If you are a statistical analyzer, and break down numbers on
NFL games with any sort of regularity, you know that TO margin is an incredibly
important statistic in the outcome of all games. Straight up, ATS – doesn’t matter. Perform well in this area, be it taking care
of the ball, or taking the ball away from your opponent on a regular basis, and
the likelihood you are playing football come January greatly increases. Most NFL fans, even the casual fan that
doesn’t get too involved in breaking stats down is aware of this
phenomenon. But how good a tool can it
be to predict future outcomes, whether it be on a week to week basis trying to
pick straight up winners in your office pool, picking a few games to wager your
hard earned cash on, or even trying to predict the chances your team has at
making the playoffs? It’s very useful,
and below I will discuss various angles and ways to utilize this one stat in
your handicapping efforts.
Initially let’s examine the relationship between turnovers,
points scored vs. points against (which is a team’s points margin), and
projected records based on those two stats.
|
TEAM
|
POINTS
|
TURNOVER
|
NORM
|
ACTUAL
|
NEW
PROJ
|
WIN
|
2+
|
|
|
MARGIN
|
ADV/(DIS)
|
MARGIN
|
RECORD
|
RECORD
|
VARIANCE
|
TMS
|
||
|
ARI
|
3.44
|
0.00
|
3.44
|
10-6
|
10-6
|
|
|
|
|
ATL
|
(5.63)
|
(1.25)
|
(4.38)
|
4-12
|
5-11
|
(1)
|
|
|
|
BAL
|
(2.00)
|
(1.25)
|
(0.75)
|
8-8
|
8-8
|
|
|
|
|
BUF
|
(3.00)
|
0.75
|
(3.75)
|
6-10
|
6-10
|
|
|
|
|
CAR
|
7.81
|
2.75
|
5.06
|
12-4
|
11-5
|
1
|
|
|
|
CHI
|
(2.06)
|
1.25
|
(3.31)
|
8-8
|
6-10
|
2
|
CHI
|
|
|
CIN
|
7.81
|
0.25
|
7.56
|
11-5
|
12-4
|
(1)
|
|
|
|
CLE
|
(6.13)
|
(2.00)
|
(4.13)
|
4-12
|
6-10
|
(2)
|
CLE
|
|
|
DAL
|
0.44
|
2.00
|
(1.56)
|
8-8
|
7-9
|
1
|
|
|
|
DEN
|
12.94
|
0.00
|
12.94
|
13-3
|
14-2
|
(1)
|
|
|
|
DET
|
1.19
|
(3.00)
|
4.19
|
7-9
|
10-6
|
(3)
|
DET
|
|
|
GB
|
(0.69)
|
(0.75)
|
0.06
|
8-7-1
|
8-8
|
|
|
|
|
HOU
|
(9.50)
|
(5.00)
|
(4.50)
|
2-14
|
5-11
|
(3)
|
HOU
|
|
|
IND
|
3.44
|
3.25
|
0.19
|
11-5
|
8-8
|
3
|
IND
|
|
|
JAC
|
(12.63)
|
(1.50)
|
(11.13)
|
4-12
|
3-13
|
1
|
|
|
|
KC
|
7.81
|
4.50
|
3.31
|
11-5
|
10-6
|
1
|
|
|
|
MIA
|
(1.13)
|
(0.50)
|
(0.63)
|
8-8
|
8-8
|
|
|
|
|
MIN
|
(5.56)
|
(3.00)
|
(2.56)
|
5-10-1
|
6-10
|
|
|
|
|
NE
|
6.63
|
1.75
|
4.88
|
12-4
|
11-5
|
1
|
|
|
|
NO
|
6.88
|
0.00
|
6.88
|
11-5
|
12-4
|
(1)
|
|
|
|
NYG
|
(5.56)
|
(3.75)
|
(1.81)
|
7-9
|
7-9
|
|
|
|
|
NYJ
|
(6.06)
|
(3.50)
|
(2.56)
|
8-8
|
6-10
|
2
|
NYJ
|
|
|
OAK
|
(8.19)
|
(2.00)
|
(6.19)
|
4-12
|
5-11
|
(1)
|
|
|
|
PHI
|
3.75
|
3.00
|
0.75
|
10-6
|
8-8
|
2
|
PHI
|
|
|
PIT
|
0.56
|
(1.00)
|
1.56
|
8-8
|
9-7
|
(1)
|
|
|
|
SD
|
3.00
|
(1.00)
|
4.00
|
9-7
|
10-6
|
(1)
|
|
|
|
SF
|
8.38
|
3.00
|
5.38
|
12-4
|
11-5
|
1
|
|
|
|
SEA
|
11.63
|
5.00
|
6.63
|
13-3
|
11-5
|
2
|
SEA
|
|
|
STL
|
(1.00)
|
1.75
|
(2.75)
|
7-9
|
6-10
|
1
|
|
|
|
TB
|
(6.31)
|
2.50
|
(8.81)
|
4-12
|
4-12
|
|
|
|
|
TEN
|
(1.19)
|
0.00
|
(1.19)
|
7-9
|
7-9
|
|
|
|
|
WAS
|
(9.00)
|
(2.25)
|
(6.75)
|
3-13
|
5-11
|
(2)
|
WAS
|
|
LEGEND:
Points Margin:
points scored – points allowed for 2013 regular season
Turnover
Adv/(Dis): represents the per game impact each team’s turnovers had on
their points margin. This number is
derived by taking the total TO Margin on the season, dividing by 16 to get a
“per game” TO Margin, then multiplying by 4 (or whatever value you choose to
place on turnovers – discussed below)
Norm Margin:
represents normalized margin & is calculated by taking Points Margin MINUS
Turnover Adv/(Dis). This figure
hypothetically represents what a team’s point’s margin would have been
stripping out the impact turnovers had on it
Actual Record:
straight forward, each team’s true SU record for 2013 regular season
New Proj Record:
uses the Normalized Margin calculation, and fits each into the Points Margin
Pythagorean Theory matrix. It is
generally assumed that teams who on average outscore their opponents by 1.5ppg
will go 9-7, 3ppg 10-6, 5.5ppg 11-5 and so on increasing ppg by 2.5pts for each
win – and using the reciprocal of each of those marks for losing records. Note, since these figures are quoted in
decimals & rounded, the sum of 255 wins does not equal 256, the amount we
see in an entire NFL season if there are no ties.
