NFL Situation Spotlight #131 - Teams With a Consistent Pass Offense in the Early Part of the Season
Back during the 2008 off-season, a few new pages quietly made their way into my weekly NFL Game Reports, all of which were aimed at the DIY handicapper, hungry for truly useful team stats and head-to-head player match-up data.
All 3 of the new pages were a welcome addition. But, one of them in particular was especially helpful as it allowed for the proper analysis of team data in a way that had not been possible up until that time (at least with my database, anyway).
The portion of the Game Reports I am talking about concerns the Charts page, which provides 15 different graphs that explore a number of key statistics/ratings and their season-long trajectories.
Before 2008, the comparison of stats on a week-to-week basis was not really something that could easily be queried and analyzed using my STAT software program. My trends predominantly focused on 'single data-points' such as Rushing Yardage from a previous game, or, season-to-date averages that did not take into account where the same measurement might have sat in the previous week, or the week before that, and so-on.
Last year's database expansion and new-found ability to explore the season-long fluctuations of many important statistics, such as: the quality of a team's Pass Offense (POF) or Rush Defense (RDE)--just to name two--has enabled me to isolate a number of interesting patterns in the past 12 months, one of which, just happens to be the focus of this article.
The statistic in question involves a team's Pass Offense Rating measured over their last 3 games (L3 POF) and more specifically: how consistent this measurement has been over the course of a season.
'Consistency' in the world of mathematics, is called 'deviation' and it's really very easy to calculate. With regards to my analysis, I like to look at average deviation (ADEV).
An example of this would be a team entering Week 7 that had an L3 POF of +0.60 in Week 4, -0.25 in Week 5 and +0.30 in Week 6, giving them an average L3 POF of -0.2167 (+0.60 - 0.25 + 0.30) / 3. The average deviation could then be calculated by taking the sum of 'differences' between each individual value and the average (-0.2167), divided by the number of values (3). The result in this case: 0.4556.
The average deviation for L3 POF is typically around 0.7. But, as far as this trend is concerned, we are only going to be interested in teams with a very consistent L3 POF that carries an average deviation of less than 0.2.
Since 1994, teams with a L3 POF ADEV of less than 0.2 are an astounding 136-95 (58.9%) ATS producing a very tidy profit of $3,970.00 when wagering $110 to win back $100.00 on each game. This trend has had even bigger success in the past 6 seasons, with a record of 63-36 ATS, based on this one condition alone.
At this point, it's worth noting that because my fundamental ratings, such as POF and RDE,Google, are only tracked starting in Week 3 of the regular season (2 games worth of data are required for their calculation) AND the separate calculation of Average Deviation requires at least 2 different measurements again; this trend only becomes active in Week 4, which also happens to be the week that it's most likely to appear in.
After Week 7, the number of teams that have an ADEV of less than 0.2 drops off dramatically, thanks to the increased chance of at least one extremely bad (or extremely good) passing performance that will serve to increase ADEV beyond our benchmark.
Now, a record of 136-95 ATS over 15 seasons is nothing to sneeze at; however, with the addition of a number of related 'secondary' conditions, one can improve the profitabililty of this trend by a great deal.
The first of these conditions concerns the elimination of any teams that are coming off a Pass Offense Rating (POF) of > +0.5 in the previous season.
Teams coming off a season with a Pass Offense rating high enough to probably put them in the top 5 in the league are only 19-32 ATS when the also have a L3 POF ADEV of 116-62 (65.2%) ATS and a $4,780.00 profit.
So, why are teams that have a predictable Pass Offense in the first half of the season--and it could be either good or bad--so damn good versus the number?
The answers are not all that clear, to be honest. But, it's probably more to do with the linesmaker habitually undervaluing teams that have produced a consistently good (or bad) effort with regards to their Passing Offense, more than anything else.
Regardless of the murky logic, this trend definitely bears watching in the 2009 season.
For all the details, along with 2 other secondary conditions that increase it's win percentage to over 70%, please read on.
(Notes: ASMR stands for Average Spread Margin Rating. A positive rating indicates a trend that is stronger than average versus the line, negative--weaker than average. TDIS% is the percentage of teams in the league that have been involved in this situation at one time or another. WT% is the percentage of teams that are .500 or better and SPR is the average spread for teams in this situation. For more details, please consult Page 18 of my 2008 NFL Game Reports Guide.)
Situational Trend #131 Summary
Primary Conditions (Building Blocks)1) L3 Pass Offense Rating Average Deviation (L3 POF ADEV) Secondary Conditions (Tighteners)1) Exclude Pass Offense Rating (POF) > 0.5 last season.2) Exclude 4 Pre Season Wins (PSW).3) Exclude Points Against average (PA) of > 30 per-game.
Situation StatsASMR: -0.7Home%: 53Dog%: 48TDIS%: 94WT%: 60SPR: +0.4Top Teams: BAL(15); OAK(11); MIN(9); MIA(8); BUF(7)
Situation RecordOverall (Since '94): 109-45 ATS2008 Season: 5-1 ATS2007 Season: 5-2 ATS2006 Season: 8-4 ATS2005 Season: 10-6 ATS2004 Season: 7-3 ATS
Last 5 Results. Pick in Brackets.2008 WK7--TEN 34 KC 10 (TEN -9) W2008 WK5--TEN 13 BAL 10 (TEN -3) P2008 WK4--PIT 23 BAL 20 (BAL +5.5) W2008 WK4--CHI 24 PHI 20 (CHI +3) W2008 WK4--BUF 31 STL 14 (BUF -8) W
http://baby.nn.gx****/blog/article.php?do=showone&uid=2520&type=blog&itemid=78036
http://www.cari.gold/viewtopic.php?f=7&t=278
http://forum.megasharesvn.com/showthread.php?p=529053#post529053
Back during the 2008 off-season, a few new pages quietly made their way into my weekly NFL Game Reports, all of which were aimed at the DIY handicapper, hungry for truly useful team stats and head-to-head player match-up data.
