MBKRogers/ TN Football Thread 380-320 (54%), +38.60 Units

MBKRogers · Aug 18, 2009

Welcome to my thread.

2008: 251-190 (57%) +41.0 That's 251 wins and 190 losses for a winning percentage of 57%

I am a high volume player. If you choose to follow me, you need to keep your unit size in check.

http://www.tomahawknation.com/2009/6/4/898182/tomahawk-nation-presents-for

Tomahawk Nation presents "For Entertainment Purposes Only 2009": The Pre-Season Edition. Florida State is the Atlantic Favorite, but the Value Lies with Clemson

If you're a sports fan, you know that nothing is guaranteed. The inherent drama of sport is what makes viewing so compelling. Sports are about probabilities, not absolutes. Guy's offering guaranteed victories on seemingly evenly matched teams are fools. When we evaluate a team, we attempt to ascertain just how good that team is. When sports fans talk about a prime-time matchup, they often make a prediction as to the outcome. They weigh the positives and negatives of each team, add in some other factors like injuries, home field advantage, weather, or jersey color. And it's even possible to make a great evaluation and still be wrong. Any number of non-skill factors could swing a game, like fumble recoveries, or whether an interception is returned for a touchdown. When I handicap a game, I try to answer the question "which outcome would happen more often?" I don't deal in absolutes, I deal in value. Understanding the Vegas line can offer a lot of insight into a team's true talent level.

With that, I welcome you to a season-long feature I like to call "For Entertainment Purposes Only." Last year, 251-190 (57%) +41.0 That's 251 wins and 190 losses for a winning percentage of 57%. If you were wagering $100 fake dollars per game, you would have netted $4100 at season's end. But since these are fake wagers, we refer to our wager amount as "units." First though, a disclaimer:

I want to emphasize that we don't endorse gambling at this site, and anything on here is for entertainment purposes only. Always check with your local, state, or federal laws to determine the legality of wagering.

[Editor's Note: This post discusses wagering but has a lot of football information, specifically about the 'Noles divisional opponents]

Yesterday, some of the major online casinos released their odds for the college football season. If it were legal, you could wager on who teams to win their division or conference. I provided a few of my selections, and they inspired a lot of discussion in the comment section. Inside, you'll see what got everyone so hot and bothered, and I'll give some further analysis on the initial wagers in the 2009's first edition of "For Entertainment Purposes Only!"

So, those picks...

If you find gambling tougher to understand than Chinese, don't worry, Understanding Sports Odds. In relevant part:

Step 1: Identify the type of line you are looking at. All online sports books offer you the chance to have your lines in an "American" or "Money line" version. If I were you, I would use this as my standard. An "American" line uses either a + or - before a number to indicate odds. So a -120 and a +120 are two very different odds on a team... I will explain the differences shortly. Two other less common variations exist: decimal odds and fractional odds (these are superior but Americans have trouble understanding for whatever reason, so in the interest of simplicity, I will not get into them)

Step 2: Identify the favorite. Lines with a "-" before the number (i.e. -200) indicate the favorite. A -200 should be read as: "For every $200 wagered, I win $100." When there is a negative sign, the line should always be read with relation to 100. That does not mean you have to bet that much, it's just easiest to understand! When a + sign is present, just reverse the reading, always keeping reference to 100:

Examples:

1) -150: For every $150 wagered, I win $100 ($50 wagered would win $33).
2) +300: For every $100 wagered, I win $300 ($50 wagered would win $150).
3) 100 (can be either +/-): For every $100 wagered, I win $100 ($50 wagered would win $50).
4) Most commonly: -110: For every $110 wagered, I win $100.

You see "4" most commonly because the extra $10 you have to bet to win $100 is called the "juice" that the books keep as a fee for making the line available to you. Think of it as a commission.

The most important thing you can teach yourself early on is: "Just because the books assign one side to be the favorite (even large, -200 or -300, favorites), does not mean that they will win." We have all seen favorites get upset, and it is important to avoid the temptation of finding comfort in the fact that the lines makers put one team as a favorite.

Here are mine.

Clemson to win the ACC +1000
VTech to win the Coastal +175
Nebraska to win the Big 12 North +175
Ole Miss to win the West: +350
Rutgers to win the Big East +500

I'll take them in order from least controversial to most controversial:

Rutgers to win the Big East +500 Rutgers is probably the 3rd best big east team, but they get 5 conference home games, their conference road games are @ UCONN (lost everything, unlikely to make a bowl), @ Louisville (again, terrible), and @ Syracuse (one of the worst teams in all of college football). Because they draw all their tough games at home, and because they get Cincinnati in their first game (the bearcats lost 10 of 11 defensive starters), they are a decent pick to win the division. That their odds are better than Pitt, USF, or WVU is a bonus. Again, scheduling matters. Also, they are strong along the lines despite lacking sexy named-skill position talent. There's value in +500.

Nebraska to win the Big 12 North +175 This is a schedule pick. The odds say that Nebraska and Kansas are the two favorites. This is a case of Vegas anticipating the public's laziness. It's a smart move on their part. Nebraska has by FAR the best offensive and defensive lines in the Big 12 North, and hey have excellent DB's as well. Kansas has terrible defensive linemen and linebackers. But the crux of this pick is again, scheduling.

Nebraska has: @ Missouri (who lost EVERYTHING, after a double bye week), Texas Tech at home, Iowa State, @ Baylor, Oklahoma, @ Kansas, Kansas State (the team that will finish 2nd in the north), and @ Colorado.

Compare that to Kansas' murderous schedule: Iowa State, @ Colorado, Oklahoma, @ Texas Tech, @ Kansas State, Nebraska, @ Texas, Missouri.

Nebraska dodges Texas and gets the rebuilding teams EARLY, before they can rebuild. Kansas plays Texas and has to play the rebuilding teams late (Missouri).

I wasn't sure I'd play this one, but then I saw that Colorado lost its best player (WR Smith, transfer). That sealed the deal.

VTech to win the Coastal +175 Virginia Tech is probably the best team in the Coastal division. I don't think there's much separation between them, GT, UNC, and Miami, and to be honest, I really am not comfortable calling them the best. That said, let's again go to the schedule, specifically each of the fout team's three Atlantic division games:

VT: Boston College, @ Maryland, NCST

GT: Clemson, @ FSU, Wake Forest

Miami: @ FSU, Clemson, @ Wake

UNC: FSU, @ BC, @ NCST

I'm very confident in saying Clemson and Florida State are the most talented teams in the Atlantic division. VT avoids both. They get NCST in Blacksburg. The other three contenders have difficult Atlantic division schedules. Thus, VTech is the pick.

Ole Miss to win the West: +350 Ole Miss is this year's sexy pick. Our readers remember that I was on them last year, even after they lost @ Wake Forest. I think the public is a year late on this team, but I still see value here. I don't believe Ole Miss is the best team in the SEC West. I think Bama is better and that Bama will have the 2nd best defense in the country (UF #1). This might come as a shock, but again, this is a schedule play. Ole Miss doesn't play UGA or UF. They get Bama, LSU, Arkansas, and Tennessee all at home. their conference road games rad at South Carolina (rebuilding year, and they get them early), @ Vanderbilt (not a likely bowl team), @ Auburn (should be better but not a ton better), and @ Mississippi State (awful).

I dismiss LSU's chances to win the division because they play @ UGA and also play UF, which Ole Miss and Bama do not play. This comes down to Ole Miss v. Bama. Ole Miss is +350, Bama is +200. I do not think that Bama is twice as likely to win this division as is Ole Miss, and thus I select Ole Miss as a value selection, despite not believing they will actually win the division.

Now to the pick y'all care about:

Clemson to win the ACC +1000 Hold on Noles fans. Let me explain! Where is the value in football? Along the lines and on defense. Quarterbacks are sexy. Linemen are not. This guy will never be on the cover of GQ:

via www.nflgridirongab.com

But he'll be paid millions upon millions of dollars because he weighs as much as a small elephant yet has the feet to win dancing with the stars and that wins games.

And Vegas has to overrate Quarterbacks, Wide receivers, and running backs because they have to create betting lines that don't make them susceptible to overexposure to certain highly public wagers. And where does the public form their opinion? The media, and most importantly, ESPN. And who produces content for ESPN? Writers, most of which don't necessarily scout these teams very well. And why should they? The average person doesn't want to know about a lineman. They want the running back.

Why else doesn't the public know about linemen? Sportswriters don't know what to look for. As you'll see in an upcoming piece (later this month) about FSU center Ryan McMahon, who is largely a product of FSU's media hype and playing as a freshman. ESPN's Bruce Feldman also comments on this:

If one coach really makes a strong case to some media members, he can get a certain player on or off an all-conference or even All-American team, especially if the player is a linemen or defensive back. Those positions are more dependent on the opinions of guys who have coaching insight.

Fantasy football has skewed the perception of what makes a quality football team. Ever draft an offensive lineman in your 12 team league? Didn't think so.

Honestly, ask yourself, when did you start hear about the linemen drafted in this past draft? March, right? Around the time of the combine, and long after the college season was over?

So back to Clemson, and I don't want to write an entire Clemson preview here, but I want to explain the justification for my wager.

Let's start with Talent. Clemson has that in bunches. You'll remember our article on FSU's failed 2006 and 2007 recruiting classes, where Jeff Bowden and his horrible staff failed to evaluate recruits and then took wild chances on kids who didn't want to play for FSU or who had serious academic and character issues. FSU has only 9 starters form those two recruiting classes. Only 9 senior or junior starters? Ouch! Recruiting really helps or hurts a team 3 and 4 years down the road.

Clemson's recruiting classes in 05-07 ranked 15th, 13th, and 16th nationally. FSU's ranked better, but those rankings don't account for kids who never qualify. In terms of who is still here from those classes, Clemson blows FSU away. Where FSU has 9 starters from the 05-07 classes, Clemson has 15. That's 15 physically mature grown men who were very talented to begin with.

And Clemson isn't sexy. They lost their best wide receiver (Aaron kelley) and the 2nd leading all-time rusher in James Davis. They also lost their quarterback. Similar to UGA, people are writing Clemson off because they lost their recognizable players. They did not, however, lose their best players, and that's what I care about.

