Boise State has yet to play its first game of the 2009 season but most of us know that this
year’s team has the potential of returning to another Bowl Championship Series
(BCS) bowl game. Though the
BCS
system continues to be chided by fans and many in the media, it is the system
we must live with at present. As
most of us already know, this system is composed of three separate polls:
the USA Today Coaches Poll (which consists of 61 coaches), the Harris
Poll (a motley crew of 114 college football “experts”), and six computer
polls (Kenneth Massey, Jeff
Sagarin, Richard Billingsley, Anderson
and Hester, Peter Wolfe,
and the Colley Matrix).
What part do computer rankings play in the overall
BCS
ranking for a team?
Rather
than providing several details regarding the history of how computer polls made
their way into the
BCS
mix and, even more, how the six specific computer polls currently used by the
Bowl Championship Series came into play, let’s take a brief look at some of
the highlights. From 1998 to 2003,
the
BCS
tinkered with a few different ranking models.
As we know, the Associated Press Poll was used as one of the human polls
during that time (they remained until 2005).
In addition, there were set formulas for determining a team’s strength
of schedule (aside from computers), a quality win factor was used from
2001-2003, number of losses was explicitly used as a value to be deducted from a
team’s ranking score, and anywhere from three to eight computer polls were
used (additional computer polls included the New York Times, Richard Dunkel,
Herman Matthews/Scripps Howard, and David Rothman).
Some of these
computer rankings used margin of victory in their calculations and were later
forced out of the
BCS
unless they could provide a method that did not use margin of victory. Due
to several controversies relating to the final rankings of some teams and to the
heavy weight that computer polls had in the process, the entire system was
recreated in 2004, which is the system we now have.
Good,
bad, or indifferent, each of the three polls currently used by the BCS holds
equal weight (1/3 each) in the overall ranking score.
As an example, the final BCS rankings for 2008 (before any bowl games
were played) had
Oklahoma
in the #1 spot with a .9757 BCS average. This
score was determined by averaging Oklahoma’s Coaches Poll score of .9718, its
Harris Poll score of .9554, and its computer poll score of 1.000 (.9718 + .9554
+ 1.000 = 2.9272 and 2.9272/3 = .9757). To
determine a team’s BCS score for each poll, the total number of votes or
points received in the poll is divided by the maximum number of votes or points
available in the poll.
When
applied to the computer polls, a first-place vote equals 25 points as in the
human polls. But unlike the human
polls, the highest and lowest computer poll scores for each team are thrown out
when calculating a team’s BCS computer poll average. In
the case of Oklahoma above, they received a 1.000 average in the computers
because, after throwing out one of their high scores of 25 points and their
lowest score, they still held the number #1 spot in the four remaining computer
polls. Thus, they had received 100
points, the maximum number of points available in the computer polls (100/100 =
1.000).
Computers vs. Humans
Both
human and computer polls have their critics and supporters, and in many
instances both sides are correct in their opinions.
It’s interesting that the non-human polls are even called computer
polls. I mean, my own computer
rankings (junkysrankings.com) could
technically be called Junky’s Excel Spreadsheet Rankings because that is the
program I use to enter in all the data and to perform the calculations.
In reality, computer polls are nothing more than mathematical polls in
which a computer is used to present the data.
Even more, computer rankings are truly human polls that use fixed
conditions and methods developed by their human creators to evaluate the
performance of college football teams. The
so-called human polls, on the other hand, are subject to changing
thought-processes and whims of those individuals who participate in them
(assuming they all put much thought into their decisions).
Please
understand that though I’m a big supporter of computer polls, I’m very aware
of their shortcomings and, at the same time, realize that the human polls have
some important advantages. Many
consider computer polls to be fairer because they evaluate all teams based on
the same criteria. In the same
sense, however, this could be considered less fair because the computers
aren’t able to account for changing game plans, injuries, team improvement,
unfortunate calls by referees, weather conditions, or in the case of the BCS
computers, margin of victory (Kenneth Massey’s rankings actually claim to
account for some of these factors and Richard Billingsley attempts to control
for changes occurring from week to week). Those
voting in the USA Today Coaches Poll and the Harris Poll can view the games on
television or collect other information about the games and then consider the
effort made by the losing team or decide that one team got some lucky or
unfortunate breaks. You need to look
no further than the Oklahoma-Oregon game of 2006 as an example of where the
subjective eye of pollsters would be important.
