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You
have probably noticed how very few statistics, when held to
the light alone, genuinely expose a team’s strengths/weaknesses. But if you poignantly align the right numbers
and use simple logic, much is revealed as to why a team is
performing a certain way. Trends definitely exist in some
areas. And of course, decipher what you will. But we think we have a few cleaver, multi-faceted insider angles
for how to break this information down.
We compare/contrast certain basic vital statistics
that, when focused together, can give a better understanding
as to why a team does/doesn’t do well.
Most
important is this - what
one set of stats means to one team, when mimicked, often leads
to a totally different statistical and final outcome for another. In other words, there is no statistical prototype for success. But unlike
our first Stat Class 101 that covered third-down efficiency,
there is much more consistency in this analysis.
Enough of the top
teams do share enough of a similar play-calling ratio
such that comparing/contrasting their numbers/rankings brings
light to the plight of each and all.
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How
to Read
- For our analysis, the table presented above takes the Top 30 teams (via the latest Associated Press poll) into consideration.
We first list each
team and its record, by order of rank.
The next column (A)
gives the total number
of runs called and in parenthesis is what percentage of the total plays called has been
runs. The next
column (B) gives the national ranking for that team’s rushing offense. The fourth column (C) gives the total number
of passes, with the parenthesis giving us the percentage of total plays called that have been throws. The next column (D) gives the national rank
of the team’s passing offense (via yards), which is directly
next to (and different than) the team’s pass
efficiency ranking in column (E). We then list the team’s total offense ranking (F) and their scoring ranking (G).
This
week is pretty basic. You don’t need to be working for ESPN
to realize how many of the top 30 run
more often than they pass.
Most run it between 50-70%
of the time. Four teams (Oklahoma
State, West Virginia, Minnesota, and Navy) run it more
than seven out of ten times, and they only have two losses
amongst them. Note
that though these teams have predictably low passing totals,
each rank in the top 20 for pass efficiency. In other words, a good, strong, consistent
running attack definitely opens up the passing game and leads
to overall success.
Only
Texas Tech throws
quite a bit more than they run.
And since this fact is well-known by all, foes anticipating
such have made Tech’s top-ranked
passing attack rate only 38th for overall efficiency. Though they still have the country’s #1 total and #8 scoring offenses,
their predictable play-calling
(and subsequent 112th ranking for rushing offense)
has led to two early losses. Running more could improve things against
certain teams, but we are weary to suggest anything to a group
that trounced Nebraska so bad.
Passing
well is important; passing often isn’t.
Getting the opponent to think first that they need
to stop your rushing attack lures them into the box and optimal
aerial results follow. Accordingly (and unlike the
Red Raiders), notice how most top
teams have a higher pass efficiency ranking than they do for
their total passing (yardage).
Look carefully at Oklahoma
State’s and Navy’s low pass totals (both at 47). And to prove our point - these 114th- and 116th-ranked
passing offenses, respectively, have the 4th- and
6th-ranked air efficiency. Both remain unscathed. Florida
State is the only top 10 squad with similarly inverse
numbers. Only Ohio
State has a substantially negative differential (18 places)
between their total passing and efficiency passing, which
seemingly earns them their 97th-ranked total offense and
two losses.
The
Buckeyes also prove that, even with a good play-calling
balance, yardage and/or success is/are never guaranteed. Florida,
another team with a (close to) 50-50
split of run and pass, also sports two in the loss column. But the Gator’s
37th-ranked total offense gives them much more
promise, while Ohio State needs to go back to the drawing
board. Even when they
were winning (up through their last two games), OSU
prematurely showed these statistical
signs of weakness, so many of us realized it would be
only a short time until they lost.
A
team can run from numerical facts, but statistical realities
usually catch up with lacking efforts and produce losses.
So
we have hopefully proven play-calling balance is a key for
offensive production. Accepting
the fact that almost all of the top 30 teams do gauge a healthy
mix of run and pass, note how only seven rank in the bottom
half (lower than 60th) for total offense.
And of those seven, only Wisconsin and Ohio State also fail
to rank in the upper half (top 60) for scoring – therefore,
we can safely conclude that efficient
scoring offsets marginal yardage totals.
Of those seven weaker offenses, three still remain
undefeated, and it is evidently Wisconsin’s #1 and Miami’s #2 total defenses
that keep them this way.
Consistently, Georgia,
Virginia Tech, and Michigan all land in the top 20 defensively.
And of the seven, Southern
Mississippi alone ranks in the bottom half defensively,
too. Yet somehow, the Golden Eagles turn it on at all the right
times – they are still undefeated, even with such average
numbers. (Southern Mississippi, though, does have
the 10th-ranked
pass efficiency defense, clearly proving they know just how/when to bend and not break.)
Play-calling
intricacies do change at specific times in a game (such as
in first- and third-down situation, or at times whether a
team is ahead or behind) and can obviously be further broken
down and then applied much differently than just in the simple
way we do here. But for our basic approach, the above-stated information tells much.
Follow play-calling balance(s), along with subsequent
efficiencies, and then ultimately results, to see just how
far you can expect certain teams to go.
Statistics tell
the truth, and the truth tells all.
We
welcome any and all comments that add to or affect our statistical
analysis.
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