The Tigers were widely picked to dominate the ACC last year and flopped so poorly that their coach was fired. Clemson was as overhyped last year as they are underhyped this year. To understand why, let's review what happened to Clemson. They got smoked by an Alabama team who nobody realized was elite until much later in the season, then they suffered huge injuries to both lines, particularly on the offensive line where they had 9 different guys start a game! Their QB had serious personality issues, and he was a pocket passer type who suddenly had no pocket. They were 3-1, but then lost three very close games @ Maryland, @ Wake Forest, and against Georgia Tech by an average of less than a field goal. Know what I call that? Bad luck, and it's proven that everyone wins about half of their games decided by 7 points or less. Winning or losing a huge percentage of those is truly, variance. Clemson was 1-4 in games decided by less than a touchdown. If they had gone 3-2, they'd probably be ranked in the top 15 to start this year.

So, did anything good come of their season? You bet. First, they got an excellent defensive coordinator in Kevin Steele to replace an already excellent defensive coordinator in Vic Koenig. Steele was at FSU forever, is considered one of the best defensive coordinators in America, and knows FSU's defensive scheme inside and out. Second, Clemson already played excellent defense last year. They were the 4th best defense in the country. Their points per game numbers don't show it because the offense set them up in horrible field position after horrible field position, but that ultra comprehensive system (linked above) shows their true performance level. Clemson will, have a defense that is in the conversation for "best in America", and I expect as many as 7 of these defenders to eventually be drafted in the first 3 or 4 rounds of the NFL draft. Finally, they got a lot of guys experience on the offensive line. Much like FSU, their offensive linemen took their lumps last year and will be much better this year. Clemson's OLine is probably more talented than FSU's, though I am not prepared to call them better. They received far less press because they didn't become so inexperienced until later in the season (brought on by injuries), while the Noles were very young from day one and got a lot of preseason press. This year, they bring back all 5 of their main starters and these guys are very talented and experienced.

At receiver, they return 2 of the top 3, and also have Dye (high 4* Junior), Jones (high 4* soph), McNeal 5* frosh, and Rendrick T aylor (Unproven, but a a greg jones/ cornelious ingram type athlete at tight end). Jamie Harper (another high 4* RB) is more than capable of replacing James Davis at running back to go with CJ Spiller. And at QB, they have Willie Korn and Kyle Parker (5* and 4* respectively). Both are very talented players and the chance is that one WILL play pretty well. Plus, Clemson's staff won't ask them to do much at all. Behind a very talented line, and a great running game (likely), they will be asked to manage the game, throw play action passes, and make plays with their legs. It's been proven that if you play NFL style ball (ultra conservative, defense, field position) in the ACC, you’ve been successful. Eventually, that’ll stop because teams like FSU or Miami will just be better than folks and will play offense AND defense, but for my money, not this year.

What about the schedule? Clemson starts off @ GT on a Thursday night, but they play Middle Tennessee state before that, so it's effectively a bye week, they then get BC at home, go @ Maryland, get a bye week, play Wake Forest at home, then travel to Miami. They then come home and get Coastal Carolina (a bye week) before hosting FSU. FSU hasn't won at Clemson since before Chris Rix! They then play @ NCST, and finish at home with Virginia.

FSU's schedule, as you know, does not have Virginia or Duke and that is a major detriment. Clemson gets de facto bye weeks before their two most difficult games, and draw Virginia out of the Coastal, which is HUGE, which means that in addition to playing FSU in Clemson, the Tigers do not have to play 3 difficult Coastal games, but rather two.

I believe FSU is as good as Clemson and that the 'Noles have a decent shot to win the division, but Clemson has the better schedule to win the division. Florida State faces a massive battle with attrition as they trek through this schedule. The Noles do not have quality depth because while Jeff Bowden was busy being one of the worst coaches ever, Tommy was busy killing his dad and brother in recruiting (when you consider who actually made it into school and not prison). Those players, from 2006 and 2007 are now Seniors and Juniors, and their veteran talent is much better than ours. Playing at home, getting a bye week before FSU, and getting to play UVA gives Clemson a shot to win the division.

Let's play guess a team, okay? In 2007, this team:

*Went 0-3 in games decided by 4 points or less (the bad luck factor) (Like Clemson)
*Was loaded with talent from a coach who was a great recruiter (like Clemson)
* Went 3-10, including 0-8 in conference despite high expectations.
* Fired their coach at the end of 2007. (Like Clemson)

In 2008, this team:

* Returned 16 starters from those elite recruiting classes, including 9 on defense. (Clemson returns 15, including 8 or 9 on defense)
* Had a Five-Star QB who hadn't played before (Much like Clemson's Willie Korn)
* Had an elite defense, especially their defensive line. (Much like Clemson)
* Had a new coach to energize the program, including quality coordinators. (Like Clemson)
* Brought back an offensive line entirely of talented upperclassmen (Like Clemson)
* Had a distinct lack of name skill-position talent (Like Clemson, except spiller)

And won the cotton bowl. This team was Ole Miss.

The point of this exercise is that just because you haven't heard of the talent on a squad doesn't mean that the talent is not there. Sometimes all it can take is a new coach or a lack of really bad luck to allow that talent to shine through. Ole Miss won games with excellent defense, and a 5* QB who had tons of time behind a talented and more experienced OLine, despite Jevan Snead being a first time starter.

But this does not mean I am picking Clemson to WIN the division. Rather, this means that I believe Clemson has about a 40% chance to win, as does FSU. NCST has about a 15% chance (I don't buy their defense yet), with everyone else having major rebuilding years in the Atlantic. The reason I make this wager is value. If I feel two teams have an equal or similar shot to win something, I will choose the team with the +350 odds over the team with the +120 odds. It's just value.

But I didn't pick them for the Division at +350, I picked them for the conference, at +1000. The rationale is simple: If I like them for the division, would I like them for the championship game as well, and if so, how much added value is needed to make the Conference wager more attractive than the division wager? I do like them for the division at +350, and +1000 is 50% more than double +350 for a game they'd probably be slight underdogs in, so the conference selection is more attractive than the division selection.

The Noles can beat the Tigers for the Atlantic, but they have work to do and will need to catch a few breaks as well. You can bet Jimbo Fisher knows this. I've had multiple readers tell me he believes Clemson is the team to beat in the Atlantic.
Click to expand...

MBKRogers · Aug 18, 2009

For Entertainment Purposes Only: Season Win Totals

http://www.tomahawknation.com/2009/6/27/926845/for-entertainment-purposes-only

Maryland Terrapins under 6 wins at -165. (Fake wager of 165 to net 100)

First, I don't think Maryland is a good team. They only return 9 starters, a number that could drop to 7 if they lost Left Tackle Bruce Campbell or Nose Guard Travis Ivey to grades. They schedule is tough, and they will be definitive underdogs in at least 6 of their 12 games (@ Cal, Clemson, @ Wake, @ NCST, VTech, @ FSU) . Of the remaining 6, 4 look to be tossups (Rutgers, UVA, @ Duke, BC). Only James Madison and Middle Tennessee appear to be sure wins for the Terps- and Zach_Nole reminds us that MTSU BEAT Maryland last year. Their recruiting has really dropped off in recent years and since their magical 2001-2003 run (31-8 record), they have just two winning seasons. I also prefer to wager on unders whenever possible, because the public is typically overly optimistic about every team, and lines often fail to properly account for the possibility of attrition. I predict Maryland to finish at 5 wins, and will say that I believe they are more likely to finish with 4 than 6.

Texas Over 10 wins at -155 (Fake Wager of 155 to net 100)

Hook 'Em! This one is fairly simple. The Longhorns are one of the best teams in the country. They return 9 offensive starters and an excellent college quarterback in Colt McCoy. In a conference that doesn't play defense and produces no defensive talent, having a great quarterback can really make all the difference. Texas' non-conference schedule is beyond pathetic and they will be 4td+ favorites in each of those contests. Their Big 12 road games are @ Missouri (should be significantly down), @ Oklahoma State (a dangerous game), @ Baylor (not in Texas' league yet, but improved), and @ Texas A&M (a stagnant below average program that should go bowling by virtue of a terrible non-conference slate). They of course do play Oklahoma. After looking at this schedule, I estimated that that Texas will drop 0 or 1 games more than 60% of the time, which makes this profitable. Further, we have the opportunity to "push" (no winner, money returned), if Texas wins only 10 games. Do you think Texas is losing 3 games with their schedule? Thought not.

Rutgers over 8 wins at -200

This is some heavy juice to pay. In order to win this, Rutgers would need to win 9+ games 66% of the time. You already know that we are holding Rutgers at +500 to win the Big East. Again, enthusiasm for the Scarlet Knights is all about schedule and style. Rutgers loses 3 of their top four receiving threats and their all time leading passer in Mike Teel. Yet the guys they lost weren't all that special; they just started for a long time. So, how does Rutgers get to 9 or more wins? Start with a terrible non-conference schedule (a theme I like to emphasize). They play Howard, Florida International, Texas Southern, and @ Army. That's about as sure a four wins are you can get. We'll call it 3.7 Then they travel to a very iffy Maryland team, who could overlook the Knights as they have Clemson the next week. We'll say 4.2 wins in the non-conference. So, can Rutgers find 5 wins in their 7 conference games? They get Cincinnati (lost their entire defense) at home to open the season, and get Pittsburgh at home on a Friday night and have a bye week before the panthers. Touchdown or greater favorites in both of those games? Likely. Let's call is 5.6 wins through 7 games. 5 left and we need to find 3.4 wins. @ a highly suspect UConn team seems like a game Rutgers will be a slight favorite in, so we'll say .7 wins there, putting the Knights at 6.3 wins. Then they get another weekday night home game off a bye week, hosting USF (USF is also off a bye). I'll say .6 wins there, putting them at 6.9 wins with three games to go. @ Syracuse should be a piece of cake, as the Orange are terrible. .85 seems reasonable there, bumping them to 7.75 wins. Two to go. @ a bad Louisville team in a conference road game on a Friday- I'll say .6 wins- putting the Knights at 8.35 wins. They finish out the season hosting WVU in a game that should send them to get pummeled in the Orange Bowl by the ACC champ. With the extra day (they played Fri @ Louisvile), they'll beat WVU .65, putting them at 9 wins even. Oh, and that style thing? Schiano just wants to run the ball and play defense. With the league's best offensive line and a very solid defense, the Knights could conceivably extend their current undefeated streak from 7 to 19- and play for the National Championship. Doubtful- but they'll still win 9+ games and our wager.

