Jessica Clemmensen and I, Wilson Lyle, did our final project on big data analytics of eventual Super Bowl champions.
Here is the link to the video tutorial we recorded:
http://www.youtube.com/watch?v=jX9K82aE4so
Below is the paper. If anyone is interested in reading it as a PDF as opposed to a blog post, contact Jessica or me and we would be more than happy to send it to you. Enjoy.
Wilson Lyle:
WWL0002@auburn.edu
Jessica Clemmensen: JLC0030@auburn.edu
Conference Paper
Big Data Analytics
Football Statistics Super Bowl Winning Teams
This paper was written as a summary for a project that was
completed for INSY 4970 at Auburn University.
Unless otherwise specifically stated, the information contained
herein is made available to the public by Jessica Clemmensen, Wilson Lyle and
Auburn University for reference or non-profit use. The intent of this paper was to further
understand the sport of football and how success can be attained.
Neither Jessica Clemmensen, Wilson Lyle, Auburn University
nor any other agency or entities thereof, assumes any legal liability or
responsibility for the accuracy, completeness, or usefulness of any
information, product or process disclosed in this paper.
Neither Jessica Clemmensen, Wilson Lyle, Auburn University
nor any other agency or entities thereof, assumes any liability or
responsibility for money or any other assets lost due to gambling based on the
conclusions stated within the paper; this is a purely academic paper and should
not be used for profit. Any other use is
prohibited.
Reference herein to any specific commercial product,
process, service by trade name, trademark, manufacturer, or otherwise, does not
constitute or imply its endorsement, recommendation, or favoring by Jessica
Clemmensen, Wilson Lyle, Auburn University or any entities thereof.
The views and opinions expressed in this paper do not
necessarily state or reflect those of Auburn University or the Auburn
University Industrial and Systems Engineering department.
Executive Summary
At the beginning of the semester
in INSY 4970, the project group decided to develop a big data, data mining
project that would seek to develop a model that would accurately predict Super
Bowl winners. In order to complete the project, the group downloaded statistics
from ESPN’s website which lists statistics for all teams beginning in 2002 for
regular and playoff seasons.
In order to narrow the scope of
the project, a set of hypothesis and research questions were utilized to
explore data within the regular and playoff seasons. While these questions
focused on the statistical data from ESPN, miscellaneous circumstances were
also considered such as emotional effects on player performance and
circumstances surrounding the Super Bowl. Microsoft Excel, Minitab, and Orange
Canvas software packages were used during data analysis.
The project group analyzed a
variety of statistics, looking for trends within the data that was unique to
Super Bowl winning teams. The team analyzed effects of first round byes,
compared playoff performance to regular season performance, and attempted to
define the concept of an “elite” quarterback during the playoff season. Regular
season analysis included developing correlated characteristics of eventual
winners, defining the term “hot” when referring to teams, examining offensive
and defensive strategies, as well as establishing conference difficulty.
After data analysis was
completed, the project team determined that there was not a complete model that
would accurately predict the outcome of the Super Bowl for all teams over the
last 11 seasons. However, there were characteristics that were common to all
Super Bowl winning teams. Such characteristics are a balanced offense, as well
as game strategy. Championship winning teams tend to balance both offense and
defense, but both offense and defense are above average. Quarterbacks tend to
improve between the regular and playoff seasons. Conference difficulty,
historically, does not indicate which team will win the Super Bowl. Though
emotional influence cannot be quantified, the project group believes this is a
motivating factor in previous championship teams.
Introduction
The Super Bowl can be
called the television event of the year, garnering a staggering 109 million
viewers in 2013 alone. Viewers tune in to watch their favorite team try to
capture the Lombardi Trophy, placing bets and yelling at the television.
Americans across the country end their Sunday evenings at least once year happy
or miserable, depending on whether or not the final score favored their wishes.
The score may indicate the
outcome of the game, but what factors contribute to a team’s win? This question
has many possible answers, but it requires an in-depth analysis of statistics
for the winning teams of past Super Bowl games. If one could accurately predict
the winner of the Super Bowl based on statistics, not only bragging rights
could be earned but financial gain. In 2012, Super Bowl viewers wagered an
estimated $93,899,840 on various bets. Predicting the winner of the Super Bowl
with confidence could finance a college education, buy a new car or a trip
abroad, and with so many viewing and actively invested in the game, there is a
plethora of data to analyze.
