Diaries

EDIT:  Added further justification to point 5.

I put a bit of effort in cleaning up my code to address some of the problems from last week:

1. Undefeated and winless teams:  I added the fictitious tie.  Now there's a path from every team to every other team.  While this is a hack, it's reasonable and about as far as I really care to go with it.  I also calculated the round-robin winning percentage (RRWP) and KRACH strength of schedule (SOS).  These are useful metrics for comparing teams that don't line up very well, as per John Whelan's KRACH site.  Now the undefeated and winless teams can be compared with multiple data elements.
2. Rasmus:  Open source:  The original Pairwise and KRACH code was freely given to me years ago by John Whelan.  This code is my own, based only on the information given in his KRACH site;  given that, I'm comfortable sharing my code, so long as the user gives due credit to John and Ken Butler.
3. joeyb:  Undefeated teams will always rate better than teams with losses:  To investigate this, I created a fictitious team that was 10-0 with wins against only the bottom ten teams.  It rated out at #37, well below several 1- and 2-loss teams.
4. SpartanDan:  Top teams are ranked backwards:  You are correct, sir.  I mistakenly assumed that a larger deviation from predicted meant a less accurate KRACH.  This has been fixed, as in 1) above, with the RRWP, SOS and fictitious tie.
5. Seth9:  The rating doesn't apply to college football:  You may be right on this. It's still curious, and now trivial, for me to crunch the numbers.  EDIT:  But is this any less applicable than any of the computer ratings used in the BCS?  And it certainly does not have the bias and politicking associated with opinion polls.

As points of comparison, I've included the latest BlogPoll results and BCS standings.

Again, this rating includes all D-IA games through 15 November 2009:

Team	BlogPoll	BCS	Rank	KRACH	RRWP	Record Rank	W	L	T	Win %	SOS Rank	SOS
Alabama	1	2	1	50.413	0.953	1	10	0	0	1.000	10	2.401
Florida	3	1	2	43.477	0.946	1	9	0	0	1.000	15	2.288
Cincinnati	5	5	3	36.050	0.937	1	9	0	0	1.000	23	1.897
TCU	4	4	4	29.380	0.926	1	9	0	0	1.000	35	1.546
Texas	2	3	5	26.406	0.920	1	10	0	0	1.000	52	1.257
Boise State	6	6	6	21.243	0.906	1	9	0	0	1.000	59	1.118
Georgia Tech	7	7	7	13.464	0.870	2	9	1	0	0.900	18	2.126
LSU	11	8	8	9.599	0.837	5	8	2	0	0.800	2	2.823
Oregon	9	11	9	9.141	0.832	5	8	2	0	0.800	3	2.688
Pittsburgh	8	9	10	8.655	0.826	3	8	1	0	0.889	37	1.527
Ohio State	10	10	11	7.289	0.807	4	9	2	0	0.818	22	1.918
Iowa	14	13	12	6.292	0.789	5	8	2	0	0.800	24	1.851
Virginia Tech	16	15	13	6.103	0.785	8	7	3	0	0.700	1	2.848
Oklahoma State	12	12	14	5.436	0.770	6	7	2	0	0.778	26	1.812
Penn State	15	14	15	5.061	0.761	5	8	2	0	0.800	40	1.489
Oregon State	19	19	16	4.967	0.758	9	6	3	0	0.667	4	2.675
USC	21	18	17	4.954	0.758	8	7	3	0	0.700	14	2.312
Wisconsin	17	16	18	4.574	0.747	6	7	2	0	0.778	38	1.525
Miami (FL)	20	20	19	4.544	0.746	9	6	3	0	0.667	8	2.447
Clemson	18	23	20	4.393	0.741	9	6	3	0	0.667	12	2.365
Rutgers			21	4.372	0.741	7	5	2	0	0.714	20	1.987
Utah	23	21	22	3.965	0.727	5	8	2	0	0.800	56	1.166
Stanford	13	17	23	3.825	0.721	8	7	3	0	0.700	27	1.785
California		25	24	3.679	0.716	9	6	3	0	0.667	21	1.981
Temple			25	3.607	0.713	3	8	1	0	0.889	88	0.636
North Carolina	25		26	3.504	0.708	11	5	3	0	0.625	16	2.230
Arizona			27	3.496	0.708	11	5	3	0	0.625	17	2.224
Georgia			28	3.049	0.687	14	5	4	0	0.556	7	2.494
South Florida			29	3.048	0.687	13	4	3	0	0.571	11	2.370
Navy			30	3.001	0.685	8	7	3	0	0.700	46	1.400
Brigham Young	22	22	31	2.900	0.680	5	8	2	0	0.800	71	0.853
Boston College			32	2.855	0.677	9	6	3	0	0.667	36	1.537
Arkansas			33	2.826	0.675	14	5	4	0	0.556	13	2.312
Houston	24	24	34	2.817	0.675	6	7	2	0	0.778	63	0.939
West Virginia			35	2.665	0.666	9	6	3	0	0.667	41	1.435
Auburn			36	2.379	0.648	12	6	4	0	0.600	31	1.647
Notre Dame			37	2.252	0.639	12	6	4	0	0.600	33	1.559
Mississippi			38	2.228	0.637	11	5	3	0	0.625	43	1.418
Central Michigan			39	2.091	0.627	6	7	2	0	0.778	84	0.697
Nebraska			40	1.994	0.619	8	7	3	0	0.700	64	0.931
Florida State			41	1.985	0.618	16	4	5	0	0.444	9	2.426
South Carolina			42	1.748	0.597	15	5	5	0	0.500	28	1.748
Kentucky			43	1.717	0.594	14	5	4	0	0.556	44	1.405
Oklahoma			44	1.709	0.593	14	5	4	0	0.556	47	1.398
UCLA			45	1.680	0.590	15	5	5	0	0.500	29	1.680
Tennessee			46	1.648	0.587	15	5	5	0	0.500	30	1.648
Minnesota			47	1.549	0.576	15	5	5	0	0.500	34	1.549
Missouri			48	1.498	0.570	14	5	4	0	0.556	54	1.225
Troy			49	1.447	0.564	8	7	3	0	0.700	86	0.675
Texas Tech			50	1.400	0.559	14	5	4	0	0.556	57	1.145
Mississippi State			51	1.372	0.555	20	3	6	0	0.333	5	2.548
Connecticut			52	1.289	0.544	18	3	5	0	0.375	19	2.026
Air Force			53	1.239	0.537	12	6	4	0	0.600	70	0.858
Michigan State			54	1.223	0.535	15	5	5	0	0.500	55	1.223
Washington			55	1.187	0.530	21	3	7	0	0.300	6	2.543
Nevada			56	1.179	0.529	8	7	3	0	0.700	95	0.550
Fresno State			57	1.152	0.525	14	5	4	0	0.556	62	0.942
Northwestern			58	1.151	0.525	12	6	4	0	0.600	76	0.797
UCF			59	1.125	0.521	14	5	4	0	0.556	65	0.921
East Carolina			60	1.061	0.510	14	5	4	0	0.556	69	0.868