Illinois over 7 1/2 wins @ -150

Another moderately high juice wager. Quick! How many Big 10 teams won 8 or more regular season games last year? FIVE. Everyone agrees that Penn State and Ohio State are 1-2 in some order. Then we come to a group that includes Iowa, Illinois, Michigan State and Wisconsin. I happen to think Illinois will finish 3rd or 4th in the Big 10. Their non-conference schedule is manageable, and their in-conference road games outside of Ohio State are a joke (@ Indiana, @ Purdue, @ Minnesota). Teams that are favored in 3 of their 4 conference road games win more than 8 games. Illinois will win 9 games and cash this wager. Yes, I do think the Big 10 is a joke conference trending downward. Only PSU, Ohio State, and Michigan are serious big boy programs focused on football. Northwestern, Minnesota, and Purdue would be gifts on the schedule of any other major conference team. Illinois has decent talent and will take care of business.

South Carolina under 7 wins @ -145.

I'll come out and say it. I'd bet them Under 5.5 wins. I think the Gamecocks win 4 games. They should be TD or more underdogs in all of their road games ( @ NCST, @ UGA, @ BAMA, @ TENN, @ ARK), and should also be TD dogs at home to Clemson and Florida (in Florida's case, probably three-TD dogs). To have any shot at topping 7 wins, the Gamecocks almost must sweep their remaining five home games (FAU, OLE MISS, SCST, UK, VANDY). I see at least one loss in those 5. This team lost a lot and unfortunately for them they have to play the West's top two teams and must travel to Arkansas. Arkansas- not bowling, and losing out on Marcus Lattimore. Fun stuff.

Tennessee Over 7 wins at +140 (wager of 100 to win 140)

I think UT wins between 7 and 9 games. UT had 8 home games, and does not play LSU from the west division. They have three gimme non-conference games and while they do play a much improved UCLA, they should win that one too. Even assuming three losses from @UF, @BAMA, UGA, and @OLE MISS, they can pull out 3 wins from , Auburn, South Carolina, Vandy, and Kentucky to reach 8. This is still a very talented defense and their offense should be a lot more cohesive.

Arizona State Under 6.5 at -110

I don't think Dennis Erickson is a good coach. He does some things well but his game management is poor. Arizona State got really lucky to go 5-7 last year with a very good college quarterback who has now graduated. This year they don't look much improved. I'll give them 1.98 wins for Idaho State and Louisiana Monroe. They are large underdogs at Georgia, I'll say .2 wins there, bringing them to 2.18. They are no better than .6 against Oregon State, so 2.78. Then @ WAZU, still terrible but its a road test, so .5, putting them at 3.28. Washington at home and then Stanford on the road is good for 1 wins, bringing them to 4.28. Then the fun starts. They will be underdogs to Cal and USC in the desert, and I'll give them .65 for those two combined (generously), bringing them to 4.93. At Oregon and @ UCLA are good for a .65 as well, bringing them to 5.58. Zona at home is a coin flip type game, bringing them to 6.08. I'll take the half win for the coin flip, knowing they will be underdogs or very small favorites in 9 of their games.

Georgia Tech Under 8.5 at -120

I think Tech is a very good team, but they have a brutal ACC Atlantic draw. I believe the ACC has 7 have's and 5 have not's. The have not's: Wake, BC, Maryland, UVA, and Duke. On a Neutral field, all of the 7 are TD+ faves over those 5. If tech had BC and Maryland instead of FSU and Duke, I'd be all about the over, but they don't, and their schedule is very difficult. I have them at 7.5 wins and believe they will win 8.

Missouri Under 7.5 @ -185.

I think Missouri caught everyone off guard with their offense but college football is catching up. They have to play Texas and OK St. from the south, and play very losable games @ Nevada and v. Illinois in the non-conference. 5.15 wins is my calculation and I wouldn't be surprised to see a 4-8 campaign. 8 wins would be a big surprise.

Alabama over 9.5 @ +145

This is sort of a hedge for our Ole Miss SEC West play at +350. I think Bama is more likely to win 10 games than they are to lose 4. Saban has to avoid losing to a bad team here, but if they beat VT this should cash. I believe that Alabama is slightly better than Ole Miss and LSU. They get the tigers at home but must play at the Rebels. The Tide will be favored in at least 10 and as many as 12 of their games. Bama's defense will keep them in a lot of games this year and at +145, I like my chances for double digit wins by the tide.

Others...

Southern Cal Under 10.5 -140

Arizona Over 6.5 wins at -110

VTech Under 9.5 wins at -155
Click to expand...

MBKRogers · Aug 18, 2009

09/01- 09/07

These are week 1 openers and they have and will move and change a lot before game time. That is the nature of the beast. Find your best number. Don't chase over key numbers.

http://www.tomahawknation.com/2009/8/11/984826/for-entertainment-purposes-only

North Carolina State -3 over So Carolina I discussed this game here. Since then, NCST lost linebacker Nate Irving.
Wisconsin -15.5 over Northern Illinois. NIU not on par with Wisky
Toledo/ Purdue Under 52 I do not trust Purdue's offense at all.
Utah State +22 this game means more to Utah State and Utah should be rusty.
Notre Dame -12.5 I actually like Nevada here but this lins should climb a bit and if it gets to 17 I will take Nevada and play for the middle.
North Texas +21 Too many in the opener.
Ohio +4.5 Uconn is probably not better than Ohio early in the year.
Louisiana Tech -13. laying points with Auburn is scary, but this coaching staff will run the score up given the opportunity.
Oklahoma -21 BYU's defense is very bad and OU should cover if they take care of business early.
Illinois -4 Illinois has much better athletes and I expect Mizzouri to take a step back this year.
San Jose State +35.5 There is no way USC is focused for this game as Ohio State looms large.
Idaho +6 Idaho is likely the better team.
Georgia +6 Georgia returns more starters than OKST. They are a better team now and were a better team last year and are more talented than OKST. OKST's defense is seriously bad. And Georgia did this while also dealing with 44 starts lost to injury. Do not overlook UGA this year. They are loaded with guys you have not heard of yet.
UAB -3 Rice lost a ton and I expect UAB to win by 10+
Texas A&M -10 New Mexico just isn't a good team and A&M will score points.
Bama/ VTech Under 37.5 Both defenses are excellent and both offenses could struggle.
Wake Forest -1 Wake has an experienced OLine and a 4-year starter at QB. Baylor got lucky last year and the Big 12 really made their offense look better than it was.
FSU/ Miami over 48.5 Both defenses struggled last year and both offenses return almost everyone.
Clemson -19.5 This is the home opener for a team needing to make a big impression. Death valley at night.
Cal -20 Maryland lost their nose guard.
Ohio State -21, Navy lost their best defensive player.

So, Current Wagers for the season are:

Clemson to win the ACC +1000
VTech to win the Coastal +175
Nebraska to win the Big 12 North +175
Ole Miss to win the West: +350
Rutgers to win the Big East +500

Maryland Terrapins under 6 wins at -165
Texas Over 10 wins at -155
Rutgers over 8 wins at -200
Illinois over 7 1/2 wins @ -150
South Carolina under 7 wins @ -145
Tennessee Over 7 wins at +140
Arizona State Under 6.5 at -110
Georgia Tech Under 8.5 at -120
Missouri Under 7.5 @ -185
Alabama over 9.5 @ +145
Southern Cal Under 10.5 -140
Arizona Over 6.5 wins at -110
VTech Under 9.5 wins at -155
Click to expand...

MBKRogers · Aug 20, 2009

Re: MBKRoger's Season Long Wagering Thread: 2008: 251-190 (57%) +41.0

Section 1: Sports Betting as an Investment

Making Money by Betting on Sports

Most people think that sports betting is about finding 'sure things,' but in reality such 'locks' are nothing more than gamblers' fancy. Just as in real estate, currency, stocks, or any other speculative market, 'sure things' simply do not exist. As a professional sports bettor, my goal is to find and exploit many small edges over a long period of time to earn a compounding return. Winning 56% of games is very significant, and with very conservative bet sizing, you can grow your return very quickly. Investing $10,000 into the stock market for a year and earning a 10% return is considered a great investment - but over the past 10 years I have averaged a 73.0% return each year on my NFL, NBA and NCAA football and basketball picks. (With 10% juice already factored in).

If you had invested $10,000 in my picks each year since 1999, you would have seen an average profit of $7,298 per year. Furthermore, if you had invested $10,000 in my picks in 1999, and had let your winnings compound weekly without removing any profits, you would have turned that $10,000 into $349,112 in ten years.

In the past, I've suggested betting 2% per star in football, 1.5% per star in Basketball and 1.6% and 1.2% (respectively) with the Combo package. That bet sizing produced the following results:

Combined Football and Basketball Packages 1999-2008.

1999-00 +162.6%
2000-01 +140.2%
2001-02 +165.2%
2002-03 -49.7%
2003-04 +62.9%
2004-05 +81.6%
2005-06 +210.7%
2006-07 -1.1%
2007-08 -77.4%
2008-09 +34.8%
Average +73.0%
Football Package 1999-2008.

1999-00 +111.4%
2000-01 +132.2%
2001-02 +51.4%
2002-03 +19.6%
2003-04 -100%
2004-05 +115.4%
2005-06 +95.4%
2006-07 +8.4%
2007-08 -80.6%
2008-09 +50.8%
Average +40.4%
Basketball Package 1999-2008.

1999-00 +91.8%
2000-01 +43.1%
2001-02 +155.1%
2002-03 -81.8%
2003-04 +199.1%
2004-05 -13.4%
2005-06 +168%
2006-07 -9.8%
2007-08 -16.2%
2008-09 -7.4%
Average +52.9%
In fact, my picks have yielded a much higher risk adjusted return than the stock market over the last 10 years. Obviously, the variance from season to season is formidable, but as anyone who had a significant amount invested in stocks or real estate in 2008 can tell you, such swings aren't limited to sports. In the long run, my edge in what I do is far greater than the edge that you could hope to gain in any other speculative market. See my Past Performance for further explanation.

