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Wal's College Football Week #3

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wal66

wal66

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There was some interest in this last season so I am at least starting out with them again this season. These ARE NOT opening lines, these are my adjusted lines. I use this as a jump start for narrowing down the scope of games I want to concentrate on.


NC State -7.5
Kansas -4.5
California -10
Troy -20.5
Kentucky -41
Indiana -5.5
Missouri -16.5
Ole Miss -6
Marshall -5.5
Miami-Ohio -8
Oklahoma St -7
Oregon St -16.5
Temple -2.5
Northwestern -11.5
Texas A&M -19.5
UL Monroe -3
Central Florida -7.5
Central Michigan -22
Purdue -13
Kansas St -4.5
Army -4
West Virginia PK
Illinois -14.5
Penn St -30
Ohio St -35
North Carolina -5.5
Arkansas -2
East Carolina -2.5
Hawaii -2
FSU PK
USC -25
Alabama -30
Oklahoma -9.5
Nebraska -7
Wisconsin -7
Florida -15
SMU -12
TCU -17.5
Auburn -9.5
Memphis PK
Western Michigan -2
Navy -4.5
LSU -13
Michigan St PK
Fresno St -18
Texas -7
Utah -18
Boise St -27
UTEP -9
Iowa -2.5
Houston -8.5
Idaho -3
Stanford -13
 
P-Roid,

I attempt to predict which way the market will go but I haven't been tracking. I sometimes check after the games but haven't documented.

I will give this a try this season.

I have paid attentio to when the program indicates the wrong team is favored but again it's not documented so while I am certain there is something substantial there I have no data.
 
wal66
P-Roid,

I attempt to predict which way the market will go but I haven't been tracking. I sometimes check after the games but haven't documented.
Click to expand...

When you're using a random X variable (like you said, although there is some thought behind the "randomness" so it's not totally random) which can't be backtested easily since X might change every week then a good way or measuring how well your model works is comparing it to the closing line. And actually, that method of tracking will really help you in making your X variable more effective since you might win ten games in a row but if you don't beat the closing line on each of those ten games then either the market is inefficient (less likely) or your model isn't efficient but you've got lucky (more likely).
 
Personally I like several plays this week that I am not on. I really like Alabama, Kentucky and Rice this week but am trying to show some discipline since this is the first week for the program. I only hope I have made a good decision on the games I am on.

If I have time I might try to do some write-ups but with everything thats going I just dont know if I will get around to it. Really though write-ups are mainly for those trying to convince others or yourself and Im not doing either.

Plays:

NC State -2
Troy -4.5
Maryland +11
East Carolina +17
Hawaii +11
Fresno St -6

All plays are to win 1 unit.
 
Coug's I have some concerns with just about every play on my card.

Fresno on the road and always tight with Utah St.
Hawaii's ability to stop the run and being off the island.
Cincinnati's steaky and could easily go off against NC State.
Virginia Tech humiliated themselves last weekend and is East Carolina playing that damn good or have teams been less than what was expected?
I'm good with Troy though.
 
wal66
Personally I like several plays this week that I am not on. I really like Alabama, Kentucky and Rice this week but am trying to show some discipline since this is the first week for the program. I only hope I have made a good decision on the games I am on.

If I have time I might try to do some write-ups but with everything that’s going I just don’t know if I will get around to it. Really though write-ups are mainly for those trying to convince others or yourself and I’m not doing either.

Plays:

NC State -2
Troy -4.5
Maryland +11
East Carolina +17
Hawaii +11
Fresno St -6

All plays are to win 1 unit.
Click to expand...

gl wal
 
mmike, roughly 53% across the board. The key to it's success is choosing the games with the largest margins and then of those finding the games with the biggest advantage. As I've said, this works for me because I believe it works and mainly because it narrows my search and helps to eliminate personal bias.



Kato, thanks bro, good luck to you as well.
 
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