfully or if its out-of-sample prediction is unsuccessful.

I will make a start:

Elo and Glicko systems on tennis. Tried to optimize the parameters using brute-force optimization and also Nelder-Mead optimization. Out-of-sample prediction very lacking.

[Miz]

Anything that involves unsupervised neural network classifiers... for things like "win" in parimutuels. No matter what 56 graduate theses suggest.

[Miz]

Ken Pomeroy's line projections. These don't work, but they are a gift that keeps giving, as lines tend to converge to them.

[HUY]

Ken Pomeroy's line projections. These don't work, but they are a gift that keeps giving, as lines tend to converge to them.

If the lines converge to them why don't they work?

[HUY]

Anything that involves unsupervised neural network classifiers... for things like "win" in parimutuels. No matter what 56 graduate theses suggest.

What input data did you give to the neural network?

[Miz]

If the lines converge to them why don't they work?

I should've said they don't work unilaterally. Sometimes they are incorrect and the lines seem to converge anyway... creating value... It's the Sagarin effect basically.

[Miz]

What input data did you give to the neural network?

Tried various things. Supervised learning seems to work better. I also dislike the qualitative output, because it is harder to evaluate how accurate it is (harder to tell how big your adv is). There are better ways to include qualitative things like track conditions in a horse race, or "coming off a loss" in sports betting.

It isn't that NN's don't work for prediction. Supervised is usually better than unsupervised.

Many times for things like parimutuels where people like to try to use them, they are no better than the crowd (and usually worse than the crowd)... -EV