[h=3]I hope the format will not be crushed when copying from my blog.
Introduction[/h] I’ve worked out an interesting trend for week one and wanted to share it. But first of all, let me make a few things clear: technical handicapping can be a great complement, but you always have to put everything into perspective. Always question a trend, think about the background and about how much it can be applied to that certain situation. You will find clowns who say “Chargers are 8-1 ATS in their last 9 games at Mile High, so Chargers are the play” or “The Dolphins are 10-2 ATS in their last 12 home games when the total is below 45”. Those trends are bullshit. You always have to apply a trend on any given team in a certain situation, whether it’s the Pats or Titans. You can only compare the same with the same. That being said, let’s go!
Three weeks ago, I posted a write-up on Seasonal Regression Methods in the NFL. These regression methods can be used as an indicator for public perception early in the season and especially in week one. Public perception creates valuable lines for handicappers. Teams get over- and undervalued early in the season due to public perception. It’s the cappers’ job to take advantage until lines adjust.
[h=3]The System[/h] I went back to 2002 and analyzed all week one games in which a matchup had a difference of atleast 1.9 estimated wins and pythagorean wins. Meaning that one team over- and the other underperformed, or just a huge over- or underperformance by one team the year before. I have also noticed that in all except one matchup, the close win differential was the same direction than the win differential. So you can basically assume that the difference lies in all three categories. Then I checked back the ATS margin on the closing line. If there was a difference in all three categories, the teams went 26-11 ATS since 2002. The trend is 16-3 ATS since 2009. So it went 10-8 ATS from 2002 to 2008. Here are the games dating back to 2009:
2015: NYG (2.7 est; 2.7 pyth; 6 close) lost 26-27 as +6 underdogs at DAL
NO (4.2 est; 3.1 pyth; 5 close) lost 19-31 as +2 underdogs at ARI
2014: ATL (3.5 est; 2.1 pyth; 5 close) won 37-34 as +2.5 underdogs vs. NO
DET (2.2 est; 2.9 pyth); 3 close) won 35-14 as -6.5 favorites vs. NYG
2013: DET (4.8 est; 3.6 pyth; 10 close) won 34-24 as -4.5 favorites vs. MIN
OAK (4.5 est; 3.9 pyth; 10 close) lost 17-21 as +10 underdogs at IND
NO (3.3 est; 3.0 pyth; 6 close) won 23-17 as -3.5 favorites vs. ATL
PIT (2.1 est; 2.1 pyth; 2 close) lost 9-16 as -6.5 favorites vs. TEN
SD (3.3 est; 2.8 pyth; 8 close) lost 28-31 as +4 underdogs vs. HOU
2012: SEA (4.2 est; 2.3 pyth; 6 close) lost 16-20 as -2.5 favorites at ARI
2011: GB (2.7 est; 3.8 pyth; 6 close) won 42-34 as -4.5 favorites vs. NO
DET (3.1 est; 3.1 pyth; 4 close) won 27-20 as -1.5 favorites at TB
TEN (4.1 est; 4.2 pyth; 8 close) lost 14-16 as +2 underdogs at JAX
HOU (3.7 est; 1.9 pyth; 3 close) won 34-7 as -9.5 favorites vs. IND
2010: NE (3.2 est; 3.3 pyth; 5 close) won 38-24 as -5 favorites vs. CIN
HOU (3.9 est; 3.6 pyth; 9 close) won 34-24 as +1 underdogs vs. IND
KC (2.1 est; 2.3 pyth; 10 close) won 21-14 as +4.5 underdogs vs. SD
2009: JAX (5.1 est; 2.9 pyth; 9 close) lost 12-14 as +6.5 underdogs at IND
PHI (4.3 est; 4.2 pyth; 8 close) won 38-10 as -2.5 favorites at CAR
GB (2.6 est; 3.3 pyth; 6 close) won 21-15 as -4 favorites vs. CHI
[h=3]Results[/h] Dogs went 7-1 ATS, home dogs went 5-0 ATS. Favorites went 9-2 ATS, home favorites went 7-1 ATS. So home teams went 12-1 ATS whereas away teams went 4-2 ATS.
The results are highly interesting. Of the 7 winning dogs, 3 won straight up and 4 didn’t. It’s a small sample size, but you notice that getting more points due to public perception can be very valuable.
[h=3]Qualifying Matchups for Week One[/h] Here is the data for week one:
A negative number favors the road team, a positive one the home team. The qualifying teams are Seahawks, Lions and Titans. Chargers come VERY close to qualifying. Steelers and Browns are also worth a look. The Titans and Chargers also have a huge differential regarding turnovers. The Titans really are a prime example, posting a huge regression advantage in every category. Current lines from 5Dimes:
Titans +3 +105
Seahawks -10 -110
Lions +5 -110
Chargers +7 -115
Browns +6 -110
Steelers -3 -120
Like I’ve said before, you should put everything into perspective, but this system can help you evaluating lines in week one.
Introduction[/h] I’ve worked out an interesting trend for week one and wanted to share it. But first of all, let me make a few things clear: technical handicapping can be a great complement, but you always have to put everything into perspective. Always question a trend, think about the background and about how much it can be applied to that certain situation. You will find clowns who say “Chargers are 8-1 ATS in their last 9 games at Mile High, so Chargers are the play” or “The Dolphins are 10-2 ATS in their last 12 home games when the total is below 45”. Those trends are bullshit. You always have to apply a trend on any given team in a certain situation, whether it’s the Pats or Titans. You can only compare the same with the same. That being said, let’s go!
