I'm going to withhold the total predictions for this week, but I'll put up the winners and spreads. This week's predictions are going to sound ridiculous. I was actually thinking about waiting another week before putting them up, but I went back and looked at the simulation of last season's week 2 to see how they compared. In both weeks 2 there were many picks of huge underdogs, but that week actually turned out to be a very good one. I won't give out that week's exact numbers because I don't want heightened expectations. Remember, use these at your own risk. If you're in a suicide / survivor pool and you see that SF wins 79% when they're a 7 point dog, use some discretion. Bear in mind it's the beginning of the season, so the picks may not be as reliable because of a lack of data. That said, here we go...
If the confidence is 50%, I'll just write EVEN.
NYG -8.5 at STL
Winner: NYG, 76%
Spread: EVEN
IND -2 at MIN
Winner: MIN, 54%
Spread: MIN +2, 58%
NO -1 at WAS
Winner: NO, 61%
Spread: NO -1, 60%
CHI at CAR -3
Winner: CAR, 51%
Spread: CHI +3, 61%
BUF at JAX -5
Winner: BUF, 64%
Spread: BUF +5, 77%
TEN at CIN -1
Winner: TEN, 79%
Spread: TEN +1, 80%
GB -3.5 at DET
Winner: DET, 59%
Spread: DET +3.5, 64%
OAK at KC -3.5
Winner: KC, 81%
Spread: KC -3.5, 72%
SF at SEA -6.5
Winner: SF, 79%
Spread: SF +6.5, 89%
ATL at TB -7
Winner: ATL, 80%
Spread: ATL +7, 92%
SD -1 at DEN
Winner: DEN, 74%
Spread: DEN +1, 75%
BAL at HOU -4.5
Winner: BAL, 70%
Spread: BAL +4.5, 80%
NE at NYJ -1.5
Winner: NYJ, 53%
Spread: EVEN
MIA at ARI -6.5
Winner: ARI, 57%
Spread: MIA +6.5, 61%
PIT -6 at CLE
Winner: PIT, 91%
Spread: PIT -6, 79%
PHI at DAL -7
Winner: PHI, 65%
Spread: PHI +7, 84%
Friday, September 12, 2008
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Lots of gutsy picks, should be fun! (Not that a simulation knows that it's gutsy...)
ReplyDeleteThanks for taking the risk and posting early season simulations. Way more interesting to see the predictions generated from very sparse data.
Are you using monte carlo to help you simulate your games? I've been trying to understand Monte Carlo but to no avail.
ReplyDeleteRight now i just have a pretty basic formula to caluclte league wide power ratings. It takes into account home field and strength of opponent so maybe its no so basic afterall.
What I'm getting at, I guess, is that it's nothing like what you have here.
I hope to learn a thing or to more and be able to simulate games like you have. Get player statistics to help me with my fantasy team as well as the final game scores.
I definately need to add a few more statistical tools to my toolbag and then run backtests.
It's a shame HS Statistics only gets one so far. Never took any Stats Classes in College.
Enough rambling on that.
I was wondering what your top 5 and bottom 5 teams are. My model has the top as Denver, Phila, Arizona, Dallas and Chicago. The bottom (32nd on up) as Oakland, St. Louis, Cleveland, Indy and Seattle.
GL this week.
singletrack...It'll be an interesting Sunday. Lots of dogs to root for.
ReplyDeletenick...When I started this project I didn't know about Monte Carlo simulations, though I suppose it kind of turned into one. But no, it's not specifically Monet Carlo.
I think incorporating opponent strength is a good move with power rankings. I'll start posting my ratings next week, so we can compare those.
Don't overestimate the importance of a stats class. When I wrote the program I had never taken a stats class, so it's definitely possible to do it on your own. I'd say being able to program is much more important. As you work, you'll develop an intuition for statistics. That said, I just finished up a stats class; I think I'll be applying what I learned in the class to the program. Programming/problem solving is 90%, formal knowledge of statistics is 10%.
If you want fantasy advice, let me know which players you need to choose from and I'll tell you who performed better in the sim's.