NCAA football

Rather than visiting multiple sites to check out all of the reviews for NCAA football 11, check out metacritic.com.  All of the reviews in one place...it looks like it is going to be a must get this year.

Hope this is helpful!

http://www.metacritic.com/games/platforms/xbox360/ncaafootball11?q=NCAA Football 11

As if we haven't already discussed this, but another set of opinions on how to divide the Big Ten and PAC-Ten into divisions.

Link:

http://collegefootball.rivals.com/content.asp?CID=1097701

Please, please, please let football season start!  

The demo for NCAA 2011 dropped this morning for 360 and PS3. Thought I'd share while downloading. There are 8 playable teams in the demo, to give a feel for all the different types of offenses. Match-ups are: Miami vs OSU, Oklahoma vs Texas, Clemson vs Mizzou, and Florida vs Florida State. I think I'm going to play with the U first. Demo includes four two minute quarters. I'll post my impressions after I play. Also, they are allowing everyone to unlock the Nike Pro-Combat uniforms for all the teams included by playing as them and winning in the demo, the uniforms will then be unlocked for you when you play the retail version coming July 13th. Here's to a month of playing the hell out of the demo.

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Preface/Disclaimer:

All of this data is based on the rosters listed on ESPN.

The average NFL team has approximately 63 players listed on their roster on the ESPN page (varying from the Vikings having 55 to the Bills having 73).  Now, this is clearly more than the NFL limit of 53 players on the active roster, so the numbers in the rest of this diary won’t be 100% correct, but they are based on what ESPN says.  Additionally, there were a few quirks in the positional listings, such as occasionally having OL listed, instead of OT or G.  In one of the analyses I did, I just changed all listings of OL to OT (because I was lazy), but in another I looked up the few players that were different.  This could result in some error.

 

In case anyone wants to toy around with anything I’ve worked on, I have all of my spreadsheets uploaded here:

http://sharebee.com/7880bf69

 

WARNING: Wall of text and data ahead.  I thought about breaking this up into multiple parts, but decided against it.

Other warning, some of the charts at the end are a bit on the large side and may leak over into the sidebar.

 

 

 Ranking the top 50 colleges by their number of players in the NFL.

Rank	COLLEGE	Total
1	Texas	42
2	Louisiana State	41
3	Miami (Fla.)	40
4	USC	39
	Georgia	39
6	Ohio State	37
7	Tennessee	36
8	Nebraska	34
9	Michigan	33
10	Florida State	32
	Florida	32
12	Notre Dame	30
	California	30
14	Oklahoma	29
	Auburn	29
16	Iowa	28
17	Penn State	27
18	Purdue	26
	Louisville	26
20	Virginia Tech	25
	Virginia	25
	North Carolina	25
23	Oregon	24
	Michigan State	24
	Maryland	24
	Boston College	24
	Alabama	24
28	Texas A&M	22
	Oregon State	22
	Georgia Tech	22
31	Wisconsin	21
	Pittsburgh	21
	Illinois	21
34	South Carolina	20
	Rutgers	20
	North Carolina State	20
	Mississippi State	20
	Mississippi	20
39	UCLA	18
	Kansas State	18
	Colorado	18
	Arizona	18
43	San Diego State	17
	Fresno State	17
	Clemson	17
	Arkansas	17
	Arizona State	17
48	Washington State	16
	Utah	16
50	Wake Forest	15
	Central Florida	15
	Brigham Young	15

 

Top Schools by Position:

(# from – school from)

Centers: 4 – California, North Carolina, Notre Dame, Texas A&M

Cornerbacks: 7 – Ohio State, 6 – Auburn, South Carolina, 5 – Michigan, Pittsburgh, Texas

Defensive Ends: 8 – Georgia, 7 – Florida, 5 – North Carolina

Defensive Tackles: 6 – Texas, Texas A&M, 5 – Florida State, Miami (Fla.), Michigan State, Tennessee

Fullbacks: 2 – Alabama, West Virginia

Guards: 5 – LSU, 4 – Iowa, Nebraska

Linebackers: 8 – Miami (Fla.), Ohio State, USC, 7 – Michigan, Nebraska, Purdue

Offensive Tackles: 5 – Boston College, Florida State, 4 – Michigan, Notre Dame, Texas, Virginia

Punters: 2 – Ball State, Notre Dame, Tennessee

Place Kickers: 2 – Florida State, Louisiana Tech, Nebraska, North Carolina, Texas, Washington State

Quarterbacks: 4 – USC, 3 – Boston College, Fresno State, Louisville, Michigan, Oregon, Purdue

Running Backs: 4 – Georgia, Texas, USC, Virginia

Safeties: 5 – LSU, 4 – Clemson, Georgia Tech, Miami (Fla.), Oklahoma, Virginia Tech, Washington State

Tight Ends: 5 – Virginia, 3 – Arizona State, California, Colorado, Georgia, Georgia Tech, Iowa, Maryland, Miami (Fla.), Notre Dame, Penn State, Texas

Wide Receivers: 8 – Ohio State, 6 – LSU, Miami (Fla.), Virginia Tech, 5 – North Carolina, Oklahoma

 

 

The Eagles have 5 players from Georgia, and the Chiefs have 5 players from LSU.  No other NFL team has as many players from a single school.

