11institutetext: Old Dominion University, Department of Computer Science
Norfolk, VA 23529

# The Performance of Betting Lines for Predicting the Outcome of NFL Games

Greg Szalkowski  Michael L. Nelson

###### Abstract

We investigated the performance of the collective intelligence of NFL fans
predicting the outcome of games as realized through the Vegas betting lines.
Using data from 2560 games (all post-expansion, regular- and post-season games
from 2002-2011), we investigated the opening and closing lines, and the margin
of victory. We found that the line difference (the difference between the
opening and closing line) could be used to retroactively predict divisional
winners with no less accuracy than 75% accuracy (i.e., “straight up”
predictions). We also found that although home teams only beat the spread 47%
of the time, a strategy of betting the home team underdogs (from 2002-2011)
would have produced a cumulative winning strategy of 53.5%, above the
threshold of 52.38% needed to break even.

##  1 Introduction

Week one of the 2007 National Football League (NFL) season had the New England
Patriots on the road against the New York Jets. The sportscasters had been
talking about how the Jets were looking good this year and were ready for
payback from the previous year when New England beat them in the playoffs.
While New England had performed well in the preseason games, they had two
starters out with injuries and Randy Moss was questionable for the week one
game. The future was looking grim for the Patriots and Vegas was favoring the
Jets at home by 6 points. When betting opened, many people placed bets on New
England to win even though the experts predicted that the Jets would triumph.
The lopsided nature of the betting forced the sportsbooks to move the line
repeatedly in order to keep the volume of bets even on both sides of the game.
Eventually the line moved a total of 13 points to New England being a 7 point
favorite by game day. New England went on to win this game 38 to 14, easily
covering the spread. This is one example where the collective intelligence of
the NFL fans was confident that New England would win even when the “experts”
thought otherwise.

Collective intelligence is a way of synthesizing information from a group of
people that no one person would have known on their own. Collating information
from many individuals and making new conclusions based on the assimilated
knowledge is the core of collective intelligence. Harnessing this collective
intelligence using network applications is one of the fundamental ideas
underlying Web 2.0 [10]. The archetypical example of collective intelligence
is the “guess how many jelly beans in a jar” prediction. Ask a group of people
to estimate the number of beans in a jar, and many of the answers will be far
from correct. But their average answer will be quite close to the real answer
and maybe even closer than the prediction of most of the people who guessed.
“Under the right circumstances, groups are remarkably intelligent, and are
often smarter than the smartest people in them”. Surowiecki [15] outlines four
conditions that must hold for collective intelligence:

> There are four key qualities that make a crowd smart. It needs to be
> diverse, so that people are bringing different pieces of information to the
> table. It needs to be decentralized, so that no one at the top is dictating
> the crowd’s answer. It needs a way of summarizing people’s opinions into one
> collective verdict. And the people in the crowd need to be independent, so
> that they pay attention mostly to their own information, and not worrying
> about what everyone around them thinks.

The NFL betting line implements each of the four qualities that Surowiecki
mentions as requirements for a crowd to be smart. NFL fans come from all walks
of life and while focused in the United States, they are a quite diverse group
of people. The line is initially set by sportsbooks but it then moves from its
initial point due to forces applied by betting volume and it neatly summarizes
the views of the betting pool. The betting line itself could be considered a
form of feedback so each of the individual bettors is not completely
independent but each person is acting in their own self interest. This
research is focused on leveraging the collective intelligence of the football
community realized in the NFL point spreads and how they change as a result of
the bets placed on the outcome of NFL games placed by many people [15].

Our question is: can the collective intelligence of the NFL fans consistently
be leveraged to predict the outcome of future games?

##  2 The Point Spread

Frequently teams have a talent discrepancy that makes predicting the outcome
easy; “straight up” is the term used for simply predicting the outcome of the
game and a “point spread” is used to handicap the prediction process. The
point spread by itself is not intended to predict a winner but to ensure that
the sportsbooks make money. The sportsbooks make money by charging a
commission, sometimes called the vigorish, on each bet placed. The
sportsbook’s intentions are to separate the betting population in half in
order to minimize risk and maximize profit. In order to split the population
of bettors in half, the point spread on a particular game may have to be
adjusted due to betting pressure on one side or the other. This way, the
bookmaker can guarantee 5% profit regardless of the outcome of the game (10%
from the losing half of the betting population).

Every week of the NFL season the initial point spreads are established at the
beginning of the week by a small group of sportsbooks and consultants. Las
Vegas Sports Consultants [3] bills itself as a provider of sports betting
lines to most of the Las Vegas casinos. The initial point spreads are
initially opened for bidding to a group of high level knowledgeable bettors
and the line is adjusted based on the bets placed by those bettors [7]. This
initial line is sometimes referred to as the “virgin” line and it moves to
equalize the early betting so that the amount of money bet on either side of
each game is roughly equal. After this initial adjustment the point spread is
released to the public and opened for betting as the opening line.

A bettor can place a bet on either team. The bettor wins if the bet is on the
favorite and the favorite wins by more than the point spread, or if the bet is
on the predicted loser and the predicted loser either wins or loses by fewer
points than the point spread. If the predicted winner wins by a margin equal
to the point spread, a tie is declared, and the bettor neither wins or loses.
This is usually referred to as a “push”. In all other cases, the bettor loses.
As bets are placed the point spread values will move to continue to keep an
equal volume of betting on both sides of each game.