Duke			61	1.056	0.509	15	4	4	0	0.500	60	1.056
Arizona State			62	0.993	0.499	20	3	6	0	0.333	25	1.845
Idaho			63	0.961	0.493	10	7	4	0	0.636	92	0.577
Middle Tennessee State			64	0.943	0.490	8	7	3	0	0.700	102	0.440
Southern Methodist			65	0.924	0.486	14	5	4	0	0.556	80	0.756
Iowa State			66	0.874	0.477	15	5	5	0	0.500	67	0.874
Virginia			67	0.820	0.466	20	3	6	0	0.333	39	1.523
Southern Miss			68	0.787	0.459	14	5	4	0	0.556	87	0.644
Texas A&M			69	0.757	0.452	15	5	5	0	0.500	79	0.757
Wake Forest			70	0.757	0.452	21	3	7	0	0.300	32	1.622
Baylor			71	0.738	0.447	20	3	6	0	0.333	48	1.370
Purdue			72	0.736	0.447	19	4	7	0	0.364	53	1.227
Louisville			73	0.726	0.445	20	3	6	0	0.333	49	1.348
Kansas State			74	0.718	0.443	16	4	5	0	0.444	66	0.878
Kansas			75	0.683	0.434	16	4	5	0	0.444	73	0.835
Northern Illinois			76	0.675	0.432	9	6	3	0	0.667	108	0.364
Bowling Green			77	0.624	0.419	15	5	5	0	0.500	89	0.624
Ohio			78	0.624	0.419	9	6	3	0	0.667	113	0.336
Marshall			79	0.612	0.415	16	4	5	0	0.444	81	0.748
Louisiana-Monroe			80	0.578	0.406	14	5	4	0	0.556	99	0.473
Michigan			81	0.577	0.406	17	4	6	0	0.400	74	0.833
Wyoming			82	0.564	0.402	16	4	5	0	0.444	85	0.689
UAB			83	0.511	0.385	15	5	5	0	0.500	97	0.511
North Carolina State			84	0.510	0.385	22	2	6	0	0.250	50	1.326
Syracuse			85	0.476	0.374	23	2	7	0	0.222	42	1.428
Indiana			86	0.407	0.349	21	3	7	0	0.300	68	0.872
Colorado			87	0.390	0.342	21	3	7	0	0.300	72	0.835
Illinois			88	0.380	0.338	23	2	7	0	0.222	58	1.140
UNLV			89	0.363	0.331	21	3	7	0	0.300	78	0.777
Toledo			90	0.324	0.313	17	4	6	0	0.400	100	0.468
Colorado State			91	0.314	0.309	23	2	7	0	0.222	61	0.943
San Diego State			92	0.303	0.303	20	3	6	0	0.333	93	0.562
Kent State			93	0.298	0.301	16	4	5	0	0.444	107	0.364
Louisiana-Lafayette			94	0.273	0.289	16	4	5	0	0.444	114	0.334
Western Michigan			95	0.243	0.272	17	4	6	0	0.400	111	0.351
Florida Atlantic			96	0.242	0.271	20	3	6	0	0.333	101	0.449
Maryland			97	0.226	0.262	26	1	8	0	0.111	51	1.282
Hawaii			98	0.223	0.260	20	3	6	0	0.333	103	0.413
Washington State			99	0.222	0.259	27	1	9	0	0.100	45	1.403
Tulsa			100	0.205	0.248	20	3	6	0	0.333	106	0.380
Louisiana Tech			101	0.204	0.248	23	2	7	0	0.222	90	0.611
Buffalo			102	0.185	0.235	23	2	7	0	0.222	94	0.555
UTEP			103	0.165	0.220	21	3	7	0	0.300	110	0.353
Army			104	0.162	0.218	20	3	6	0	0.333	116	0.300
Florida International			105	0.155	0.213	21	3	7	0	0.300	115	0.332
Tulane			106	0.136	0.197	23	2	7	0	0.222	104	0.407
Memphis			107	0.126	0.189	26	1	8	0	0.111	83	0.713
Vanderbilt			108	0.124	0.187	27	1	9	0	0.100	77	0.787
Utah State			109	0.117	0.180	23	2	7	0	0.222	112	0.350
Akron			110	0.107	0.171	26	1	8	0	0.111	91	0.606
Arkansas State			111	0.106	0.170	25	1	7	0	0.125	96	0.528
Miami (OH)			112	0.105	0.169	28	1	10	0	0.091	82	0.734
New Mexico State			113	0.079	0.141	23	2	7	0	0.222	118	0.236
Rice			114	0.076	0.138	27	1	9	0	0.100	98	0.479
North Texas			115	0.064	0.123	24	2	8	0	0.200	119	0.218
San Jose State			116	0.048	0.101	29	0	8	0	0.000	75	0.815
Ball State			117	0.032	0.074	26	1	8	0	0.111	120	0.179
New Mexico			118	0.019	0.049	29	0	10	0	0.000	105	0.395
Western Kentucky			119	0.019	0.049	29	0	9	0	0.000	109	0.353
Eastern Michigan			120	0.012	0.033	29	0	10	0	0.000	117	0.250

