So this is late, but I already did the analysis and I wanted to share my results for posterity. I used the same method as last time to try to evaluate who should be picked in a survival football pick 'em. This method basically only tries to figure out which teams, or combinations of teams, give you the best chance of getting all of your picks correct for the entire year. Obviously you want to pick a team this week that has the best chance of winning. But you do not want to pick a team that also has a lot of future value, where they will be favored by quite a bit in their remaining games. To reiterate, the way I did this is that I randomly picked a teams for the remainder of the season. I then used the Las Vegas point spreads of all the games to give determine the probability of winning each game and the rest of the games. Finally, I compared the average probability of winning every game when picking team X next week to the average probability of winning every game when picking a random team next week. I express this as a ratio - the higher, the better.
There are a couple of differences to how I did this last week.
What this is saying is that you are about 48% more likely to go 16 - 0 for the rest of the year if you choose Green Bay than if you pick a random team.
One aspect this method does not address is that your goal is not always to have a perfect season. If the league is small enough, you just want to outlast your opponents. My league only had about 10 people to start. If you looked at Yahoo!'s dashboard, you'd see that 56% of people chose GB this week. If they go down and you didn't pick them, your chances of winning increase dramatically. That is why I didn't choose GB and why I lost.
I chose DAL, because the spread was high, the percent taking them was low, and they had less future value. It was almost entirely on the recommendation of Vegas Watch. Maybe I should have looked at my ranking and picked Oakland, Atlanta or Cleveland since there was even a lower percentage picking those teams and a higher ranking than Dallas. With hindsight, we now know that Green Bay, Oakland, and Atlanta won and Cleveland and Dallas lost. But there was a strategy to it because 4 of the 9 teams that picked took Green Bay. If they lost and my team won, I would have had a much better chance of winning.
I will brainstorm about a way to factor the pick distribution into the rankings, but I do not think it is possible the way it is currently set up.
There are a couple of differences to how I did this last week.
- I modified the sampling of teams so that it does not choose teams on a bye week. This saved a lot of wasted simulations, and helps to make the results more stable. Therefore, I only simulated 100,000 seasons worth of picks compared to 1,000,000 last week.
- I omitted the team I picked last week from consideration of being picked for the rest of the season. Last week I picked Chicago, so they are not included.
- The bar charts on the graphic start at zero. I committed a cardinal sin of graphing last week because of Excel's defaults.
One aspect this method does not address is that your goal is not always to have a perfect season. If the league is small enough, you just want to outlast your opponents. My league only had about 10 people to start. If you looked at Yahoo!'s dashboard, you'd see that 56% of people chose GB this week. If they go down and you didn't pick them, your chances of winning increase dramatically. That is why I didn't choose GB and why I lost.
I chose DAL, because the spread was high, the percent taking them was low, and they had less future value. It was almost entirely on the recommendation of Vegas Watch. Maybe I should have looked at my ranking and picked Oakland, Atlanta or Cleveland since there was even a lower percentage picking those teams and a higher ranking than Dallas. With hindsight, we now know that Green Bay, Oakland, and Atlanta won and Cleveland and Dallas lost. But there was a strategy to it because 4 of the 9 teams that picked took Green Bay. If they lost and my team won, I would have had a much better chance of winning.
I will brainstorm about a way to factor the pick distribution into the rankings, but I do not think it is possible the way it is currently set up.