The question the project
team sought to answer is: are there certain patterns or trends that eventual
Super Bowl winning teams show? In order to provide a solution to this question,
it is necessary to develop an initial starting point. As a team, it was decided
that the best approach would be to group questions by regular season and
playoff season performance. The questions are listed below.
During the playoffs:
1. How many eventual
winners had first round byes?
2.
Does the way the teams perform during the regular season line up with how they
played during the playoffs or do many teams just go on an unprecedented
Championship run?
3.
Most experts would say that you must have an “Elite QB” to win a Super Bowl (10
of last 12). What makes a QB Elite? Is this a catch-22? Many QB’s
aren’t actually considered elite until after they win a super bowl. This
begs the question of how you determine if a QB is on his way to becoming elite.
During the regular season:
4. Are there specific stat
categories that eventual champions excel at (PPG, OPPG, YPG, 4th
down conversion percentage etc.)?
5.
Many teams enter the playoffs “Hot” (Giants 2012, Packers 2011). Do most
eventual champions end the regular season “Hot”? How do you consider a
team “Hot”? The last 8 games? 4 games?
6. Do most eventual
champions run similar offenses? Pass/Run/Balanced?
7. Do most eventual
champions rely on their Defense? Offense? Neither?
8. Running
back-by-committee has becoming increasingly popular in the last 10 years.
Do most eventual champions rely on a single back or multiple?
9. Does the eventual
champion come from the “easier” conference?
The three italicized
questions are believed to provide the most insight to the question at hand. The
group also considered miscellaneous circumstances surrounding the Super Bowl
such as Ray Lewis’ impending retirement this year and the effect of Hurricane
Katrina in the 2010 match up.
Method
The group retrieved all
data except AFC and NFC records from ESPN’s website (http://espn.go.com/nfl/statistcs). AFC and NFC
records were downloaded from a visualization website (http://visual.ly/afc-vs-nfc-records-year?view=true). The data was compiled
using visual basic; however, at times during data compilation it was necessary
to manually input data in Excel. The manually input data included byes, running
back-by-committee, and data associated with miscellaneous circumstances. Though
data collection included both manual and coded input components, it was all
gathered from ESPN.com except for the aforementioned conference records. For
data group members did not enter manually, Visual Basic Macros were written to
compile and manipulate the data. The Visual Basic Macros created
workbooks which housed statistical data for seven categories: defense, downs,
passing, receiving, returning, rushing and total, and each category’s data was
stored in three workbooks. Of the three workbooks, the first held the
statistics for the regular season for every team. The second consisted of
statistics from the playoffs for all teams that made it. The third
workbook was comprised of all opposing team statistics for each team.
Unfortunately, the group could not find anywhere that had playoff statistics
for opposing teams, so the opposing team statistics were limited to the regular
season. The group consequently did not analyze the opposing team statistics. All
of the workbooks had essentially the same macro, which consisted of a single
“for” loop. Each “for” loop; the Macro created a new spreadsheet with the name
of the year, and then inserted the data from ESPN.com. Lastly, it deleted all
of the unnecessary rows before and after the necessary data. Unnecessary data
in these rows included links to other aspects of the ESPN website which did not
pertain to the project. The only difference between workbooks was the specific URL
because a different statistic was downloaded each time.
The next step the group
needed to complete was to compile all of the data into a single spreadsheet.
This was accomplished through a two-step process. The first step involved
creating a Macro that assembled all regular and post-season statistics of Super
Bowl winning teams into a single workbook with each worksheet representing a
year. The second step did not involve writing a Macro; rather, it proved to be
simple. The group manually copied and pasted the 11 worksheets into a single worksheet
in another workbook.
The group analyzed the data
in a variety of ways. A number of conclusions could be made by simply looking
at the data once it had been arranged in a single workbook. Other data needed
formatting using Excel; however, the majority of the project group’s
conclusions were drawn through data analysis in statistical software packages
Minitab and Orange Canvas. When analyzing whether having a bye affected whether
or not a team won the championship, it was as simple as looking at the
spreadsheet and seeing that only five of the last 11 teams had first round
byes. The running back-by-committee and miscellaneous analysis were
similar to the bye analysis. Other categories utilized Excel in order to
draw conclusions. These included offensive strategy, team strength and
conference difficulty. They only required excel to calculate averages, medians,
probabilities and standard deviations. There were two categories, the “meat” of
the project, which were analyzed using statistical software. Minitab was used
to compare regular season and playoff performances through a series of t-tests.
