I hope everyone is enjoying the college football offseason thus far!
I'm nearly done with a final #s review of every team's cumulative 2017 performance (PR #s with various breakdowns). Will share more of that later when I'm finally ready... guessing a week or two depending upon how busy I am at work.
The idea is to have an accurate full season PR# going into the offseason, then simply adjust up or down accordingly going into Week 1 2018. Easier said than done. And I'm sure all of us go to different levels of detail for their offseason analysis and adjustments. This exercise also forces me to see how well a team did against different groupings of opponents (vs. top 10 teams, vs. #11-25, etc. whatever breakdown makes sense as dictated by their schedule), or how much worse a team performed on average when a key player was out with an injury, etc, etc.
Anyway, I'm not quite ready yet, but Mr. Connelly is.... see the article posted below for a good summary of returning production for 2018. Mr. DoubleUp's Mississippi State team is mentioned among others. Really like Clemson as well. The entire D-line turning down the NFL tells us something IMO. They have their eyes on the prize.
Looking forward to comparing offseason notes for the next several months. Take care guys. And enjoy the Super Bowl this weekend.
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To remove first post, remove entire topic.
I hope everyone is enjoying the college football offseason thus far!
I'm nearly done with a final #s review of every team's cumulative 2017 performance (PR #s with various breakdowns). Will share more of that later when I'm finally ready... guessing a week or two depending upon how busy I am at work.
The idea is to have an accurate full season PR# going into the offseason, then simply adjust up or down accordingly going into Week 1 2018. Easier said than done. And I'm sure all of us go to different levels of detail for their offseason analysis and adjustments. This exercise also forces me to see how well a team did against different groupings of opponents (vs. top 10 teams, vs. #11-25, etc. whatever breakdown makes sense as dictated by their schedule), or how much worse a team performed on average when a key player was out with an injury, etc, etc.
Anyway, I'm not quite ready yet, but Mr. Connelly is.... see the article posted below for a good summary of returning production for 2018. Mr. DoubleUp's Mississippi State team is mentioned among others. Really like Clemson as well. The entire D-line turning down the NFL tells us something IMO. They have their eyes on the prize.
For the record I believe Michigan may be the most underrated team going into 2018 season.........I think that offense will be much better after only having just a couple returning starters on offense coming in to 2017.......There defense brings back a ton of production in an already elite defense..................I actuall have them ranked 5th overall.
Obviously gotta go with Bama Clemson GA and OHST to fill out the top 5
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Great stuff TD
THNX for sharing.
For the record I believe Michigan may be the most underrated team going into 2018 season.........I think that offense will be much better after only having just a couple returning starters on offense coming in to 2017.......There defense brings back a ton of production in an already elite defense..................I actuall have them ranked 5th overall.
Obviously gotta go with Bama Clemson GA and OHST to fill out the top 5
TD, So good your doing some Hot Stove football. Hope the family is well.
I couldn't care less about the top 5. Bore me. I'll make hundreds of bets this year and that top 5 will seldom be mentioned. I'm looking forward to info on all but the so called top 5.
I'm scuffling around trying to figure some Tex. area teams. Nothing positive to say now but maybe will have a couple I think will disappoint.
My mind is numb from playing NCAAB. I'm not going to study and play 250 teams playing twice a week any more. It's arduous and not particularly profitable. I suck betting NCAAB
Dilly, Dilly
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TD, So good your doing some Hot Stove football. Hope the family is well.
I couldn't care less about the top 5. Bore me. I'll make hundreds of bets this year and that top 5 will seldom be mentioned. I'm looking forward to info on all but the so called top 5.
I'm scuffling around trying to figure some Tex. area teams. Nothing positive to say now but maybe will have a couple I think will disappoint.
My mind is numb from playing NCAAB. I'm not going to study and play 250 teams playing twice a week any more. It's arduous and not particularly profitable. I suck betting NCAAB
Interesting link, TD. I like Connelly's take on returning starters based on production. But it's also interesting that #1 is MSU.......followed by Kansas........1-11 Kansas. As has been said many times over the years here, having a lot of returning starters from a really bad team isn't always a positive. But I get the gist of it.
Good info.
