April 20, 2014

2014 Recruiting Footprint

Jeff Scott

There are 20 points on the map because there are two each from Clemson and Columbia. The Atlanta area was very very good to Clemson.

2014 Recruiting Numbers by State
2014 Recruiting Footprint

Figure The Odds: Early Odds for 2014 Clemson Football

CK

The probabilities below are based on the estimated total yards gained by each team and the results of 1,900 college football games between 2011 and 2013.

There are many other factors that could (and perhaps should) be included in an analysis of this type, but I’ve found that total yards gained is the most important one, statistically speaking.

Another caveat is that these numbers are based on the 2013 statistics each team – obviously next year’s teams will be different for both sides. No Tajh and Sammy, no Teddy Bridgewater, no Aaron Murray.

My guess is that most would swap the results of Louisville and South Carolina and perhaps change the result of the Georgia game.

All this is to say that these numbers are a starting point and an attempt to lay the groundwork for next season and defining realistic expectations.
2014 Probs

Win Probabilities vs. Ohio State

Random Numbers

Below is my first shot at a win probability graph. I realize this graph is not valuable to everyone and, as a matter of fact, some probably think it’s useless.

I’ll just say that the goal of this site is to offer data that you won’t find on other sites and this is an example. But more than that, seeing data like this has changed the way I look at a football game and generally makes me calmer during games (for the most part).

Before I get to that, here’s a quick recap of how these probabilities are determined. I use what I believe is the most important metric, total yards, and turnovers. It’s really simple, the more total yards you have in relation to your opponent the better chance of a victory.

Why do teams with more total yards lose sometimes? There can obviously be a variety of reasons, but generally it’s turnovers. So by combining these two statistics and projecting the total yard differential at various points of the game I know what the likely outcome of the game is going to be.

If Clemson has 20 more yards at the end of the first quarter, this obviously projects to 80 more yards for the game and I know (from other research) what % of teams win when they gain 80 more yards than the other team.

However, if a team outgains the opponent by 80 yards, but is negative 2 in turnovers the win % is going to be less and as you can see from when Tajh Boyd threw the second interception it has a huge effect on the win probabilities.

Win Prob vs Ohio State

The problems with this approach? It’s pays no attention to the score. Despite being down 29-20 the model said that Clemson still had a 60.9% chance of winning (because of the total yard differential) and that proved true.

Theoretically, the score of a game could be 28-7 with 30 seconds to go and the win probability wouldn’t be at 100% because the total yard differential may be closer than normal. It happens. Not often, but it happens. I would simply suggest that in a 28-7 game a win probability graph is probably not as useful as in a game like what occurred Friday.

While I understand that many non-metric fans will not agree with this, there is no better metric at determining winner or loser in college football than total yards. Not turnovers, penalties, rushing yards or any other metric.

So even down 29-20 in the 3rd quarter I knew Clemson had a fantastic chance of winning because they were moving the ball. Makes complete sense to me and that’s why it’s changed the way I look at a football game. In the past I would have been petrified at the blown opportunities in the first half and points Clemson had given away and thought “there’s no way we are going to win this game”. Friday, while I certainly wasn’t confident, I knew Clemson had a decent chance to win.

Many of the critics of this type of analysis will point out many times that the team with more total yards doesn’t win. This is true and I would suggest that if you are looking for a model that will tell you with 100% accuracy who will win and lose you’ll be looking forever. These are probabilities. By definition, when I tell you there is a 60.9% chance Clemson will win that also means there’s a 39.1% chance they will lose.

Win probabilities change literally on every play and I could create a graph that reflected that, but chose to chart the turning points in the game. The graph above shows that when Clemson went up 20-9 the win probability was at 100.0%. The assumption was that if the yard and turnover rates stayed the same from that point on there was no way Ohio State was going to win. That obviously changed and the win probability reflected those changes.

Because Clemson was never behind in total yardage and had only one turnover for the majority of the game the model indicates that the Tigers odds of winning never dipped below 50.0% and that occurred when Ohio State tied the game (and total yardage) at 7-7 with 5:44 to go in the first quarter.