Game Set

• MSU (17-11, 5-11, RPI: 33) @ #14 Michigan (22-7, 10-6, RPI: 13)
• Wednesday November 18
• 6pm
• Cliff Keen Arena
• TV: BigTenNetwork
• Radio: MGoBlue

Michigan returns to the floor for it's second Wednesday night special of the season against Michigan State as the two teams set out to battle for the State Pride Flag. Michigan currently leads the race including a 3-0 win over MSU by scores 33-31, 25-22, and 26-24. That's really close for a 3-0 sweep, and by all indications, that'll probably be the case again tonight.

Momentum Coming In

Michigan enters this game on a 3 game winning streak, but all three of those wins came against the 3 of the bottom four teams in the BigTen. To make it sound worse, two of those games involved Michigan squeaking by in 5 sets. That's not the way a top 15 team should be winning.

The Spartans also have struggled of late, including losing 3 of their last four. One of those losses was a respectable sweep by Illinois (the Illini are a top ten team), but the other two came at the hands of Northwestern and Indiana, teams guaranteed to finish the conference season with a losing record.

Michigan Injuries

A little bit has changed since the last time Michigan faced the Spartans. In that last game, Lexi Zimmerman dislocated her thumb, and that has definitely made a noticeable difference in the team's play. While Lexi's thumb should be getting closed to healed. The last time I saw it was two weeks ago, still wrapped in the support brace. In the picture to the right, from mgoblue, you can see the thumb wrap at it's largest. You can also see some excellent control of her body.

Another player that has been missing is middle blocker Courtney Fletcher. While I don't have the exact diagnosis, her being on crutches with the ankle soft cast (or that's what it appeared to be, feel free to leave what you know in the comments) is still a fixture on the sidelines.  Karlee Bruck has taken her place on the court, and has done well so far. Bruck was the starter to finish last season and has plenty of experience. She should be a solid replacement on the net.

The real loss with Fletcher's injury is on the serve. Bruck's serve is the reason she lost her starting job to Courtney in the first place. To try and keep a solid 6th server, Coach Rosen has been using Maggie Busch as a defensive specialist as a substitute. Busch has already had 7 service aces on the season, but she's very inconsistent, claiming 7 service errors as well.

What to Watch

While I think Michigan should be a heavy favorite across the board in this game, the Wolverines have been letting too many teams stay in close games. With teams like Indiana and Purdue, our team's talent can pull through. Against Michigan State, they'll have that added motivation which could really hurt Michigan. We need to keep the intensity high and not take the boot off their throats. If Michigan starts to let MSU go on any 5-0 run or better and we haven't called timeout, I'll start to worry.

Oh, and we need to cut down on the service errors.

Team Comparisons

Again, I'm not sold on these charts yet, but I think they may put a couple things in perspective.

Michigan		Michigan State		Advantage
Serving
Aces/Game	1.7	SR Errors/Game	1.0	Michigan
Serve Errors/Game	2.5	Serve Errors/Game	1.9	MSU
SR Errors/Game	1.0	Aces/Game	.9	Michigan
Reception%	.947	Reception%	.950	PUSH
Hitting
Attack%	.235	Blocks/Game	2.3	MSU
Kills/Game	13.9	Digs/Game	12.7	Michigan
Digs/Game	15.0	Kills/Game	13.5	Michigan
Blocks/Game	1.7	Attack%	.250	MSU
Other
Passing Errors	29	Passing Errors	37	Michigan

I was inspired to write this diary after reading Brian's account of his trip to Columbus in 2002.  I've never experienced anything nearly as gratuitous as what Brian described, but I have stories.  Believe everything you hear about Buckeye fans.....Everything.  Warning:  Content has not been editted for language, because believe it or not, Buckeye fans love to curse at Michigan fans.