The Power of Compounding Return

Some investors choose to start each year by investing a fixed percentage of their overall holdings (maybe $10,000, or 5% of their liquid assets) and take a profit every year. This conservative approach has yielded incredible results - in the last ten years, I have had an average return of 73%. My best year was +211%, and my worst year was -77%. If you carefully invest year after year without being intimidated by short term variance, then you will eventually see fantastic returns.

Other investors choose much longer periods of investment - they lock in their money for five, or even ten years. Rather than bet a fixed percentage of their initial bankroll per star, their bet sizes fluctuate each week. Since my investments have a long-term upward trend, these investors earn a compounding return on their money. The swings get larger as the bankroll grows, but the trend is always upward. An investor who invested $10,000 in my picks in 1999 and pursued an optimal growth strategy would have seen their portfolio value swell to $349,112 by the end of the 2008-2009 seasons. This concept of optimal growth is discussed in much greater detail in my Money Management Articles.

Juice, and the power of 56%

Even though most sports bettors are losers in their own right (as a whole, bettors actually win an average of only 48% of their bets - less than they would expect to win if they just flipped a coin for every game), their losses are compounded by the fact that the house takes a cut of winnings, also known as the 'juice' or 'vig.' Most sports books charge a 10% commission on wins, which means that a bettor must actually win 52.4% of his games just to break even. (Wagering $100 per game, a bettor loses $100 with a loss and wins $90.91 with a win, so he must go 11-10 (11/21 = 52.38%) to break even). Recently, some online books have started to offer lower juice, betting exchanges and deposit bonuses, which reduce the house edge.

In order to beat the juice and win in sports betting, a bettor must employ a disciplined approach in their analysis of each game using methods that have proven to be successful in the long run. I discuss my math models and analytical metrics in my Handicapping Methods essay, but you must realize that only the best and most knowledgeable handicappers can win 55% of their games. In their 2007 two page article about my handicapping success, the Wall Street Journal wrote, "...fewer than 100 people can sustain (win rates of 55%) over time. Most of them belong to professional betting syndicates that hire teams of statisticians, wager millions every week and keep their operations secret." Even fewer bettors can hit 56-57% over a 20 year period as I have.

Touts often claim to be able to hit 60% or higher, but as I explain in my essay on Bayesian Probability, anyone who tells you that their long term expected winning percentage is higher than 60% is deluding themselves. For a bettor to claim a greater than 60% long term expected win percentage, that would be mean that Vegas would have to consistently release lines with egregious errors, and that simply just does not happen often enough for claims of greater than 60% long term expected win percentages to be caused anything other than blind, short-term luck.

I often hear amateur gamblers erroneously claim that winning 56% of games isn't even enough to beat juice. As demonstrated above, a bettor only needs to win 52.4% to break even, and a 56% bettor will be profitable in the long run if they pursue an optimal money management strategy.

Of course, as in any game of chance, there is variability in the actual results and just because you have won 56% in the past and expect to win 56% in the future doesn't mean that you're going to win 56% this upcoming season. There is variance in sports betting, as there is in most investments, and I calculate the standard deviation to figure out how much of my bankroll I can safely wager on each game during the season to accommodate potential negative swings while having very little chance of exhausting my bankroll. I have extensively quantified the variance that exists in sports betting, and use mathematical formulas to dictate the exact optimal amount to invest so as to maximize the ratio of profits to variance.

A football season with 56% winners (my long term percentage) on 400 units or 'stars' would on average yield +30.04 stars ( (400*.56) - (400*.44)*1.1 ). Even using a very conservative 1.5% per star (in the past, I have recommended 2%), that's an expected return of 45.06%.

A basketball season with 54% winners (my long term percentage) on 1,050 units or 'stars' would on average yield +35.7 stars ( (1050*.54) - (1050*.46)*1.1 ). Using a conservative 1.1% per star (in the past, I have recommended 1.5%), that's an expected return of 39.27%. So, despite a lower overall winning percentage, I expect a season's worth of basketball wagers to be almost as profitable as a season of football in the future. Over the last ten years, my basketball wagers have actually been more profitable than football, but I have done a lot of analysis this summer and I expect my football wagers to be even better next year than they have been in the past.

Money Management

Money Management is as critical to a sports investor as picking winners. I have devoted many hours of careful analysis and math to optimal money management systems, which I have painstakingly outlined in my Money Management articles. Sports betting is more high risk (higher volatility and standard deviation of return) than stocks, and also much more high return. When compared with stocks or bonds or real estate, it also has a much higher risk adjusted return (Sharpe), and is a more attractive overall investment. (My investments have a Sharpe Ratio of 0.715 compared to the S&P 500's SR of 0.406)

My Money Management articles outline how to adjust your bet sizing based on your goals (expected return vs. probability of positive returns), your investment length (one season or many), your growth preference (flat or compounding), your risk tolerance (high or low) and the proportion of your overall bankroll which is made up by sports betting.

In general, I would recommend that beginners wager 1.5% of their initial bankroll per star in football. I have won 56% of my Football Best Bets over 22 years, and while I continue to improve my methods I will use 56% as my expected win percentage. I anticipate approximately 160 Best Bets, which should add up to around 400 stars. For basketball, I would recommend about 1.1% of initial bankroll per star. Over 22 years, I have won 54.6% of games, and I will conservatively set my win expectation at 54% going forward into 2009, and I expect to bet approximately 400 Best Bets and 1,050 stars. It is always better to set conservative expectations to avoid over betting. For subscribers who purchase the Combo package, I recommend 1.4% per star in football and 1.0% per star in basketball, as overall variance is greater (although variance as a percentage of total wagers is lower).

Factoring in the Cost of my Service

The cost of my football service is $1,295, so you must factor in that cost when doing the above analysis. If my expected win percentage is 56% (my long term percentage) on 400 Stars per season, then the average profit would be +30.4 Stars per season. If you had $10,000 that you invested in a football season and $1,295 went to pay for my service, then you would have $8,705 left for wagering. At 1.5% per star, you have an expected profit of 45.6%, and with a bankroll of $8,705 that's an expected net of +$2,675 after factoring in the cost of my service.

Many subscribers save money by buying my Football-Basketball Combo package each year for $2495 (rather than $1295 for football and $1695 for basketball, or $2990). If you have $12,000 to invest for the year and decide to pay for my Football-Basketball Combo package for $2495 then you have $9,505 remaining for your working bankroll. When using the Combo package, I recommend 1.4% of your bankroll per Star for football and 1.0% per Star for basketball if you plan on using one bankroll to cover both seasons. I expect about 30.4 Stars of profit per year in football and about 35.7 Stars of profit per year in basketball, which translates into an expected profit of +42.56% of your bankroll in football (30.4 stars times 1.4% per star) and an expected profit of +35.7% of your bankroll in basketball (35.7 stars times 1.0% per star). That's a total expected profit of +78.26% of your bankroll, which is about $7,438 in the case of a $12,000 total investment ($9,505 * .7826). After taking out the cost of the service ($2495), you still would have $4,943 total profit, which is an astounding 41.2% expected return on 56% winners on my Football Best Bets and 54% winners on my Basketball Best Bets. Of course, there are going to be years better than that and some years worse than that, but it doesn't get much better than an average return of 41.2%.

What is a Point spread?

Before I delve into rigorous explanations of how a bettor can gain an advantage against the point spread, it is important to understand what the spread actually represents. Point spreads were invented to keep bettors interested in games between teams of different talent levels - if a perennial powerhouse like Florida plays a mid level team such as Southern Miss, you'll find very few people willing to bet on which team will win the game since Florida would be such a prohibitive favorite. However, most are willing to bet on whether Florida will 'cover the point spread' and win by a certain number of points. If the point spread is 21.5, then Florida must win by 22 or more points for their side of the bet to cover, while USM must either win outright or lose by 21 or less to cover their side. Point spreads are designed so that the probability of each outcome is roughly equal, and are generally set so as to approximate the median score differential between the two teams.

However, skewed public perception, results-oriented analysis, and unsound metrics result in point spreads that are often slightly biased one way or another. While the casual bettor does not possess the capacity to exploit these advantages, I have used mathematical models, situational analysis, significant trends and quantitative player analysis that are far more complex and accurate than anything else on the market to gain an advantage, which is why I have won 54 to 59% of my Best Bets (depending on the sport) over the last 22 years.

How are the lines set?

While the odds makers do to try approximate the median margin of victory between two teams, they also try to reduce their exposure to risk by setting lines such that the public money will fall evenly on both sides of a game, so that they can offset the bets against each other and earn a profit on the juice (cut of winnings taken by the house, explained below) without exposing themselves to large potential losses. Thus, odds makers are often in the business of gauging public perception rather than team performance, and therefore the betting public actually sets the line. If Georgia is 4 points better than Georgia Tech according to my advanced metrics and analysis, but the aggregate public perception is that Georgia is 7 points better than Georgia Tech, then the posted point spread is likely to be closer to 6.5 or 7 points (public perception) than it will be to 4 points (the realistic difference between the teams). This makes my job as a professional handicapper much less daunting; not only can I exploit lines where the odds makers leave an edge, but I can also exploit the uniformed opinions of the general betting public.

Isn't gambling risky?

I don't believe that the term 'gambling' applies to what I do. I sell information to subscribers, with which they can take positive expectation positions in uncertain markets. With correct financial optimization and bankroll management, long term risks are nominal compared to the risks of investing in other, more conventional markets. Just as a single stock may go up or down in a day, any one team may win or lose a given game. But as long as the investor maintains a long-term perspective, understands variance, and doesn't over-extend themselves or bet more than they can easily handle, risk can be highly mitigated, and they can earn a very attractive risk adjusted return.

MBKRogers · Aug 20, 2009

Re: MBKRoger's Season Long Wagering Thread: 2008: 251-190 (57%) +41.0

Section 2: Handicapping Theory 1/3 (Model Handicapping)

Handicapping Theory

There are three general theories of how a bettor can gain an edge handicapping sports: Model Handicapping, Fundamental Analysis and Technical Analysis. In this three-part article, I explain each of these theories independently, and how I combine them to produce my Best Bets.