It
is the opinion of this college football fan that both computer and human polls
are necessary when evaluating and ranking the teams.
There will certainly continue to be a healthy debate about which factors
are critical and which are more important than others when it comes to computer
polls, which is good; because without a playoff, we really have no definitive
way to determine who the best teams are.
What do the computer rankings evaluate?
Each
of the six current BCS computer poll creators provide some details regarding
their methods, but some provide more information than others.
They each state the factors they feel make their rankings unique or
better than other run-of-the-mill computer polls.
Some of them do this using highly technical jargon, while others speak in
generalities. If you aspire to work
for NASA someday, or you like to spend your evenings reading the works of
Einstein, Tesla, or Pythagoras, you might enjoy reading Colley Matrix’s
dissertation on how to best rank college football teams.
Otherwise, you can simply take the poll creator’s word for it that
they’ve got a well-run system and that the factors they use are all that are
needed.
For
those of us who like things put in simple terms, I will attempt to summarize how
each of these six
BCS
computer polls ranks the 120 teams that make up what most of us know as
Division 1-A college football (or what the NCAA likes to call the Football Bowl
Subdivision). Before looking at each
computer poll, it is important to note that all of these computer polls have
some things in common. For instance,
all have some kind of method of determining a team’s strength of schedule (
SOS
), which is required by the
BCS
. However, the
SOS
method is not standardized and is therefore left up to the poll creators to
determine. The six computers also
look at a team’s number of wins and losses in some way or another and most of
them (aside from Peter Wolfe) use a strict retrodictive process in ranking the
teams. A retrodictive process is
simply one in which current accumulated data is used to justify or explain a
team’s ranking (rather than looking at their potential).
A predictive process could be used to determine where a team will be
ranked at the end of the season, or to forecast the outcome of an upcoming game.
Jeff Sagarin’s poll is one that has both retrodictive and predictive
rankings for each team, though only the retrodictive ones are used for the
BCS
.
One
of the conditions the BCS sets on the computer polls is that they are not
allowed to use Margin of Victory (MOV) in their BCS ranking formulas.
Kenneth Massey and Jeff Sagarin have specific BCS-compliant rankings in
addition to their regular rankings in which they factor in a MOV value.
When looking at Sagarin’s rankings website, he lists his official BCS
rankings for each team under a column labeled ELO-CHESS, his regular rankings
that use a MOV value are listed under RATING, and his predictive rankings are
listed under the PREDICTOR column.
As
I’ve already mentioned, each computer poll creator has his own philosophy on
how to best rank the teams from 1 to 120. Without
going into the geeky details about how these calculations work (many of which
are proprietary anyway), let’s just look at some basic similarities and
differences between the polls. For
each poll listed below, I will list the poll creator’s/owner’s name(s),
whether they provide separate rankings than those used by the BCS (such as those
using a MOV factor or predictor), a brief explanation of what factors are most
critical in their ranking calculations, how they determine strength of schedule
for a team, if they rank 1-AA/FCS teams in the same pool as the 1-A/FBS teams
(or if they simply exclude 1-AA/FCS teams and games), if there is any carry-over
in rankings from the previous season, if conference strength (however defined)
is used in their ranking formulas and who they ranked number #1 and #2 at the
end of the 2008-2009 season (post-bowl).
ANDERSON & HESTER
Creators/Owners: Jeff
Anderson and Chris Hester (part of BCS rankings since 1998)
Rankings Provided: Retrodictive
BCS-compliant Rankings Only. Rankings
posted after 5th week of season is complete.
Key Ranking Factors (BCS Only):
Quality Wins, Presumably Wins and Losses, Strength of Schedule
SOS Determination: Opponent’s (and Opponents’
Opponents) W/L Record as well as Opponent’s Conference Strength (defined as a
conference’s record against non-conference opponents and the W/L record of the
conference’s non-conference opponents).
Ranking of 1-AA/FCS Teams:
Not ranked and do not appear to factor into 1-A/FBS rankings
Season Carryover: None
(each team starts with a clean slate each season)
Conference Strength Factor:
Used as part of a team’s SOS calculation.
Conferences are individually ranked as well.