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Interesting link, TD. I like Connelly's take on returning starters based on production. But it's also interesting that #1 is MSU.......followed by Kansas........1-11 Kansas. As has been said many times over the years here, having a lot of returning starters from a really bad team isn't always a positive. But I get the gist of it.
Ever look at those Against the Spread (ATS) statistics at
the end of the year and notice that some teams were great ATS (2017:Iowa State 11-1-1 and Fresno State 11-2-1)
and some teams were terrible ATS (2017:Hawaii 1-10-1, UTEP 2-9-1, and WKU 3-10)?What does this mean?¡. Some teams were undervalued most of the
season from their true value and other teams were overvalued most of the season
from their true value.So if a team¡¯s
true CUMULATIVE value (numerically ¨C their PR#) was ¡°100% accurate¡± all season
long, then theoretically their ATS record is 6-6 and their full season Against
the Spread margin is net zero (*ATS margin being the number of points they did
or didn¡¯t cover by).I can tell you that
¡°100% accurate¡± doesn¡¯t exist for determining a ¡°one size fits all¡± PR#, but
going through the process of trying to get close to a 6-6 ATS record and net
0.0 margin for ATS margin does make you notice things about each team.Some teams are quite consistent week to week
and other teams are more volatile in their performance.I also notice tiers for a lot of teams.As just one example, a team might have what looks like a pretty
accurate PR# against most of the teams on their schedule in the ¡°same weight
class¡± but as soon as they play a top 10 team they can¡¯t keep up even with the
point spread.And/or the opposite also
occurs; they may not only beat but also cover vs. terrible teams.So, slight adjustments up or down to a team¡¯s
base PR# for a certain situations may be necessary.Some good to great teams choose not to run up
the score on lesser opponents.Some ball
control teams do well at keeping it close and covering vs. tough
opponents.The list of different
situations goes on and on.A lot of this is intuitive and not
surprising, but going through the numbers review process I find to be both
fascinating and rewarding in the long run.
I was done with this final 2017 review effort, then recently decided
to take one more pass at it and adjust individual team ratings based on
conference ratings (i.e., the entire conference¡¯s combined non-conference ATS
record and ATS margins¡ªbecause their in-conference ATS record and ATS margin
are 0.500 and net zero by definition). Basically, slide the entire conference up or down together.
So, reasons to look back at the previous season, rather than
starting over from scratch every year:1. Establish an accurate starting point for offseason adjustments.2.Noticing how well a team improved (or regressed) in the last 4-8 games
of the season (especially with first or second year head coaches).3.Noticing how bad a team performed with a lame duck coach that ends up
being fired ¨C i.e., the new head coach inherits a team that is not as bad as
the raw #s indicate.4.Noticing what types of tier teams exist and
being on the lookout for similar teams in the upcoming season. 5. Determining what types of teams are consistent or volatile.
Anyway, here are my final 2017 post season ratings.I¡¯ll try to get into some more detailed
breakdowns later this weekend for each team.And there do end up being a few teams that I intentionally keep at a PR#
that results in an ATS W/L record a couple notches above or below a 6-6 record
for various reasons.Specific examples
later as I find time to post (Pretty damn busy lately).And before I forget to mention, I choose to
count any ATS margin result that is between +0.99 and -0.99 (less than a full
point win or loss) as a push in their ATS record.This just helps me notice when a team is very
consistent (some teams have 2, 3, or 4 pushes in a season with this approach).
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Alright team, I¡¯m a little later than estimated.