Model Handicapping

The core of my handicapping comes from the mathematical model I have built which predicts the results of games more accurately than the public or Las Vegas odds makers. Less sophisticated simulators that try to come up with a formula to predict future games tend to make the same mistake; they use regression analysis to find the correlation between different statistics and point differential. While that exercise is very useful for explaining which statistics impact a game's result, regression is not necessarily useful in using past statistical averages to predict future results since some important statistics simply don't correlate very highly to the future. For example, turnovers are the number one factor in point differential in football, but turnovers are also the least predictable statistic. A model that is based on regression analysis will weigh turnovers very highly, but since past turnovers do not correlate highly with future turnovers such models will over-weigh the affect of past turnovers - creating a model that is good at explaining what has happened but not very good at predicting what will happen.

Fumbles in particular are almost 90% due to variance - that is to say that historically, if you took all of the teams that fumbled a lot over the first 8 games of a season, and all of the teams that fumbled very little over the first 8 games, those two groups of teams fumbled at a similar rate over their last 4 games. In other words, fumbles are almost completely random, and if 119 teams play 8 games each, you'll have some team with lots of fumbles and some teams with almost no fumbles, but going forward, those statistics are not predictive of future performance. In other words, when the talking heads on ESPN praise teams that 'hold on to the football' and criticize teams with 'fumbilitis,' one must realize that such labels are just fooling you with randomness, and that in future games, the 'hold on to the football' teams will not necessarily fumble less than the 'fumbilitis' teams. (Of course, when this happens, the talking heads then say, "Iowa fumbled 10 times in the first 5 games, but has only fumbled once in the 5 games since then. They have learned how to take care of the football!") This is just the most obvious of literally hundreds of different metrics which are factored into my mathematical model, and is one of the reasons that my model is much better than regressive models and has a consistent, winning track record to prove it.

My math model incorporates the predictability of past statistics to future games and uses each team's compensated statistics rather than their raw stats, which adds to the accuracy of my prediction. Compensated statistics are derived by comparing a team's statistics to the statistics of the opponents that they have faced.

For instance, if Oregon is averaging 3.6 yards per carry, and Rutgers is averaging 4.0 yards per carry, but Oregon's opponents (when adjusted for schedule strength) only project to allow a combined 3.4 ypc against an average opponent, and Rutgers' opponents project to allow a combined 4.2 ypc against an average opponent, then compensated statistical analysis (which I have tested over a sample size of tens of thousands of games) predicts that Oregon is actually likely to fare better rushing against an average run defense than would Rutgers, despite the fact that Rutgers is running at a rate of 4.0 ypc to Oregon's 3.6. Using compensated statistics in combination with the predictive nature of each statistic used in my model produces an accurate measure of the true differences between two teams future performances - not the difference between their past performances.

I also adjust my projected numbers based on current personnel for each team and those extra hours of statistical work have paid off handsomely over the years (and I get better each year at making those adjustments). A lot of my edge comes from some complex defensive player analysis models which I have built to evaluate the effects of defensive injuries. Most other handicappers - even the most sophisticated ones - all but ignore defense, and their lines are often off as a result. I also remove meaningless plays from my data set such as kneel downs at the end of a half or game and quarterback spikes, so the game statistics that I use are more representative of a team's performance than the statistics used by other handicappers who take a lazier approach and just plug in box scores.

I have been using my current NFL model for 8 years, and my NCAA math model since 2001 (with major upgrades in 2005). As you can see in my Past Performance (link to Past Performance Page) section, the results have been fantastic. The NFL model begins in Week 1 of each year (since I have so much reliable data from previous years), while the NCAA model kicks in Week 5.

Determining Value

One of the critical advantages of Model Handicapping is that it allows me to quantify my edge. That is to say, that over many years my model not only identifies advantageous lines, but also can give me a rough percentage estimate of how likely a given team is to cover. We delve much further into the significance of this in the Investment Strategies (link to MM Summary Essay) article, but briefly, quantifying my edge allows bettors to adjust my bet sizes for optimal bankroll growth, which allows my customers to make more money.

It takes years of careful tweaking and analysis to really determine how much value each point of difference between a bettor's own lines and Vegas' lines is worth. I have used statistical software to create a regression equation predicting home team spread result as a function of the line differential of my power ratings/math model from the actual line. For instance, I have 10 years using my NFL math model and the equation to predict the chance that the home team covers the spread is .505 + 0.0128xLD, where LD is the line differential between my math model prediction and the line. So, for every point differential, I can add 1.28% to my chance of winning. Each NFL game has an unknown hypothetical 'perfect' line where each side would cover exactly 50% of the time, and I try my best to arrive at that line. If I had 'perfect' lines, then I would have about a 3% advantage per point differential between my lines and the Vegas' lines. Over the last two decades, my advantage has been 1.28% per point, meaning that I have clearly demonstrated that my lines are superior to Vegas' lines, but that they aren't 'perfect' yet. I spend the majority of each summer researching my methods and fine tuning my analysis, and my lines have become more and more accurate each year.

Remember, it doesn't matter how much of a differential there is between your ratings/math model if your line is not proven to be better than the actual point spread, as my lines have!

Power Ratings

Many handicappers have a set of ratings, most often referred to as power ratings, that gauge the overall strength of each team in comparison to every other team. They then take the difference in ratings between two teams as the predicted point differential between the teams if they met on a neutral field. Of course, teams don't usually meet on a neutral field so points are added to the home team to compensate for the advantage that most teams have playing at home. The home field advantage can be a set amount for all teams (such as 2.5 or 3 points in the NFL) or can vary from team to team depending on their individual variance in their level of play at home and on the road.

While the concept of power ratings is rather simple, it is very difficult to come up with a set of accurate ratings. The problem with most power ratings methods is that the ratings are generated using some sort of mathematical process based on the past performance of each team and the level of opposition that they have faced. An example of this is the Sagarin Ratings seen in USA Today each week. I've talked to many amateur handicappers that use the Sagarin Ratings to figure out if the point spread is too high or low on a particular game. What is important to remember is that the Sagarin Ratings, and any other mathematically produced set of ratings, explain what has already happened rather than what will happen. In other words, while it is true that these ratings accurately reflect the difference in the performance of each team up to that point of the season they are not a predictive tool to be used to forecast the future performance level of teams, which is what we are truly interested in as handicappers.

If beating the point spread were as easy as picking up the Tuesday USA Today, checking the Sagarin Ratings and making your wagers based on that, then everyone would be winning and sports books would all be out of business. Obviously, that is not the case. So, while the Sagarin Ratings can be used to see how teams have performed up to that point of the season, do not depend on them to forecast how teams will perform in their next game.

Power ratings are typically based off of the final scores of games - in football, there is a lot of 'noise' and 'variance' in scoring, and points are not nearly as useful for predicting the outcomes of games. Furthermore, power ratings which reduce every team to a single number ignore the enormous importance of matchups. If Texas Tech and Georgia Tech have similarly rated offenses, then you would expect them to fair similarly against a defense that had an average rating across the board in all defensive metrics. However, against a defense with an average overall rating, but on a more specific level, with very high run-defense ratings (allowing 3.1 ypc against opponents who combine for an adjusted 4.5 ypc) and very bad pass-defense ratings (allowing 9.8 ypa against opponents who combine for an adjusted 6.4 ypa), you would expect Texas Tech's pass-heavy offense to fair comparably better than Georgia Tech's run-heavy offense, even though the two offenses are rated similarly overall. Obviously analyzing matchups is much deeper and more complex than this, and often gets into very technical data concerning advantages at individual positions, but this simple example illustrates the overall concept of how power ratings do not factor matchups.

MBKRogers · Aug 20, 2009

Re: MBKRoger's Season Long Wagering Thread: 2008: 251-190 (57%) +41.0

Section 3: Handicapping Theory 2/3 (Technical Analysis & Team Trends)

Fundamental Analysis:

Fundamental analysis is the old fashioned way of handicapping. Fundamentalists look at matchups or try to envision how a game will play out. They study the strengths and the weaknesses of teams and try to determine if one team has a significant advantage over another based on their ability to exploit a team's weakness with their strength. For example, a fundamental analyst might claim that, 'Denver is a great running team and they should have no problem running against a team like Cincinnati that has trouble stopping the run.' Or that, 'Carolina's All-Pro defensive end will take advantage of their opponents rookie left tackle and be in the quarterback's face all day.'

Such a style of handicapping depends on a very keen knowledge of each team and their personnel. The problem with this sort of fundamental analysis is that most mis-matches in a game are already reflected in mathematical models, as well as in the point spread.

The key to fundamental analysis is finding statistical indicators that have led to point spread success, beyond the most obvious observations which everyone already knows, and odds makers have already taken into account. Handicapping based solely on fundamental analysis (which is what you'll see from 99% of touts and analysts) is lazy, inaccurate and unlikely to gain an edge over odds makers and/or other bettors. However, more nuanced fundamental analysis can be a useful addition to more comprehensive and predictive analytical models.

Technical Analysis:

Technical analysis is the study of patterns and is based on the psychological ups and downs of teams as well as the psychological patterns of those that bet sports. Obviously teams have their ups and downs, and I use trends and situations to identify when teams are likely to play well and when they are not based on patterns that have lead to point spread success and failure in the past.

Team Trends:

The most widely used and understood type of technical analysis is the study of team specific patterns which I simply call team trends. When I started handicapping back in the mid-80's team trends were an important part of selecting which teams I bet on. I would go back into my logs of results and study how teams performed at home and on the road, as a favorite or as an underdog, after a win or after a loss, and under other circumstances. I found that the most insightful team trends were the ones that were based on recent performances and explained how teams performed after good and bad performances. For instance, the 49ers were 48-17 ATS (Against The Spread) from 1981 through 1997 when they lost straight up and failed to cover the point spread in their previous game. What this told me is that the 49ers had a strong tendency to be more focused after a poor outing. This is a trend that worked for many years despite coaching and player changes over the years. Of course, the 49ers have basically had the same type of team over all of those years, and the 49ers' tradition started under Bill Walsh in the early 80's has been handed down through the generations of players. It also helped that San Francisco had only two quarterbacks during those 17 years, and that both Joe Montana and Steve Young had the personality types that made them perform better after poor outings.