As an example of the betting process, in the introduction New England was a 6
point underdog to the Jets in the opening line. The point spread was New
England +6 or New York -6. In order to place a bet, a potential bettor must be
willing to risk $11 to win $10. When betting opened, many people started
betting on New England to win, so the sportsbooks were forced to move the line
toward New England in order to entice people to bet on New York so that there
would be an even betting volume on both sides. By taking an equal volume of
bets on each side, the sportsbook is in effect charging the players betting
the losing side an extra dollar. When the game is over and the closing line
was New England favored by 7 points, a person who bet $10 on New England would
receive $21 (the $11 risked plus the $10 won) while the player who bet on New
York loses $11. The sportsbook then makes $1 on this transaction, the $11 lost
by the New York bettor less the $10 won by the New England bettor.

If a $100 bet is placed ($110 with the ante) and wins, the bettor is paid $100
and keeps the ante; if the bettor loses she pays $110. In order to break even
the bettors must win at least 52.38% of their bets. This can be found solving
equation (2). Let the win ratio (WR) be the proportion of winning games,
therefore 1-WR is the number of losing games. In order to determine the
proportion of winning bets, WR, necessary to break even on wagering, set the
expected winnings equal to the expected losses and solve for WR. Solving
yields WR=0.5238 or 52.38% to break even.

$\displaystyle 100*WR=110(1-WR)$ (1)

Every sportsbook that takes bets has the same incentive to maintain an equal
volume of bets on each side of a given game. The line offered by each
sportsbook is unique to that sportsbook and they can change independently of
other sportsbooks. There is an entire industry related to “line shopping”,
that seeks to exploit differences in lines offered by different sportsbooks.
If the line for a certain game is trending down, the “line shoppers” attempt
to place bets at sportsbooks that move a little slower and have not adjusted
down as quickly as other. Overall the entire “sportsbook system” has a form of
collective intelligence feedback loop that keeps most of the lines offered by
all of the sportsbooks very close if not equal to each other. In comparing
against the spread records, there may be some differences when using line
values from different sources. The majority of line values used in this study
were obtained from The GoldSheet [16].

A significant factor in determining the line is the home field advantage. Home
teams typically have an advantage because of familiarity, travel requirements,
and factors related to the crowd [13]. The magnitude of this advantage is
typically realized in the betting market by a three point benefit for the home
team. At first glance the betting line appears to be a good indicator of
performance, as home teams have won 58% of the NFL games from 1981 to 1996
[18] and 57% from 2002 to 2011. However winning against the spread was found
to be much less decisive with the home team beating the spread in only 48.9%
of the games from 2002 to 2011 and 49.9% of the games from 1981 to 1996 [18].
Home team performance against the spread for 2002 to 2011 is detailed in Table
1. Historically it appears that the home game factors may have been
overcompensated as the performance is below 50%. One data point that sticks
out is the performance of the home underdog. The home-underdog effect has been
the subject of some research and has been attributed to late season biases [2]
and weather conditions [1]. Betting on the home team underdog from 1973 to
1979 would have resulted in a 58.1% win ratio [5], a 52.5% win ratio from 1981
to 1996 [18] and 53.5% from 2002 to 2011. Results from a number of studies
indicate that the home-underdog bias has been diminishing over the years [5,
6]. This diminishing bias is quite evident when plotted over time as seen in
Figure 1.

Figure 1: Home Underdogs That Covered the Point Spread Table 1: Home Team Records Against the Spread | Favorites | Underdogs | Pick-ems | All Home Games
---|---|---|---|---
| Win | Lose | WR | Win | Lose | WR | Win | Lose | WR | Win | Lose | WR
2002 | 80 | 93 | 0.462 | 51 | 36 | 0.586 | 1 | 1 | 0.5 | 132 | 130 | 0.504
2003 | 90 | 86 | 0.511 | 36 | 35 | 0.507 | 2 | 2 | 0.5 | 128 | 123 | 0.510
2004 | 77 | 94 | 0.450 | 40 | 40 | 0.500 | 1 | 4 | 0.2 | 118 | 138 | 0.461
2005 | 91 | 76 | 0.545 | 31 | 45 | 0.408 | 1 | 0 | 1 | 123 | 121 | 0.504
2006 | 74 | 101 | 0.423 | 45 | 32 | 0.584 | 3 | 1 | 0.75 | 122 | 134 | 0.477
2007 | 82 | 79 | 0.509 | 46 | 44 | 0.511 | 0 | 1 | 0 | 128 | 124 | 0.508
2008 | 81 | 96 | 0.458 | 35 | 41 | 0.461 | 2 | 0 | 1 | 118 | 137 | 0.463
2009 | 78 | 84 | 0.481 | 39 | 46 | 0.459 | 3 | 2 | 0.6 | 120 | 132 | 0.476
2010 | 80 | 86 | 0.482 | 41 | 38 | 0.519 | 2 | 2 | 0.5 | 123 | 126 | 0.494
2011 | 83 | 93 | 0.472 | 45 | 39 | 0.536 | 0 | 0 | 0 | 128 | 132 | 0.492
Total | 816 | 888 | 0.479 | 409 | 396 | 0.508 | 15 | 13 | 0.535 | 1240 | 1297 | 0.489
0.500 |  | z= | -1.858 |  | z= | 0.404 |  | z= | 0.044 |  | z= | -1.907
0.524 |  | z= | -4.002 |  | z= | -0.955 |  | z= | -0.165 |  | z= | -5.977