Ever look at those Against the Spread (ATS) statistics at
the end of the year and notice that some teams were great ATS (2017:Iowa State 11-1-1 and Fresno State 11-2-1)
and some teams were terrible ATS (2017:Hawaii 1-10-1, UTEP 2-9-1, and WKU 3-10)?What does this mean?¡. Some teams were undervalued most of the
season from their true value and other teams were overvalued most of the season
from their true value.So if a team¡¯s
true CUMULATIVE value (numerically ¨C their PR#) was ¡°100% accurate¡± all season
long, then theoretically their ATS record is 6-6 and their full season Against
the Spread margin is net zero (*ATS margin being the number of points they did
or didn¡¯t cover by).I can tell you that
¡°100% accurate¡± doesn¡¯t exist for determining a ¡°one size fits all¡± PR#, but
going through the process of trying to get close to a 6-6 ATS record and net
0.0 margin for ATS margin does make you notice things about each team.Some teams are quite consistent week to week
and other teams are more volatile in their performance.I also notice tiers for a lot of teams.As just one example, a team might have what looks like a pretty
accurate PR# against most of the teams on their schedule in the ¡°same weight
class¡± but as soon as they play a top 10 team they can¡¯t keep up even with the
point spread.And/or the opposite also
occurs; they may not only beat but also cover vs. terrible teams.So, slight adjustments up or down to a team¡¯s
base PR# for a certain situations may be necessary.Some good to great teams choose not to run up
the score on lesser opponents.Some ball
control teams do well at keeping it close and covering vs. tough
opponents.The list of different
situations goes on and on.A lot of this is intuitive and not
surprising, but going through the numbers review process I find to be both
fascinating and rewarding in the long run.
I was done with this final 2017 review effort, then recently decided
to take one more pass at it and adjust individual team ratings based on
conference ratings (i.e., the entire conference¡¯s combined non-conference ATS
record and ATS margins¡ªbecause their in-conference ATS record and ATS margin
are 0.500 and net zero by definition). Basically, slide the entire conference up or down together.
So, reasons to look back at the previous season, rather than
starting over from scratch every year:1. Establish an accurate starting point for offseason adjustments.2.Noticing how well a team improved (or regressed) in the last 4-8 games
of the season (especially with first or second year head coaches).3.Noticing how bad a team performed with a lame duck coach that ends up
being fired ¨C i.e., the new head coach inherits a team that is not as bad as
the raw #s indicate.4.Noticing what types of tier teams exist and
being on the lookout for similar teams in the upcoming season. 5. Determining what types of teams are consistent or volatile.
Anyway, here are my final 2017 post season ratings.I¡¯ll try to get into some more detailed
breakdowns later this weekend for each team.And there do end up being a few teams that I intentionally keep at a PR#
that results in an ATS W/L record a couple notches above or below a 6-6 record
for various reasons.Specific examples
later as I find time to post (Pretty damn busy lately).And before I forget to mention, I choose to
count any ATS margin result that is between +0.99 and -0.99 (less than a full
point win or loss) as a push in their ATS record.This just helps me notice when a team is very
consistent (some teams have 2, 3, or 4 pushes in a season with this approach).
Alabama 98.2 Georgia 96.2 Clemson 95.4 Oklahoma 95.4 Penn State 95.1 Ohio State 94.9 Auburn 91.8 Wisconsin 91.2 Notre Dame 89.6 Washington 89.1 Oklahoma State 87.7 TCU 86.4 LSU 85.0 Central Florida 84.7 Mississippi State 83.3 Miami 83.2 USC 82.3 Texas 82.3 Virginia Tech 81.9 Stanford 81.5 NC State 81.4 Northwestern 80.6 Michigan 80.5 Iowa 80.4 Louisville 80.2 Florida State 79.9 Memphis 79.9 Wake Forest 79.6 Iowa State 79.4 Michigan State 79.2 Oregon 78.4 Georgia Tech 77.9 Boise State 77.7 Kansas State 77.1 Florida Atlantic 77.0 Washington State 76.9 Purdue 76.3 Boston College 76.2 Syracuse 75.7 *based on games with QB Dungey healthy.
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7800 characters.... really!?
Alabama 98.2 Georgia 96.2 Clemson 95.4 Oklahoma 95.4 Penn State 95.1 Ohio State 94.9 Auburn 91.8 Wisconsin 91.2 Notre Dame 89.6 Washington 89.1 Oklahoma State 87.7 TCU 86.4 LSU 85.0 Central Florida 84.7 Mississippi State 83.3 Miami 83.2 USC 82.3 Texas 82.3 Virginia Tech 81.9 Stanford 81.5 NC State 81.4 Northwestern 80.6 Michigan 80.5 Iowa 80.4 Louisville 80.2 Florida State 79.9 Memphis 79.9 Wake Forest 79.6 Iowa State 79.4 Michigan State 79.2 Oregon 78.4 Georgia Tech 77.9 Boise State 77.7 Kansas State 77.1 Florida Atlantic 77.0 Washington State 76.9 Purdue 76.3 Boston College 76.2 Syracuse 75.7 *based on games with QB Dungey healthy.