Clemson Receiver Detail – Final Edition

Sammy Watkins

Sammy Watkins finishes the season with 101 catches in 135 targets. 72 (53.3%) of those targets were at or behind the line of scrimmage leaving me to wonder who fills that role next year. My guess is Germone Hopper will get the first shot, but several incoming freshmen may help. Let’s not forget Charone Peake will return next season.

Rod McDowell was very good out of the backfield despite a drop against Ohio State and whoever lines up behind whoever the quarterback will has a high standard to reach.

Stanton Seckinger played a big role in the Orange Bowl catching the winning touchdown pass just as he did in the opener against Georgia and Jordan Leggett had a big play earlier in the game.

The tight ends seem to catch the ball fine, but in my opinion their blocking (as a group) leaves a lot to be desired.


**Does not include 0 yard routes in avearge
*Routes at or behind line of scrimmage are counted as 0 yard routes

Tajh Boyd by Distance and Area of Field – Final Edition

TBoyd

Interesting notes and minutiae on Tajh Boyd’s 2013 season.

  • 27 of Boyd’s 40 passes versus Ohio State were at or behind the line of scrimmage.
  • Boyd was 23 of 27 (85.2%) for 219 yards and a TD on passes at or behind line of scrimmage.
  • Boyd threw 17 passes to the left side of the field. 14 of those were behind the line of scrimmage, two were 3 yard TDs to Martavis Bryant. Only once did a pass to the left side travel more than 3 yards downfield.
  • For the season to deep (20+ yards) right side of field Boyd finishes 15/25 for 649 yards & 9 TDs. 26.0 yards per pass attempt and 36.0% TD rate.

TB 13

Updated Play by Play Data

Random Numbers

I’ve updated the play by play data to include all 12 games in 2013.  One project I hope to complete in the off-season is to combine all three years worth of data into one database vs. 3 separate databases.  This will make it easier to query across seasons.

Receiver Detail Through 12 Games

Sammy Watkins

Sammy Watkins finished the season with 60 targets past the line of scrimmage and 58 at or behind the line of scrimmage.

In a note that may only interest me, Adam Humphries actually had a higher completion ratio than Watkins, despite having a lower percentage of targets at or behind the line of scrimmage.

**Does not include 0 yard routes in avearge
*Routes at or behind line of scrimmage are counted as 0 yard routes

Boyd by Distance and Area of Field Through Game 12

TBoyd

Of Tajh Boyd’s 27 passes Saturday night 7 were at or behind the line of scrimmage and 11 of the 27 targeted Sammy Watkins.  As a matter of fact,  Watkins was targeted on 11 of Boyd’s first 22 pass attempts and at one point, across 3 series, was targeted on 5 straight Boyd pass attempts.

Boyd’s strength is throwing to the right and while he completed 8 of 11 passes to the right those passes were good for only 46 yards (and 1 interception) and 25 of those 46 yards came immediately prior to Boyd’s second interception (i.e. the game was over).
Boyd 12

Targets 12

Receiver Detail Through 11 Games

Mike Williams, Walker Smith

Sammy Watkins was targeted 8 times against The Citadel, 7 of them on passes at or behind the line of scrimmage and the other only 8 yards downfield. For the season 47.7% of the time Watkins has been targeted it has been at or behind the line of scrimmage.

In two years versus South Carolina Watkins hasn’t done a whole lot, 14 targets, 8 receptions, 0 touchdowns and 1 explosive play. There are multiple reasons for those numbers, but they are what they are.


**Does not include 0 yard routes in avearge
*Routes at or behind line of scrimmage are counted as 0 yard routes

Boyd by Distance and Area of Field Through Game 11

TB112413

On passes of 20 yards or more (these numbers cross over two categories below) against The Citadel Tajh Boyd was 6 for 9 for 153 yards and 2 touchdowns.

Only 10 of Boyd’s 28 passes were to the right side of the field and 11 (39%) were behind the line of scrimmage.
Boyd 11
Targets 11