Over the years a number of researchers have investigated betting strategies in
relation to NFL football [19, 17]. Numerous market efficiency metrics have
found the betting market to be efficient [18, 11]. A common theme in much of
the research was searching for a bias in the betting line. When investigating
the home field advantage for bias it would be useful to compare the line to
two win rates, 0.5 for a straight up win and 0.5238 to cover the spread [18].
The data in Table 1 shows that the favorite home team lost more often than it
won from 2002 to 2011 with a z of -1.85 compared to 0.5, which is similar to
the -1.36 for 1981 to 1996 [18]. The against the spread comparison was worse
with a z of -4.002 compared to 0.5238 for 2002 to 2011 and -3.66 for 1981 to
1996.

##  3 Comparing the Line with Reality

The NFL box scores and line values for all NFL games from 2002 to 2011 were
collected from the Internet and inserted into a MySQL database. The box scores
contain over 30 statistics about each game from the final score, to statistics
about individual players. A starting year of 2002 was chosen as this was the
first year after the last NFL expansion and included all of the current 32
teams. Data collection resulted in 2560 regular season games in the database
at 256 games per year over 10 years.

A histogram of the values of the point spreads for regular season games from
2002 to 2011 appears in Figure 2. The most popular point spread values 3, -3,
and 7 are evident and quite logical as they are common values for NFL scores
[9]. Visually there appears to be a bias towards positive values. A positive
point spread value in this system indicates that the home team is favored,
reinforcing the concept that the home team is favored more often than the
visitor. Principal component analysis was used to dimensionally reduce the box
score data and investigate the importance of each of the box score statistics.
The betting line value had a high coefficient in almost every analysis and was
ranked better than the other box score statistics with a high orthogonal
variance.

Figure 2: Most Popular Closing Line Values (2002-2011); positive values
indicate the home team is favored.

To compare the point spread with actual game results, the Margin of Victory
(MOV) will be compared to the point spread for each game. The MOV is
traditionally calculated as shown in equation (2).

$MOV=WinnerScore-LoserScore$ (2)

The MOV and the line values can be seen in Figure 3. The absolute value of the
closing line is on the x-axis and the MOV on the y-axis. If MOV was accurate
then the data in the scatter plot should be clustered about the diagonal. The
actual results are much different and show that the line is not a good match
for the actual MOV. Indeed the main purpose of the line is not to predict the
actual MOV but to split the betting population in half. The games above the
diagonal are games in which the favorite won. There were 1194 games in which
the favorite beat the spread. The games below the diagonal but above the
x-axis are games in which the favorite won but did not cover the spread. Games
below the x-axis are games in which the favorite lost. There were 412 games
where the favorite did not cover and 853 losses for a total of 1265 games in
which the favorite lost against the spread for a loss rate of 51.5%. There
were 101 games which resulted in a push and are not included in the numbers on
the graph. The line value on its own is not a good indicator of the actual
MOV.

Figure 3: Closing Line vs. MOV (2002-2011); above the diagonal represents
beating the spread, between the diagonal and above the x-axis is winning
straight up but not beating the spread, below the x-axis is losing.

In order to overcome the deficiencies of the MOV we defined a similar metric
called the Line Difference (LD) shown in equation (3). The LD for a given game
is the magnitude of how far off the point spread was from the actual outcome
of the game. A positive value will indicate that the favorite was undervalued
or the underdog overvalued, and a negative value indicates that the favorite
was overvalued or the underdog undervalued.

$LD=(FavoriteScore-UnderdogScore)-|ClosingLine|$ (3)

Figure 4: Histogram of Line Difference (LD) values (2002-2011)

A histogram of the Line Difference values appears in Figure 4. Visually the
data appears close to a normal distribution with a mean of -0.009 and standard
deviation on 13.588. This is similar to a mean of 0.07 and standard deviation
13.86 obtained from NFL seasons 1981 to 1984 [14], 1980 to 1985 [4], and data
for NFL seasons 1992 to 2001 [12]. A comparison of the mean and standard
deviation of the LD values from 1992 to 2011 is shown in Figure 5. The mean
Line Difference form 1992 to 2011 is rather close with a value of -0.009 but
the standard deviation of 13.588 which is close to two touchdowns demonstrates
quite a bit of volatility in the line values. Additionally it appears that the
line values have not improved over the years as the Line Difference values
going back to at least 1980 demonstrate a rather amazing consistency.

Figure 5: Mean and Deviation of Line Difference (1992-2011)

A chi-squared goodness of fit test comparing the LD curve to a Gaussian
distribution with a mean of 0 and a standard deviation of 13.588 indicates
that the distribution of LD is not statistically different. Given that our
data with a mean of 0 and standard deviation of 13.588 is an adequate
approximation of a normal distribution, we can use the cumulative distribution
function to approximate the probability of winning a game. Equation (4) was
used with p = point spread for an individual game to determine the probability
that a p-point favorite would win the game [14]. Going back to our New England
example, New England was a 7 point favorite at game time. Using equation (4)
with p=7 results in a New England win probability of 69.6%. The resulting
normal approximations for the probability of victory of a sample of point
spreads is shown in Table 2.

