August 29, 2014

Episode 21: Brandon Rink talks Georgia

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Brandon Rink of orangeandwhite.com joins me to discuss Zac Brooks injury, Shaq Anthony transfer, offensive line depth, cornerback depth chart and Georgia while giving Tiger fans everywhere a prediction on Saturday’s game.

You can also download current and previous episodes and subscribe to the Podcast via iTunes by clicking here.

Thanks to Brandon for participating and Clemson graduate Adam Eargle for the podcast artwork and all SUR graphics.  Need a graphic artist? Check out some of Adam’s work here.

Fun with Point Spreads

Random Numbers

Below are the straight up winning % and % against the spread for favored teams from 2011-2013, FBS vs. FBS only, including Bowl Games.

The straight up column contains 2,105 games – 11 “Pick ‘em” games were excluded because there was no “favorite”.  The games that ended as a “Push” are not included in the “Cover % by Spread” table.

Going by spread, the biggest upset of the last 3 years was Lousiana-Monroe’s upset of Arkansas in week 2 of the 2012 season when the Warhawks were 30 point underdogs.

Biggest cover? Florida State beat Idaho 80-14 last season to cover a 59 point spread.

The most frequent spread over the last 3 years? No surprise – 3 points – as it showed up in 133 games (6.3%).

There are some strange anomalies in the data, such as 11 point favorites win 64.5% of the time and cover only 48.4% of the time, but 11.5 point favorites are 18-0 and cover two-thirds of the time.

There’s also a weird little thing between 31.5 and 32.5 spreads where the teams are 23-0 straight up (expected) and 18-5 ATS (not so expected) which is very dissimilar to the spreads immediately preceding (3-7 ATS) and after (0-3).

 

Spread 1thru14Spread 145thru28Spread285thru42Spread425Plus

 

Episode 20: Brandon Rink talks fall camp, QBs, OL, Peake’s health, cornerbacks and more

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Brandon Rink joins me to discuss the Clemson fall camp, quarterbacks, offensive line, Charone Peake’s health, Nickels and SAMs, cornerbacks and more.

You can also download current and previous episodes and subscribe to the Podcast via iTunes by clicking here.

Thanks to Brandon for participating and Clemson graduate Adam Eargle for the podcast artwork and all SUR graphics.  Need a graphic artist? Check out some of Adam’s work here.

Geek Speak: Kmeans Clsutering with College Football Defenses

Random Numbers

Editors Note: Paul Chimenti is a marketing analyst and provides statistical analysis for Seldom Used Reserve. The tables below originated with analysis done by Paul. For more detailed information on methodology, data, assumptions, etc., please contact chimenti80@gmail.com.

The data below includes games from 2011-2013 and includes games between “Big 5″ teams only (or those that will be this season such as Louisville) and is an attempt to “cluster” defenses together using Kmeans clustering. If you’re not familiar with Kmeans clustering you may want to read this page prior to attempting to digest the data.

First, let’s clarify what the data includes (and doesn’t include):

• Includes all games between two “Big 5″ teams (ACC, SEC, Big 10, PAC 12 and Big 12), Notre Dame and teams (like Louisville) that will be in a Big 5 conference this season.
• For teams like Louisville (and Notre Dame) only games against Big 5 teams are included.
• Does not include games against FCS teams or games with teams outside of the Big 5 – i.e. Clemson vs. Citadel and Clemson vs. Troy, for example, are not included.

The data does not purport to tell you which defensive style (“cluster”) is better than another – Cluster 1 is not necessarily better than cluster 2, just different – but rather gives you an idea of defenses with similar attributes.

I’ll have to admit that I was surprised to see Michigan in Cluster 1, but 348.1 yards per game is not a bad average these days, even if the Wolverines do play in the Big 10.

D Cluster 1 2013
Cluster 2 is where the Tigers reside and that’s probably about right over the last 3 seasons.  I would classify these as “decent”, but not top tier defenses.  An interesting side note here (at least for me) is that Arizona gave up 95.4 more yards per game than Clemson, but only 1.8 more points.  Perhaps turnovers were the key as the Sun Devils averaged a half more turnover per game than Clemson.

D Cluster 2 2013

Cluster 3 introduces us to some of the more problem defenses and includes one that many see as “good” – Ohio State.  Again, we have to remember this is a 3 season window, not a look back at 2013 and the clustering is not a referendum on “good” or “bad”, but rather grouping like defenses.