Most of the personality of a team comes from the head coach, and I have noticed that patterns follow coaches from team to team. For instance, Jon Gruden's teams have a tendency to play well as a favorite after a loss (10-5 ATS from 1998 through 2001 in Oakland and 17-10-2 ATS from 2002 through 2008 with Tampa Bay) and poorly when favored by three or more the week following a victory (5-15 ATS with Oakland and 9-17 ATS with Tampa Bay).

On the other hand, there are some types of team trends that simply do not predict what will happen in the future. Over the past 10 years, I have studied the results of all statistically significant team trends that I have used in my game notes and tallied to results, broken down by the type of trend it is. The type of team trends that were the best indicators are what I call personality trends, which are the trends that explain how teams react to recent performances, such as how a team performs after a win or a loss, or after two straight spread wins, or after allowing 28 points or more (like the ones I used in the examples above). Certain types of team trends don't work at all, such as series history trends or trends that deal with a specific game number. A series history trend is a trend that states that Team A has covered 10 straight times against Team B. I have found that regardless of how many times in a row a team has covered against another team, the chance that they cover in the next meeting remains unaffected. A trend that says that Team A has gone 13-1 in their second road game of the season or is 8-0 in week number 5 doesn't make any sense and does not have any value in predicting the future. I know what types of trends tend to work and to what degree they work.

While the use of team trends worked very well during the 80's and through the mid-90's, the advent of free agency and the constant changing of head coaches in the league changed the personalities of teams every few years, making previous patterns of these teams meaningless in most cases. I tend to shy away from most team oriented trends unless the head coach or core of star players has been intact over the term of the trend. I certainly wouldn't pay much attention to a Carolina Panthers trend that included games prior to the arrival of head coach John Fox, who changed the personality of that team. On the other hand, longer term trends of the Philadelphia Eagles do have some validity due to the long tenure of head coach Andy Reid - even though the personnel have changed over the years.

Team trends can still be a very effective handicapping tool, but I do not use` team trends that no longer explain the personality of a team. From a statisticians' point of view, a trend is basically a sample of games taken from a pool of results. When the pool from which the sample was taken changes, the sample of games is no longer representative of that pool and should thus not be used as a forecasting tool. Thus, team trends work best with teams that have had the same coach or core group of players for at least as far as the trend goes back.

Situational Trends:

As the use of team trends became more limited because of free agency and coaching changes, I began looking for patterns that explained the results of all teams that were in the same set of circumstances. For instance, how did all teams perform following consecutive games in which they allowed less than 10 points? Or, what is the record of Monday night home underdogs? These league-wide patterns are referred to as situational trends. I have found that situational trends are better indicators of future point spread results than team trends, because team specific changes (such as coaching changes and free agency) have little effect on league wide patterns. The patterns that exist in the NFL and in college football have existed for years and are based on the psychological ups and downs that exist in all teams and in the wagering habits of the betting public.

While all of my situations deal with the patterns that exist in team performance, some of them also are enhanced by the betting patterns of the public, who are influenced by more recent performances of a team. A lot of the situations that I use deal with playing on teams that have been playing below expectation (bounce-back situations) and playing against those that are playing well in recent weeks (letdown situations). Bounce-back and letdown situations work partly because the betting public overreacts to a string of good and bad performances and bets accordingly. A team may become out of favor after a couple of terrible performances while other teams may get more support than warranted from a couple of very good performances. Since the point spread is as much a measure of public perception as it is a projection of a median outcome, the point spread gets over-adjusted in conjunction with the public's fear of betting on a team on the slide or with their eagerness to bet on a team playing especially well in recent weeks.

This sort of betting behavior based on short-term results gives the smart player line value and that is why these sort of bounce-back or letdown situations produce good results. For instance, NFL teams that win back-to-back games straight up as an underdog are just 52-72-3 ATS (Against The Spread) in their next game if they are on the road and not getting more than 7 points (since 1981), including 25-51-2 ATS if visiting a non-divisional opponent.

There are a couple of reasons for this. First, teams that win back-to-back games as an underdog tend to get more support from the betting public and as a result the point spread deviates from a realistic point spread to a line that represents current public perception of the "hot" team. At the same time, the team that has just won back-to-back upsets generally is not quite as hungry, as the coach has less ammunition to motivate them with. Also, the coaching staff is generally reluctant to change the offensive and defensive schemes that produced the two victories. Their next opponent, however, has two weeks of films to figure out how to find a weakness in the schemes that have been so successful and these opposing players and coaches will prepare for the game against the "hot" team with more focus because of their recent success. So, not only is the "hot" team in this situation due to play at a lower level but the point spread has also moved in our favor to create value because the betting public is generally afraid to bet against a "hot" team. Playing against a non-divisional opponent gives the team off two upset wins even less reason to get fired up. This situation does not work nearly as well when playing against home teams off back-to-back upset wins because it is easier to maintain a high level of intensity in front of the home fans.

Not all situations are in the bounce-back or letdown mode and take advantage of misguided public perception and the natural fluctuations of team performance. There are also what I call momentum situations and these deal with playing on teams that are playing well and playing against teams that are playing poorly. For instance, in the NFL home underdogs are 193-136-10 ATS if they won straight up as an underdog the previous week. Bad teams in the NFL generally lack the confidence to beat a good team and there is nothing like an upset win to boost confidence. The confidence of winning as an underdog is enhanced by playing in front of the home fans and thus creates a good momentum situation. In general, the NFL is a contrary league, meaning that most of the situations involve going against teams that have been playing well and going with teams that have been struggling. College football, meanwhile, is more of a momentum sport and many more of the good technical situations in college football involve playing on a team that has been playing above expectations.

Does Technical Analysis Work?

Technical analysis has come under scrutiny by fundamental handicappers and some sports bettors due to the fact that anybody searching a database randomly for patterns will find situations that have produced very good results. However, the key is to look for situations that make sense. I don't use trends such as "The Steelers are 13-2 in week number 7" (Do they actually know that week 7 is their week and gain confidence from it?) or "bet on home dogs from +2 to +4 if it's a weeknight MAC game" (the more narrow the point spread range is the more likely it is a random occurrence and not a true indicator of a real pattern).

So how can I be sure that technical analysis works? At the beginning of each year, I make a list of the situational angles that I think are meaningful (they are all easily statistically significant). At the end of the year, I tally the results of these angles. In the last 10 years of doing this, I have found that the situational angles that I use (remember, if you're angles don't make sense they are not going to hold up as well) have won at a profitable rate of 55%, and that the situations with a higher statistical significance (i.e. a higher t-value) have proven to be even more predictive.

Many handicappers tend to back-fit past data by adding more and more factors (parameters) to a situation until they have a very high percentage angle (but also a much smaller sample size). However, my research has shown that a situation's predictability is sacrificed with each parameter added to derive that situation. For instance, a situation with a record of 50-20 (71%) that is derived using 10 factors isn't as predictive as the 59% home underdog situation that I presented above, which has just 4 parameters (this game home, this game dog, won last game, dog last game) and a much larger sample size. It's easy to find a very high-percentage situation if you use an unlimited number of parameters to get to that situation, but all that will result in is a situation that explains what has happened rather than something that helps predict what will happen.

My research, and the theories of statistics, shows that more predictive angles have fewer factors and a larger sample size, rather than a smaller sample situation with a high winning percentage that was derived by using too many parameters. Further research I did in the Summer of 2004 (which I update each summer) enables me to accurately assess a situation's future performance based on the win percentage, sample size, number of parameters and more recent performance (i.e. record of the angle over the past 3 seasons). That research led to a more realistic use of situational analysis than I've employed in the past. For instance, I can now tell you that a situation with a record of 140-60-5 ATS that uses 6 parameters has a 56.8% chance of winning the next time it applies if the line is otherwise fair according to my metrics. Having a realistic expectation of a situation's value has helped my overall analysis immensely, and I will continue to devote time each summer to update the research on the predictability of my situational analysis.

Remember, just because a situation is 70% over 200 games in the past does not mean that it will win 70% of the time in the future. A 140-60 situational trend is simply a sample of 200 games selected from a population consisting of all NFL games. Since the NFL is constantly changing (although the league as a whole doesn't change nearly as quickly as most individual teams do), the results of the same situation in the future will not fully reflect the past. Also, by definition, a statistically significant trend has a 5% probability of being caused by no more than chance variation, and the record of those trends can be expected to be 50% as a whole, bringing down the overall percentage of all significant trends. There is also going to be a certain level of back-fitting involved in finding a situation, which also lowers the future percentage of the situation. Of course, the better the record, the greater number of games in the sample, and the fewer parameters there are in an angle the more likely that the situation is real and not just random.

MBKRogers · Aug 20, 2009

Re: MBKRoger's Season Long Wagering Thread: 2008: 251-190 (57%) +41.0

Section 4: Handicapping Theory 3/3 (Combined Analysis & Best Bets)

Combined Analysis:

The key to the 2004 research on my methods was finding a way to combine my situational analysis, fundamental indicators and my math model to give me an overall chance of a team covering at any given number. My performance on my Best Bets the last 5 seasons is an indication that I succeeded in that endeavor and I will continue to refine the accuracy of my methods each year.

As an example, consider a hypothetical game between the New Orleans Saints and Oakland Raiders. New Orleans applies to a 140-60-5 ATS situation that uses 6 parameters, but Oakland also applies to a statistical profile indicator with a record of 86-28-4 ATS. My NFL math model favors New Orleans by 12.4 points when they are only a 7 point favorite in reality. As discussed above, a situation with a record of 140-60-5 and 6 parameters has a 55.8% chance of winning if the line is fair. The fundamental indicator favoring the Raiders has a 56.1% chance of winning given a fair line, and my math model would give the Saints a 56.9% chance of covering at a line of -7 points. The trick is assigning a point value to the situation and the fundamental indicator based on their chance of covering at a fair line. I simply put everything in terms of points based on the relationship between point differentials and the chance of covering of my math model. Each point difference in my math model is worth about 1.3% in chance of covering, so each percentage point is worth about 0.8 points (1/1.3). In this case, the situation favoring New Orleans is worth 4.5 points while the fundamental indicator favoring Oakland is worth 4.8 points. My math model favors New Orleans by 12.4 points, so adding the value of the situation and the indicator would result in an overall prediction of the Saints by 12.1 points (+4.5 - 4.8 + 12.4 = 12.1), which would give them a 56.5% chance of covering at the line of -7 points. Obviously, things can become a lot more complicated when there are multiple situations and indicators applying to a particular game - which is most often the case, but my years of studying probability theory have given me the tools to sort through it all and come up with an accurate measure of the overall affect of the situations and indicators.