$Pr(F>U|P=p)=\phi\left(\frac{p}{13.588}\right)$ (4)

Table 2: The Normal Approximation and the Empirical Probability of Winning Point Spread | $Pr(F>U|P)$ | Actual
---|---|---
1 | 0.529 | 0.509
3 | 0.587 | 0.581
5 | 0.644 | 0.597
7 | 0.697 | 0.689

The probability of winning a given game could be calculated using Equation (4)
and the probability of a team winning multiple games could be envisioned as a
sequence of events and calculated as the product of each of the individual
events. For example, New England is favored by 7 points in one game and 4
points in another. The probability of New England winning both games is
$\phi\left.(\frac{7}{13.588})\right.\cdot\phi\left.(\frac{4}{13.588})\right.=0.429$.
The probability of winning k games in a season can be found by adding the the
probabilities for all $\binom{16}{k}$ sequences of outcomes. The probabilities
for all games in each season from 2002 to 2011 were calculated and each season
was simulated 1000 times and the results averaged. The results can be found in
Tables 4 to 13. The estimates from the normal approximation are consistent
with the results obtained from the actual games. The predicted division
winners for each season were calculated and compared to the actual outcome.
The rules for division winners become detailed in the event of ties. When
there is a tie in the predicted division winner, the predicted outcome was
decided in our favor. The results of the predictions are in Table 3.

Table 3: Predicting the Division Winners Year | Correct
---|---
2002 | 7/8
2003 | 7/8
2004 | 6/8
2005 | 8/8
2006 | 6/8
2007 | 7/8
2008 | 7/8
2009 | 7/8
2010 | 6/8
2011 | 6/8

Because we used the point spreads for the entire regular season, this is
essentially a retrospective analysis and does not in isolation lend itself to
being a good predictor of an entire season. In the 1970s a study of over 1,800
horse races and the accompanying betting odds was conducted. The winning
frequency of every horse in every race was compared to the odds in the betting
market. With only a few exceptions the betting odds precisely predicted the
actual order in which the horses finished [8]. Both the NFL and the horse
races were retrospective but they reinforce the concept that the line (or
odds) would be a valid input into a prediction algorithm.

##  4 Line Movement

In football, sports bettors have a whole week to bet on games. This is a week
in which new information becomes available daily. Sports Illustrated puts a
team on their cover111The Sports Illustrated cover jinx is an urban legend
that states that individuals or teams who appear on the cover of the Sports
Illustrated magazine will subsequently be jinxed (experience bad luck).,
injury reports are released, weather reports become more accurate, and paid
handicappers release their picks to the public. All of these things have an
influence on the volume and direction of betting, which directly impacts the
direction and magnitude of line movement.

Line movement is essential to sports betting. It is a game that begins
immediately after the line makers create the virgin line. During early betting
the sportsbooks will adjust the spread on these early lines until they get an
equivalent volume of betting on both sides. Sportsbooks do not hope for, but
expect, this early action to help firm the line. If the volume of betting is
equal on both sides, the line is considered good and will not move. If the
line is biased to one side or the other, heavier betting will occur one one
side and continue until the line has reached a point that the collective
intelligence indicates is a “correct” line for that particular game. Once the
line is firmed up, it will not move much until game time, but it still can.

Sportsbooks are typically reluctant to move the line too far due to the
possibility of losing money on both sides of the bet. If the opening line has
a team favored by 7 points and heavy betting forces the sportsbook to adjust
the line to 9 points and the team wins by 8 points the sportsbook could be
forced to pay out on bets on both sides and lose money. The most infamous
example of this is the Stardust during the 1978 Superbowl [19]. For Superbowl
XIII the line varied between 3.5 and 4.5 with Pittsburgh favored. Pittsburgh
won the game by 4 points resulting in a push for a majority of the bets. The
sportsbooks do not make money off a push and they lost millions of dollars
that year. Frequently it is observed that a spread may only move from 2 to 2.5
or from 3 to 3.5 before there is an equal amount of bets coming in on both
sides. The half point moves help the sportsbooks to prevent having to pay out
to both sides and to eliminate the probability of a push. If the games results
in a push then the sportsbook needs to refund many of the bets and the
overhead costs typically outweigh any profit from that game.

Figure 6: Line Movement in Points (2002-2012)

For the purposes of this study, the line movement is defined as the difference
between opening line and the closing line. The line movement for each of the
regular season games from 2002 to 2011 was calculated and a histogram of the
values is shown in Figure 6. Out of the 2560 regular season games, over 2000
games had a line movement of 1 point or less, 1548 games had a line movement
of 0.5 points or less. Therefore only 20% of the games had a line movement
greater than one point. In testing the opening line versus the closing line it
was found that the opening line had a smaller mean square error than the
closing line but the difference between the lines was generally small and
statistically insignificant [4]. Subsequently all of the calculations for this
study were then completed with both the opening line and the closing line. The
results were compared and resulted in no statistically significant difference
between the results.

There is anecdotal evidence of using the temporal movement of the line for
prediction of the game. Similar to the opening line, if sportsbooks receive
more money on just one side of the spread during the week, they will adjust
the line. This is a much slower process as bets come slowly during the week,
and sportsbooks do not like to move the line once it has stabilized. Unless a
team has a major injury or venue change, the spread should not move at this
point 95% of the time.