Three weeks ago, I posted a write-up on Seasonal Regression Methods in the NFL. These regression methods can be used as an indicator for public perception early in the season and especially in week one. Public perception creates valuable lines for handicappers. Teams get over- and undervalued early in the season due to public perception. It’s the cappers’ job to take advantage until lines adjust.
[h=3]The System[/h] I went back to 2002 and analyzed all week one games in which a matchup had a difference of atleast 1.9 estimated wins and pythagorean wins. Meaning that one team over- and the other underperformed, or just a huge over- or underperformance by one team the year before. I have also noticed that in all except one matchup, the close win differential was the same direction than the win differential. So you can basically assume that the difference lies in all three categories. Then I checked back the ATS margin on the closing line. If there was a difference in all three categories, the teams went 26-11 ATS since 2002. The trend is 16-3 ATS since 2009. So it went 10-8 ATS from 2002 to 2008. Here are the games dating back to 2009:
2015: NYG (2.7 est; 2.7 pyth; 6 close) lost 26-27 as +6 underdogs at DAL
NO (4.2 est; 3.1 pyth; 5 close) lost 19-31 as +2 underdogs at ARI
2014: ATL (3.5 est; 2.1 pyth; 5 close) won 37-34 as +2.5 underdogs vs. NO
DET (2.2 est; 2.9 pyth); 3 close) won 35-14 as -6.5 favorites vs. NYG
2013: DET (4.8 est; 3.6 pyth; 10 close) won 34-24 as -4.5 favorites vs. MIN
OAK (4.5 est; 3.9 pyth; 10 close) lost 17-21 as +10 underdogs at IND
NO (3.3 est; 3.0 pyth; 6 close) won 23-17 as -3.5 favorites vs. ATL
PIT (2.1 est; 2.1 pyth; 2 close) lost 9-16 as -6.5 favorites vs. TEN
SD (3.3 est; 2.8 pyth; 8 close) lost 28-31 as +4 underdogs vs. HOU
2012: SEA (4.2 est; 2.3 pyth; 6 close) lost 16-20 as -2.5 favorites at ARI
2011: GB (2.7 est; 3.8 pyth; 6 close) won 42-34 as -4.5 favorites vs. NO
DET (3.1 est; 3.1 pyth; 4 close) won 27-20 as -1.5 favorites at TB
TEN (4.1 est; 4.2 pyth; 8 close) lost 14-16 as +2 underdogs at JAX
HOU (3.7 est; 1.9 pyth; 3 close) won 34-7 as -9.5 favorites vs. IND
2010: NE (3.2 est; 3.3 pyth; 5 close) won 38-24 as -5 favorites vs. CIN
HOU (3.9 est; 3.6 pyth; 9 close) won 34-24 as +1 underdogs vs. IND
KC (2.1 est; 2.3 pyth; 10 close) won 21-14 as +4.5 underdogs vs. SD
2009: JAX (5.1 est; 2.9 pyth; 9 close) lost 12-14 as +6.5 underdogs at IND
PHI (4.3 est; 4.2 pyth; 8 close) won 38-10 as -2.5 favorites at CAR
GB (2.6 est; 3.3 pyth; 6 close) won 21-15 as -4 favorites vs. CHI
[h=3]Results[/h] Dogs went 7-1 ATS, home dogs went 5-0 ATS. Favorites went 9-2 ATS, home favorites went 7-1 ATS. So home teams went 12-1 ATS whereas away teams went 4-2 ATS.
The results are highly interesting. Of the 7 winning dogs, 3 won straight up and 4 didn’t. It’s a small sample size, but you notice that getting more points due to public perception can be very valuable.
[h=3]Qualifying Matchups for Week One[/h] Here is the data for week one:
| AWAY | HOME | DIFF | DIFF | CLOSE | TO |
| TEAM | TEAM | ESTIM. | PYTH | WIN DIFF | DIFF |
| CAR | DEN | 2.6 | 0.3 | 0 | 24 |
| GB | JAX | 0.9 | 1.9 | 1 | 15 |
| BUF | BAL | 1.7 | 0.5 | 2 | 20 |
| CHI | HOU | -2 | -0.5 | -1 | -9 |
| CLE | PHI | -1.7 | -1.3 | -6 | -4 |
| TB | ATL | -2.9 | -0.2 | -1 | 2 |
| MIN | TEN | 2.9 | 3 | 6 | 19 |
| CIN | NY | -0.6 | 0.3 | 2 | 5 |
| OAK | NO | -2.2 | -0.5 | -1 | -1 |
| SD | KC | -1.6 | -1.7 | -6 | -18 |
| MIA | SEA | 2.7 | 2 | 3 | -10 |
| DET | IND | -2.9 | -1.9 | -3 | -1 |
| NYG | DAL | -1 | -0.3 | -1 | 29 |
| NE | ARI | -0.3 | -0.6 | -1 | -2 |
| PIT | WASH | -2 | -1.5 | -1 | -3 |
| LA | SF | -1.8 | -0.7 | -2 | 10 |
Titans +3 +105
Seahawks -10 -110
Lions +5 -110
Chargers +7 -115
Browns +6 -110
Steelers -3 -120
Like I’ve said before, you should put everything into perspective, but this system can help you evaluating lines in week one.