#1 Team from 2008-2009:
Utah
Utes
#2 Team from 2008-2009:
Florida
Gators
COLLEY MATRIX
Creator/Owner: Wesley
Colley, Ph.D. (part of BCS rankings since 2001)
Note: Of the
six BCS computer polls, the Colley Matrix poll seems to be the most
mathematically intense. In fact,
Wesley Colley provides a 23 page detailed explanation of his rankings that can
be accessed by anyone who visits his rankings website.
Rankings Provided: Retrodictive
BCS-compliant Rankings Only
Key Ranking Factors (BCS Only):
Wins and Losses and Strength of Schedule
SOS
Determination: Running several
iterations of opponents’ ratings based upon the initial ratings contrived from
wins and losses until there is little movement left in teams’ ratings in the
entire poll (for further explanation, please visit his site and read The
Colley Matrix Explained.
Ranking of 1-AA/FCS Teams:
Not ranked and do not factor into 1-A/FBS rankings
Season Carryover: None
(each team starts with a clean slate each season)
Conference Strength Factor:
Not used though individual conferences are ranked separately.
#1 Team from 2008-2009:
Florida
Gators
#2 Team from 2008-2009:
Texas
Longhorns
JEFF SAGARIN
Creator/Owner: Jeff
Sagarin (part of BCS rankings since 1998)
Rankings Provided: Retrodictive
BCS-compliant Rankings (ELO-CHESS), Retrodictive Rankings Using Margin of
Victory (RATING), and Predictive Rankings (PREDICTOR)
Key Ranking Factors (BCS Only):
Wins and Losses, Quality Wins, Game Location, and Strength of Schedule
(no information is provided as to how these factors apply but it is inferred
they are used from his rankings and explanations)
SOS Determination: Rating
of opponent plus game location
Ranking of 1-AA/FCS Teams:
Ranked with all 1-A/FBS teams and games factored into all rankings
Season Carryover: Temporarily
but previous season is removed from calculations after sufficient number of
games have been played to “connect” all teams
Conference Strength Factor:
Uncertain if this is used in formula, though individual conferences are
ranked separately.
#1 Team from 2008-2009:
Utah
Utes (BCS) /
Florida
Gators (MOV)
#2 Team from 2008-2009:
Florida
Gators (BCS) / USC Trojans (MOV)
KENNETH MASSEY
Creator/Owner: Kenneth
Massey (part of BCS rankings since 1999)
Rankings Provided: Retrodictive
BCS-compliant Rankings and Retrodictive Rankings Using Margin of Victory
Key Ranking Factors (BCS Only):
Wins and Losses, Game Location, Date of Game, and Strength of Schedule
SOS Determination: Rating
of opponent plus game location
Ranking of 1-AA/FCS Teams:
Not ranked and games do not appear to factor into 1-A/FBS rankings
Season Carryover: Temporarily
but previous season is not influential in calculations after certain number of
games have been played
Conference Strength Factor:
Not used in formula, though individual conferences are ranked separately
#1 Team from 2008-2009:
Utah
Utes (BCS) /
Florida
Gators (MOV)
#2 Team from 2008-2009:
Florida
Gators (BCS) / USC Trojans (MOV)
PETER WOLFE
Creator/Owner: Peter
R. Wolfe (part of BCS rankings since 2001)
Rankings Provided: Retrodictive
BCS-compliant Rankings with a Predictive Element
Key Ranking Factors (BCS Only):
Wins and Losses, Game Location, Mutual Opponent Comparison, and Strength
of Schedule (implied)
SOS Determination: Unknown
Ranking of 1-AA/FCS Teams:
Ranked with all 1-A/FBS teams and games factored into all rankings
Season Carryover: Unknown
but appears no carryover is used
Conference Strength Factor:
Not used in formula and unknown if conferences are ranked separately
#1 Team from 2008-2009:
Utah
Utes
#2 Team from 2008-2009:
Florida
Gators
RICHARD BILLINGSLEY
Creator/Owner: Richard
Billingsley (part of BCS rankings since 1999)
Rankings Provided: Retrodictive
BCS-compliant Rankings Only
Key Ranking Factors (BCS Only):
Starting Position (final rank from previous season), Strength of
Schedule, Win Value (based on single game and accumulated from week to week),
Losses, Game Location, and Head to Head Rules
SOS Determination: Based
solely on the Rank and Rating of Opponents
Ranking of 1-AA/FCS Teams:
Not ranked with 1-A/FBS teams and games not factored into rankings
Season Carryover: Yes,
though teams are able to quickly overcome previous season’s rankings
Conference Strength Factor:
Not used in formula and unknown if conferences are ranked separately
#1 Team from 2008-2009:
Florida
Gators
#2 Team from 2008-2009:
USC Trojans
What seem to be the most critical factors in determining
if a team has a good computer ranking?
