ESPN writer covers new college football prediction model

ESPN’s coverage, a new system to bet ncaa college football

ESPN writer covers new college football prediction modelBack in August, Chad Millman, Editor in Chief of ESPN The Magazine wrote a popular article on a new system to bet college football stemming from a conversation with an MIT graduate at the MIT Sloan Sports Analytics Conference.

Now this new system to bet ncaa college football is not system endorsed by ESPN, but what resonates strongly are two key points:

  1. The research and inspiration of the prediction model from the MIT graduate aligns with to prove sports is a sound “investment asset” within an investment portfolio, along with equities and other investment vehicles.
  2. The new ncaa football prediction model was produced from a graduate of MIT, where sports prediction model was inspired from scholar research at the MIT Center for Collective Intelligence, and Harvard.

Chad sat on the panel about the world of sports betting today, and wrote about a discussion he had with an MBA graduate of MIT (Massachusetts Institute of Technology) by the name of Mike Wohl.

The Sloan Sports Analytics Conference tends to attract a variety of members who are hard-core gambling fans, sports investors and sharps – along with a few other MIT graduates who probably have the next best prediction system for betting on sports. Why attend? It’s a place where the brainiac statisticians, sports fanatics, and computer engineers (likely from MIT) come together who have a focus in sports, and lead the evolution in sports prediction modelling and sports betting.

Chad goes on to speak of individuals like Richard Stand, who won the Las Vegas Hilton SuperContest a couple of years ago but moonlights as a high-ranking financial officer of a public company. The current crop of MBAs are version 2.0 of these quantitative analysts, and they will be able to take advantage of new laws legalizing sports betting in the next few years.

Chad’s article talks of a conversation he had with this MIT graduate (Mike Wohl) about a study called the  “The Missing Asset Class”. Obviously an oblivious title that means jack-sharp nothing, but he goes on to write about wrote about how to examine various sports bets in different ways, such as ncaa football picks, through the same prism of opportunity and analysis that Wall Streeters view stocks.

Now of course this article resonates well with us here at, as the foundation of our the Intelligent prediction model grew from these exact model types tested at Harvard and MIT. And if you have seen these studies, I encourage you to read for yourself the behavioural economics of collective intelligence and decision 2.0 from Harvard and MIT centre for collective intelligence.

The background of Mike Wohl’s is that he was a soccer player at Amherst where he spent four years after college working as a financial adviser for a financial firm specializing in high-net-worth individuals and families (avg. bankroll of $22 million).

Wohl had spent some time like the average sports bettor dropping a few bets on the games. And along his time betting on sports and the transition into the financial world, he realized that the risk and return from sports wagering was equal to or better than investing in the market. The primary difference however was that the time for potential return on investment was shorter, and each sports bet lived independent from another.

To translate Wohl’s discovery, losing a Green Bay Packers bet had nothing to do with the three other NFC North teams losing. But with stocks, a 500-point drop in the Dow is bad for all.

Chad wrote that Wohl was smart enough to recognize that making a bet on the point spread didn’t exactly offer a level playing field. That a minimal 10 percent commission (vig) most vegas and online sports books charge got in the way, forcing bettors to win at least 52.3 percent of the time to see any profit. Thus, he went on a journey for, all together now, “The Missing Asset Class.”

Out-of-box thinking like Wohl’s is what makes entreprenuers succeed by challening the norm, seeing what others see but doing what others don’t, and seeing taking a view from a different lens. You don’t have to look to far to see some of the most success brands over the last few years, or gang buster IPO’s did just that (Apple anyone?).

And Chad said it best that places like MIT, and other high-end business schools understand: Ideas win.

Chad wrote how Wohl went on his analytical search for betting advantages and word spread of his efforts, he says he “went from being that dude who went to Vegas to bet on college football all fall to being the guy with the innovative idea.”

And soon after Wohl found the  asset, or at least one of them, in college football money-line bets.