D Cluster 3 2013

Each team in Cluster 4 gave up at least 411.6 yards per game and a minimum of 29.9 points per game.  Ouch.

It’s also notable that 4 ACC teams reside in this cluster.  And remember how there were 5 Big 12 teams (of 10 conference teams) in Cluster 1 on the offensive side?  Well, there are 5 Big 12 teams in Cluster 4 on defense.

D Cluster 4 2013

 

The Way Back Machine: Jim Phillips Calls Treadwell Field Goal to Beat UGA in Athens (1986)

Treadwell

In less than 2 weeks the Tigers will be in Athens to face Georgia.  While we impatiently wait for that day and game, here’s a blast from the past: Jim Phillips calling David Treadwell’s game winning field goal in Athens circa 1986.

Jim Phillips voice is part of the reason I became a Tiger fan and ended up at Clemson.  I spent many hours in the front (and back) yard of my house pretending to be a Clemson Tiger with Jim Phillips making the calls.

Fond memories that return every fall about this time.

Geek Speak: Kmeans Clustering with College Football Offenses

Random Numbers

Editors Note: Paul Chimenti is a marketing analyst and provides statistical analysis for Seldom Used Reserve. The tables below originated with analysis done by Paul. For more detailed information on methodology, data, assumptions, etc., please contact chimenti80@gmail.com.

The data below includes games from 2011-2013 and includes games between “Big 5″ teams only (or those that will be this season such as Louisville) and is an attempt to “cluster” offenses together using Kmeans clustering. If you’re not familiar with Kmeans clustering you may want to read this page prior to attempting to digest the data.

First, let’s clarify what the data includes (and doesn’t include):

• Includes all games between two “Big 5″ teams (ACC, SEC, Big 10, PAC 12 and Big 12), Notre Dame and teams (like Louisville) that will be in a Big 5 conference this season.
• For teams like Louisville (and Notre Dame) only games against Big 5 teams are included.
• Does not include games against FCS teams or games with teams outside of the Big 5 – i.e. Clemson vs. Citadel and Clemson vs. Troy, for example, are not included.

The data does not purport to tell you which offensive style (“cluster”) is better than another – Cluster 1 is not necessarily better than cluster 2, just different – but rather gives you an idea of offenses with similar attributes.

Most of these are givens – you won’t get much argument about Clemson, Oregon, Oklahoma State and Texas Tech being clustered together.

But what about Indiana? The data shows they performed worse in every category than other cluster 1 teams except for turnovers.

However, the Hoosiers played fast, averaging 2.85 plays per minute of possession, which by the way was second only to Oregon’s 2.86 in cluster 1.
O Cluster 1 2013
In other words, Indiana played fast but not efficient and in that sense in makes sense they’re included in Cluster 1.

Cluster 2 also makes sense to me as it contains some very good, but not fast paced offenses like Florida State, Alabama, Georgia and Louisville.
O Cluster 2 2013

Conversely, many would question Auburn in the 3rd cluster, as I did. But remember, this is a 3 year window so the horrid Tiger offense of 2011 most likely offset the torrid Auburn offense of 2013.
O Cluster 3 2013

Cluster 1 confirms a couple of long held assumptions of mine.

First, the Big 12 has been the “fastest” offense in college football with 5 of the 11 teams in the cluster coming from that league (there are only 10 teams in the conference).  3 more come from the PAC 12. That means 8 of the 11 teams in Cluster 1 come from conferences that are known for fast paced offenses.

Secondly, Clemson is the lone ACC team in cluster 1 and that gives the Tigers an advantage over the rest of the ACC in general, Florida State’s dominance notwithstanding.

That doesn’t mean Clemson will win every game, offense is only half the battle, but absent a stout defense (i.e. Florida State) it means the Tigers have a decided advantage in most ACC games.

The Effect of Explosive Plays on Defense

Robert Smith

VenablesIt’s a well known fact that the Clemson defense has given up more than it’s share of big plays over the last few seasons, but the graphics below shows just how much the plays have effected the Tigers defensive numbers.

Of 918 competitive plays (3 kneel down plays were excluded) the Tiger defense gave up 102 explosive plays (11.1% or 1 of every 9 plays).

However, that 11.1% of plays accounted for 57.7% of yards given up as the graphics below indicate.

Red slices are explosives and blue are all other plays/yards.