When
all is said and done, there really isn’t that much to understanding how a team
ends up ranked very high in the computer polls versus the middle or bottom.
In fact, you’ll find that most of the disparities that exist between
different computer polls aren’t seen in the rankings of the best and worst
teams as much as they are seen in the teams in the middle of the pack.
When you think about it, teams like Duke, Idaho, and Louisiana-Monroe
(perennial bottom-dwellers) seem to find their way into the bottom of all the
computer polls no matter what the minute differences are in their ranking
formulas. Where the computers seem
to differ most is when comparing those teams with 4 to 8 wins (using a 12-game
season). For instance, is a 3-9
Mississippi
State
team better than a 7-5
Arkansas
State
? Some computers would say yes
because they weight SOS or some sort of conference-weighting value much higher
than wins and losses. Whether this
is proper or not is up to each college football fan to decide and many of these
computer-ranking gurus will give you their spiel as to why their method is
better than another.
Before
we go any further, we have to remember that six of the BCS bowls offer
guaranteed births to the conference winner of an automatic-qualifying (AQ)
conference (ACC, Big East, Big 10, Big 12, and PAC-10).
So when discussing the polls and their influence on a team’s
BCS-worthiness, we have to realize that that this only applies to at-large
selections and to those teams attempting to qualify for the BCS Championship
game. We also need to accept the
fact that each AQ conference and all the non-AQ conferences combined are limited
to two BCS bowl spots.
Remember,
the six computer polls can’t use margin of victory in their formulas, so
winning big only helps in the human polls. So
when it comes to the computers, what separates the top teams from the middle of
the pack, and further, what matters most when trying to qualify for an at-large
BCS spot or the BCS Championship game? If
you look at just three critical factors, I think you can narrow things down
pretty quickly. The first factor is
Total Number of Losses, the second is Quality Wins, and the last is Opponents’
Win-Loss Record.
#1
– Number of Losses: This probably
seems obvious but too many losses will disqualify a team from
BCS
contention faster than any other factor. The
threshold for an at-large
BCS
birth has historically been two losses (though all non-AQ teams have gone
undefeated, they could conceivably make it with one or even two losses).
Since 2004 (the year the current
BCS
formula started) only one at-large team has ever had three losses.
That team was
Illinois
and they lost to USC in the Rose Bowl 49-17.
Technically, the exact number of losses isn’t really the key because a
three-loss team could conceivably play in the championship game in the unusual
event that no other team in the league had fewer than three losses.
However, there have historically been plenty of at-large teams meeting
the two-loss threshold and so it is statistically very difficult to earn a
BCS
birth with three or more losses. By
the way, if you are looking for a #1 or #2 ranking in the computers, then the
threshold moves to one loss, though LSU won the championship in 2007 with two
losses (since 2004, five teams participating in the championship game have had
no losses, four have had one loss, and LSU is the only team to have had two
losses).
#2
– Number of Quality Wins: Computer
polls play a major role in verifying what qualifies as a quality win.
Preseason rankings and victories over perceived quality teams early in
the season can skew the human polls in the first few weeks.
However, computer polls do bit have this problem as the quality win
factor is free from prejudice. This
factor is important because it helps separate teams having the same number of
losses and can boost teams above others that have more losses than themselves.
LSU’s
appearance in the 2007
BCS
Championship game with two losses supports this argument.
Using
Anderson
and Hester’s results from that season, LSU had victories over five teams
ranked in the top 25 of that poll. Its
only two losses were to #25
Kentucky
and #32
Arkansas
. A simple rule of thumb when
looking at quality wins would be to look at how many victories a team has over
teams in the top 25, the top ½ of the rankings, and the bottom ½ of the
rankings. When comparing two teams
with an equal number of losses between them, or that are within one to two
losses of each other, it is almost certain that the team having more wins
against teams in the top 25 and top ½ of the rankings is more likely to be
ranked ahead of the other team, especially if one has a majority of their wins
against teams in the bottom ½ of the list.