It was 1994 and I was attending my first road game at the invitation of my cousin-in-law Michael.  Now Mike is an OSU fan.  I tolerate this because, for one, he is married to my cousin, second, he resides in Columbus, making getting down there to watch The Game convenient, and finally, he is not the typical rub-it-in, asshole brand of Buckeye fan that so many of us are familiar with.  I can only assume this is due to the fact that Mike was afforded the advantage of growing up in Texas, rather than Ohio, and splits his college allegiance between his grad alma mater, OSU, and his undergrad alma mater, Texas A&M.

1994 was a milestone year for the Buckeyes, since this was their first win over Michigan since 1988 {chuckle}.  The day started uneventful enough.  We arrived early and tailgated near the stadium.  Much to my surprise neighboring Buckeye fans were for the most part civilized.  My Michigan sweatshirt drew the occasional "BOO!" as somebody walked by, but for the most part nothing.  What I didn't realize is that OSU fans were about to come out of their Cooper-induced hibernation.

The game went badly for Michigan and after a Todd Collins interception with around 5 minutes to go, Mike indicated we should leave and try to beat traffic.  Frustrated with the effort, I agreed and got up to leave.  Seconds after I stood I began to hear them.  "Go the fuck back to Ann Arbor!"  "Fuck Michigan!"  The words didn't bother me as much at the pizza box lid as Chinese throwing star that narrowly missed crashing into my skull or the empty soda cups that didn't.  After exiting the stadium, I turned to Mike and asked if that was normal.  His answer was interrupted by an incredibly intoxicated OSU student who leaped in front of me, stuck his face 2 inches from mine, and with breath that reeked of Jack Daniels asked me, "Hey fucker, what's the SCORE?"  I elbowed past him, annoyed and shocked at such boorish behavior.  Little did I realize......

I missed the 1998 trip to Columbus, but four of my friends had managed to get tickets and made the trek.  Their report started with their arrival to their seats, where they were greeted by a single OSU fan chanting, "ASSHOLES!" from 2 rows behind them for 5 minutes.  He only stopped when his neighboring cohorts told him shut up.  As the game progessed and it became clear that OSU would prevail one of the neighboring fans turned to my pals and said, face completely serious, "I don't think I'd stick around here if I were you."  Alarmed, but untimidated, they stuck it out to the end and then made for the exit to the requisite jeers of "Fuck Michigan!", "Assholes", and the ever popular "Faggots!".

This was not their enduring parting memory of Columbus though.  What WAS the enduring memory was the mob of OSU fans outside the stadium hurling rocks at a charter bus of Michigan fans getting ready to leave while 2 Ohio state troopers watched from 30 feet away.  Apparently, OSU fans find it appropriate to treat visitors the same way ancient Hebrews treated adulterers, and apparently Ohio troopers can't do anything about it because strict Hebrew interpretation of the 10 Commandments trumps the United States Consitution.  As my friend Charles recalls, "At this point, I was starting to get scared".  They have never been back to Columbus since.

The final chapter to this story is the 2006 game.  When I told my friends I had scored tickets, they wondered if it was wise to even attend and surely it would be unwise to show up dressed conspicuously as a Michigan fan.  Defiant, I resolved to dress over-the-top in Wolverine gear.  I attended the game with Mike again and some other family members.  Mike decided this time that we skip the tailgate because it would be easier to just be dropped off.  I agreed.

Arriving, there was a surprising lack of enmity.  A couple of more elderly fans stopped me and expressed their sadness over the passing of Bo.  Things looked good at I made my way to my seat.  These hopes were dashed a few minutes later when 2 OSU fans, visibly intoxicated and reeking of Jack Daniels (what is it with OSU fans and Jack Daniels?), roughly elbowed their way past me to their adjoining seats.  One of them paused in front of me, looked me over and asked in slurred speech, "What the fuck do you think you're doing wearing that shit in here?  This is OUR HOUSE!".  I explained to him that he was sitting in the Michigan fan section.  "Fucking faggots!" he muttered as he moved along.

