So I modeled the season (and you aren't going to like it)

[Happy Panther]

Does the model take into account weather in the late months? For example GB or NE will more than likely be much tougher at home in Nov, Dec than they would in say September, even if said team is already tough to beat at home.

Another good question.

 

Ignoring stats for a moment, I suspect that the correlation between weather and scores is pretty weak and wouldn't add much to the model. Just anecdotally I feel like I have watched plenty of games where it is pouring snow and you assume that it will be a 10-6 snore game and the final is 35-32. We had a snow week last year but i seem to recall scores were pretty high. Could be wrong.

 

But from a stats standpoint...if I went back and tried to do a historical analysis on what the effect of weather was on northern home teams during December over the last 10 years I doubt it would be material. In other words I'm guessing good teams still win in the cold and bad teams don't. Or at least the predictive variable isn't December.

 

edit: and that analysis would take weeks with a team of math dorks. I'm not dodging the exercise.

[Scrumtrilescent]

I wrote into my programmers goals this year to work on more efficient expression algorithms.  I think we can easily tweak them a bit to cover other experiments besides biological systems.  If they want their raises, I mean.  

[Happy Panther]

The last part is what I have a problem with. Why would you randomly select the scores individually, unless I am misunderstanding. You should randomly select the scores of 1 of the 10,000 games, not randomly select one score, then randomly select the other. Otherwise, with that number of simulations, you could just put up every possible combination of scores, and select 2 numbers at random and get the same results.

Not sure the question but look at it this way.

 

Pretend there are only 5 possible scores for any NFL game. I have 10,000 but pretend there are 5.

 

7

10

13

21

28

 

My algorithm says for the very first game which is CAR @ TB give each team a random score and that determines who wins.

 

Random algorithm thingy runs and scores are assigned:

 

CAR 21 TB 13

 

Carolina is 1-0

 

Then just do that 255 more times and you have simulated the season on a coin flip level.

[Happy Panther]

I wrote into my programmers goals this year to work on more efficient expression algorithms.  I think we can easily tweak them a bit to cover other experiments besides biological systems.  If they want their raises, I mean.  

lol I will send you my model once it is cleaned up and you can run with it. Unfortunately once excel get's bogged down i am done.

[pstall]

[Rise of The Hive]

The fug?

[jrh687]

I can see us going 7-9.

 

I can't see us starting 2-9 and then winning the last 5 games. If were 2-9, were probably gonna end with 3 or 4 wins total.

[Ivan The Awesome]

Assumptions if anyone cares

 

1) I assumed that each team in each games scores independently of each other. While this obviously isn't true, trying to correlate scores in individual games is too complex and probably doesn't make a difference.

2) I assumed the average score is 24 and there will be roughly 1 team that scores under 12 and 1 that scores over 35 on average. I generated 10,000 scores (normal distibution) and each game randomly picks 2 scores from those 10,000

3) I assigned a relative strength to each team based on the previous season's results plus an assumption that good teams will get slightly worse and bad will get slightly better.

4) For each individual game the better team gets spotted a certain number of points based on strength. As an example Carolina gets 4 extra points in the opener (vegas line is -2.5) while Cincinnati gets 10 against Houston.

5) While i wanted the simulation to be random I also had to set some filters to make the choice itself random. So I demanded that 6 teams have a record at least 4 games different than the previous year and that both Seattle and Denver have winning records.

5) Once every game is simulated you just add up wins and losses.

 

 

 

You know what they say when you assume, right? Right?

[Happy Panther]

I can't see us starting 1-3 and ending up 12-4 with a home playoff game

...

[h0llywood]

Give the man his credit he spent his time doing a simulation and you guys half applaud him you Peter King aholes.

J/k. Good job putting that together but I hate simulations. If rather watch the season.

[davos]

Cleveland, Buffalo, and Jax all with winning records....

 

 

[Carolina Cajun]

[MadHatter]

 especially since your algorithms have the whole league 30 games above .500

Yep

 

NE, Buffalo, Cleveland, jacksonville, SF, etc  projections

 

Entire league is 30 games above .500

 

If this was done for a class, the OP would receive a massive failing grade.

[Happy Panther]

Yep

 

NE, Buffalo, Cleveland, jacksonville, SF, etc  projections

 

Entire league is 30 games above .500

 

If this was done for a class, the OP would receive a massive failing grade.

Thanks as always for your useless post.

 

It was a simple formula error I fixed and has been noted earlier in the thread. The panthers simulation was correct though.

 

It isn't for class if you knew how to read. It is something I banged out in an afternoon for fun. I will spend another couple of weeks cleaning it up.

 

But I build these for clients all the time and they pay me a shitload of money for it since very few people can even begin to try. Haven't found an error yet in what I produce.

 

Now run along and troll political.