Welcome to Week 1! I can’t tell you how excited I am for this football season to get started. Vegas set the lines for these games almost a month ago, before preseason even began, and there’s been a bit of movement in a few games since then, but most lines have stayed pretty much the same. I think many people put a lot of stock in the changes that teams make from season to season (draft picks, free agent signings, coaching and system changes) but in truth the players and philosophy that make up the team’s core rarely change all that much during the offseason. Vegas tends to use this misconception to set a few traps. The computer’s historical performance in Week 1 doesn’t deviate much from its overall performance; the Weighted Win Percentage (WWP) is 54.3% compared to a lifetime WWP of 55.6%. Hedge bets during the first week tend to perform poorly, but this is balanced by the flip bets, which do quite well. There’s only one team that the computer is 90% confident about this week, and that’s the Atlanta Falcons. But the largest bet of the week is actually Philadelphia, where over 1/3 of the models call for a bet on the Eagles with 85% confidence. The other big bets are hedges on Denver, Detroit, and Oakland. It’s important to remember that the lines you see here are from when I placed my bets, and don’t necessarily reflect the final lines on each game. It’s also important that you listen to the computer, not me. The computer is better at this than I am. (more…)
Author Archive
I call him GAMBLOR! (Part 2)
Last week I explained a bit about how my prediction model came into being, and how it works. The “flip” component of it, which identifies questionable spreads and presumes a trap is being set, is responsible for the majority of the money the computer bets each week and primarily focuses on favorites. But there’s also a “hedge” component, which bets exclusively on underdogs.
In general, bettors prefer favorites, mainly as a psychological spillover of the fact that favorites win the actual game 66% of the time. But the spread is a powerful equalizer. The Detroit Lions lost all eighteen games last year. They were the worst team in NFL history. They got beat by more than 17 points seven times. They kept the final score within a touchdown only three times. And yet Vegas set the spread at 11 points or greater only seven times during the season. And every single time – every single time – the Lions were able to keep the game close enough to cover it.
In the last twelve years, underdogs have won 51.5% of the games against the spread. So statistically, the underdog is the better side to bet. And the prospect of betting on home dogs has been well-publicized; they have won money 52.2% of the time in the last twelve years. But the last two years have been pretty tough on home dogs; their overall record against the spread is 76-90-3 – a win percentage of just 47.5%. Blanket projections like this are a dangerous way to bet, as are most trends – they’re statistical fluctuations which are just as likely to bounce back (particularly if they are well-known) as they are to uphold their performance.
There’s a statistical phenomenon called “regression to the mean” which refers to the tendency of sequential outcomes to find their way back towards the center of the distribution. The further away from that “center” that any one particular outcome lies, the more likely it is that the following outcome will be closer to the center than the last. The easiest way to envision this is with the classic game show “Card Sharks” (or perhaps the drinking game high-low-red-black). Players would be dealt a card and have to guess whether the next card would be higher or lower. When presented with a low card (for example, a four), most contestants would (wisely) predict that their next card would be higher. Of course, there is no inherent skill involved in this process; it’s simply a matter of luck. And attempting to apply this to football, where skill is very much a factor in determining the outcome of a game, must be done carefully. But luck is also a factor in football games, as well as the tendency of lousy teams to improve and talented teams to deteriorate. And in cases where the spread is perfectly balanced, the underdog has an inherent edge because both of these factors work in its favor.
Each week the computer identifies four or five favorites that pass muster under the “flip” method. Based on the model’s past performance, these favorites should have an above-average chance of winning. Which means that amongst the remaining games, any given underdog has a slightly higher chance than the base of 51.5%. It only takes a small edge to get over the breakeven point – and by taking the games where the computer sees a spread as being set perfectly (less than a 55% confidence level), it’s possible to take advantage of this plus the “regression to the mean” potential. The hedge bets aren’t as powerful – they only hit 53.4% of the time, but they make things more interesting.
The betting scheme I used last year and plan to continue this year is based on a steady growth strategy. Hedge bets are given a weighting of 1. Confidence levels of 85% are given a weighting of 3. Confidence levels of 90% are given a weighting of 8. Confidence levels of 95% or better are given a weighting of 25. Each week, the base amount grows by 1%. For the first week of 2009, the base amount is $0.21. Which means that if ten of the models picked Dallas at a confidence level of between 85% and 90%, the total bet for the game would be $6.24 (0.21 x 3 x 10).
Occasionally, when one of the teams has particularly volatile statistics, it’s possible for the “flip” scheme and the “hedge” scheme to come into conflict. In this case, the computer will side with whichever scheme dominates (after weighting) and subtract the weaker side’s component from the overall bet. Sometimes the two schemes agree, in which case the total bet is the sum of the two. The graph below shows the performance after eleven years, based on a starting value similar to what I’ll be starting with this year.
It would have taken quite a bit of faith to stick with the computer during the lean years of 2000 to 2001. But the boom years between 2002 and 2006 more than made up for it. The model took a bit hit at the end of 2007 and the beginning of 2008, but with sufficient faith it’s shown a pretty spectacular return. This progression is based on 10% juice (and includes pushes), but I make an effort to take advantage of nickel (5%) juice whenever I can – it makes a pretty significant difference over time.