West Virginia 75.2 Texas A&M 74.9 Utah 74.8 South Carolina 74.7 Texas Tech 74.5 Houston 74.5 Missouri 74.2 Arizona 74.1 San Diego State 73.8 Duke 73.7 Fresno State 73.6 Pittsburgh 73.6 Arizona State 73.4 Toledo 73.3 UCLA 72.4 Mississippi 72.0 Troy 71.7 California 71.5 Indiana 71.4 Navy 70.1 Ohio U. 69.7 Army 69.6 Nebraska 69.5 Appalachian State 69.3 Florida 68.7 Virginia 68.4 Kentucky 68.0 Minnesota 67.4 SMU 67.3 Arkansas 67.2 Northern Illinois 67.0 Arkansas State 66.9 Colorado 66.4 Colorado State 66.1 North Carolina 66.1 Western Michigan 65.7 Wyoming 65.7 Temple 65.6
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West Virginia 75.2 Texas A&M 74.9 Utah 74.8 South Carolina 74.7 Texas Tech 74.5 Houston 74.5 Missouri 74.2 Arizona 74.1 San Diego State 73.8 Duke 73.7 Fresno State 73.6 Pittsburgh 73.6 Arizona State 73.4 Toledo 73.3 UCLA 72.4 Mississippi 72.0 Troy 71.7 California 71.5 Indiana 71.4 Navy 70.1 Ohio U. 69.7 Army 69.6 Nebraska 69.5 Appalachian State 69.3 Florida 68.7 Virginia 68.4 Kentucky 68.0 Minnesota 67.4 SMU 67.3 Arkansas 67.2 Northern Illinois 67.0 Arkansas State 66.9 Colorado 66.4 Colorado State 66.1 North Carolina 66.1 Western Michigan 65.7 Wyoming 65.7 Temple 65.6
Baylor 65.1
Central Michigan 64.8
Eastern Michigan 64.7
Maryland 64.1
Utah State 63.7
Middle Tennessee 63.5
Tennessee 63.2
Tulane 63.0
Air Force 63.0
Marshall 63.0
Rutgers 63.0
Vanderbilt 62.8
Buffalo 61.9
Southern Miss 71.8
North Texas 61.0
Louisiana Tech 60.0
Texas San Antonio 59.9
Miami (Ohio) 59.8
Massachusetts 59.2
New Mexico State 58.9
Nevada 58.9
Illinois 58.7
BYU 58.2
Idaho 58.7
UAB 57.3
Tulsa 57.2
UNLV 57.2
Louisiana Monroe 56.5
Akron 56.5
Florida International 55.5
Oregon State 55.5
Cincinnati 55.4
South Alabama 54.6
Connecticut 54.4
Georgia State 53.6
Western Kentucky 53.2
New Mexico 52.8
Bowling Green 52.7
East Carolina 51.9
Kansas 51.2
Coastal Carolina 50.9
Hawaii 50.0
Georgia Southern 49.8
Louisiana Lafayette 48.9
Old Dominion 47.5
Charlotte 44.8
Kent State 44.0
Rice 43.3
Texas State 41.7
San Jose State 40.0
UTEP 39.0
Ball State 36.4
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Baylor 65.1
Central Michigan 64.8
Eastern Michigan 64.7
Maryland 64.1
Utah State 63.7
Middle Tennessee 63.5
Tennessee 63.2
Tulane 63.0
Air Force 63.0
Marshall 63.0
Rutgers 63.0
Vanderbilt 62.8
Buffalo 61.9
Southern Miss 71.8
North Texas 61.0
Louisiana Tech 60.0
Texas San Antonio 59.9
Miami (Ohio) 59.8
Massachusetts 59.2
New Mexico State 58.9
Nevada 58.9
Illinois 58.7
BYU 58.2
Idaho 58.7
UAB 57.3
Tulsa 57.2
UNLV 57.2
Louisiana Monroe 56.5
Akron 56.5
Florida International 55.5
Oregon State 55.5
Cincinnati 55.4
South Alabama 54.6
Connecticut 54.4
Georgia State 53.6
Western Kentucky 53.2
New Mexico 52.8
Bowling Green 52.7
East Carolina 51.9
Kansas 51.2
Coastal Carolina 50.9
Hawaii 50.0
Georgia Southern 49.8
Louisiana Lafayette 48.9
Old Dominion 47.5
Charlotte 44.8
Kent State 44.0
Rice 43.3
Texas State 41.7
San Jose State 40.0
UTEP 39.0
Ball State 36.4
*Notice the very first team is an example of you can¡¯t have
it both ways (Record suggests they are overvalued, Margins suggest they are
undervalued). This is obviously because
when they won, they won big.
