Sports Wagering

How Dr. Bob Uses Football Analytics for Profitable Sports Gambling

August 24, 2012 By Ed Feng Leave a Comment

While most people see football analytics as up and coming, one man has been using it for a long time.

Bob Stoll, the man behind Dr. Bob Sports, has used football numbers for over twenty years to provide winning football picks against the Vegas line. In a business in which only one number matters, he beats the line 56% of the time over the last 13 years of college football. Since a winning percentage greater than 52.4% is profitable, this is a significant edge for bettors who subscribe to his service.

Let’s take a look at how he does it.

The Math Model behind the Football Analytics

Luckily, Dr. Bob is quite open about his methods. His website has an extensive set of essays about his techniques. First, we’ll focus on his football analytics, or what he calls his math model.

He begins by warning about using a regression model on football statistics to predict the future. The problem is that some statistics do not predict the future because their early season values don’t strongly correlate with late season values. This weak correlation implies that randomness plays a large role in these numbers, not the skill of the team. Dr. Bob claims that fumbles are 90% random. A model that uses turnovers will accurately describe what has happened already but doesn’t project well into the future. In a similar fashion, Bill Barnwell of Grantland found that turnovers forced by a defense are 98% random. Hence, teams that enjoy a large turnover margin are getting lucky and shouldn’t expect this luck to continue.

So what statistics does Dr. Bob use in this model? He mentions rush yards per carry. It’s remarkable how simple division can make a world of difference in the meaning of numbers. Rush yards per game is a silly statistic since a triple option team like Georgia Tech runs the ball almost every down while a spread offense team like Houston barely runs the ball. It’s much more meaningful to divide those total yards by the number of rush attempts. Comparing the pass yards per attempt for an offense against the pass yards allowed per attempt by an opposing defense allows Dr. Bob to analyze how two teams match up. These football specific numbers goes into the weekly analysis he provides to his subscribers.

Moreover, Dr. Bob discusses using compensated statistics that account for strength of schedule instead of the raw numbers. If Oregon and Rutgers rush for 3.6 and 4.0 yards per carry respectively, one might think that Rutgers has the better rushing offense. However, Rutgers put up those numbers against defenses that gave up 4.2 yards per carry. Oregon earned those yards against defenses that allowed 3.4 yards per carry. Relatively, Oregon rushed for more yards per attempt than the opposing defenses allowed, while Rutgers didn’t. Dr. Bob’s research over tens of thousands of games shows that Oregon will outrush Rutgers against an average defense.

Above and beyond to find an edge

Dr. Bob doesn’t stop at compensated yards per attempt. The most impressive aspect of his analysis is accounting for injuries. In his essay on his math model, he speaks vaguely about evaluating for defensive injuries. In a panel at the 2012 Sloan Sports Analytics Conferences, he gave us some more juicy information. His research shows that cornerbacks are more important than linebackers. He sighted some examples from the 2011 NFL season in which some defenses performed just fine without a star linebacker but just crumbled without an average cornerback. For example, the Steelers played well without James Harrison, while the 49ers were just as good in 3 games without Patrick Willis.

Moreover, he also considers situational trends in making his betting picks. For example, consider NFL teams that win back to back games as the underdog. In the next game, suppose these teams are

on the road
playing a non-division opponent
less than a 7 point underdog

These teams are 29-57-2 (33.7%) against the spread in this game. The math shows that this trend is statistically significant. However, that’s not enough, since there’s a 5% probability that the trend exists due to pure randomness. Dr. Bob only uses trends that make sense. In the above example, the team is ripe for a letdown after winning two games. Visiting a non-division team makes that letdown even more likely. For further reading on situational trends, check out Chad Millman’s article on the Buckeye Database.

Money Management

Having an edge over the Vegas books is nice, but it does not guarantee making a profit from gambling. Bankroll management is crucial. Even with a 56% winning percentage, there will be losing streaks. Putting down too much money on games can wipe out a bankroll. In the extreme example, suppose someone puts all of his bankroll on one game. Even with a 60% win rate, there’s a 40% chance of going broke.

To properly manage a bankroll, one must understand the Kelly criteria. It’s a mathematical formula that relates one’s edge in a game to a bankroll fraction that maximizes long term growth. No one can claim to use mathematical principles for investing without understanding this idea. This applies to quantitative hedge funds as well as the sports market. Dr. Bob’s essays on the Kelly criteria are the most complete discussion on the web. For further reading, we highly recommend William Poundstone’s book Fortune’s Formula.

Dr. Bob uses the Kelly criteria to suggest betting 1.5% of the bankroll per star on his football picks. That’s the fraction that maximizes the rate of growth of the bankroll. To get a perspective on the potential long term growth, Dr. Bob claims that $10,000 would turn into $349,112 over 10 years. He considers sports an investment, not gambling.

But what if he’s just getting lucky?

Dr. Bob’s college football gambling picks win 56% of the time, going 575-453 over the last 13 years.