#3
– Opponents’ Win-Loss Record: This
factor is generally what the computer polls refer to as strength of schedule.
In
Boise
State
’s case, this factor alone has probably kept them from reaching the highest
spots in some computer polls. The
Western Athletic Conference’s performance in non-conference games has often
been a hindrance. Any conference
that loses more non-conference games than it wins is going to have an adverse
affect on all teams in that conference, no matter how good their top team is.
When a conference finishes its
non-conference schedule, the win-loss record for each team in the conference, as
far as it applies to
SOS
, has basically been set.
Because all teams in a conference play each other (with a few
exceptions), the inter-conference win-loss record is going to be .500.
For instance, in the nine-team WAC, the total number of wins and losses
in inter-conference play will always come out to 72 wins and 72 losses. In
reality, individual conference games generally have no bearing on a team’s
SOS
score. In some conferences, such as the Big-10
and Big-12, conference games would seem to have some bearing on
SOS
because not every team in the conference plays each
other; but regardless, many of the computer polls completely disregard inter-conference
games when calculating
SOS
. So,
instead of the WAC winning 37 percent (or 11 out of 30) of their non-conference
games against 1-A/FBS teams, as they did last season, let's assume the WAC were
to win 75 percent, or 23 out of those 30 games. If that were to occur, the
SOS
for each team in the conference would greatly improve
and, in turn, boost every conference member’s ranking in the computer polls.
Non-conference dominance is a major reason why teams in the SEC
and Big 12 do so well in the computer rankings even when they have a significant
number of losses after conference play. Though playing soft
non-conference schedules (such as those often found in the SEC) can adversely
impact team rankings for those in the conference, winning those non-conference
games appears to have a greater influence on the rankings.
Though it's critical that conference-mates perform well in non-conference
games, the success of a team’s own non-conference opponents is even more
important. This is because there are
more games affecting a team's
SOS
from non-conference opponents than from conference
opponents (the PAC-10 may be an exception).
If
Boise
State
were to play four 1-A/FBS non-conference teams,
those four opponents could play up to 11 other 1-A/FBS teams during the season
(assuming a 12-game schedule). That would provide 44 games (four teams x
11 games) compared to a maximum of 32 from conference opponents (eight teams
x four non-conference games) in a 12-game schedule, assuming all
non-conference opponents are 1-A/FBS members.
Games
versus 1-AA/
FCS
teams are often looked down upon (and often do no count in calculating
SOS
) but these games can be helpful if the only other option were to be a game
versus a 1-A/FBS team likely to have a losing record.
On the other hand, these games are detrimental if they could be replaced
by 1-A/FBS opponents likely to have winning records.
What does this all mean for
Boise
State
?
For
Boise
State
to appear in a
BCS
game this year, they cannot afford to lose more than one game but probably will
need to go undefeated with other non-AQ teams in the mix again (such as TCU).
It is unlikely they could play in the
BCS
Championship game simply because not enough teams on their schedule appear (at
present) to qualify as quality opponents (
Oregon
,
Tulsa
, and
Nevada
are probable quality opponents). If
the WAC can have a banner year, with more victories than losses in
non-conference play this will greatly benefit
Boise
State
and the WAC as a whole. There are
plenty of quality opponents in WAC non-conference games and beating a good
number of these teams is also key (some of these quality teams could include
USC,
Oregon
, Stanford, Notre Dame,
Cincinnati
, LSU,
Auburn
,
Missouri
, Texas A & M,
Ohio State
,
Wisconsin
, BYU,
Utah
, and
Tulsa
). Obviously, the human polls will
carry more weight than the six
BCS
computer polls (making up 2/3 of the
BCS
ranking), but a respectable or poor showing in the computers could make the
difference if things gets close.
In accumulating a high
SOS
, it is more important for a conference to win games than it is to schedule
quality opponents. A conference
member scheduling a “bodybag game” is doing a disservice not only to
themselves but to their conference mates. A
team’s computer rating will always go up if they win and down if they lose;
it’s that simple.
I will be providing ratings at my site during the
season—feel free to drop by and check on them.
If
Boise
State
is in the hunt for a
BCS
Bowl (as it has been for four of the past five years), we will chronicle their
quest on a weekly basis on the Blue Turf Board.