 

The average NFL team has (listed on their ESPN roster):

Centers: 3.063, Giants and Jets only have one each, Bills have six

Cornerbacks: 6.875, Vikings, Cowboys, Dolphins, and Raiders have only five, Packers have ten

Defensive Ends: 5.344, Ravens only have one, Raiders and Rams have eight

Defensive Tackles: 4.969, Cowboys only have one, Panthers and Ravens have eight

Fullbacks: 1.313, a number of teams have zero, a few have three

Guards: 3.188, Titans, Raiders, and Falcons only have one, Chiefs have six

Linebackers: 8.281, Broncos only have five, Chargers and Patriots have eleven

Offensive Tackles: 4.906, Chiefs only have two, a bunch of teams have seven

Punters: 1.188, Most teams only have one, Buccaneers have three

Place Kickers: 1.188, most teams have one, a few have two

Quarterbacks: 3.25, Jaguars and Packers only have two, Panthers have five

Running Backs: 4.438, it’s pretty evenly spread from three to six

Safeties: 4.563, Chargers and Panthers only have two, Falcons and Texans have seven

Tight Ends: 3.938, Jaguars and Patriots only have two, Bengals and Colts have seven

Wide Receivers: 6.406, a few teams have only five, Buccaneers have nine

 

 

Ranking the FBS conferences by the average number of players that end up in the NFL per team*

 

Conference	AVG # Players/Team	Total # of Players
SEC	24.75	297
Big Ten	22.72727273	250
ACC	22.66666667	272
PAC-10	21	210
Big 12	18.33333333	220
Big East	15	120
MWC	10.625	85
WAC	7.777777778	70
C-USA	6.583333333	79
MAC	5.230769231	68
Sun-Belt	3.444444444	31

 

 

*Note that I did not factor in the service academies, as there is only one player from the any of them playing in the NFL (Kyle Eckel, from Navy).  I assume that is due to the 5 year requirement of being in their corresponding service after graduating.  As such, I also did not include the Independents in the chart, as it would have just been Notre Dame.

 

Ranking the different conferences by position

 

Center:

Conference	AVG/Team	Total #
Big Ten	1.090909091	12
ACC	1.083333333	13
Big 12	1	12
PAC-10	1	10
SEC	0.916666667	11
C-USA	0.75	9
WAC	0.555555556	5
MAC	0.384615385	5
MWC	0.375	3
Big East	0.25	2
Sun-Belt	0	0

 

Cornerback:

Conference	AVG/Team	Total #
SEC	2.75	33
Big East	2.625	21
Big Ten	2.090909091	23
Big 12	2	24
ACC	1.916666667	23
PAC-10	1.7	17
WAC	1.333333333	12
MWC	0.875	7
Sun-Belt	0.777777778	7
C-USA	0.5	6
MAC	0.461538462	6

 

Defensive End:

Conference	AVG/Team	Total #
SEC	2.75	33
Big Ten	1.818181818	20
Big 12	1.75	21
ACC	1.75	21
PAC-10	1.6	16
Big East	1.25	10
MWC	0.75	6
WAC	0.555555556	5
MAC	0.307692308	4
Sun-Belt	0.111111111	1
C-USA	0	0

 

Defensive Tackle:

Conference	AVG/Team	Total #
SEC	2.583333333	31
Big 12	2.25	27
Big Ten	2.181818182	24
ACC	2.166666667	26
PAC-10	1.5	15
Big East	0.75	6
MWC	0.5	4
MAC	0.461538462	6
Sun-Belt	0.333333333	3
C-USA	0.25	3
WAC	0.111111111	1

 

Fullback:

Conference	AVG/Team	Total #
Big East	0.75	6
SEC	0.5	6
PAC-10	0.4	4
ACC	0.333333333	4
Big 12	0.25	3
Sun-Belt	0.222222222	2
Big Ten	0.181818182	2
MWC	0.125	1
WAC	0.111111111	1
C-USA	0.083333333	1
MAC	0.076923077	1

 

Guard:

Conference	AVG/Team	Total #
SEC	1.666666667	20
Big Ten	1.363636364	15
Big 12	1.166666667	14
PAC-10	1	10
Big East	0.875	7
ACC	0.666666667	8
WAC	0.666666667	6
MWC	0.5	4
C-USA	0.25	3
MAC	0.153846154	2
Sun-Belt	0.111111111	1

 

Linebacker:

Conference	AVG/Team	Total #
ACC	3.916666667	47
Big Ten	3.545454545	39
SEC	3.25	39
PAC-10	2.6	26
Big 12	2.166666667	26
Big East	2.125	17
MWC	2.125	17
C-USA	0.75	9
WAC	0.444444444	4
Sun-Belt	0.444444444	4
MAC	0.384615385	5

 

Offensive Tackle:

