Turnover Analysis - Part 2: Do Turnovers = Turnaround?

Submitted by Enjoy Life on November 30th, 2009 at 5:46 PM
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In Part 1(mgoblog.com/diaries/turnover-analysis-part-1-it-all-just-luck-1), a statistical analysis concluded: Luck is primarily responsible for TOM of approximately 80% of FBS football teams. For the other 20%, team performance (good or bad) is primarily responsible for TOM.

In Part 2, I’ll look at the question, “Do Turnovers = Turnaround”. Each year, Phil Steele includes his “Turnovers = Turnaround” article in his College Football Preview. A couple of quotes:

“Teams with a positive double-digit TO ratio had the same or weaker records 77% of the time since 1996.

“Teams with a negative double-digit TO ratio had the same or stronger records 80% of the time since 1996.”

Steele bases his analysis on the premise that turnovers are primarily luck and that teams “rarely get a repeat of that good fortune.”

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

Phil Steele is wrong. Turnovers do NOT equal turnaround.

The teams Steele isolates (those with double-digit turnovers) are the teams whose TOM is primarily due to performance and not luck. Therefore his basic premise is incorrect.

Even if the double-digit TOM was primarily due to luck, there is no cause and effect. The percentage of teams that “turnaround” the next season is approximately the same when TOM is completely ignored.

From 1999 to the present, 72% of all FBS teams that had a winning record of at least +2 (7-5 or better) had the same or weaker records the next year regardless of TOM. This includes approximately 50 teams each year. (Steele: 77% for the teams with double-digit turnovers.)

From 1999 to the present, 74% of all FBS teams that had a losing record of at least -2 (5-7 or worse) had the same or stronger records the next year regardless of TOM. This includes approximately 43 teams each year. (Steele: 80% for the teams with double-digit turnovers.)

Basically, it is very difficult for winning teams to keep on winning at the same rate and very difficult for losing teams to keep on losing at the same rate regardless of what TOM happens to be.

Problems With Analysis of TOM

It is very difficult to analyze TOM. It is undeniable that a single turnover can determine which team wins the game. It is also undeniable that teams win games with large negative TOM (Michigan had a -4 TOM and beat a favored Florida team in the 2007 Capital One Bowl). And, teams lose games with large positive TOM (Michigan lost to osu in 2006 with a +3 TOM).

Obviously, the impact of TOs is situational and difficult to analyze. But, using TOTAL TOM for a year in any analysis is foolhardy. Every football game is decided individually and TOs occur uniquely for each game. Averaging this data over a year just does not make sense.

This table shows the Top 10 teams from 2008, their Average TOM Per Year, and their yearly Win/Loss Record and yearly TOM for the last 8 years.









Florida (+7.4)

8-5 (-9)

8-5 (+7)

7-5 (+4)

9-3 (+18)

13-1 (+5)

9-4 (+5)

13-1 (+22)

Utah (+7.7)

5-6 (-1)

10-2 (+9)

12-0 (+15)

7-5 (-1)

8-5 (+8)

9-4 (+11)

13-0 (+13)

USC (+13)

11-2 (+18)

12-1 (+20)

13-0 (+19)

12-1 (+21)

11-2 (+4)

11-2 (+2)

12-1 (+7)

Texas (+6.1)

11-2 (+17)

10-3 (+2)

11-1 (+5)

13-0 (+7)

10-3 (+9)

10-3 (+1)

12-1 (+2)

Oklahoma (+9.9)

12-2 (+19)

12-2 (+17)

12-1 (+4)

8-4 (-1)

11-3 (-1)

11-3 (+8)

12-2 (+23)

Alabama (+6.7)

10-3 (+15)

4-9 (+1)

6-6 (+6)

10-2 (+8)

6-7 (+7)

7-6 (+4)

12-2 (+6)

TCU (+8.1)

10-2 (+15)

11-2 (+4)

5-6 (+4)

11-1 (+21)

11-2 (+7)

8-5 (-7)

11-2 (+13)

Penn State (+2.6)

9-4 (+14)

3-9 (-6)

4-7 (-3)

11-1 (+3)

9-4 (+1)

9-4 (+2)

11-2 (+7)

Ohio State (+3.7)

14-0 (+13)

11-2 (+1)

8-4 (-1)

10-2 (-9)

12-1 (+9)

11-2 (-3)

10-3 (+16)

Oregon (+2.1)

7-6 (+5)

8-5 (-5)

5-6 (-2)

10-2 (+13)

7-6 (-10)

9-4 (+9)

10-3 (+5)

As you can see, there are wild swings in Average TOM Per Year for many of the teams with no apparent correlation to wins and losses.

A Bit More About Turnovers, Luck, and Michigan

In Part 1, I looked at a statistical analysis to evaluate whether turnovers are based on luck or team performance. It is also worthwhile to examine TOs individually and make a determination of whether luck or team performance is primarily involved. The criteria I used are shown in the table below.

Is the player doing what he should be doing and has been coached to do?

Does the TO occur rarely?

If the answers to these questions is “Yes”, the TO is primarily bad luck. If the answers are “No”, the TO is primarily due to poor performance. This year for M.










Team (Those bad snaps)



























This year Tate/Denard/Team fumbled the ball 19 times and lost 8. Almost all of these were due to poor performance and not bad luck (the lost fumble in the Iowa game, I would classify as bad luck because it was caused by weather). The good news is that these should be correctable.

In 2008, Jimmy Clausen had 17 interceptions. This year he has 4. Does anyone actually believe that is just due to bad luck last year and good luck this year? Most interceptions are performance based.

This year the freshman QBs threw 14 interceptions. These are mostly due to poor performance and should be correctable.

Last year, Michigan was -10 in TOM and ended up 3-9. This year, Michigan was -12 in TOM and ended up 5-7. Obviously a better TOM was not the cause of the “turnaround”. Why would TOs actually increase in the second year of the Coach Rod offense? Is this a major cause of concern?

The answer I think is obvious – freshmen QBs were the cause of the poor TOM!

 I will readily admit I was one of the MGoBloggers that did not listen when several of our fellow MGoBloggers warned us all that things would not end well with freshmen QBs. They were absolutely right, I was absolutely wrong. Many of the advances in team performance this year were masked by the inexperience and inconsistency of the freshmen QBs. This should not be a cause for concern since neither will be a freshman ever again.