The last period is which the line can move is on game day. Big bettors that
did not hammer the opening line often place their bets a few hours up to 15
minutes before game time. These bettors come with large amounts of money and
desire the most accurate information available before the game starts. They
utilize accurate weather reports and have access to the most current injury
reports as they have delayed placing bets until the last day. This last minute
action can be large enough to move the line right up until game time. While
the temporal aspect of line movement may be interesting we did not have the
historical data to include in our study. The low frequency of late week line
changes would tend to lead to small sample sizes that would not have much
statistical significance.

Figure 7: Percentage of Games with Line Movement $\geq 1$ By Week (2002-2011)
Figure 8: Percentage of Games with Line Movement $\geq 2$ By Week (2002-2011)

One aspect of temporal line movement that we could analyze is to compare the
amount of line movement by week to see if there is a difference. Because the
number of games per week is not consistent throughout the year, we plotted the
number of games that had a line movement greater than or equal to one point in
Figure 7 and two points in Figure 8. The movement was calculated as the
difference between the opening and closing line. The values were plotted along
with the mean and standard deviation in Figure 7 and Figure 8. The peak in
week 1 could be attributed to indeterminate performance as it is the first
week of the regular season and the first time that actual game play strategies
are being revealed. The peak in week 17 is usually attributed to teams that
have locked in playoff positions who rest their starting lineup or teams that
have to win to make it into the playoffs. With so few games demonstrating line
movement of this magnitude we were unable to find a statistically significant
difference that could be leveraged as a predictive value.

##  5 Future Work and Conclusions

We investigated many aspects of the betting line and its relation to NFL
football. In comparing the opening and closing lines we found no statistically
significant difference in the predictive values. Line movement was
investigated and typically only 10% of the games experience a difference of 2
or more points between the opening and closing line and those games did not
demonstrate any consistent behavior. We showed that the line value using the
cumulative distribution function is a good predictor of the team that will win
the game straight up but historically less than 50% accurate at predicting the
winner against the spread.

Our investigation and the Principal Component Analysis of the box score data
demonstrate that the line value would be a valuable input to a machine
learning algorithm for predicting the outcome of NFL games.

We also found that from 2002-2011, a strategy of betting the home underdog
would produce a cumulative winning percentage of 53.5%, which is above the
threshold of 52.38% needed to break even. Pre-2002 data suggests that while
this strategy was historically effective, its effectivness has been reduced in
later years.

Future work will include incorporating the closing line as a feature in a
machine learning algorithm for predicting the outcome of games, and
investigation to determine if the results here hold in college football games
as well as professional (the former has more teams, but the latter has more
parity between the teams).