But maybe it’s just luck. When estimating this win percentage, there’s an element of randomness. The real win percentage might be 57.3%, or 52.1%. Using a little probability, we can estimate the distribution of this win rate given the number of games in Dr. Bob’s college football sample. The width of this distribution decreases with more games.

With this analysis, there’s only a 1% chance that Dr. Bob’s true win rate in college football is less than 52.4%. Those are some good odds. To put this in perspective, a result from a medical study has a 5% chance of being wrong. This results from the uncertainty in picking a small number of patients to participate in the study. The odds that Dr. Bob is not profitable is significantly lower than the odds your doctor is providing treatment based on a bogus study.

Dr. Bob is the one guy in the world I wouldn’t invite into my college football pool. To see the pricing of his subscription services, click here.

Thanks for reading.

Top 10 Moments from the Sloan Sports Analytics Conference 2012

March 6, 2012 By Ed Feng Leave a Comment

Dorkapalooza. That’s what Bill Simmons called the Sloan Sports Analytics Conference in 2009. Back then, a bunch of mathematically inclined dudes like Dean Oliver and John Hollinger debated advanced stats in a classroom. However, things have changed. Simmons’ article made the conference popular among more main stream sports fans. The 2012 version was Suitapalooza. The average attendee was a few months from earning an MBA, dressed in a suit and spent the conference begging for a job in the sports world. This year, they even stuffed the research paper talks in the far corner of the Hynes convention center in Boston. I didn’t go for the math; I went to talk with people in the halls. Here is my very personal take on the 2012 conference.

10. The gambling panel. When Daryl Morey asked Jeff Ma to participate on this panel again this year, Jeff said he wouldn’t do it unless he could be the moderator and pick the panel. Morey acquiesced, and the panel morphed from stale last year to exciting this year. Bob Stoll of Dr. Bob Sports dished out the football analytics behind his consistently winning picks. Michael Craig of Right Angle Sports was in the audience. The presence of these two companies, the only sports handicappers with profitable picks over the long run, legitimized the entire conference. Add in some tension between other panelists, and it became the hit of the conference. Of course, I wasn’t there because I was at the…

9. The football analytics panel. Disappointing. Can it really be a football analytics panel when no one says the words “expected points”? It didn’t even come up when they discussed Bill Belichick’s famous decision to go for it on 4th and 2 from their own 28 against the Colts. Brian Burke of Advanced NFL Stats used this concept to show that Belichick’s decision was reasonable. Expected points is the cornerstone of football analytics, a concept we explained in this article. No one mentioned it once.

8. Total QBR. I left ESPN’s talk about their Total Quarterback Rating more impressed than when I arrived. Unlike the football analytics panel, they mentioned and even used the concept of expected points. Moreover, the team with a higher Total QBR wins the game 86.8% of the time, a higher percentage than with the traditional quarterback rating. However, they also count clutch plays more, even though no one has ever found statistical evidence of clutch hitting in baseball, a sport with a much larger sample of events. Talking heads like Trent Dilfer insisted on the inclusion of this clutch factor.

7. The line for Bill Simmons. There was no line for the food at lunch. There wasn’t even a line for drinks at the cocktail reception, most likely because each attendee was limited to single free drink. However, there was a long line to get Simmons to sign the copy of Grantland that came in the goodies bag. Just further evidence how his column in 2009 has grown this conference to over 2200 attendees this year.

6. Moneyball. The stage behind many of the panels showed the original cover of Moneyball, the classic book in which Michael Lewis brought Bill James and baseball analytics mainstream. Bill Simmons may have popularized this conference, but Michael Lewis brought numbers in sports from weird hobby to real job possibility for the hundreds of MBA students. Without Moneyball, Sloan sports analytics might refer to an elective class instead of huge conference sponsored by ESPN.

5. Dean Oliver. A few month ago, my friend Chris Ritchie saw my copy of Basketball on Paper on my shelf. He mentioned that Dean Oliver, the author, used to work at his environmental engineering firm and had even interviewed him before leaving for the sports world. Oliver is now part of the analytics group at ESPN, and I told him this story at the conference. He didn’t remember Chris, but he certainly remembers taking time off to write Basketball on Paper, the seminal work on basketball analytics. Dean showed genuine interest in The Power Rank, particularly how our college basketball rankings contrasted with their own Basketball Power Index.

4. Chad Millman. This author of The Odds and the gambling blog on ESPN recently became editor of ESPN The Magazine. He’s done an amazing job with improving the writing in the magazine. The recent analytics issue features excellent stories on how Brandon McCarthy used analytics to save his pitching career and marry a model (see her on the cover) and how Floyd Fielding earns six figures as an old fashion bookie. I caught Chad in the hallway and complimented him on this work at the magazine. He asked for more features on The Power Rank. They’re coming, Chad. Look for a makeover this upcoming college football season.

3. The NFL’s sophisticated technology. The NFL tracks who takes the field on every play. To do this, they have people take pictures from a few different angles around the stadium. The images are transferred to a human team that writes down the identity of each player by hand. Are you kidding me? Commissioner Goodell, there’s been a recent invention called the personal computer. It sits on your desk and can analyze images. Some smart people in Silicon Valley have already started applying this invention to sports video analysis.

