Haralabos voulgaris nba betting odds
It was hardly a fair fight. Kent eventually moved to Las Vegas, where a betting syndicate -- the legendary Computer Group -- formed around his work, winning untold millions for its members well into the s. Kent continued to develop models and bet on sports up until seven years ago. He is now retired, according to his lawyer, his whereabouts closely guarded. Billy Walters, a core member of the Computer Group, has, however, stayed in the game; he now has a staff of consulting mathematicians who have built advanced predictive models to project scores.
Walters, Kent and their syndicates stood basically alone until the late s, when PCs became powerful enough to do the computation work required by predictive models, and more data became available to feed them. Voulgaris was well aware of these predecessors. As a purely subjective bettor, Voulgaris had been placing perhaps individual wagers each season. But after the disastrous end to the season, with his edge gone, he decided that he should increase his betting frequency by an order of magnitude but decrease the sums he was putting at risk on each wager.
It only made probabilistic sense. If his return on investment ROI fell from 20 percent to, say, 5 percent, that was okay. This new approach would require an enormous amount of research and analysis. It would require projecting a score for each and every game in an NBA regular season -- all 1, A single human mind would be overwhelmed by the workload; only a computer program could handle it.
Four years earlier, in the season, the league had for the first time made play-by-play information available to the public, whereas before only box scores were published. This trove of fresh information had no immediate practical value, except perhaps to assuage fan curiosity. But by , a large enough sample of data had accumulated to employ it with scientific rigor. To help him build his model, Voulgaris required a specialist in the field, a mind trained in the codes of statistics, mathematics and computer science.
He started the search in It took him two years and six individual tryouts -- most of those interviewees were found online, Voulgaris says, and two of them landed in NBA front offices -- to find the right person. The right person was a literal math prodigy. As a preteen, he had won national math contests; he had been the subject of awestruck articles in major newspapers.
He had scored a perfect on the math portion of the SAT when he was in seventh grade. At the time of his interview with Voulgaris, he had just quit a high-paying job designing algorithms for an East Coast hedge fund with a roster of Nobel-grade quant talent. To do so, they would have to break the game down into its basic unit, the possession. Each simulation would therefore be a series of mini-simulations.
First, the program would have to predict the number of possessions each matchup would likely produce. Then it would need to judge the likeliest outcome of each possession: Score or no score; one point, two points or three; micro-forecasts ascertained from historical performance data. It would also have to take into account a vast number of potential occurrences, each missed shot or successful rebound creating the possibility of still other occurrences -- a garden of explosively forking paths, as if in parallel universes.
The program would run tens of thousands of simulations for each matchup, discarding the most outlandish or improbable results. It would be a black box -- prophecy as output. Between the statistical analysis, the algorithms and the programming, it took two years to create their first model, version 1. Voulgaris continued to bet subjectively, marking time until the model was ready. When they finished, they called it Ewing. At some point in the process of breaking the game down into its component parts, they realized that Ewing would also require a kind of feeder model, one that could forecast the lineups a team would most likely use each game and the minutes each player was likely to see on the court.
They called that model Van Gundy. Van Gundy, in turn, required its own feeder tool, one that would track the overall roster patterns for each team, the trades, the draft picks, the midseason player-acquisition tendencies. That database, less intricate than the other two, they at times jokingly referred to as Morey, as in Daryl Morey, the quant-minded GM of the Rockets.
Ewing, Van Gundy, Morey. Player, coach, GM. The names of each corresponding, of course, to the job of each tool. Every score the model spit out was higher than the average lines produced by the bookmakers -- the standard by which they would be judging themselves.
The model, in other words, was recommending that Voulgaris bet the over in every single game. After weeks spent poring through code, Voulgaris finally caught the flaw. In more advanced versions of Ewing, they would jettison this primitive free throw method.
The key is to find those scraps that are more predictive than others. Each player has two values -- on offense and as a defender -- and those values are constantly changing. So Voulgaris and the Whiz created, for Ewing, an aging component. Further number-crunching revealed that different types of players, based on position and size, will reach their zeniths at different ages and on trajectories that are possible to predict.
Ewing now grasps the curve of the lifespan of the point guard, the shooting guard, the forwards, the center -- and predicts the downslope and expiration date of every NBA career. When Ewing went live with actual betting for the first time toward the end of the season, Voulgaris was not yet sold on its powers.