Conference	AVG/Team	Total #
ACC	2.166666667	26
Big Ten	1.818181818	20
SEC	1.75	21
Big 12	1.416666667	17
MWC	1.375	11
PAC-10	1.1	11
Big East	0.875	7
WAC	0.666666667	6
MAC	0.615384615	8
C-USA	0.583333333	7
Sun-Belt	0.111111111	1

 

Punter:

Conference	AVG/Team	Total #
PAC-10	0.4	4
Big East	0.375	3
Big Ten	0.363636364	4
Big 12	0.333333333	4
MAC	0.307692308	4
SEC	0.25	3
ACC	0.166666667	2
C-USA	0.166666667	2
WAC	0.111111111	1
MWC	0	0
Sun-Belt	0	0

 

Place Kicker:

Conference	AVG/Team	Total #
Big 12	0.583333333	7
ACC	0.5	6
Big Ten	0.454545455	5
PAC-10	0.4	4
WAC	0.333333333	3
Big East	0.25	2
SEC	0.166666667	2
C-USA	0.166666667	2
Sun-Belt	0.111111111	1
MAC	0.076923077	1
MWC	0	0

 

Quarterback:

Conference	AVG/Team	Total #
PAC-10	1.5	15
Big Ten	1.181818182	13
Big East	1	8
SEC	0.916666667	11
C-USA	0.75	9
ACC	0.666666667	8
MWC	0.625	5
Big 12	0.583333333	7
WAC	0.555555556	5
MAC	0.384615385	5
Sun-Belt	0.111111111	1

 

Running Back:

Conference	AVG/Team	Total #
Big Ten	1.818181818	20
SEC	1.75	21
PAC-10	1.7	17
Big 12	1.25	15
ACC	1.166666667	14
Big East	1	8
MWC	0.875	7
C-USA	0.583333333	7
MAC	0.307692308	4
WAC	0.222222222	2
Sun-Belt	0.111111111	1

 

Safety:

Conference	AVG/Team	Total #
PAC-10	2.1	21
SEC	2	24
ACC	1.916666667	23
Big Ten	1.363636364	15
Big 12	1	12
Big East	0.75	6
MWC	0.75	6
WAC	0.555555556	5
Sun-Belt	0.555555556	5
C-USA	0.333333333	4
MAC	0.307692308	4

 

Tight End:

Conference	AVG/Team	Total #
ACC	1.833333333	22
PAC-10	1.6	16
Big Ten	1.181818182	13
Big 12	1.166666667	14
SEC	1.083333333	13
Big East	0.875	7
WAC	0.555555556	5
MWC	0.5	4
C-USA	0.5	6
MAC	0.461538462	6
Sun-Belt	0.111111111	1

 

Wide Receiver:

Conference	AVG/Team	Total #
ACC	2.416666667	29
SEC	2.416666667	29
PAC-10	2.3	23
Big Ten	2.272727273	25
Big 12	1.416666667	17
MWC	1.25	10
Big East	1.125	9
WAC	1	9
C-USA	0.916666667	11
MAC	0.538461538	7
Sun-Belt	0.333333333	3

 

Some more data related to individual conferences:

ACC:

Duke has the least players in the NFL of any ACC team, with only three.

The Falcons, Giants, and Texans have more players from the ACC than any other team, with 14 each.  The Packers have the fewest ACC players, with only three.

Big 12:

Kansas has the least players in the NFL of any Big 12 team, with only six.

The Buccaneers have more players from the Big 12 than any other team, with 12.  The Dolphins, Giants, and Raiders have the fewest Big 12 players, with only two each.

Big East:

South Florida has the least players in the NFL of any Big East team, with only six.

The Colts, Eagles, Jaguars, and Panthers have more players from the Big East than any other team, with 7 each.  The Bears, Browns, Chiefs, Redskins, and Vikings each only have one Big East player.
Big Ten:

Indiana has the least players in the NFL of any Big Ten team, with nine.

The Jets have more players from the Big Ten than any other team, with 13.  The Eagles have the fewest Big Ten players, with only one.

C-USA:

Southern Methodist and UAB are tied for the least players in the NFL of any C-USA team, with only two each.

The Raiders have more players from C-USA than any other team, with six.  The Cowboys, Redskins, and Seahawks all have no players from C-USA.

MAC:

Buffalo has the least players in the NFL of any MAC team, with only two.

The Lions have more players from the MAC than any other team, with five.  The Cardinals, Jets, Redskins, and Seahawks all have no MAC players.

MWC:

Ignoring the Air Force’s zero, UNLV has the least players in the MWC with three.

The Texans have more players from the MWC than any other team, with eight.  The Broncos, Chiefs, Falcons, Patriots, and Titans all have zero MWC players.

PAC-10:

Stanford and Washington have the least players in the NFL of any PAC-10 team, with 13.

The Seahawks have more players from the PAC-10 than any other team, with 14.  The Broncos and Dolphins have the fewest PAC-10 players, with only three each.

SEC:

Kentucky has the least players in the NFL of any SEC team, with only seven.

The Chiefs have more players from the SEC than any other team, with 16.  The Giants have the fewest SEC players, with five.