Inconsistent teams for ATS margin lead me to do a standard deviation on
this statistic so that I could then rank all teams from most consistent ATS
margins to most volatile ATS margins.
Will post that info later.
Clemson:
Power Rating = 95.4
ATS record: 7-7
ATS margin: +0.2 ppg
ATS margin first 10
regular season games: -1.4 ppg (ATS record: 4-6) *Also, for games 6-10,
they were 0-5 ATS. Clemson seems to
intentionally put it on cruise control until their best effort is needed.
ATS margin last 3
regular season games: +10.7 ppg (ATS record 3-0) *I do include conference championship
games as regular season (both teams are motivated, unlike some bowl games).
ATS margin
average/mean vs. #1 -#25 rated opponents:
+3.4 ppg (ATS record: 4-2) *This
figure was +7.1 ppg going into the Alabama game. Clemson plays well (generally) against better
opponents.
ATS
margin average/mean vs. #26 -#45 rated opponents: -3.5 ppg (ATS record: 2-4) *Anybody
have Wake Forest +21 against Clemson last year?
Never in doubt, right!!?? Down
28-0 with 12 minutes left to play. Dabo
tends to go on cruise control vs. opponents in this tier. Same thing happened in the 2016 championship
season, they turned it on when they needed to.
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Just a sample of what I'll try to show next (running out of time tonight):
ACC:
Boston College:
Power Rating = 76.2
ATS record: 5-7-1 *suggests
they should be rated lower to improve their ATS record.
ATS margin: +3.1 ppg
*suggests they should be rated higher to lower their ATS margin
toward 0.0.
**This ATS margin figure decreases to +0.8 if the Syracuse
game is excluded (QB Dungey out).
ATS margin first 6 regular
season games: -9.0 ppg (ATS record: 1-5)
ATS margin last 6
regular season games: +16.1 ppg (ATS record 4-1-1)
*Notice the very first team is an example of you can¡¯t have
it both ways (Record suggests they are overvalued, Margins suggest they are
undervalued). This is obviously because
when they won, they won big.
Inconsistent teams for ATS margin lead me to do a standard deviation on
this statistic so that I could then rank all teams from most consistent ATS
margins to most volatile ATS margins.
Will post that info later.
Clemson:
Power Rating = 95.4
ATS record: 7-7
ATS margin: +0.2 ppg
ATS margin first 10
regular season games: -1.4 ppg (ATS record: 4-6) *Also, for games 6-10,
they were 0-5 ATS. Clemson seems to
intentionally put it on cruise control until their best effort is needed.
ATS margin last 3
regular season games: +10.7 ppg (ATS record 3-0) *I do include conference championship
games as regular season (both teams are motivated, unlike some bowl games).
ATS margin
average/mean vs. #1 -#25 rated opponents:
+3.4 ppg (ATS record: 4-2) *This
figure was +7.1 ppg going into the Alabama game. Clemson plays well (generally) against better
opponents.
ATS
margin average/mean vs. #26 -#45 rated opponents: -3.5 ppg (ATS record: 2-4) *Anybody
have Wake Forest +21 against Clemson last year?
Never in doubt, right!!?? Down
28-0 with 12 minutes left to play. Dabo
tends to go on cruise control vs. opponents in this tier. Same thing happened in the 2016 championship
season, they turned it on when they needed to.
Nice work , TD . Digging into the " Science " of establishing " Power ratings " can lead to some great breakthroughs into how to get maximum accuracy and benefit from them. Digging into the analytics is not for everyone , but I've found that in doing so it leads to more questions that demand answers . It's actually a fun journey and leads to some great rewards. Well done , Bud. You might consider taking your research just a bit farther and use your ratings to establish a " win probability " for each matchup as well.