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Table 4: Results of 1,000 Simulations of the 2011 NFL Season 2011
---
Team | Division | Predicted Wins | Actual Wins | Outcome
New England | AFC East | 11 | 13 | Division Winner
NY Jets | AFC East | 9 | 8 |
Buffalo | AFC East | 7 | 6 |
Miami | AFC East | 7 | 6 |
Baltimore | AFC North | 11 | 12 | Division Winner
Pittsburgh | AFC North | 10 | 12 | Wild Card
Cincinnati | AFC North | 8 | 9 | Wild Card
Cleveland | AFC North | 6 | 4 |
Houston | AFC South | 9 | 10 | Division Winner
Tennessee | AFC South | 8 | 9 |
Jacksonville | AFC South | 6 | 5 |
Indianapolis | AFC South | 5 | 2 |
Denver | AFC West | 7 | 8 | Division Winner
San Diego | AFC West | 9 | 8 |
Oakland | AFC West | 8 | 8 |
Kansas City | AFC West | 6 | 7 |
NY Giants | NFC East | 8 | 9 | Division Winner
Dallas | NFC East | 9 | 8 |
Philadelphia | NFC East | 10 | 8 |
Washington | NFC East | 7 | 5 |
Green Bay | NFC North | 12 | 15 | Division Winner
Detroit | NFC North | 9 | 10 | Wild Card
Chicago | NFC North | 8 | 8 |
Minnesota | NFC North | 6 | 3 |
New Orleans | NFC South | 11 | 13 | Division Winner
Atlanta | NFC South | 9 | 10 | Wild Card
Carolina | NFC South | 7 | 6 |
Tampa Bay | NFC South | 7 | 4 |
San Francisco | NFC West | 9 | 13 | Division Winner
Arizona | NFC West | 7 | 8 |
Seattle | NFC West | 6 | 7 |
St. Louis | NFC West | 5 | 2 |
Table 5: Results of 1,000 Simulations of the 2010 NFL Season 2010
---
Team | Division | Predicted Wins | Actual Wins | Outcome
New England | AFC East | 10 | 14 | Division Winner
NY Jets | AFC East | 9 | 11 | Wild Card
Miami | AFC East | 8 | 7 |
Buffalo | AFC East | 6 | 4 |
Pittsburgh | AFC North | 10 | 12 | Division Winner
Baltimore | AFC North | 10 | 12 | Wild Card
Cleveland | AFC North | 6 | 5 |
Cincinnati | AFC North | 7 | 4 |
Indianapolis | AFC South | 10 | 10 | Division Winner
Jacksonville | AFC South | 7 | 8 |
Houston | AFC South | 8 | 6 |
Tennessee | AFC South | 8 | 6 |
Kansas City | AFC West | 8 | 10 | Division Winner
San Diego | AFC West | 11 | 9 |
Oakland | AFC West | 7 | 8 |
Denver | AFC West | 7 | 4 |
Philadelphia | NFC East | 10 | 10 | Division Winner
NY Giants | NFC East | 9 | 10 |
Dallas | NFC East | 8 | 6 |
Washington | NFC East | 6 | 6 |
Chicago | NFC North | 8 | 11 | Division Winner
Green Bay | NFC North | 10 | 10 | Wild Card
Detroit | NFC North | 6 | 6 |
Minnesota | NFC North | 8 | 6 |
Atlanta | NFC South | 10 | 13 | Division Winner
New Orleans | NFC South | 11 | 11 | Wild Card
Tampa Bay | NFC South | 7 | 10 |
Carolina | NFC South | 5 | 2 |
Seattle | NFC West | 8 | 7 | Division Winner
St. Louis | NFC West | 7 | 7 |
San Francisco | NFC West | 6 | 6 |
Arizona | NFC West | 7 | 5 |
Table 6: Results of 1,000 Simulations of the 2009 NFL Season 2009
---
Team | Division | Predicted Wins | Actual Wins | Outcome
New England | AFC East | 10 | 10 | Division Winner
NY Jets | AFC East | 9 | 9 | Wild Card
Miami | AFC East | 7 | 9 |
Buffalo | AFC East | 6 | 6 |
Cincinnati | AFC North | 8 | 10 | Division Winner
Baltimore | AFC North | 10 | 9 | Wild Card
Pittsburgh | AFC North | 11 | 9 |
Cleveland | AFC North | 5 | 5 |
Indianapolis | AFC South | 10 | 14 | Division Winner
Houston | AFC South | 9 | 9 |
Tennessee | AFC South | 8 | 8 |
Jacksonville | AFC South | 8 | 7 |
San Diego | AFC West | 10 | 13 | Division Winner
Denver | AFC West | 8 | 8 |
Oakland | AFC West | 4 | 5 |
Kansas City | AFC West | 5 | 4 |
Dallas | NFC East | 10 | 11 | Division Winner
Philadelphia | NFC East | 10 | 11 | Wild Card
NY Giants | NFC East | 10 | 8 |
Washington | NFC East | 7 | 4 |
Minnesota | NFC North | 11 | 12 | Division Winner
Green Bay | NFC North | 10 | 11 | Wild Card
Chicago | NFC North | 8 | 7 |
Detroit | NFC North | 4 | 2 |
New Orleans | NFC South | 11 | 13 | Division Winner
Atlanta | NFC South | 8 | 9 |
Carolina | NFC South | 7 | 8 |
Tampa Bay | NFC South | 4 | 3 |
Arizona | NFC West | 9 | 10 | Division Winner
Seattle | NFC West | 8 | 8 |
San Francisco | NFC West | 7 | 5 |
St. Louis | NFC West | 4 | 1 |
Table 7: Results of 1,000 Simulations of the 2008 NFL Season 2008
---
Team | Division | Predicted Wins | Actual Wins | Outcome
Miami | AFC East | 8 | 11 | Division Winner
New England | AFC East | 10 | 11 |
NY Jets | AFC East | 9 | 9 |
Buffalo | AFC East | 8 | 7 |
Pittsburgh | AFC North | 9 | 12 | Division Winner
Baltimore | AFC North | 8 | 11 | Wild Card
Cincinnati | AFC North | 5 | 4 |
Cleveland | AFC North | 6 | 4 |