Sun-Belt:

FAU has zero players in the NFL.

The Falcons, Giants, and Panthers have more players from the Sun-Belt than any other team, with three each.  Thirteen NFL teams have zero Sun-Belt players.

WAC:

New Mexico State has the least players in the NFL of any WAC team, with only two.

The Redskins, Packers, and Jaguars have more players from the WAC than any other team, with five each.  The Bengals, Chiefs, Lions, Steelers, and Texans all have no WAC players.

I also compiled a list of the all of the players in the NFL from Michigan, Michigan State, Notre Dame, and Ohio State:

	Michigan
33	Team
15	Offense
3	QB	Tom Brady	Chad Henne	Todd Collins
1	RB	Mike Hart
0	FB	----
4	WR	Braylon Edwards	Steve Breaston	Jason Avant	Mario Manningham
0	TE	----
4	OT	Jake Long	Jeff Backus	Jon Runyan	Jon Jansen
3	G	Steve Hutchinson	David Baas	Jonathan Goodwin
0	C	----
17	Defense
2	DT	Alan Branch	Gabe Watson
2	DE	James Hall	Tim Jamison
7	LB	David Harris	Dhani Jones	Prescott Burgess	Shawn Crable	Larry Foote	LaMarr Woodley	Pierre Woods
5	CB	Charles Woodson	Ty Law	Marlin Jackson	Leon Hall	Morgan Trent
1	S	Jamar Adams
1	Special Teams
1	PK	Jay Feely
0	P	----

 

 

	Michigan State
24	Team
12	Offense
2	QB	Drew Stanton	Brian Hoyer
1	RB	Javon Ringer
0	FB	----
3	WR	Devin Thomas	Derrick Mason	Muhsin Muhammad
2	TE	Kellen Davis	Chris Baker
2	OT	Flozell Adams	Peter Clifford
0	G	----
2	C	Kyle Cook	Chris Morris
11	Defense
5	DT	Ogemdi Nwagbuo	Clifton Ryan	Brandon McKinney	Domata Peko	Kevin Vickerson
2	DE	Ervin Baldwin	Robaire Smith
2	LB	David Herron	Julian Peterson
0	CB	----
2	S	Eric Smith	Renaldo Hill
1	Special Teams
0	PK	----
1	P	Brandon Fields

 

 

	Notre Dame
30	Team
16	Offense
1	QB	Brady Quinn
2	RB	Ryan Grant	Julius Jones
0	FB	----
2	WR	Maurice Stovall	Arnaz Battle
3	TE	John Carlson	Anthony Fasano	John Owens
4	OT	Mark LeVoir	Ryan Harris	Jordan Black	Mike Gandy
0	G	----
4	C	John Sullivan	J.J. Jansen	Dan Santucci	Jeff Faine
12	Defense
2	DT	Trevor Laws	Derek Landri
4	DE	Victor Abiamiri	Justin Tuck	Bertrand Berry	Renaldo Wynn
2	LB	Corey Mays	Rocky Boiman
1	CB	Mike Richardson
3	S	Tom Zbikowski	Chinedum Ndukwe	David Bruton
2	Special Teams
0	PK	----
2	P	Hunter Smith	Craig Hentrich

 

 

	Ohio State
37	Team
15	Offense
1	QB	Troy Smith
1	RB	Beanie Wells
0	FB	----
8	WR	Ted Ginn Jr.	Anthony Gonzalez	Roy Hall	Santonio Holmes	Michael Jenkins	Joey Galloway	Brian Hartline	Brian Robiskie
1	TE	Ben Hartsock
1	OT	Orlando Pace
1	G	Rob Sims
2	C	Nick Mangold	Kevin Houser
22	Defense
1	DT	Ryan Pickett
3	DE	Jay Richardson	Will Smith	Kenny Peterson
8	LB	Larry Grant	Vernon Gholston	Bobby Carpenter	A.J. Hawk	Matt Wilhelm	Na'il Diggs	Mike Vrabel	James Laurinaitis
7	CB	Ashton Youboty	Chris Gamble	Nate Clements	Antoine Winfield	Shawn Springs	Donald Washington	Malcolm Jenkins
3	S	Donte Whitner	Will Allen	Donnie Nickey
0	Special Teams
0	PK	----
0	P	----

 

 

 

So there you have it.  A whole mess of data about things you may or may not have been curious about.  And to think that I originally only wanted to compile the list of Michigan players in the NFL...

Prior to 1993 (this is the date the rule was changed in the NFL, not sure if NCAA changed the same year or a few years later), if a QB was under duress and threw the ball where there were no receivers in the area, IT WAS INTENTIONAL GROUNDING. Obviously this was a judgement call but so are a lot of other penalties (holding, pass interference, etc.)

The obvious reason was to allow the defense to get the benefits of a great play.

Then, the NFL decided it was an offensive league (pun probably intended), and put in the new rule that the QB can just throw the ball away as long as they are outside the tackle box and get it past the line of scrimmage.

This rule has ruined college football.