When analyzing championship characteristics, the group used Orange Canvas
software to create scatter-plots for each of all statistics downloaded. The
group analyzed each statistic for all teams, coloring championship teams blue
instead of red. This was done so Super Bowl winning teams were prominent in the
scatter plots. Trends for these statistics were then noted and analyzed.
Results & Conclusions
Byes
The project group looked to
answer the nine questions outlined in Research Hypothesis and Method. The
initial question was established in order to look for correlations between
eventual Super Bowl winning teams and whether or not these championship teams
had a first round bye. A first round bye is awarded to a team if the team
finishes first or second in their respective conference and won their division.
This means that a team does not have a game the first week the playoff season
begins. It also guarantees that first round bye teams play their first playoff
game in their home stadium. Initially, the project group hypothesized that
eventual Super Bowl winners would have, for the majority, first round byes. It
was believed that because a team played well enough in the regular season to
earn a first round bye, that they would be qualified to win the super bowl as
well. It was also believed that a first round home game would help a team
win the game, and beginning the playoff season with a win would then help a
team gather momentum and garner an emotional edge. The group predictions proved
to be inaccurate. When analyzing the data, only five out of the 11 Super Bowl
winning teams evaluated from 2002 to 2012 had a first round bye. The Saints,
Steelers, Patriots (2), and Buccaneers had first round byes while the Colts,
Giants (2), Steelers, Packers, and Ravens did not. The Steelers had a first
round bye in 2008 but not in 2005. This indicates that the first round bye was
not necessary for the Steelers to win the Super Bowl. The Patriots had first
round byes for all four of their Super Bowl appearances in the last ten years;
however, they only won two of the four games. Consequently, it cannot be
determined that the first round byes were the reason the Patriots had
successful Super Bowl appearances. Generally, championship teams have first
round byes less than half of the time over the last ten years. Because more
teams do not have first round byes than do, the project group concluded that
first round byes do not appear to affect the outcome of the Super Bowl.
Playoff vs. Regular Season Performance
The project group also
looked to evaluate whether or not a team’s regular season performance was
indicative of the playoff season performance, noting that it was possible for a
team to achieve unprecedented success in the playoff season due to a
championship drive not executed in the regular season. The group analyzed
regular and playoff season performances by subgrouping data into two
categories: offense and defense. Offense performance was broken down into
passing, rushing, and down conversions. Defense performance was evaluated
through tracking the number of tackles and interceptions.
Quarterback Rating
Because the quarterback is
the heartbeat of the passing game, analysis of the quarterback rating was
hypothesized to increase after the regular season. The quarterback rating is a
formula established by the NFL that essentially looks to assign a value to a
team’s quarterback. The formula is comprised of various equations based on
completions per attempt, touchdowns per attempt, interceptions per attempt, and
yards per attempt. Dean Oliver of ESPN writes that the QBR was established in
order to evaluate a quarterback’s expected number of points and probability of
winning. The average quarterback rating for Super Bowl winning teams was 90.2
during the regular season and increased to 98.5 in the playoff season. A t-tail
hypothesis test was performed in order to determine whether the average
quarterback ratings between the regular and playoff season were the same. With
a p-value of 0.15, the group reasoned that on average, quarterback ratings for
eventual super bowl winning teams do not remain the same between playoff and
regular seasons. Based on historical data, quarterbacks for champions
outperform their regular season appearances.
Interceptions: Offense
Interceptions are also an
indication of success in the passing game. A low value for interceptions per
attempt indicates that the quarterback is accurate and precise in executing
passing plays. Interceptions per attempt is averaged at 0.025 during the
regular season for the past 10 years and decreases to 0.014 during the playoff
season. Again, a t-tail hypothesis test was performed, which yielded a p-value
of 0.047. Approximately 96% of the time, interceptions per attempt differs
during the regular and playoff seasons, and interceptions per attempt is on
average lower in the playoff season. This was expected as teams are not able to
make mistakes such as interceptions and still win games. Reducing interceptions
between the regular season and playoff season is a strong characteristic of
Super Bowl winners.
Third
Down Conversion Percentage
Third down conversion
percentages allow offenses more time on the field and also demonstrate the
effectiveness of an offense’s ability to continue to move down the field toward
the opponent’s end zone. Third down conversion percentages t-tests produced a
p-value of 0.112. Again, this indicates that the hypothesis that the average
third down conversions for regular and playoff seasons are the same is not true.
Regular season conversion percentages are 41.14 on average and 44.87 in the
playoff season. Based on historical data, conversion percentages increase in
the playoff season which is logical in that teams must move down the field more
once less capable opponents are eliminated from the game pool.