For those new sports gamblers and bettors who don’t know, money lines takes the place of a point spread. Money line betting is simply wagering on a game or match based on a given price rather than a point spread.

The team wagered on has to win the game outright, regardless of the score. The minus sign (e.g.-130) always indicates the favorite and the amount you must bet to win $100.

The line without the minus sign (e.g.120) always indicates the underdog and the amount you win for every $100 bet. Example: an individual bets $130 to win $100 on the favorite, while for the underdog you would bet $100 to win $120.

In simple form, you to pick a straight-up winner rather than betting against the spread. Now the catch is that betting on the favorite usually costs a lot more as the sportsbooks set and adjust their lines based on the information the bookies receive. And yes, as betting syndicates or sharp players can move the lines, the sportsbook will try to adjust the lines to cover and attract bettors on both sides.

Wohl discovered however there is a long-tail advantage — if you are willing to be patient, which a) is something most bettors have a problem with, and b) an integral quality that applies to the sports investors betting strategy. Warren Buffett ring a bell to anyone?

Wohl found his advantage in betting the money lines for college football teams that were favored by 20-25 points. He wrote in his paper: “There were 376 games in the last six seasons (approximately 62 per season or approximately 4.5 per week) that had spreads of between 20.0 and 25.0. Of those 376 games, 94.95 percent of the favorites won the game outright. Investing equal amounts on all 376 contests produces six straight years of profitable returns with an average annual (non-compounded and non-annualized) return of 12.24 percent.”

Now even the best handicapper who runs their statistical analysis each week on the games to generate their college picks may have limitations to finding anomalies as this, but through machine learning and complex computer algorithms sifting through heaps of data would under cover statistical opportunities as this and adjust in any predictive modelling and its algorithms. See the performance of our sports prediction modelling results and algorithms (11 algorithms to-date) that address this machine learning and data mining sophistication.

Interesting enough the statistical data Wohl used in his modelling he pulled from the Covers website, inputting 1,591 games that had a spread between 10 and 25, as well as their money-line price.

Why the 25 points? Wohl said he chose the 25 points because offshore sportsbooks offered money line prices on up to the 25-point favorite.

Even the most novice sports bettor understands you’re not going to get rich betting heavy on the money-line favorites, nor the prudent patience and tolerance to place big bets that yield more consistence, yet smaller returns.

Wohl said, “Seeking a 5-15 percent return over the course of 15 weeks betting college football is hard, especially when it is risking tens of thousands of dollars to potentially win seven or eight hundred dollars.”

Chad noted a more specific breakdown of his math from Wohl’s paper:

“This asset class would start with $100,000 of capital and would experience 376 wagers of $10,000 a piece over six years. 357 would be winning investments and 19 would be complete losses. The following table summarizes each year’s activity.”

Games Fav Wins Fav Losses Gains Losses Profit Return Std Dev
2006 49 46 3 $33,093.53 ($30,000,000) $3,093.53 3.09% 23.99141
2007 63 59 4 $42,142.86 ($40,000,000) $2,141.86 2.14% 24.40201
2008 68 66 2 $47,826.09 ($20,000,000) $27,826.09 27.83% 16.90802
2009 58 54 4 $41,538.46 ($40,000.00) $1,538.46 1.54% 25.35904
2010 71 68 3 $49,635.04 ($30,000.00) $19,635.04 19.64% 20.13138
2011 67 64 3 $49,230.77 ($30,000.00) $19.230.77 19.23% 20.69711

The essence of Wohl’s study is the fact he’s not looking to get rich quick through gambling. Rather he has a bigger vision to prove you can invest in sports betting as a sound portion of an overall investment strategy. Hey – we agree 100% with Wohl’s vision and encourage anyone to benchmark their Dow or their diversified investment portfolio, weighted with the a fair spread of investment options (stocks, bonds, etc.) and compare the results of the sports investment model our members use with

That is what Wohl is going to do this year. In Vegas, with his own money, and throughout the season he’ll provide Chad updates on his progress.

Ref: Millman: a new system for betting college football

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