When the defense makes a mistake, there is no rule that allows them to recover. But, now when the defense makes a great play, it is often completely negated. Instead of a 10-15 yard loss, we just forget about it and bring it back to LOS.

In Saturday's game, Pryor messed up and failed to get the ball back to the LOS. So, penalty called and a great play by the D was maintained. The D then forced a punt. Imagine on that drive what might have occurred if Pryor had merely gotten the ball past the LOS. Second and 10 not second at 24!

I hate this rule!!

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A comprehensive analysis of turnovers is a bit too much to cover all at once. So, I’ve decided to break it up into a few parts. Part 1: Are Turnovers Just A Matter of Luck?

Some folks claim that turnovers are primarily a matter of luck and that teams have little or no control over turnover margin (TOM). Phil Steele is one of the most notable advocates that turnovers are primarily luck. Each year, Steele includes his “Turnovers = Turnaround” article in his College Football Preview. A couple of quotes:

“Teams that benefitted from double-digit turnovers the previous year rarely get a repeat of that good fortune.”

“Let’s take a look at some teams who had terrible luck (lots of turnovers) in one year and then drastically improved over the next year without those turnovers.”

In Part 2 of the Turnover Analysis, I’ll look at Steele’s theory about turnovers being a cause of turnarounds. But, for now, let’s just look at whether turnovers are primarily luck.

Let’s first define the term: “Luck is a belief in good or bad fortune in life caused by chance which happens beyond a person's control.” As applied to turnovers, this would mean they simply happen at random (i.e. chance) and a football team has no control over TOM.

Executive Summary: The gory details are below but for those in need of instant gratification here is the synopsis:

Disclaimer: There is obviously an element of luck and an element of skill involved in the sport of football. As you’ll see, the analysis is to determine the “primary” cause of turnovers. It is not attempting to conclude that turnovers are completely luck or completely skill.

Basis: All 120 FBS teams over the last 10 years (1999 through 2008); Total TOM Per Year over the last 10 years. Bowl games excluded before 2002.

Here is a table of the very good teams and very poor teams with their Average TOM per year over the last 10 years and their Average WLM (Win/Loss Margin) over the same 10 years. Similar to TOM, the win/loss margin is merely games won minus games lost. For example, a team that is 7-5 has a WLM of +2 and a team that is 5-7 has a WLM of -2. I decided to use WLM because it provides data that is in the same format as TOM (i.e. net numbers).

Table Showing Very Good and Very Poor Teams in the FBS
Average TOM/Yr and Average WLM/Yr: 10 Years (1999 through 2008)
		TOM	WLM				TOM	WLM
Team	CONF	AVG	AVG		Team	CONF	AVG	AVG
USC	PAC10	10.2	7.1		Florida Intl	SunBelt	-5.1	-5.4
Oklahoma	Big12	8.1	8.4		Utah St	WAC	-5.4	-5.0
West Virginia	BigEast	7.8	3.8		Baylor	Big12	-5.8	-4.7
Virginia Tech	ACC	7.7	7.1		Idaho	WAC	-7.9	-5.6
TCU	MW	7.4	6.0		SMU	CUSA	-8.4	-5.5
Texas	Big12	7.3	8.4		Army	Army	-10.1	-7.0
Florida	SEC	6.1	6.4
Utah	MW	5.2	4.6
Boise St	WAC	4.9	8.5
Boston College	ACC	4.7	4.8
Georgia	SEC	4.7	6.7
Michigan	Big10	4.5	4.7
Florida State	ACC	4.2	5.1
Oregon	PAC10	4.1	4.6

And, yes, that does say that USC has averaged over +10 TOM Per Year for the last 10 years (that includes one year at -19 TOM, one year at +21, and six years with double digit positive TOM).

The Gory Details – TOM Simulation

To determine if TOM was primarily due to luck, I designed a simulation to provide TOM data that was based entirely on luck. The simulation is based on rolling 2 dice. Rolling dice involves random, independent events and the results are based purely on luck. Instead of adding the numbers on the two dice (craps), the numbers are subtracted.

One die is red for TO Lost and one is green for TO Gained. Obviously, TOM = Green – Red. Thus, the maximum TOM would be +/- 5 per game which is very consistent with actual data. Also, the distribution curve of turnovers is bell shaped with the most likely value being 0 (17% Chance) and least likely being 5 (3% Chance for + and 3% chance for -). See the table below for a comparison of the simulation distribution curve versus the theoretical curve.

Actually rolling the dice enough time to get statistically meaningful data would have taken way too long and would be prone to error. So, I used an EXCEL spreadsheet to accomplish the same results. EXCEL has formulas to generate random numbers within a range. This actually works better than the dice because I could set the lower range at -0- (the fewest possible turnovers a team could experience in a game) and the higher range at +5 (the most possible turnovers a team would experience in a game).

 I created a formula to subtract one random number from a second random number which results in TOM per game. I then created a table with 12 columns (one for each game in a year) and 1200 rows (120 FBS teams over 10 years). Each time the F9 key is pressed, the random numbers and TOM are recalculated for all 120 teams over 10 years (14,400 games). I used 10 trials and took the composite of all the trials (144,000 games). The composite is based on a count of the number times each TOM occurs.