Explosive Plays on Defense

To put that into context a bit, the Clemson offense had an explosive play on 14.8% of their plays which accounted for 56.3% of their yards. Opponents had a smaller % of explosive plays, but those plays accounted for a higher percentage of total yards.

Put another way, Clemson known for it’s explosive offense, averaged 24.7 yards per explosive play while opponents aveaged 26.2 yards per explosive play.

Any way you slice it, the Tigers have got to do a better job of cutting down on the number and yardage given up on explosive plays.

2013 Offensive Explosives By Game

Morris

Clemson Football - Cole StoudtThe chart below shows Clemson’s explosive offensive plays for the 2013 season. This is something that I’ll track, compare and contrast to this season – the Tigers first in 3 years without Tajh Boyd, Sammy Watkins and Martavis Bryant.

2014 can be viewed as a referendum on the Morris offense and whether it’s “the system” or “the players”.

While Sammy Watkins doesn’t come along every day, I personally think there’s not much of an argument. It’s the system AND the players, the players AND the system. You wouldn’t see the numbers of the last 3 years in the Billy Napier offense even with Watkins, Boyd and Bryant and you wouldn’t have seen 6,000+ yards per season under Morris without Boyd, Watkins, Bryant and others.

2013 Explosives by Game

The numbers exclude 12 “Team Rushes” (aka “kneel down” plays) that were non-competitive.

The 2013 Georgia game is a great example of the importance of explosive plays.  The 9 explosive plays Clemson had that night accounted for 269 of the Tigers 467 total yards (53.3%).  The other 65 plays accounted for 218 total yards (3.4 yards per play).

2013 Opponents Success By Down

Stephone Anthony 42

The concept of successful and non-successful plays is not a new one, but one we’ll track during the season on both sides of the ball. As stated in the linked article success is defined as 50% of needed yards on first down, 70% of needed yards on second down, or 100% of needed yards on third or fourth down. It helps to describe a team’s ability to stay on schedule and avoid obvious passing downs and third and long when the odds of gaining a first down are smaller.

The same idea can be applied in reverse to defenses.  Defenses that are successful on first down force their opponents into more predictable play calling on 2nd and 3rd downs thereby reducing the offenses odds of gaining a first down.

Using this theory one could argue that first down is just as important, if not more, than third down.  A team stops their opponent for a 1 yard gain on first down, which leads to a pass on 2nd and 9 which falls incomplete, bringing up a 3rd and 9 where another pass falls incomplete and the defense is credited with a 3rd down stop.

Which play is more important, the first down that forced more predictable play-calling and reduced the odds of the offense getting a first down or the third down that had little chance of success?

2013 Defense Success by Down Chart

2013 Defense Success by Down Graph

 

Of the 921 total plays by the Clemson defense, 918 of them were “competitive”, meaning they were not “Team Rush” (aka “kneel downs”) type plays at the end of games/halves (which is why the numbers don’t exactly match the “official stats”).

Clemson showed a remarkable consistency across downs in 2013 on the defensive side.  The numbers above indicate that almost 65% of the time opponents faced 2nd and at least 6 (assuming a first and 10).

I believe the first and second down defense was just as important as the third down defense.

2013 Defensive 3rd Downs

Vic Beasley

Below are the metrics for third downs for the Tiger defense in 2013. While the overall numbers are good – 30.8% – it’s interesting to note the anomalies in the data such as 3rd and 4 (small sample size), 3rd and 6 and to a lesser extent 3rd and 8.

These numbers include goal to go situations so that made a difference on 3rd and 4, where opponents were 3 for 3 on goal to go situations, but only 2 for 5 otherwise.

Overall opponents were 4 for 8 on third and goal situations against Clemson in 2013.

2013 Defensive 3rd Down Conversions Graph2013 Defensive 3rd Downs Chart

Here’s a look at the tackles made on third downs and those (“Stopped” column) that resulted in the opponent being stopped on third down. In short, every tackle (8 of which were sacks) Vic Beasley was a part of on third down meant a 4th down followed.

The difference in third down production between Beasley and Corey Crawford is notable.

Finally, the chart also shows that replacing Spencer Shuey may be easier said than done. Shuey had more primary stops on third down than any other Tiger and perhaps this means Shuey was attacked more on 3rd down, but nonetheless he forced just one less 4th down than Stephone Anthony did with the same number of overall opportunities.

3rd Down Stops 2013