Win Variance:
calculated by taking Actual Record MINUS New Projected Record. Teams highlighted in red may have played
worse than their record indicated last season (this analysis could certainly be
used tying into my first post about +/- 4 wins along with betting future season
win totals) while green teams would be bullish targets as far as this analysis
goes.
Next big piece of this analysis is to explain how we valued
the turnover. Most analysts who work
with turnovers in their models will value these at approximately being worth 4
points. Of course this number is not set
in stone, and can be debated & supported at various “point” impacts – but
for this analysis I will be using 4 points.
In reality, any number you select within reason – the number has to be
worth anywhere between a minimum 2pts and a maximum 5pts because a turnover
either way leads to the possibility of scoring or allowing points – working
with estimated % chances of scoring/allowing a FG/TD will allow you to derive your
own worth of a turnover; so long as you have support & utilize a consistent
value for all teams your analysis will be sound. By using that method of “valuing” turnovers,
we can calculate a new point’s margin based on a team’s pure play performance –
stripping away the advantage/disadvantage turnovers had on their point’s margin. This is a valuable way to place a barometer
on how team’s truly performed, statistically speaking, in their games.
Now that we have explained all the data, here is where it
gets useful. As mentioned, a “model” or
any analysis is only good if you back-test it, and prove that it has worked in
the past. While any model may add value
for a short period of time or even a year the ones that offer the best value
& assistance in your handicapping efforts are those where you apply your
theory for a minimum of 5 years back & check how significant its results
are vs. actual results. Especially in
this day & age there are tons & tons of new statistics, analysts &
bloggers publishing their work – but the biggest issue I see many have is
information overload. Sure most of the
new statistics & theories can help you predict outcomes of sporting events
but you should attempt at mastering a small data set & metrics, knowing how
to utilize those the best you can to handicap games; you do not want to be a
jack of all trades / master of none – too many times I read on Twitter
handicappers using tons of different analytics & metrics every other night –
there is a such thing as information overload, which is where many people go
wrong. Remember my old saying – give me
either side of any game & I can give you a write-up supporting that play….
Back to this analysis, let’s first focus on the teams I have
highlighted in red – teams that achieved a record in 2013 that was above and
beyond their actual performance stripping the impact of turnovers. These team’s we forecast to drop in wins from
2013 to 2014 because as we know, turnovers typically, but not always, revert
back to the mean – so a team’s performance that was positively impacted by a
strong TO margin the prior season often flips in the very next season. Numerous articles have been posted on this
topic to my blog over the years & this has proven to be a solid leading
indicator for the following season, barring IMPACT signings or SIGNIFICANT free
agency defections. For the 2014 season
here are some teams we would be bearish on, i.e. those that are likely to win
less than they did last season:
- CHI
- IND
- NYJ
- PHI
- SEA
Now let’s move onto the team’s that were negatively impacted
by TO margin in 2013, which means we expect this group to have a stronger
record in 2014 comparing to 2013:
- CLE
- DET
- HOU
- WAS
As I continually stress back-testing must be completed on
any analysis to confirm its accuracy. We
have done that the last few years with this analysis, but for ease let’s
examine below which teams we expected to slide in the 2013 season vs. the 2012
season (posted here last summer):
Team’s that were likely to see a drop in their 2013 record went
3-1-1 as I show games won in 2012 vs. 2013 in parenthesis:
- ATL (13 to 4): CORRECT
- HOU (12 to 2): CORRECT
- IND (11 to 11): SAME
- TEN (6 to 7): INCORRECT
- WAS (10 to 3): CORRECT
Team’s that were likely to win at least one more game in
2013 vs. 2012 went 2-0-1 as change in wins is shown in parenthesis:
- DET (4 to 7): CORRECT
- KC (2 to 11): CORRECT
- PIT (8 to 8): SAME
In summary, using TOM’s impact on NFL Pythagorean Theory,
heading into the:
- 2011 season there were 13 teams that were projected to slide up or down in wins – 10 moved the way we projected while 2 stayed the same; the only one that missed was SD who went from 9 to 8 wins & we forecasted a rise
- 2012 season there were 12 teams that were projected to slide up or down in wins – 10 moved the way projected while 2 slid opposite (SD again & PHI both were projected to drop but increased their wins vs. 2011)
- 2013 season there were 8 teams that were projected so slide up or down in wins – 5 moved the way projected while 2 stayed the same; the only one that missed was TEN who went from 6 to 7 & we forecasted a drop
- SUMMARY: over last three seasons we have projected 33 teams to shift their wins either up or down – only FOUR of those THIRTY THREE moved the opposite way (just 12%).
Going into the 2014 season we have 9 teams projected to
slide up or down in wins based on this initial look at TOM & Pythagorean
Theory.
That will conclude Part I of our Turnover Margin
analysis. Trust me when I say I have a
lot more great analysis related to turnovers that will be discussed in the
coming week’s right here on my blog.
Thanks for reading, just a few weeks till preseason football
gets going!
Email me directly: boss@thesportsboss.com
Visit my website: www.thesportsboss.com
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