These t-tests produced
logical p-values. The statistical tests essentially demonstrate that
quarterbacks of eventual championship winning teams play better in the playoffs
than the regular season, which is to be expected. It also makes sense
interceptions per attempt decrease at a more significant level than quarterback
rating increases, because winning teams do not turn the ball over.
Tackles
& Defensive Interceptions
T-tests were also performed
on defensive statistics. Teams averaged 62.92 tackles per game in the regular
season and 63.28 in the playoffs. The p-value testing the equivalence of
playoff and regular season average tackles is 0.015. This p-value suggests that
the hypothesis that teams make the same amount of tackles in the playoff and
regular seasons is not true. The project group found this particular statistic
to produce surprising results as the average values for playoff and regular
season tackles both round to 63 per game. Interceptions the defense is able to
execute is 1.295 and 1.659 for regular and playoff seasons respectively. The
p-value for this particular test is 0.128, so it would hold that defenses are
able to complete varying average interceptions per game, historically
increasing during the playoff season.
Elite Quarterbacks
Super Bowl winning teams
have to be able to score which requires a strong offense. The quarterback
captains the offense. Quarterbacks are called “elite” when they are likely to
be inducted into the National Football League’s Hall of Fame. Since 2002, nine
of the 11 teams had quarterbacks who are now considered elite. However, a
definition or clear qualities to define an elite quarterback before this occurs
would indicate that a team is likely to appear in and win the Super Bowl. The
group initially hoped to provide a definition based on offensive statistics
though this proved to be a question that remains unanswered. Eventual Super
Bowl winning teams all have quarterbacks with a high QBR and high yards per
attempt passing completions. These were the only qualities consistent across
all championship teams analyzed; however, these alone do not classify a
quarterback as elite as many non-elite quarterbacks meet this criteria.
Concluding, Super Bowl winning teams had elite offensive leaders 82% of the
time over the last ten years. Elite quarterbacks appear in the Super Bowl the
majority of the time, yet there is no clear definition which will indicate
whether a quarterback in the here and now will be considered elite in the
future.
Championship Characteristics
Regular season performance
determines whether or not a team is eligible for the playoffs which then
determine the teams facing off for the Lombardi trophy. The project group
sought to find characteristics of championship teams that were prominent during
the regular season. Implementing scatter plots in Orange Canvas, the group
looked at each statistic for these teams beginning in 2002 until present day.
Of all the data, four statistics were consistent across all teams, with these
teams predominantly clustered at an above average level. Such statistics being
quarterback ratings, passing yards per attempt, rushing attempts per game, and
sacks per game which is logical. A high quarterback rating indicates a high
probability of a team winning games as well as a high number of expected points
scored. This would demonstrate a qualified quarterback leading the offense
which was characteristic of winning teams. An increase in passing yards per
attempt and rushing attempts per game indicate that champions run the ball at a
high rate and then capitalize on their passing attempts. Sacks per game
illustrates defensive strengths which prevents opponents from outscoring Super
Bowl winning teams; eventual winners are able to penetrate the defense and
apply pressure on the opposing team’s quarterback and offense in general.
Classification:
“Hot”
Sports analysts continually
discuss whether a team is considered “hot” before entering the playoffs and
whether or not this is a deciding factor in the Super Bowl. The group set out
to determine whether or not data can be used to identify which teams are “hot”
and whether said teams have a chance at winning the super bowl. The group
looked at how many eventual champions won their last game of the season, the
record for their last five games of the regular season, and the record for
their last eight games of the playoff season. Surprisingly, there was no
consistency for any of the three. The group initially predicted that eventual
champions would consistently win games, cementing their team strengths before
playing in the Super Bowl. The Ravens, Super Bowl winner in 2012, went 1-4 in
their last five games, including a loss for the final game of the year. Only
one team in the last seven seasons won at least four of their last five games;
that team being the Steelers who went 4-1. None of the last seven Super Bowl
Champions have won the final five regular season games. Although the Ravens
lost their last game of the regular season, many eventual champions do in fact
win their last game. The Giants (2011) and the Packers (2010) had to win
their last two games of the season in order to be eligible for the playoffs.