TD , Great work , Buddy
Helping Kama'aina to beat their " Local " since 1994.
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Nice work , TD . Digging into the " Science " of establishing " Power ratings " can lead to some great breakthroughs into how to get maximum accuracy and benefit from them. Digging into the analytics is not for everyone , but I've found that in doing so it leads to more questions that demand answers . It's actually a fun journey and leads to some great rewards. Well done , Bud. You might consider taking your research just a bit farther and use your ratings to establish a " win probability " for each matchup as well.
Where are all the offseason college football fans!? - I'm just too d@mn busy with work and coaching baseball and soccer to post very many of my thoughts for going into the 2018 season. Hopefully I'll find a little more time in the coming weeks to post.
And whoever is moving a couple week 1 lines in my favor... Thanks!
2018 Week 1 plays locked in:
Texas -7 (hook) (0.2u) *posted previously in my bowl thread actually -- grabbed it that early.
Added this morning:
Washington +5.5 (0.5u)
Florida Atlantic +24 (0.3u)
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Where are all the offseason college football fans!? - I'm just too d@mn busy with work and coaching baseball and soccer to post very many of my thoughts for going into the 2018 season. Hopefully I'll find a little more time in the coming weeks to post.
And whoever is moving a couple week 1 lines in my favor... Thanks!
2018 Week 1 plays locked in:
Texas -7 (hook) (0.2u) *posted previously in my bowl thread actually -- grabbed it that early.
How much tougher does wagering on college football get from beginning to end of a season?
1 year sample size only, but here is what I've found for 2017.
Using the cumulative full season PR#s I listed above to determine a point spread vs. the actual Las Vegas point spread (closing lines each week per this site), the following are the average difference in point spread per game from a * "true line":
Weeks 0 / 1: All games = 10.0
FBS vs. FBS = 9.0
FBS vs. FCS = 11.0
Week 2: All games = 6.7
FBS vs. FBS = 6.5
FBS vs. FCS = 7.1
Week 3: All games = 6.8
FBS vs. FBS = 6.3
FBS vs. FCS = 8.7
Week 4: All games = 6.5
Week 5: All games = 5.3
Week 6: All games = 6.8
Week 7: All games = 4.8
Week 8: All games = 4.6
Week 9: All games = 4.7
Week 10: All games = 4.3
Week 11: All games = 2.8
Week 12: All games = 3.4
Week 13: All games = 2.8
Week 14: All games = 3.6
Week 15: 1 game = 2.0
Bowls: All games = 2.9
* I put an asterisk by "true line" because my PR# per team is a full season cumulative # (because my offseason effort is an overview to find a starting point for 2018 offseason adjustments. My week to week in-season approach would further correct the late season ebbs & flows indicated above). In reality I think LV gets sharper every week late in the year. Late season injuries, lame duck coaches, etc. are the explanation for the #s above bouncing around late in the year. And week 14 is a smaller sample size as well. Think of the full season cumulative PR# as if Team A plays Team B all 12 weeks, what is the one single baseline point spread for all 12 games between these two teams. In-season, the PR#s (and the resulting point spread) adjust slightly week to week.
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How much tougher does wagering on college football get from beginning to end of a season?
1 year sample size only, but here is what I've found for 2017.
Using the cumulative full season PR#s I listed above to determine a point spread vs. the actual Las Vegas point spread (closing lines each week per this site), the following are the average difference in point spread per game from a * "true line":
Weeks 0 / 1: All games = 10.0
FBS vs. FBS = 9.0
FBS vs. FCS = 11.0
Week 2: All games = 6.7
FBS vs. FBS = 6.5
FBS vs. FCS = 7.1
Week 3: All games = 6.8
FBS vs. FBS = 6.3
FBS vs. FCS = 8.7
Week 4: All games = 6.5
Week 5: All games = 5.3
Week 6: All games = 6.8
Week 7: All games = 4.8
Week 8: All games = 4.6
Week 9: All games = 4.7
Week 10: All games = 4.3
Week 11: All games = 2.8
Week 12: All games = 3.4
Week 13: All games = 2.8
Week 14: All games = 3.6
Week 15: 1 game = 2.0
Bowls: All games = 2.9
* I put an asterisk by "true line" because my PR# per team is a full season cumulative # (because my offseason effort is an overview to find a starting point for 2018 offseason adjustments. My week to week in-season approach would further correct the late season ebbs & flows indicated above). In reality I think LV gets sharper every week late in the year. Late season injuries, lame duck coaches, etc. are the explanation for the #s above bouncing around late in the year. And week 14 is a smaller sample size as well. Think of the full season cumulative PR# as if Team A plays Team B all 12 weeks, what is the one single baseline point spread for all 12 games between these two teams. In-season, the PR#s (and the resulting point spread) adjust slightly week to week.