For another, the model was performing unremarkably with their money on the line -- right above the break-even line. So too has the frequency of his wagering. In a season, he now regularly puts down well over 1, individual bets. It might come as little surprise to learn that Voulgaris has intermittently dreamed of becoming the general manager of an NBA franchise. If not maybe all. As with everything Voulgaris does, it was a calculated move. He wanted to burnish his bona fides as a quantitative basketball expert.
And it worked. Despite the fact that he was giving up a yearly income that he says would dwarf all but the highest-paid executive in the NBA -- who is Jerry West of Golden State, Voulgaris is quick to point out -- he stopped gambling and signed a contract during the season with one of the co-owners of an NBA franchise to consult on matters of player acquisition and roster assembly.
The owner, according to Voulgaris, made certain alluring pledges. The quant revolution in sports at large has brought these two worlds closer together than ever before, at least intellectually. He says he advised his co-owner client on several trades.
The stats nerds have no chance of ever becoming general managers. During the NBA regular season, which he splits between any number of North American and international ports of call, he watches as many games as he can, clocking more than 80 hours a week. He typically faces a wall against which rises a rack of Samsung flat-screens: a inch central TV flanked on both sides by vertical ranks of three inch screens, each showing a game.
From here, he orchestrates his wagering: Ewing spits out a projected score and a number representing its level of confidence for each potential wager. This is a lifestyle not exactly conducive to relationships: He partly attributes his recent breakup with a girlfriend of five years to his odd hours during the season.
Despite it all, Voulgaris faces the same issue that all sharps face: the sustainability of his edge, no matter how sophisticated the model that produces it. When he returned to gambling for the season, Voulgaris says Ewing clocked an ROI of more than 6 percent. By , it had fallen to 5. Of course, the lockout-shortened season made for a bizarre outlier year, and Voulgaris and the Whiz had to adjust.
Basically they subtracted a varying amount from the scores Ewing gave them, trying to account for the rust that kept scoring low at the start of the season, and the compressed number of games that later fatigued players -- and also kept scoring low -- toward the end of the season. That dual role has raised eyebrows among at least some of his peers.
An employee in the analytics department of an N. The N. When the professional gambler Haralabos Voulgaris, a computer-modelling expert who made millions betting on basketball, took a job as director of quantitative research and development with the Dallas Mavericks, of the N. Voulgaris left the Mavericks this fall amid reports of management infighting. That is a recipe for disaster. You have to explain that his gambling work is totally separate.
He would not identify those franchises, but two N. The Eagles declined to comment. He sat in the basement of his house, in the D. He had installed a twelve-foot projector screen, a curved fifty-five-inch TV, two thirty-two-inch TVs, and a desk, on which he placed six computer monitors. He works in the basement, alone, crunching numbers, conferring with gambling confidants, writing reports, appearing on TV shows and podcasts, and watching every N. Although his role as a commentator makes him highly visible, the other worlds that Sharp operates in are characterized by a high degree of secrecy.
The gambling world has long been even more secretive, and remains so, even as much of the action becomes legal. Sharp urges touts to document their picks in order to gain credibility, but no longer discloses his own history of bets, because, he says, someone could try to reverse engineer his model.
After our initial conversations over Zoom, I discovered that Warren Sharp is a pseudonym, something that Sharp has never publicly disclosed. My own reporting did not indicate any obvious reason that he would need to keep his real name a secret; there was also, I told him, in the end, no pressing need for me to print it. But his adamance was somewhat puzzling, given how visible he has become. Sharp grew up in Virginia. He began placing small bets on N.
Competitive and math-oriented, he became fixated on predicting outcomes more accurately than the oddsmakers, and spent about a year designing and backtesting models that took into account injuries, coaching styles, and any other variable he could think to plug in. By the time he had graduated and got his job at the accounting firm, in D.
I know my stuff is good. I should be on there. He also started a Web site, Sharp Football Analysis, which offered tips to the betting public. Before long, he got a note from a sports bettor named Bill Krackman Krackomberger. The two exchanged messages for months, then arranged to meet during a trip to Las Vegas that Sharp had planned. Because football is such a popular sport to bet on, it is hard to gain a consistent gambling edge, but Sharp insisted that he had one.
Krackman denied being part of a syndicate. Sports books impose limits on bettors, especially those known for cleaning up, and a syndicate tries to bet the limit at as many books as possible, often also using bots to enter offshore bets.
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