Best Bets:

Over the last 5 seasons I have gone 572-414-28 (58%) on a Star basis on my NCAA Football Best Bets and 210-168-2 (56%) on my Strong Opinions, and I have gone 1131-853-42 (57%) on a Star basis on my NCAA Football Best Bets and 327-266-8 (55%) on my Strong Opinions over the last 10 seasons. On NFL sides, I have gone 845-754-52 (53%) on a Star basis on my NFL Best Bets, and 159-132-5 (55%) on my Strong Opinions. In Basketball, I have gone 2168-1843 (54%) over the last 10 years.

Going forward, I expect to continue my success, and to win around 59% of my 4-Star bets, 56% of my 3-Star bets, 55% of my 2-Star bets, and 54% of my Strong Opinions in Football. In Basketball, I expect to win around 57% of my 4-Star bets, 55% of my 3- Star bet, and 54% of my 2-Star bets. A lot of handicappers use situational analysis and math models in their handicapping but few, if any, of them have studied the predictability of their methods, as I have, or found a realistic way of combining their methods for an overall measure of predicted success on every game.

MBKRogers · Aug 29, 2009

Re: MBKRoger's Season Long Wagering Thread: 2008: 251-190 (57%) +41.0

Helping our DR. BOB this year.

MBKRogers · Sep 1, 2009

Re: MBKRoger's Season Long Wagering Thread: 2008: 251-190 (57%) +41.0

VT +7.5, http://www.tuscaloosanews.com/article/20090831/NEWS/908319862/1007?Title=Alabama-football-player-Brandon-Deadrick-shot-twice

MBKRogers · Sep 2, 2009

Re: MBKRoger's Season Long Wagering Thread: 2008: 251-190 (57%) +41.0

New Mex State -2.5

MBKRogers · Sep 2, 2009

Re: MBKRoger's Season Long Wagering Thread: 2008: 251-190 (57%) +41.0

Date Wager Stake
F Clemson to win the ACC +1000 100
F VTech to win the Coastal +175 100
F Nebraska to win the Big 12 North +175 100
F Ole Miss to win the West: +350 100
F Rutgers to win the Big East +500 100
F Maryland Terrapins under 6 wins at -165 165
F Texas Over 10 wins at -155 155
F Rutgers over 8 wins at -200 200
F Illinois over 7 1/2 wins @ -150 150
F South Carolina under 7 wins @ -145 145
F Tennessee Over 7 wins at +140 100
F Arizona State Under 6.5 at -110 110
F Georgia Tech Under 8.5 at -120 120
F Missouri Under 7.5 @ -185 185
F Alabama over 9.5 @ +145 100
F Southern Cal Under 10.5 -140 140
F Arizona Over 6.5 wins at -110 110
F VTech Under 9.5 wins at -155 155
Week 1 North Carolina State -3 110
Week 1 Wisconsin -15.5 110
Week 1 Toledo/ Purdue Under 52 110
Week 1 Utah State +22 110
Week 1 Notre Dame -12.5 110
Week 1 North Texas +21 110
Week 1 Ohio +4.5 110
Week 1 Auburn -13 110
Week 1 Oklahoma -21 110
Week 1 Illinois -4 110
Week 1 San Jose State +35.5 110
Week 1 Idaho +6 110
Week 1 Georgia +6 110
Week 1 UAB -3 110
Week 1 Texas A&M -10 110
Week 1 Bama/ VTech Under 37.5 110
Week 1 Wake Forest -1 110
Week 1 FSU/ Miami over 48.5 110
Week 1 Clemson -19.5 110
Week 1 Cal -20 110
Week 1 Ohio State -21 110
Week 1 NMST -2.5 110
Week 1 VT +7.5 110

New:

Week 1 Bowling Green +7.5 110
Week 1 Ball State -16.5 110
Week 1 Tulane +14.5 110
Week 1 Nevada +14.5 110
Week 1 Akron +27 110
Week 1 Missouri +7 110
Week 1 Syracuse +7.5 110
Week 1 New Mexico +14.5 110
Week 1 SDSU +20 110
Week 1 FAU +23.5 110
Week 1 ULM +42 110
Week 1 Washington +17.5 110
Week 1 Wake over 53 110

MBKRogers · Sep 2, 2009

Re: MBKRoger's Season Long Wagering Thread: 2008: 251-190 (57%) +41.0

Week 1 Memphis +17 110
Week 1 Colorado -10 110

MBKRogers · Sep 2, 2009

Re: MBKRoger's Season Long Wagering Thread: 2008: 251-190 (57%) +41.0

Week 1 UGA U62 110
Week 1 UGA +5 (yes, a 2nd wager) 110

MBKRogers · Sep 3, 2009

Re: MBKRoger's Season Long Wagering Thread: 2008: 251-190 (57%) +41.0

Oregon +130

MBKRogers · Sep 3, 2009

Re: MBKRoger's Season Long Wagering Thread: 2008: 251-190 (57%) +41.0

Oregon O28 2H

MBKRogers · Sep 4, 2009

Re: MBKRoger's Season Long Wagering Thread: 2008: 251-190 (57%) +41.0

Okie State -4
Okie State -4

Swt · Sep 4, 2009

Re: MBKRoger's Season Long Wagering Thread: 2008: 251-190 (57%) +41.0

Why don't you like Georgia anymore?

FSUsem · Sep 4, 2009

Re: MBKRoger's Season Long Wagering Thread: 2008: 251-190 (57%) +41.0

Where are you getting OSU -4?

Swt · Sep 4, 2009

Re: MBKRoger's Season Long Wagering Thread: 2008: 251-190 (57%) +41.0

I see WSEX? at -4.5

Weedlord420 · Sep 4, 2009

Re: MBKRoger's Season Long Wagering Thread: 2008: 251-190 (57%) +41.0

still -5.5 at bookmaker for me

FSUsem · Sep 4, 2009

Re: MBKRoger's Season Long Wagering Thread: 2008: 251-190 (57%) +41.0

Cover1 said:

I see WSEX? at -4.5
Click to expand...

4.5 is the lowest it's at anywhere.

MBKRogers · Sep 7, 2009

Re: MBKRoger's Season Long Wagering Thread: 2008: 251-190 (57%) +41.0

Matchbook. ESPN Radio was reporting that Joe Cox was out.

MBKRogers · Sep 8, 2009

Re: MBKRoger's Season Long Wagering Thread: 21-23 (47%) -6.3

Week 1: 21-23, -6.3 Units.

Not what I wanted.

MBKRogers · Sep 9, 2009

Re: MBKRoger's Season Long Wagering Thread: 21-23 (47%) -6.3

MBKRogers said:

Week 1: 21-23, -6.3 Units.

Not what I wanted.
Click to expand...

Should be 20-24, -6.3 Units.

MBKRogers · Sep 9, 2009

Re: MBKRoger's Season Long Wagering Thread: 20-24 (47%) -6.3

For week 2:

Clemson +4.5 (+100)
Toledo +4
Iowa -6
Iowa Over 45.5
Ohio -3 (+100)
Oregon -11
South Carolina +7
Idaho/Washington Over 52
Virginia +11.5
Virginia Under 41
Wake Forest -3
Wake Forest Over 43.5
La Tech +7.5
Tulane +17.5
Miss St/ Auburn Over 40 (-120)
Marshal +19
Wyoming +33.5
Eastern Michigan +20
Penn State -28
Miami (OH) +37
Houston +15.5
Hawaii -2
Hawaii Over 51
Central Michigan +14.5
Notre Dame -3 (-118)
Idaho +21
Minnesota -3 (-115)
Indiana -1
Florida -36
Florida Under 59.5
West Virginia -6.5 (+100)
Tulsa -17 (-114)
Arkansas State +22
Middle Tennessee State U -0.5
Middle Tennessee State Under 52.5
UL-Lafayatte +245
UCF +14.5
UCF/ So Miss Under 51

Explanations here, very very long: http://www.tomahawknation.com/2009/9/9/1023207/for-entertainment-purposes-only

MBKRogers · Sep 10, 2009

Re: MBKRoger's Season Long Wagering Thread: 20-24 (47%) -6.3

MBKRogers said:

For week 2:

Clemson +4.5 (+100)
Toledo +4
Iowa -6
Iowa Over 45.5
Ohio -3 (+100)
Oregon -11
South Carolina +7
Idaho/Washington Over 52
Virginia +11.5
Virginia Under 41
Wake Forest -3
Wake Forest Over 43.5
La Tech +7.5
Tulane +17.5
Miss St/ Auburn Over 40 (-120)
Marshal +19
Wyoming +33.5
Eastern Michigan +20
Penn State -28
Miami (OH) +37
Houston +15.5
Hawaii -2
Hawaii Over 51
Central Michigan +14.5
Notre Dame -3 (-118)
Idaho +21
Minnesota -3 (-115)
Indiana -1
Florida -36
Florida Under 59.5
West Virginia -6.5 (+100)
Tulsa -17 (-114)
Arkansas State +22
Middle Tennessee State U -0.5
Middle Tennessee State Under 52.5
UL-Lafayatte +245
UCF +14.5
UCF/ So Miss Under 51

Explanations here, very very long: http://www.tomahawknation.com/2009/9/9/1023207/for-entertainment-purposes-only
Click to expand...

Adding, South Carolina/ UGA Under 38

MBKRogers · Sep 10, 2009

Re: MBKRoger's Season Long Wagering Thread: 20-24 (47%) -6.3

MBKRogers said:

Adding, South Carolina/ UGA Under 38
Click to expand...

Adding South Carolina +230

MBKRogers · Sep 10, 2009

Re: MBKRoger's Season Long Wagering Thread: 20-24 (47%) -6.3

Adding Kent +21.5

cbf0001 · Sep 10, 2009

Re: MBKRoger's Season Long Wagering Thread: 20-24 (47%) -6.3

man come on. taking that many games? let gambling have a little bit of a sport. you are a disgrace

MBKRogers · Sep 10, 2009

Re: MBKRoger's Season Long Wagering Thread: 20-24 (47%) -6.3

flow673 said:

man come on. taking that many games? let gambling have a little bit of a sport. you are a disgrace
Click to expand...