Here is a table showing all possible TOM for a game, the % that occurred in the simulation, and the theoretical % that should occur. This demonstrates the validity of the simulation.

TOM	SIM%	THRY%
5	2.7%	2.8%
4	5.6%	5.6%
3	8.3%	8.3%
2	11.0%	11.1%
1	13.8%	13.9%
0	16.7%	16.7%
(1)	14.0%	13.9%
(2)	11.1%	11.1%
(3)	8.5%	8.3%
(4)	5.5%	5.6%
(5)	2.8%	2.8%

I’ve worked with simulations involving dice before and expected some variation but, I would have to say, these results shocked me.

Simulation Results: Average for 10 Trials
120 Teams: 10 Years, 12 Games Per Year
TOM/YR	%	NO.
9+	0.1%	0
8 to 8.99	0.1%	0
7 to 7.99	0.1%	0
6 to 6.99	0.3%	0
5 to 5.99	1.5%	2
4 to 4.99	4.3%	5
3 to 3.99	7.2%	9
2 to 2.99	8.5%	10
1 to 1.99	13.7%	17
0 to .99	12.9%	16
0	1.4%	2
0 to -.99	15.2%	18
-1 to -1.99	12.5%	15
-2 to -2.99	10.0%	12
-3 to -3.99	5.7%	7
-4 to -4.99	3.5%	4
-5 to -5.99	2.3%	3
-6 to -6.99	0.3%	0
-7 to -7.99	0.1%	0
-8 to -8.99	0.2%	0
-9+	0.1%	0
	100.0%	120

Based on these simulation results, turnovers could be explained as primarily luck even with several teams experiencing up to +/- 9 Average TOM/Year over 10 years. Note that in the simulation, approximately 90% of all FBS team’s average between approximately +/- 4 turnovers over 10 years. The detailed simulation data also shows that, by just luck, many teams could experience double-digit turnovers in multiple years.

I also ran the simulation for a span of 100 years for each team. As expected the variation was reduced significantly. Approximately 80% of all teams had an average TOM/YR of less than +/- 1.0 and 100% had an average TOM/YR of less than +/- 2.0.

Here are several examples of actual data from the simulation (all examples are from the 10 year simulation).

Example 1: Actual Data from the Simulation (Large Negative Average TOM/YR)
Game-->	1	2	3	4	5	6	7	8	9	10	11	12	TOM	AVG
Year 1	(2)	3	(4)	(1)	(1)	(4)	(1)	(3)	(4)	(4)	(2)	3	(20.0)
2	(2)	3	(1)	(4)	0	1	(2)	(1)	(3)	(2)	0	(2)	(13.0)
3	4	(2)	(2)	(3)	2	1	0	(2)	(2)	0	(4)	(1)	(9.0)
4	2	0	0	(1)	(4)	3	(2)	(3)	2	3	2	3	5.0
5	(3)	(2)	(4)	0	(1)	1	0	3	5	(3)	4	(1)	(1.0)
6	(1)	2	(2)	(1)	4	(1)	0	(2)	(4)	4	3	(2)	0.0
7	2	(3)	0	(3)	(2)	3	(1)	0	(3)	(4)	0	0	(11.0)
8	0	2	2	(1)	(4)	(3)	5	(4)	(5)	(4)	(2)	(3)	(17.0)
9	1	(3)	5	1	(3)	3	(1)	(1)	(2)	(3)	(4)	(2)	(9.0)
10	(1)	0	0	1	1	3	(5)	0	(4)	1	(3)	0	(7.0)	(8.2)

 

Example 2: Actual Data from the Simulation (Large Positive Average TOM/YR)
Game-->	1	2	3	4	5	6	7	8	9	10	11	12	TOM	AVG
Year 1	1	5	4	(2)	(2)	2	0	0	(2)	0	0	1	7.0
2	(1)	2	1	0	(1)	3	(4)	4	4	(2)	(2)	0	4.0
3	4	2	3	2	3	1	(3)	1	(1)	(1)	(5)	1	7.0
4	2	3	4	(2)	(4)	3	(1)	(4)	1	(4)	0	1	(1.0)
5	5	1	3	3	2	4	0	(1)	(1)	(1)	(3)	2	14.0
6	0	5	3	3	(1)	(3)	(3)	0	(1)	3	2	0	8.0
7	2	(1)	0	0	(1)	3	2	2	4	(1)	0	4	14.0
8	0	5	3	1	(2)	(1)	(2)	0	(2)	(2)	2	1	3.0
9	0	3	2	(2)	2	1	0	0	5	(1)	1	3	14.0
10	3	5	1	4	1	2	0	3	2	0	1	1	23.0	9.3

 