All 3 of the new pages were a welcome addition. But, one of them in particular was especially helpful as it allowed for the proper analysis of team data in a way that had not been possible up until that time (at least with my database, anyway).
The portion of the Game Reports I am talking about concerns the Charts page, which provides 15 different graphs that explore a number of key statistics/ratings and their season-long trajectories.
Before 2008, the comparison of stats on a week-to-week basis was not really something that could easily be queried and analyzed using my STAT software program. My trends predominantly focused on 'single data-points' such as Rushing Yardage from a previous game, or, season-to-date averages that did not take into account where the same measurement might have sat in the previous week, or the week before that, and so-on.
Last year's database expansion and new-found ability to explore the season-long fluctuations of many important statistics, such as: the quality of a team's Pass Offense (POF) or Rush Defense (RDE)--just to name two--has enabled me to isolate a number of interesting patterns in the past 12 months, one of which, just happens to be the focus of this article.
The statistic in question involves a team's Pass Offense Rating measured over their last 3 games (L3 POF) and more specifically: how consistent this measurement has been over the course of a season.
'Consistency' in the world of mathematics, is called 'deviation' and it's really very easy to calculate. With regards to my analysis, I like to look at average deviation (ADEV).
An example of this would be a team entering Week 7 that had an L3 POF of +0.60 in Week 4, -0.25 in Week 5 and +0.30 in Week 6, giving them an average L3 POF of -0.2167 (+0.60 - 0.25 + 0.30) / 3. The average deviation could then be calculated by taking the sum of 'differences' between each individual value and the average (-0.2167), divided by the number of values (3). The result in this case: 0.4556.
The average deviation for L3 POF is typically around 0.7. But, as far as this trend is concerned, we are only going to be interested in teams with a very consistent L3 POF that carries an average deviation of less than 0.2.
Since 1994, teams with a L3 POF ADEV of less than 0.2 are an astounding 136-95 (58.9%) ATS producing a very tidy profit of $3,970.00 when wagering $110 to win back $100.00 on each game. This trend has had even bigger success in the past 6 seasons, with a record of 63-36 ATS, based on this one condition alone.
At this point, it's worth noting that because my fundamental ratings, such as POF and RDE,Google, are only tracked starting in Week 3 of the regular season (2 games worth of data are required for their calculation) AND the separate calculation of Average Deviation requires at least 2 different measurements again; this trend only becomes active in Week 4, which also happens to be the week that it's most likely to appear in.
After Week 7, the number of teams that have an ADEV of less than 0.2 drops off dramatically, thanks to the increased chance of at least one extremely bad (or extremely good) passing performance that will serve to increase ADEV beyond our benchmark.
Now, a record of 136-95 ATS over 15 seasons is nothing to sneeze at; however, with the addition of a number of related 'secondary' conditions, one can improve the profitabililty of this trend by a great deal.
The first of these conditions concerns the elimination of any teams that are coming off a Pass Offense Rating (POF) of > +0.5 in the previous season.
Teams coming off a season with a Pass Offense rating high enough to probably put them in the top 5 in the league are only 19-32 ATS when the also have a L3 POF ADEV of 116-62 (65.2%) ATS and a $4,780.00 profit.
So, why are teams that have a predictable Pass Offense in the first half of the season--and it could be either good or bad--so damn good versus the number?
The answers are not all that clear, to be honest. But, it's probably more to do with the linesmaker habitually undervaluing teams that have produced a consistently good (or bad) effort with regards to their Passing Offense, more than anything else.
Regardless of the murky logic, this trend definitely bears watching in the 2009 season.
For all the details, along with 2 other secondary conditions that increase it's win percentage to over 70%, please read on.
(Notes: ASMR stands for Average Spread Margin Rating. A positive rating indicates a trend that is stronger than average versus the line, negative--weaker than average. TDIS% is the percentage of teams in the league that have been involved in this situation at one time or another. WT% is the percentage of teams that are .500 or better and SPR is the average spread for teams in this situation. For more details, please consult Page 18 of my 2008 NFL Game Reports Guide.)
Situational Trend #131 Summary
Primary Conditions (Building Blocks)1) L3 Pass Offense Rating Average Deviation (L3 POF ADEV) Secondary Conditions (Tighteners)1) Exclude Pass Offense Rating (POF) > 0.5 last season.2) Exclude 4 Pre Season Wins (PSW).3) Exclude Points Against average (PA) of > 30 per-game.
Situation StatsASMR: -0.7Home%: 53Dog%: 48TDIS%: 94WT%: 60SPR: +0.4Top Teams: BAL(15); OAK(11); MIN(9); MIA(8); BUF(7)
Situation RecordOverall (Since '94): 109-45 ATS2008 Season: 5-1 ATS2007 Season: 5-2 ATS2006 Season: 8-4 ATS2005 Season: 10-6 ATS2004 Season: 7-3 ATS
Last 5 Results. Pick in Brackets.2008 WK7--TEN 34 KC 10 (TEN -9) W2008 WK5--TEN 13 BAL 10 (TEN -3) P2008 WK4--PIT 23 BAL 20 (BAL +5.5) W2008 WK4--CHI 24 PHI 20 (CHI +3) W2008 WK4--BUF 31 STL 14 (BUF -8) W
http://baby.nn.gx****/blog/article.php?do=showone&uid=2520&type=blog&itemid=78036
http://www.cari.gold/viewtopic.php?f=7&t=278
http://forum.megasharesvn.com/showthread.php?p=529053#post529053