As the game proceeded, my new best Columbus friend continued to become more and more belligerent with comments questioning the heterosexuality of Michigan fans and reminding us that this was "OUR HOUSE!".  Eventually, a visit from the Ohio state trooper posted near our section (they actually do enforce law and order in Columbus) convinced my buddy to tone things down...and he almost did.

Anyway, we all know how this game ends.  I left the stadium, at this point not even hearing the "Fuck Michigan" taunts because it had become just background noise.  I did encounter a young 5 or 6 year old boy outside the stadium, wearing one of those fey buckeye necklaces, who proudly shouted "Michigan sucks!" at me.  I glanced at his father waiting for some form of admonishment, but he only smiled proudly at his son.  Unfortunately for me, this was less obnoxious than the two Buckeye fans who were following about 10 feet behind me as we headed back to catch our ride home (I assume because their car was parked in the same direction, but who knows?) shouting, "Wow, you guys lost the biggest game EVER!  Woo!  You guys are LOSERS!"  This jab probably stung the most because they avoided the standard crass language and hit a much softer spot, but the fact that they kept this up for what had to have been 20 minutes puts them solidly in the asshole category nevertheless.

So, I don't like OSU fans either.  Brian's experience trumps mine, but I can confirm that the stories you hear are true.  They relish in making sure visiting fans don't enjoy themselves.  They're animals.  We're the bigger person because we can be civilized, and I'm sure that will matter not a wit to an OSU fan.  Go Blue!

 Before this turns into a huge Ron Burgundy parody, let's just get everything out of the way now: BAXTER! You ate a whole wheel of cheese? San Diaaagooo. It's German for a whale's va....ok, that's enough.

Dillon Baxter is a four star running back prospect from San Diego, CA. Though he's currently a USC commit he will be visiting this week along with CA WR Kenny Stills and CA LB/S Tony Jefferson. Yes, he plays offense. No he doesn't play defense. He's really good, though, and his highlight video is jazzy.

 

On with the interview.

TOM: This visit kind of popped up out of nowhere; how did it come about, and what piqued your interest in Michigan?

DILLON: Michigan was one of my first places I wanted to look, but I never really got a chance to. I just want to make sure USC was the right place. I have an opportunity to go see it, so I wanted to do it now.

TOM: What made Michigan a place that you liked in the beginning?

DILLON: I like the crowd, and I’ve always liked them since I was little. They were kind of my team growing up, so I’ve just always liked them.

TOM: Have you made any other visits besides USC?

DILLON: USC is really only the main place. I’ve been to UCLA a little bit, but not really for that long.

TOM: You, Tony Jefferson, and Kenny Stills will be on the visit, are you all friends? Are you all friends with Tate?

DILLON: Yeah, we’re all friends; Kenny’s going to spend the night at my house before the flight. We all know Tate (Forcier) from high school, so that should be fun to all be together again.

TOM: Have you guys every thought about all playing together, or talked about it?

DILLON: We talked about playing together all last year, so we’re all kind of looking around to find a cool place. It didn’t work out that we’d all play for USC, so we’re going to see if there’s maybe anywhere else we all like.

TOM: Can this trip change your mind? A lot of people think kids that are committed sometimes take trips just to have fun, is that the case here?

DILLON: No, if this is better than USC then yeah.  I’m pretty serious about USC, but who knows. The coaching staff at USC are all hype, and always happy to see you and teach you. I just want to find out how the Michigan coaches are, too.

TOM: What are you most excited for at Michigan?

DILLON: I can’t wait for that game; I’m excited. I don’t really know if I’ve been in front of that many people. I just want to see how the game is, the crowd is, just everything. Coach Dews is who’s recruiting me. We’ve been talking the last couple days. So, I want to build on that relationship more and meet the rest of the coaching staff. The offense is exciting, and I hope I can fit in that offense. I want to stay at running back and slot, so that would be right for their offense.

TOM: If this visit does change your mind, or make you think, when will your ultimate decision be?

DILLON: This is the first week of playoffs for us, so probably once our season is over.

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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.