Example 3: Actual Data from the Simulation (Average TOM/YR Approximately -0-)
Game-->	1	2	3	4	5	6	7	8	9	10	11	12	TOM	AVG
Year 1	1	2	0	4	(5)	(2)	3	0	0	(5)	(4)	0	(6.0)
2	(1)	0	1	1	0	4	(2)	(1)	2	0	(2)	3	5.0
3	2	(4)	0	4	(3)	0	5	(1)	1	(3)	(1)	1	1.0
4	5	(1)	5	(3)	0	0	(3)	4	(2)	(5)	(3)	0	(3.0)
5	(2)	4	0	0	(2)	3	0	0	(2)	3	1	1	6.0
6	(3)	(2)	(3)	0	(1)	4	(3)	4	(3)	0	0	2	(5.0)
7	1	(3)	(1)	0	(2)	1	3	(2)	4	(1)	(1)	0	(1.0)
8	(4)	(3)	(1)	1	1	3	2	(5)	3	5	0	(3)	(1.0)
9	3	3	(1)	(4)	1	1	3	(1)	1	2	4	2	14.0
10	(1)	(2)	1	(3)	0	0	(2)	0	5	0	2	(3)	(3.0)	0.7

 

The Gory Details – Actual Data

So, what does the actual data show? I looked at all FBS teams from 1999 to 2008. I tracked turnover margin (TOM) and win/loss margin (WLM).

Even though the simulation indicates a relatively large variation would be expected in TOM even if only luck is involved, a significant number of teams fall outside of the expected variation. Here is a table showing all the teams with average TOM per year greater than 4.0 (sorted by TOM).

Table Showing All Teams With Average TOM/Year Greater Than 4.0 (Sorted by TOM)
Team	CONF		Avg	1999	2000	2001	2002	2003	2004	2005	2006	2007	2008
USC	PAC10	TOM	10.2	14	(19)	16	18	20	19	21	4	2	7
		WLM	7.1	0	(2)	0	9	11	13	11	9	9	11
Oklahoma	Big12	TOM	8.1	(4)	6	10	19	17	4	(1)	(1)	8	23
		WLM	8.4	3	12	8	10	10	11	4	8	8	10
West Virginia	BigEast	TOM	7.8	(5)	7	(8)	19	16	3	14	7	13	12
		WLM	3.8	(3)	1	(5)	5	3	4	10	9	9	5
Virginia Tech	ACC	TOM	7.7	3	6	10	8	(1)	13	9	4	11	14
		WLM	7.1	11	9	5	6	3	7	9	7	8	6
TCU	MW	TOM	7.4	4	10	3	15	4	4	21	7	(7)	13
		WLM	6.0	3	9	1	8	9	(1)	10	9	3	9
Texas	Big12	TOM	7.3	11	8	11	17	2	5	7	9	1	2
		WLM	8.4	5	7	8	9	7	10	13	7	7	11
Wake Forest	ACC	TOM	6.3	6	(9)	(3)	18	7	7	(2)	13	9	17
		WLM	0.4	1	(7)	1	1	(2)	(3)	(3)	8	5	3
Florida	SEC	TOM	6.1	(6)	19	(4)	(9)	7	4	18	5	5	22
		WLM	6.4	6	8	7	3	3	2	6	12	5	12
S. Mississippi	CUSA	TOM	5.6	10	0	7	(3)	5	5	14	6	(1)	13
		WLM	2.5	5	3	1	1	5	2	2	4	1	1
W. Kentucky	SunBelt	TOM	5.3				17	10	8	3	(4)	2	1
		WLM	2.3				9	5	6	1	1	2	(8)
Toledo	MAC	TOM	5.2	8	22	3	7	11	(2)	5	(3)	1	0
		WLM	2.6	1	9	7	4	4	5	6	(2)	(2)	(6)
Utah	MW	TOM	5.2	8	(11)	1	(1)	9	15	(1)	8	11	13
		WLM	4.6	5	(3)	3	(1)	8	12	2	3	5	12
Air Force	MW	TOM	5.1	(4)	7	8	9	6	1	(7)	8	10	13
		WLM	1.1	1	5	0	3	2	(1)	(3)	(4)	5	3
Boise St	WAC	TOM	4.9	10	8	(8)	8	10	10	(8)	11	1	7
		WLM	8.5	5	7	4	11	12	10	5	13	7	11
Boston College	ACC	TOM	4.7	2	11	3	8	3	0	(4)	15	6	3
		WLM	4.8	5	1	3	5	3	6	6	7	8	4
Georgia	SEC	TOM	4.7	8	(1)	1	8	11	(2)	11	(1)	9	3
		WLM	6.7	3	3	5	12	8	8	7	5	9	7
Alabama	SEC	TOM	4.7	4	(8)	4	15	1	6	8	7	4	6
		WLM	2.3	8	(5)	1	7	(5)	0	7	(1)	1	10
Michigan	Big10	TOM	4.5	10	11	(4)	9	2	6	5	14	2	(10)
		WLM	4.7	7	5	5	7	7	6	2	9	5	(6)
Texas A&M	Big12	TOM	4.5	4	6	3	2	(11)	9	6	9	7	10
		WLM	1.0	5	3	3	0	(4)	2	(1)	5	1	(4)
Florida State	ACC	TOM	4.2	8	10	4	11	8	7	(4)	(8)	6	0
		WLM	5.1	11	10	3	4	7	6	3	1	1	5
Oregon	PAC10	TOM	4.1	9	3	14	5	(5)	(2)	13	(10)	9	5
		WLM	4.6	6	7	9	1	3	(1)	8	1	5	7