Tennessee | AFC South | 10 | 13 | Division Winner
Indianapolis | AFC South | 10 | 12 | Wild Card
Houston | AFC South | 8 | 8 |
Jacksonville | AFC South | 8 | 5 |
San Diego | AFC West | 10 | 8 | Division Winner
Denver | AFC West | 8 | 8 |
Oakland | AFC West | 5 | 5 |
Kansas City | AFC West | 5 | 2 |
NY Giants | NFC East | 10 | 12 | Division Winner
Philadelphia | NFC East | 10 | 9 | Wild Card
Dallas | NFC East | 10 | 9 |
Washington | NFC East | 8 | 8 |
Minnesota | NFC North | 9 | 10 | Division Winner
Chicago | NFC North | 8 | 9 |
Green Bay | NFC North | 8 | 6 |
Detroit | NFC North | 4 | 0 |
Carolina | NFC South | 9 | 12 | Division Winner
Atlanta | NFC South | 8 | 11 | Wild Card
Tampa Bay | NFC South | 9 | 9 |
New Orleans | NFC South | 8 | 8 |
Arizona | NFC West | 9 | 9 | Division Winner
Seattle | NFC West | 7 | 7 |
San Francisco | NFC West | 6 | 4 |
St. Louis | NFC West | 4 | 2 |
Table 8: Results of 1,000 Simulations of the 2007 NFL Season 2007
---
Team | Division | Predicted Wins | Actual Wins | Outcome
New England | AFC East | 13 | 16 | Division Winner
Buffalo | AFC East | 6 | 7 |
NY Jets | AFC East | 6 | 4 |
Miami | AFC East | 5 | 1 |
Pittsburgh | AFC North | 11 | 10 | Division Winner
Cleveland | AFC North | 8 | 10 |
Cincinnati | AFC North | 8 | 7 |
Baltimore | AFC North | 8 | 5 |
Indianapolis | AFC South | 11 | 13 | Division Winner
Jacksonville | AFC South | 9 | 11 | Wild Card
Tennessee | AFC South | 9 | 10 | Wild Card
Houston | AFC South | 7 | 8 |
San Diego | AFC West | 10 | 11 | Division Winner
Denver | AFC West | 8 | 7 |
Kansas City | AFC West | 6 | 4 |
Oakland | AFC West | 6 | 4 |
Dallas | NFC East | 11 | 13 | Division Winner
NY Giants | NFC East | 9 | 10 | Wild Card
Washington | NFC East | 8 | 9 | Wild Card
Philadelphia | NFC East | 9 | 8 |
Green Bay | NFC North | 9 | 13 | Division Winner
Minnesota | NFC North | 8 | 8 |
Chicago | NFC North | 8 | 7 |
Detroit | NFC North | 7 | 7 |
Tampa Bay | NFC South | 8 | 9 | Division Winner
Carolina | NFC South | 7 | 7 |
New Orleans | NFC South | 9 | 7 |
Atlanta | NFC South | 6 | 4 |
Seattle | NFC West | 10 | 10 | Division Winner
Arizona | NFC West | 8 | 8 |
San Francisco | NFC West | 5 | 5 |
St. Louis | NFC West | 6 | 3 |
Table 9: Results of 1,000 Simulations of the 2006 NFL Season 2006
---
Team | Division | Predicted Wins | Actual Wins | Outcome
New England | AFC East | 10 | 12 | Division Winner
NY Jets | AFC East | 7 | 10 | Wild Card
Buffalo | AFC East | 6 | 7 |
Miami | AFC East | 8 | 6 |
Baltimore | AFC North | 9 | 13 | Division Winner
Cincinnati | AFC North | 9 | 8 |
Pittsburgh | AFC North | 9 | 8 |
Cleveland | AFC North | 6 | 4 |
Indianapolis | AFC South | 11 | 12 | Division Winner
Jacksonville | AFC South | 9 | 8 |
Tennessee | AFC South | 5 | 8 |
Houston | AFC South | 5 | 6 |
San Diego | AFC West | 11 | 14 | Division Winner
Denver | AFC West | 10 | 9 |
Kansas City | AFC West | 8 | 9 | Wild Card
Oakland | AFC West | 5 | 2 |
Philadelphia | NFC East | 9 | 10 | Division Winner
Dallas | NFC East | 10 | 9 | Wild Card
NY Giants | NFC East | 8 | 8 | Wild Card
Washington | NFC East | 7 | 5 |
Chicago | NFC North | 11 | 13 | Division Winner
Green Bay | NFC North | 6 | 8 |
Minnesota | NFC North | 8 | 6 |
Detroit | NFC North | 6 | 3 |
New Orleans | NFC South | 8 | 10 | Division Winner
Carolina | NFC South | 9 | 8 |
Atlanta | NFC South | 8 | 7 |
Tampa Bay | NFC South | 6 | 4 |
Seattle | NFC West | 9 | 9 | Division Winner
St. Louis | NFC West | 8 | 8 |
San Francisco | NFC West | 6 | 7 |
Arizona | NFC West | 6 | 5 |
Table 10: Results of 1,000 Simulations of the 2005 NFL Season 2005
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Team | Division | Predicted Wins | Actual Wins | Outcome
New England | AFC East | 9 | 10 | Division Winner
Miami | AFC East | 7 | 9 |
Buffalo | AFC East | 7 | 5 |
NY Jets | AFC East | 6 | 4 |
Cincinnati | AFC North | 10 | 11 | Division Winner
Pittsburgh | AFC North | 10 | 11 | Wild Card
Baltimore | AFC North | 7 | 6 |
Cleveland | AFC North | 6 | 6 |
Indianapolis | AFC South | 12 | 14 | Division Winner
Jacksonville | AFC South | 9 | 12 | Wild Card
Tennessee | AFC South | 6 | 4 |
Houston | AFC South | 5 | 2 |
Denver | AFC West | 10 | 13 | Division Winner
Kansas City | AFC West | 8 | 10 |
San Diego | AFC West | 10 | 9 |
Oakland | AFC West | 7 | 4 |
NY Giants | NFC East | 9 | 11 | Division Winner
Washington | NFC East | 9 | 10 | Wild Card
Dallas | NFC East | 8 | 9 |