After looking at this data, the project group determined that what matters is
how a team is playing upon entering the playoffs at the end of the regular
season and not whether or not a team won their last five games. A great example
was the Giants in the 2007 season. The Giants lost their last game of the
season. However, they lost to the New England Patriots who, after winning
that game, went undefeated during the regular season. The Giants played
well, only losing by a three point margin which made them the team that was the
closest to defeating the Patriots. The Giants rode this momentum through the
playoffs, and eventually to the Super Bowl where they were able to defeat the
Patriots when it mattered most. Although the project group was able to
conclude that a team’s performance matters more than a team win, data did not
solidify a definition or classification tool to determine whether or not a team
was “hot.”
Offensive Strategy
Eventual Super Bowl winners
are able to effectively outscore their opponents, but this poses the question
as to how champion offenses accomplish this whether it is through the passing
or running game or a balance of the two. Historical data shows that champions’
passing attempts per game ranges between 30 and 35 for 17 of the 22 data
points, and rushing attempts per game have a wider range between 25 and 33 for
18 of the 22 teams. The New England Patriots did not follow this trend when
individually evaluating their passing attempts per game in the two playoff
seasons before their Super Bowl appearance, averaging 27 and 42 passing
attempts per game. However, when averaged, the Patriots passed the ball on
average 34.5 times each game, which falls within the aforementioned range. The
Giants were also outliers, averaging 38.31 and 40.75 passing attempts per game
in the regular and playoff seasons. The Indianapolis Colts averaged 38.5 in the
playoff season for passing attempts per game. The Baltimore Ravens, Colts, and
Steelers fall out of the range for rushing attempts per game. The Ravens and
Colts rushed the ball more during the playoff season, while the Steelers
consistently rushed the ball more throughout the entire 2005 season. This can
be attributed to running back Jerome Bettis who retired after 2005 and explains
why the Steelers were not outliers of this range during their 2008 appearance.
The ranges for passing and rushing attempts overlap. Because of this, the group
concluded that overall, balanced offenses with the ability to effectively throw
and run the ball are characteristic of Super Bowl winning teams. This
observation was predicted as teams who consistently change their offensive
strategy continually challenge opposing defenses, scoring points in ways that
are varying and difficult to predict.
Team Strength: Offense vs. Defense
After analyzing champion
offenses, it was necessary to determine whether winning teams utilized
offensive strategies more so than defensive tactics or balanced both aspects of
the team. In order to do this, the group compared average points per game,
24.76, and average opponent points per game, 20.07. The overall averages do not
indicate a large difference between winning and losing teams; however, when the
score margin is averaged, champions score on average 6.5 points more than their
opponents, a touchdown more than defense allows. The group concluded that Super
Bowl winning teams have a balanced strategy based on the low scoring margin.
Champions collectively have above average offenses and defenses without having
to particularly rely on either.
Running Back-by-Committee
The running
back-by-committee strategy is becoming increasingly popular at the collegiate
and professional level, with four of the last six champions utilizing it.
“Running back-by-committee” is defined as having at least two legitimate
rushing threats who can line up in the backfield. There are two
advantages to this strategy. The first being that extra depth at the running
back position will show as the defense line and linebackers grow tired during
the course of the game while the running backs are able to continue to play as
if it is the first quarter. The second ideology behind the strategy is the most
important, which is that the opposing team will have to split up their time in
the film room meaning a team will spend less time studying running backs
individually. Oddly enough, a championship team that only used one running back
validates this prediction. In 2010 the Packers ran the ball with Brandon Jackson
during the regular season. The Packers drafted James Starks in the previous
draft; however, he was injured in training camp and did not play until the
conclusion of the regular season. Then, in the first game of the
playoffs, Starks set the franchise record for most yards in a postseason game
for a rookie and continued to play at a high performance level for the duration
of the playoffs. James Starks clearly has talent; however, he performed at a
high level because teams did not have film on him, making it difficult to
prepare for and thwart his running skills. The conclusion the group made was
that utilizing a running back-by-committee strategy can be an advantage if
there is qualified personnel; however, it is by no means necessary to win the
Super Bowl.
Perceived Conference Difficulty
The project group predicted
that the perceived difficulty of the conference would correlate with the Super
Bowl winning teams. In order to execute this analysis, the group collected data
over the 11 seasons on how many wins each conference had when facing an
opponent from the other conference. Group predictions were that the winning
conference, which won more games, would indicate the eventual Super Bowl
winner, but in this particular case group predictions were wrong. Since 2002,
the Super Bowl winning team was from the conference that won more games only
54% percent of the time. The probability that the AFC wins the Super Bowl and
is the winning conference is around 40%. In contrast, the probability that the
NFC wins the Super Bowl and is the losing conference is around 37%. Based on
historical data, the conference difficulty only indicates the championship team
roughly half of the time. The NFC is more likely to produce a Super Bowl
winning team as a losing conference than the AFC which has a 14% chance of
happening. The probability that the NFC wins as both a conference and in the
Super Bowl is approximately 8%. With the 11 data points from 2002 to 2012,
inter-conference record is not a consistent predictive Super Bowl indicator.