 This table includes 21 teams. However, as the simulation indicates, approximately 7 teams should have TOM greater than 4 if luck is primarily responsible. So, 7 of these teams needed to be eliminated. I decided to use low WLM as the criteria to eliminate teams. Teams that are eliminated and their WLM are: Wake Forest (0.4), Texas A&M (1.00), Air Force (1.1), Alabama (2.3), Western Kentucky (2.3), Toledo (2.6), and S. Mississippi (2.5). That leaves the 14 teams in the summary table included above in the Executive Summary.

Here is a table showing all the teams with average TOM per year less than negative 4.0 (sorted by TOM).

Table Showing All Teams With Average TOM/Year Less Than Negative 4.0 (Sorted by TOM)
Team	CONF		Avg	1999	2000	2001	2002	2003	2004	2005	2006	2007	2008
Kent State	MAC	TOM	(4.2)	(11)	(2)	3	(16)	7	(1)	(11)	3	(11)	(3)
		WLM	(4.5)	(7)	(9)	(1)	(6)	(2)	(1)	(9)	0	(6)	(4)
Wyoming	MW	TOM	(4.6)	2	(9)	(3)	(2)	10	6	(12)	(4)	(12)	(22)
		WLM	(3.2)	3	(9)	(7)	(8)	(4)	2	(3)	0	(2)	(4)
Illinois	Big10	TOM	(5.0)	13	(2)	5	(8)	(18)	(6)	(11)	(15)	(2)	(6)
		WLM	(1.8)	3	(1)	9	(2)	(10)	(5)	(7)	(8)	5	(2)
Florida Intl	SunBelt	TOM	(5.1)				2	(5)	(6)	(8)	(9)	(14)	4
		WLM	(5.4)				(1)	(8)	(4)	(1)	(12)	(10)	(2)
Utah St	WAC	TOM	(5.4)	(11)	(2)	(13)	(11)	(4)	(6)	(2)	(6)	2	(1)
		WLM	(5.0)	(3)	(1)	(3)	(3)	(6)	(5)	(5)	(10)	(8)	(6)
Rutgers	BigEast	TOM	(5.7)	(5)	(7)	(22)	(13)	(6)	(7)	(3)	11	(6)	1
		WLM	(1.9)	(9)	(5)	(7)	(10)	(2)	(3)	2	9	3	3
Baylor	Big12	TOM	(5.8)	(5)	(9)	(3)	(17)	(5)	(15)	5	(7)	(18)	16
		WLM	(4.7)	(9)	(1)	(5)	(6)	(6)	(5)	(1)	(4)	(6)	(4)
Washington St	PAC10	TOM	(5.8)	(1)	(3)	(3)	1	(4)	(19)	(3)	(8)	(1)	(17)
		WLM	(1.8)	3	9	5	1	0	(10)	(7)	(2)	(5)	(12)
New Mexico St	WAC	TOM	(6.1)	6	(5)	(5)	0	(8)	5	(23)	(10)	(15)	(6)
		WLM	(3.8)	1	(5)	(2)	2	(6)	(1)	(12)	(4)	(5)	(6)
N. Carolina	ACC	TOM	(6.7)	2	(12)	(11)	(15)	(15)	(4)	(1)	(11)	(6)	6
		WLM	(2.4)	(5)	1	2	(6)	(8)	0	(1)	(6)	(4)	3
Idaho	WAC	TOM	(7.9)	0	(12)	(16)	(14)	(5)	(2)	(6)	(1)	(9)	(14)
		WLM	(5.6)	3	(1)	(9)	(8)	(6)	(6)	(7)	(4)	(10)	(8)
SMU	CUSA	TOM	(8.4)	(4)	(13)	(7)	(12)	(13)	(19)	5	1	(9)	(13)
		WLM	(5.5)	(2)	(6)	(3)	(6)	(12)	(5)	(1)	0	(10)	(10)
Army		TOM	(10.1)	(4)	(6)	(16)	(14)	(20)	3	(2)	(18)	(10)	(14)
		WLM	(7.0)	(5)	(9)	(5)	(10)	(13)	(7)	(3)	(6)	(6)	(6)

This table includes 13 teams. However, as the simulation indicates, approximately 7 teams should have TOM less than negative 4 if luck is primarily responsible. So, 7 of these teams needed to be eliminated. I used high WLM as the criteria to eliminate teams. Teams that are eliminated and their WLM are: Illinois (-1.8), Washington St (-1.8), Rutgers (-1.9), N. Carolina (-2.4), Wyoming (-3.2), New Mexico State (-3.8), and Kent State (-4.5). That leaves the 6 teams in the summary table included above in the Executive Summary.

In Part 2 of the Turnover Analysis, I’ll look at Steele’s theory about turnovers being a significant cause of turnarounds. I’ll also discuss why turnovers are (or aren’t?) important.