Stop by next week – I’ll have my picks for the first week of the season.
I call him GAMBLOR! (Part 1)
Two years ago, after being introduced to the computer program MATLAB at work, I conceived the brilliant plan of writing a computer program to pick the winners of football games against the spread. Armed with a semester’s worth of statistics expertise and a powerful processor, I was confident that I could beat the odds. It seemed so simple to outsmart all those silly rubes making bad bets based on their “gut instincts.” What fools! A clever fellow like me could harness the power of a “little magic box” and elevate myself to the financial status of a king.
My original theory was that Vegas set the point spreads for each game based on public perception – that they were angling to have about 50% of the money on each side. I figured that I could use the statistics from previous games to determine what the “true” spread should be, and it would be an easy way to pick winners. I entered five seasons’ worth of data by hand – point spreads, rushing yardage, turnovers, etc. – and set the computer loose to choose its own algorithm based on what would have worked best the previous several weeks. Based on a large-scale simulation, I thought its odds of working would be pretty good. Unfortunately, all the program really did was pick underdogs – the bigger the better. And in a season where favorites beat out underdogs 127-116 (including a Patriots squad that covered increasingly outlandish spreads the first eight weeks in a row), it turned into a pretty expensive lesson of what happens when you “overfit the curve.” (more…)
Product Review: Rosangel Tequila
It’s hard to say no to a free bottle of tequila. It’s even harder to write a review of that free tequila where you explain how the only way they could have made a less palatable product is by mashing up three turnips and a carrot, filtering the juice through a nylon stocking they found on the side of the interstate, and leaving the swill in an unwashed goldfish tank for two weeks to ferment using leftover saran wrap from a package of ground beef as a valve, then distilling it in a column made from the catalytic converter of a 1984 Oldsmobile. Fortunately, this is not that review.
The free tequila in question is Rosangel, a hibiscus-infused version of Gran Centenario Reposado tequila. It’s bottled in clear glass, which lets the pink hue of the hibiscus show. The glass is incredibly solid – great for clubbing an opponent in a bar fight or preparing root mush – but the cork adds a touch of class that’s usually only seen in a nice bourbon or scotch.
A few friends and I drank the majority of this bottle while watching the classic British gangster film The Long Good Friday. It’s well-suited to drink as a shot – the hibiscus flavor makes it much more interesting on its own – and survives the trip down your throat smoothly enough that a lime isn’t needed. As long as you’re secure enough in your masculinity to handle the pink color (or sufficiently heedless of your femininity to drink straight tequila), it’s also fine for sipping. (more…)
Motion Picture Soundtrack: “Ferris Bueller’s Day Off”
A couple weeks ago my girlfriend and I took a trip to Chicago for the weekend. The weather on Sunday was practically identical to that during Ferris Bueller’s legendary day off: mostly sunny with a high temperature of 69 degrees. It was the kind of weather that inspired Ferris (Matthew Broderick) to remark thoughtfully to the audience, “how could I possibly be expected to handle school on a day like this?” before taking off for a day of surprisingly wholesome adventures in downtown Chicago with his best friend Cameron (Alan Ruck) and his girlfriend Sloane (Mia Sara).
“Breaking the fourth wall” is an expression that refers to the imaginary “fourth wall” of a theater – the portal through which the audience watches the events of the story. Breaking the wall occurs when one of the characters acknowledges the fact that an audience exists, and speaks to them directly (or refers to them indirectly). This happens copiously in Ferris Bueller’s Day Off (1986) – in fact, it’s probably the most familiar example of this technique. Ferris talks about all kinds of things; he provides a tutorial on how to fake an illness, gripes about his lack of a car, and explains his disdain for authority – and of any ideology in general. Having Ferris speak directly to us is an important part of the movie’s charm, as Matthew Broderick’s smarmy charisma floats off of the screen and makes it clear why everyone in his high school – even the teachers in the English department – simply adore him.
The Film: Ferris Bueller’s Day Off
The Song: “Please, Please, Please Let Me Get What I Want”
The Artist: Dream Academy (more…)
Exit Music (For a Film): “Fear and Loathing in Las Vegas”
What would rock and roll be without drugs? Tomorrow is the first anniversary of the death of Albert Hoffman, the Swiss chemist who first synthesized LSD-25. Hoffman’s tale is one of the most celebrated stories of chemical discovery; he discovered the compound in 1938 but shelved it after preliminary tests on animals showed no particular pharmacological benefits (aside from “restlessness”). A “peculiar presentiment” prompted Hoffman to revisit the substance five years later, and during the synthesis process he found himself sufficiently disoriented to discontinue work. Three days later, on April 29, 1943, he intentionally subjected himself to what he thought would be a threshold dose of 250 µg (which is actually from two to five times the typical recreational dose), and the well-chronicled adventures that followed have been subsequently celebrated as “Bicycle Day” amongst the psychonaut community.