Go fuck yourself. I can do however I want and do typically turn a profit.

cbf0001 · Sep 10, 2009

Re: MBKRoger's Season Long Wagering Thread: 20-24 (47%) -6.3

you are at 47%. i would not call that a profit at all.

MBKRogers · Sep 10, 2009

Re: MBKRoger's Season Long Wagering Thread: 20-24 (47%) -6.3

flow673 said:

you are at 47%. i would not call that a profit at all.
Click to expand...

You're an idiot. Judge someone off of one week. Or, maybe judge them off a year:

2008: 251-190 (57%) +41.0 That's 251 wins and 190 losses for a winning percentage of 57%

Yeah, thought so, go fuck yourself. Gambling for profit is not fun. It's work.

cbf0001 · Sep 10, 2009

Re: MBKRoger's Season Long Wagering Thread: 20-24 (47%) -6.3

WHat is your real name?

cbf0001 · Sep 11, 2009

Re: MBKRoger's Season Long Wagering Thread: 20-24 (47%) -6.3

You are right, no one can judge someone off of one week, and you are free to do whatever you please. I know that it is a season long game. You actually have a couple of games that I am taking this weekend. My picks for the weekend are West Virginia at -6, Iowa at -6, SMU at +13, Kansas at -12, and Oregon State at -7. Take a look and let me know what you think, you seem like you know a pretty good bit about making picks.

MBKRogers · Sep 11, 2009

Re: MBKRoger's Season Long Wagering Thread: 20-24 (47%) -6.3

NFL Teasers, 6pt +100

San Diego -2.5 Carolina +8.5
San Diego -2.5 Indianapolis -1
San Diego -2.5 Seattle -1.5

Carolina +8.5 Indianapolis -1
Carolina +8.5 Seattle -1.5

Indianapolis -1 Seattle -1.5

MBKRogers · Sep 12, 2009

Re: MBKRoger's Season Long Wagering Thread: 22-24 (48%) -4.3 [Full Card Update

More

SMU +12 (-105)
Rice +28
UTEP +13 (-115)
Pittsburgh -10 (-115)
Ohio State +7.5
San Jose State +14
Jax St. +34
Jax State Over 59
Weber St. +14.5
James Madison +10
New Hampshire +7.5
SEMoSt +47
Northern Illinois -13

FULL CARD

Clemson +4.5 (+100)
Toledo +4
Iowa -6
Iowa Over 45.5
Ohio -3 (+100)
Oregon -11
South Carolina +7
Idaho/Washington Over 52
Virginia +11.5
Virginia Under 41
Wake Forest -3
Wake Forest Over 43.5
La Tech +7.5
Tulane +17.5
Miss St/ Auburn Over 40 (-120)
Marshal +19
Wyoming +33.5
Eastern Michigan +20
Penn State -28
Miami (OH) +37
Houston +15.5
Hawaii -2
Hawaii Over 51
Central Michigan +14.5
Notre Dame -3 (-118)
Idaho +21
Minnesota -3 (-115)
Indiana -1
Florida -36
Florida Under 59.5
West Virginia -6.5 (+100)
Tulsa -17 (-114)
Arkansas State +22
Middle Tennessee State U -0.5
Middle Tennessee State Under 52.5
UL-Lafayatte +245
UCF +14.5
UCF/ So Miss Under 51
Kent +21.5
San Diego -2.5 Carolina +8.5
San Diego -2.5 Indianapolis -1
San Diego -2.5 Seattle -1.5
Carolina +8.5 Indianapolis -1
Carolina +8.5 Seattle -1.5
Indianapolis -1 Seattle -1.5
SMU +12 (-105)
Rice +28
UTEP +13 (-115)
Pittsburgh -10 (-115)
Ohio State +7.5
San Jose State +14
Jax St. +34
Jax State Over 59
Weber St. +14.5
James Madison +10
New Hampshire +7.5
SEMoSt +47
Northern Illinois -13
Click to expand...

MBKRogers · Sep 12, 2009

Re: MBKRoger's Season Long Wagering Thread: 22-24 (48%) -4.3 [Full Card Up

Sem, posting this now since you loicked the official thread...

E. Mich +7.5 2H

MBKRogers · Sep 12, 2009

Re: MBKRoger's Season Long Wagering Thread: 22-24 (48%) -4.3 [Full Card Up

Iowa -0.5 2H
Iowa Over 21 2H

MBKRogers · Sep 12, 2009

Re: MBKRoger's Season Long Wagering Thread: 22-24 (48%) -4.3 [Full Card Up

UF Under 27 2H

MBKRogers · Sep 12, 2009

Re: MBKRoger's Season Long Wagering Thread: 22-24 (48%) -4.3 [Full Card Up

HOUSTON -10 2h

MBKRogers · Sep 12, 2009

Re: MBKRoger's Season Long Wagering Thread: 22-24 (48%) -4.3 [Full Card Up

Notre Dame -1.5 2H

MBKRogers · Sep 12, 2009

Re: MBKRoger's Season Long Wagering Thread: 22-24 (48%) -4.3 [Full Card Up

Notre Dame Over 23.5 2H

MBKRogers · Sep 12, 2009

Re: MBKRoger's Season Long Wagering Thread: 22-24 (48%) -4.3 [Full Card Up

West Virginia -3 2H

MBKRogers · Sep 13, 2009

Re: MBKRoger's Football Season Thread: 22-24 (48%) -4.3

Week: 39-22 (64%), +16.32

Clemson +4.5 (+100) 100 100 W
Toledo +4 110 100 W
Iowa -6 110 100 W
Indiana -1 110 100 W
Florida -36 110 100 W
Eastern Michigan +20 110 100 W
Central Michigan +14.5 110 100 W
Pittsburgh -10 (-115) 115 100 W
E. Mich +7.5 2H 110 100 W
Iowa -0.5 2H 110 100 W
Wake Forest -3 110 100 W
UF Under 27 2H 110 100 W
SMU +12 (-105) 105 100 W
Wyoming +33.5 110 100 W
Houston +15.5 110 100 W
Idaho/Washington Over 52 110 100 W
Idaho +21 110 100 W
West Virginia -6.5 (+100) 100 100 W
West Virginia -3 2H 110 100 W
Weber St. +14.5 110 100 W
Notre Dame Over 23.5 2H 110 100 W
Houston +10 2H 110 100 W
Hawaii -2 110 100 W
Hawaii Over 51 110 100 W
Miss St/ Auburn Over 40 (-120) 120 100 W
James Madison +10 110 100 W
New Hampshire +7.5 110 100 W
Minnesota -3 (-115) 115 100 W
UCF +14.5 110 100 W
UCF/ So Miss Under 51 110 100 W
Jax St. +34 110 100 W
Middle Tennessee State U -0.5 110 100 W
Middle Tennessee State Under 52.5 110 100 W
UL-Lafayatte +245 100 245 W
Tulsa -17 (-114) 114 100 W
South Carolina +7 110 100 W
Ohio State +7.5 110 100 W
Northern Illinois -13 110 100 W
San Jose State +14 110 100 W
Iowa Over 21 2H 110 0 P
Wake Forest Over 43.5 110 -110 L
Penn State -28 110 -110 L
Marshal +19 110 -110 L
Florida Under 59.5 110 -110 L
Iowa Over 45.5 110 -110 L
Kent +21.5 110 -110 L
Tulane +17.5 110 -110 L
Arkansas State +22 110 -110 L
Virginia +11.5 110 -110 L
Virginia Under 41 110 -110 L
La Tech +7.5 110 -110 L
Notre Dame -3 (-118) 118 -118 L
Notre Dame -1.5 2H 110 -110 L
Jax State OVER 59 110 -110 L
SoCarolina/UGA Under 38 110 -110 L
South Carolina +230 100 -100 L
Rice +28 110 -110 L
UTEP +13 (-115) 115 -115 L
SEMoSt +47 110 -110 L
Ohio -3 (+100) 100 -100 L
Miami (OH) +37 110 -110 L
Oregon -11 110 -110 L

MBKRogers · Sep 13, 2009

Re: MBKRoger's Football Season Thread: 22-24 (48%) -4.3

Week 1 20-24, -6.3
Week 2 39-22, +16.32

Overall 59-46 (56%) +10.02

Good week. Hope the NFL goes well tomorrow.

Even on the NFL. Updated Record:

Week 1 20-24, -6.3
Week 2 42-24, +16.32

Overall 63-48 (56%) +10.02

MBKRogers · Sep 13, 2009

Re: MBKRoger's Football Season Thread: 59-46 (56%) +10.02

Week 3

For next week

Miami -3
Fresno State -12
Army -6.5
Northwestern -3
Clemson -7
Western Michigan -16
Miss St. +8
Colorado -7
Utah +5
Washington +22.5
VTech -2.5
Nevada -1
Cal -13.5
Oklahoma -14
T A&M -17
FSU +7.5
Arkansas +1.5
Texas -17
UNLV -6
Stanford -19
LSU -27

hit some nice #'s but also missed a few bad.

MBKRogers · Sep 15, 2009

Re: MBKRoger's Football Season Thread: 22-24 (48%) -4.3

MBKRogers said:

Week 1 20-24, -6.3
Week 2 39-22, +16.32

Overall 59-46 (56%) +10.02

Good week. Hope the NFL goes well tomorrow.

Even on the NFL. Updated Record:

Week 1 20-24, -6.3
Week 2 42-24, +16.32

Overall 63-48 (56%) +10.02
Click to expand...

Updated with NFL

MBKRogers · Sep 15, 2009

Re: MBKRoger's Football Season Thread: 62-48 (56%) +10.02

Colts -3 @ Miami
Denver -3 (hosting Cleveland)

Teaser 6pt, +100 Bears +8.5/ Giants +8.5

MBKRogers · Sep 15, 2009

Re: MBKRoger's Football Season Thread: 62-48 (56%) +10.02

Jacksonville as well, reluctantly.

MBKRogers · Sep 15, 2009

Re: MBKRoger's Football Season Thread: 62-48 (56%) +10.02

MBKRogers said:

Jacksonville as well, reluctantly.
Click to expand...

Jax -3 lol

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