I should add that some of the early season opening lines are off more (some significantly more) than the data above (delta vs. closing lines).
One extreme example from 2017 week 1 would be the UAB vs. Alabama A&M game.
After a full season, the larger amount of data available arrives at a fair line point spread of right around 38 to 39 points. The actual week 1 LV closing line is listed as UAB -27.5. So, 10.5 to 11.5 points off of what the line would have been if these two teams met again at the end of the season. I was able to lock in UAB -9.5 last summer well before the season started. UAB is obviously an extreme example because books did not know how to value them early in the year.
Several other examples out there I am sure, just wanted to point out that these figures are averages. So, if week 1 lines are 'off' by 10 points on average, some games are 'off' more than 10 points, and some games are 'off' less. Makes sense. Staying ahead to find softer lines is a great approach early in the year. But the #s above clearly show how much tighter the lines get week to week as a season goes on.
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I should add that some of the early season opening lines are off more (some significantly more) than the data above (delta vs. closing lines).
One extreme example from 2017 week 1 would be the UAB vs. Alabama A&M game.
After a full season, the larger amount of data available arrives at a fair line point spread of right around 38 to 39 points. The actual week 1 LV closing line is listed as UAB -27.5. So, 10.5 to 11.5 points off of what the line would have been if these two teams met again at the end of the season. I was able to lock in UAB -9.5 last summer well before the season started. UAB is obviously an extreme example because books did not know how to value them early in the year.
Several other examples out there I am sure, just wanted to point out that these figures are averages. So, if week 1 lines are 'off' by 10 points on average, some games are 'off' more than 10 points, and some games are 'off' less. Makes sense. Staying ahead to find softer lines is a great approach early in the year. But the #s above clearly show how much tighter the lines get week to week as a season goes on.
thanks TD. Great info and truly appreciate your willingness to share. Are your Week 1 lines so far from 5D? Or is there another book offering lines now? thanks again
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thanks TD. Great info and truly appreciate your willingness to share. Are your Week 1 lines so far from 5D? Or is there another book offering lines now? thanks again
TD, thanks for the hard work and info you share with this site.
I like the UCF and Wyoming play. I too see value at those numbers. As far as the Syracuse game, what do you have as a fair line? I capped them at -5.5 and with my book already at -3.5 I have lost the edge there. Based on your number do you still see value at -3.5?
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TD, thanks for the hard work and info you share with this site.
I like the UCF and Wyoming play. I too see value at those numbers. As far as the Syracuse game, what do you have as a fair line? I capped them at -5.5 and with my book already at -3.5 I have lost the edge there. Based on your number do you still see value at -3.5?
TD, thanks for the hard work and info you share with this site.
I like the UCF and Wyoming play. I too see value at those numbers. As far as the Syracuse game, what do you have as a fair line? I capped them at -5.5 and with my book already at -3.5 I have lost the edge there. Based on your number do you still see value at -3.5?
About -7 looks right to me with a healthy Dungey and Wassink at QB for both teams. Maybe even -7.5, but that just me. IMO still some value at -3.5. Line should get bet into the -6 to -7 range is my guess.
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Quote Originally Posted by Sox:
TD, thanks for the hard work and info you share with this site.
I like the UCF and Wyoming play. I too see value at those numbers. As far as the Syracuse game, what do you have as a fair line? I capped them at -5.5 and with my book already at -3.5 I have lost the edge there. Based on your number do you still see value at -3.5?
About -7 looks right to me with a healthy Dungey and Wassink at QB for both teams. Maybe even -7.5, but that just me. IMO still some value at -3.5. Line should get bet into the -6 to -7 range is my guess.
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