The Film: Fear and Loathing in Las Vegas
The Song: “Jumping Jack Flash”
The Artist: The Rolling Stones (more…)
CD Review: Silversun Pickups, “Swoon”
In “There’s No Secrets this Year,” the first song on Swoon, the Silversun Pickups come out swinging for the fences. If the band has been bothered by frequent comparisons to the Smashing Pumpkins, they don’t show any signs of it here. It’s an auspicious track that makes the same kind of promises that “Cherub Rock” made as the opening track of Siamese Dream fifteen years ago.
The comparisons are apt. The band enthusiastically acknowledges the Pumpkins as one of their primary influences, and even the appearance, gender, and ethnicity (a lanky vocalist, a female bassist, an Asian-American male) of their lineup is similar. “There’s No Secrets this Year” showcases a number of the postive ways the Pumpkins have influenced the Pickups, as mulitple guitar overdubs and rattling drum rolls are assembled together into an ambitious, portentous opener.
Exit Music (For a Film): “The Last Temptation of Christ”
In 1998, Terrence McNally’s play “Corpus Christi” was first performed in New York City. It wasn’t hard to predict that portraying Jesus as a promiscuous homosexual living in Corpus Christi, Texas would inspire vehement condemnation from religious groups – and it most certainly did, as “Christians” spewed death threats against the members of the Manhattan Theater Group that first produced the play, and when the play opened in London in 1999 a British Muslim group issued a fatwa calling for the assassination of the playwright.
A few clues exist in the gospels that suggest Jesus’ sexual preferences might have made it a little easier to ignore the charms of the prostitutes he was willing to defend. Mentions of the “disciple who Jesus loved,” and “the kiss of Judas” provide fodder for interpretation, but in a larger sense, I think Jesus’ sexuality is entirely irrelevant with regards to the core message of his teachings. Whether Jesus had any sexual nature at all affects his legacy no more than Morrisey’s sexuality affects his lyrics or whether Kevin Spacey’s sexual preference influences the roles he inhabits.
The Film: The Last Temptation of Christ
The Song: “It Is Accomplished”
The Artist: Peter Gabriel
Popdose Gets ‘Faced: The Ultimate Drinking Mixtape
A couple months ago, Jeff mentioned to me that he was thinking of putting together the Ultimate Drinking Mixtape in time for St. Patrick’s Day. I got so excited that I persuaded him to immediately open the floor to suggestions from the Popdose writers. And the song requests flowed in.
As the resident souse of the Popdose staff, I felt it was my responsibility to filter through the ideas that emerged and weave them together into something slightly more coherent than the drunken rantings I’d occasionally find typed out on my computer during the extensive beta-testing process. Later, during gamma-testing, the songs started to find their ways into groups. Finally, when I reached the delta-testing phase, things had been organized into chapters that celebrate the many different aspects of that delightful elixir that can lift or lower our spirits, make us in turn beautiful or ugly, and loosen or tangle our tongues with equal abandon.
Here’s the full tape – 116 minutes of pure malt goodness, with some wonderful words of wisdom sprinkled in. Just add liquor.
A playlist follows the jump, but I feel I must warn you – it goes down a lot smoother if you drink straight from the bottle.
Exit Music (For a Film): “Ocean’s Eleven”
Conceptually, counting cards is incredibly simple. Take a deck of cards. With a full deck, the count is zero. Deal the cards out one by one. Each time you see a card numbered 2 through 6, add one to the count. Each time you see an ace, a face card, or a ten, subtract one from the count. That’s it. You’re done. You’ve learned the basic high-low counting system, a system that mathematician Edward O. Thorpe developed and proved by winning huge money during a single weekend.
On the technical side, the hardest part of counting cards exists in playing with perfect strategy. There are essentially 250 situations that can occur while playing blackjack, and you need to know how to play your cards in each of them. Memorizing 250 different responses might sound intimidating, but it’s no harder than memorizing the multiplication tables, and you managed to accomplish that before you were nine years old.
Put these two basic techniques together, and you’ve got an edge on the casino. All you need to do is increase the amount of your bet when the count is positive, and over the long haul you’ll win money. Of course, any dealer worth the meager wages the casinos begrudge them can count cards as easily as you – so with a basic high-low system, what you’re doing is completely transparent.
The Film: Ocean’s Eleven
The Song: “69 Police”
The Artist: David Holmes
I saw Ocean’s Eleven (2001) at a special screening in Mission Valley for Qualcomm employees and their friends. I had a roommate who was working on their digital cinema collaboration with Texas Instruments. The film was a fun bit of fluff, obviously as enjoyable for the actors to produce as it was for us to watch. The engineers at Qualcomm were deservedly proud of their work, which was absent of lint, spots, jitter or cigarette burns. It was a fun evening – the Qualcomm folks were still enjoying the tail end of the giddy stock price heights of the 2000 dot-com bubble, and I was on the tail end of my own experience at pilfering money from a Las Vegas casino. (more…)