2. Training the German national team. At last year’s conference, Mark Verstegen of Athletes’ Performance was on the opening panel that discussed the 10,000 hours in developing an elite athlete. Only later did I learn that Verstegen was the man Jurgen Klinsmann hired to train the German national team before the 2010 World Cup. I’ve always been interested in the fuzzy, nonscientific side of athletic performance. For example, Born to Run by Christopher McDougall discusses how happiness and joy carries the Tarahumara Indians of Mexico through hundreds of miles of running. Verstegen also believes in this fuzzy side of athletic performance, as his team worked with the Germans to build a culture that supported their fitness program. I didn’t get many details, but elite athletic training is more than just science.

1. Shaking hands with Bill James. This conference would not exist with Bill James. Enough said.

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3 things numbers tell us about the Super Bowl

January 31, 2012 By Ed Feng Leave a Comment

The New England Patriots will play the New York Giants in the Super Bowl this Sunday. The Power Rank offers some numbers based predictions on the outcome.

1. It is not 2008. As you might have heard, the Patriots and Giants played in the Super Bowl recently. On February 3, 2008, the Giants pulled off a 17-14 upset over Tom Brady, Randy Moss and the Patriots. While Peter King at SI.com sees many of the same characters this time, this doesn’t mean the teams are similar. The Power Rank gave the 2007 Patriots a 14.7 rating, meaning they were more than 2 touchdowns better than the average NFL team. No other NFL team this past decade has come within 2 points of that year end rating. Our methods predicted that New England would beat New York by 11.1 points in 2008. The Giants cashed in a 19% probability of winning that game. This year, New England has a 8.3 rating, and The Power Rank predicts a point spread of 3.1. The Giants have a 40% chance of pulling the upset.

2. New England’s pass defense really is bad. Many have noted New England’s inability to defend the pass this year. The Patriots ranked next to last in total pass yards given up in the regular season. However, even the best pass defense will give up yards if the opposition throws enough. A better measure of pass defense is yards per pass attempt. But the Patriots don’t look much better by this metric. New England gave up 7.1 yards per pass attempt, 29th out of 32 teams.

3. Expect a lot of points. We also use The Power Rank algorithm to rank offense and defense. These rankings amount to scoring offense and defense that account for strength of schedule. Including the playoffs, New England has scored 32.3 points per game, but we assign an offensive rating of 28.8, still 2nd best in the NFL. This lower rating suggests that New England faced poorer defenses this season. The rating also implies that New England would score 28.8 points against the average NFL defense. New York has scored 25.0 points per game but has a 25.2 offensive rating, 5th best in the NFL. The offense and defense rankings predict a 31.5-28.6 final score for the Super Bowl.

For more content, follow The Power Rank on Twitter.

–NFL rankings.
–About The Power Rank.
–Can a defense force turnovers?
–The Power Rank featured on KALX Spectrum, the science and technology show on UC Berkeley student radio.

Filed Under: Football Analytics, National Football League, New England Patriots, New York Giants, NFL 2011, NFL Predictions, Sports Wagering

NFL Predictions, Conference Championships, January 22, 2012

January 21, 2012 By Ed Feng Leave a Comment

Both games should be exciting, but if you have to pick one of the two games to watch, opt for Baltimore at New England.

1. Baltimore versus New England. (0.58)
New England (2) will beat Baltimore (5) by 4.2 at home. Baltimore has a 37% chance of beating New England.

2. New York Giants versus San Francisco. (0.54)
San Francisco (4) will beat New York Giants (6) by 4.7 at home. New York Giants has a 36% chance of beating San Francisco.

For more content, find The Power Rank on Twitter.

–About The Power Rank.
–Should Nick Saban have gone for it on 4th down against LSU?
–Can a defense force turnovers?
–College football’s incredibly slow progress towards a playoff.
–The Power Rank featured on KALX Spectrum, the science and technology show on UC Berkeley student radio.

Filed Under: Football Analytics, National Football League, NFL 2011, NFL Predictions, Sports Wagering

NFL Predictions, Divisional Playoffs, January 14-15, 2012

January 9, 2012 By Ed Feng Leave a Comment

1. New Orleans versus San Francisco. (0.71)
New Orleans (2) will beat San Francisco (4) by 0.6 on the road. San Francisco has a 48% chance of beating New Orleans.

2. Houston versus Baltimore. (0.55)
Baltimore (5) will beat Houston (6) by 3.1 at home. Houston has a 40% chance of beating Baltimore.

3. New York Giants versus Green Bay. (0.42)
Green Bay (1) will beat New York Giants (8) by 9.8 at home. New York Giants has a 23% chance of beating Green Bay.

4. Denver versus New England. (0.26)
New England (3) will beat Denver (23) by 11.9 at home. Denver has a 18% chance of beating New England.

For more content, find The Power Rank on Twitter.

Filed Under: Football Analytics, National Football League, Sports Wagering