Emotional and Circumstantial Influence
Though data is important,
the project group sought to consider the emotional aspects affecting a team and
how emotion plays into the game of football. Football is a strategic and
calculated sport; however, players, coaches and fans are all emotionally invested. When
players have other incentives for which they are playing, they are believed to
play at their best for the duration of the game rather than noticing their
body’s natural fatigue. Although emotion cannot be measured or quantified, the
group still decided to explore this aspect of the game. Emotion has been
evident in the last ten years, most recently this year with Ray Lewis and the
Ravens. Ray Lewis was injured in week six and sat out the rest of the
regular season (11 weeks). He not only returned for the playoffs but also
announced that he would be retiring at the end of the season. The fact
that this would be his last attempt for a title combined with his pastoral
pre-game speeches had the Ravens playing hard during all games throughout the
playoffs. Although Ray Lewis did not play at an all-star level during the
championship run, his drive for the win and inspiration for his team, helped
the Ravens play at a level that was necessary to end the season as champions.
Another example of an
emotional advantage is the Saints run to the Super Bowl in 2009 after the
emotional roller coaster that came in the wake of Hurricane Katrina. Katrina
devastated the city of New Orleans, killing over 1500 people in the state of
Louisiana and forcing 1.2 million people along the gulf coast to evacuate.
Almost six months after Hurricane Katrina, the Saints opted to sign a
quarterback who was undesired in the league because he was, at the time,
returning from a shoulder surgery. That quarterback, Drew Brees, was thankful
for a second chance. He moved into the city of New Orleans at a time when other
players did not want to live in a city that required rebuilding. He invested
himself in the city and helped with relief efforts. Fast forward to the 2009
season; Drew Brees has had successful seasons at this juncture. New Orleans is
beginning to see an influx of residents and visitors once more, and life in the
city is slowly moving back to its pre-Katrina pro quo. The difference in the
city this time however is that the citizens of New Orleans have a phenomenal
football team for which they can root. After a first round bye, on January 16,
2010, the Super Dome hosted a playoff game; four years earlier it was being used
as a homeless shelter for the citizens of New Orleans. The Saints went on to
win the Super Bowl after two victories in the Super Dome and one over the Colts
in Miami. After the game was over, Drew Brees was quoted as saying “We
felt as if there was no way we could lose this game...This one is for you New
Orleans.” The project group believes times like these can cause players to play
at a level they ordinarily cannot reach.
Conclusions
After utilizing ESPNs rather
large database of NFL statistics, the group was able to answer their hypothesis
questions and draw conclusions. The
group determined there were three things that do not factor into a team winning
the Super Bowl. These are conference
difficulty, whether or not they had a bye, and whether or not they used a
running back-by-committee system. The
group determined that if a team has the proper personnel, then using multiple
running backs can prove to be advantageous, however it is by no means a
requirement to win the Super Bowl.
The group determined that how a
team’s quarterback plays in the playoffs is indicative of how far that team
goes. Quarterbacks for eventual
champions play better in the playoffs, with a higher QBR and a lower
interception rate. The rest of the team
also plays better in the playoffs, historically having higher third down
conversion rates and defensive interception rates.
There also proved to be
consistencies with champions during the regular season. Teams tend to have balances offensive attacks
(rushing/passing) along with above average offenses and defenses but not to the
point where teams have to rely on either one.
There proved to be more consistencies for Super Bowl teams with specific
statistical categories. The conclusions
the group was able to draw are as follows: teams establish a solid running game
and then capitalize on their passing opportunities. Teams are also able to put pressure on
quarterbacks and disrupt the running game in a similar manner.
The group found that there are
also circumstances that affect Super Bowl outcome but cannot be
quantified. These include how “hot” a
team is and whether or not teams have an emotional advantage. It was determined that how a team is playing
at the end of the season can cause them to be “hot,” however a win/loss record
does not depict it. Teams can also have
an emotional edge if they have added incentives to play for, like the Saints in
the wake of Hurricane Katrina. Though there are common factors among
championship teams, there is not a definitive answer as to whether or not a
team will win the Super Bowl.
Sources