Philadelphia | NFC East | 8 | 6 |
Chicago | NFC North | 8 | 11 | Division Winner
Minnesota | NFC North | 8 | 9 |
Detroit | NFC North | 7 | 5 |
Green Bay | NFC North | 7 | 4 |
Carolina | NFC South | 10 | 11 | Wild Card
Tampa Bay | NFC South | 9 | 11 | Division Winner
Atlanta | NFC South | 9 | 8 |
New Orleans | NFC South | 6 | 3 |
Seattle | NFC West | 10 | 13 | Division Winner
St. Louis | NFC West | 8 | 6 |
Arizona | NFC West | 7 | 5 |
San Francisco | NFC West | 4 | 4 |
Table 11: Results of 1,000 Simulations of the 2004 NFL Season 2004
---
Team | Division | Predicted Wins | Actual Wins | Outcome
New England | AFC East | 11 | 14 | Division Winner
NY Jets | AFC East | 9 | 10 | Wild Card
Buffalo | AFC East | 8 | 9 |
Miami | AFC East | 6 | 4 |
Pittsburgh | AFC North | 9 | 15 | Division Winner
Baltimore | AFC North | 9 | 9 |
Cincinnati | AFC North | 7 | 8 |
Cleveland | AFC North | 6 | 4 |
Indianapolis | AFC South | 10 | 12 | Division Winner
Jacksonville | AFC South | 8 | 9 |
Houston | AFC South | 6 | 7 |
Tennessee | AFC South | 8 | 5 |
San Diego | AFC West | 8 | 12 | Division Winner
Denver | AFC West | 10 | 10 | Wild Card
Kansas City | AFC West | 9 | 7 |
Oakland | AFC West | 7 | 5 |
Philadelphia | NFC East | 11 | 13 | Division Winner
Washington | NFC East | 7 | 6 |
NY Giants | NFC East | 7 | 6 |
Dallas | NFC East | 7 | 6 |
Green Bay | NFC North | 9 | 10 | Division Winner
Minnesota | NFC North | 10 | 8 | Wild Card
Detroit | NFC North | 7 | 6 |
Chicago | NFC North | 6 | 5 |
Atlanta | NFC South | 9 | 11 | Division Winner
New Orleans | NFC South | 7 | 8 |
Carolina | NFC South | 8 | 7 |
Tampa Bay | NFC South | 8 | 5 |
Seattle | NFC West | 10 | 9 | Division Winner
St. Louis | NFC West | 9 | 8 | Wild Card
Arizona | NFC West | 6 | 6 |
San Francisco | NFC West | 5 | 2 |
Table 12: Results of 1,000 Simulations of the 2003 NFL Season 2003
---
Team | Division | Predicted Wins | Actual Wins | Outcome
New England | AFC East | 9 | 14 | Division Winner
Miami | AFC East | 9 | 10 |
Buffalo | AFC East | 8 | 6 |
NY Jets | AFC East | 7 | 6 |
Baltimore | AFC North | 9 | 10 | Division Winner
Cincinnati | AFC North | 7 | 8 |
Pittsburgh | AFC North | 8 | 6 |
Cleveland | AFC North | 7 | 5 |
Indianapolis | AFC South | 10 | 12 | Division Winner
Tennessee | AFC South | 10 | 12 | Wild Card
Houston | AFC South | 5 | 5 |
Jacksonville | AFC South | 7 | 5 |
Kansas City | AFC West | 11 | 13 | Division Winner
Denver | AFC West | 9 | 10 | Wild Card
Oakland | AFC West | 7 | 4 |
San Diego | AFC West | 6 | 4 |
Philadelphia | NFC East | 9 | 12 | Division Winner
Dallas | NFC East | 8 | 10 | Wild Card
Washington | NFC East | 7 | 5 |
NY Giants | NFC East | 8 | 4 |
Green Bay | NFC North | 10 | 10 | Division Winner
Minnesota | NFC North | 10 | 9 |
Chicago | NFC North | 6 | 7 |
Detroit | NFC North | 5 | 5 |
Carolina | NFC South | 9 | 11 | Division Winner
New Orleans | NFC South | 8 | 8 |
Tampa Bay | NFC South | 10 | 7 |
Atlanta | NFC South | 6 | 5 |
St. Louis | NFC West | 10 | 12 | Division Winner
Seattle | NFC West | 9 | 10 | Wild Card
San Francisco | NFC West | 8 | 7 |
Arizona | NFC West | 5 | 4 |
Table 13: Results of 1,000 Simulations of the 2002 NFL Season 2002
---
Team | Division | Predicted Wins | Actual Wins | Outcome
NY Jets | AFC East | 8 | 9 | Division Winner
Miami | AFC East | 9 | 9 |
New England | AFC East | 9 | 9 |
Buffalo | AFC East | 8 | 8 |
Pittsburgh | AFC North | 10 | 10 | Division Winner
Cleveland | AFC North | 8 | 9 | Wild Card
Baltimore | AFC North | 7 | 7 |
Cincinnati | AFC North | 6 | 2 |
Tennessee | AFC South | 9 | 11 | Division Winner
Indianapolis | AFC South | 9 | 10 | Wild Card
Jacksonville | AFC South | 8 | 6 |
Houston | AFC South | 4 | 4 |
Oakland | AFC West | 10 | 11 | Division Winner
Denver | AFC West | 9 | 9 |
Kansas City | AFC West | 8 | 8 |
San Diego | AFC West | 8 | 8 |
Philadelphia | NFC East | 10 | 12 | Division Winner
NY Giants | NFC East | 8 | 10 | Wild Card
Washington | NFC East | 7 | 7 |
Dallas | NFC East | 7 | 5 |
Green Bay | NFC North | 10 | 12 | Division Winner
Minnesota | NFC North | 7 | 6 |
Chicago | NFC North | 7 | 4 |
Detroit | NFC North | 6 | 3 |
Tampa Bay | NFC South | 10 | 12 | Division Winner
Atlanta | NFC South | 9 | 9 | Wild Card
New Orleans | NFC South | 9 | 9 |
Carolina | NFC South | 6 | 7 |
San Francisco | NFC West | 10 | 10 | Division Winner
St. Louis | NFC West | 9 | 7 |
Seattle | NFC West | 7 | 7 |
Arizona | NFC West | 6 | 5 |