Haralabos VoulgarisVerifizierter Account. @haralabob. Head of Quantitative Research and Something or Other Dallas Mavericks. Irresponsibly. Haralabos Voulgaris Poker Spieler Profil. Die aktuellsten Informationen, Gewinne und Galerie. In einem wirklich beeindruckenden Blog erzählt der kanadische Highroller und Sportwetter Haralabos Voulgaris über seine Erfahrungen mit FullTilt Poker, Ray.
Haralabos VoulgarisHaralabos VoulgarisVerifizierter Account. @haralabob. Head of Quantitative Research and Something or Other Dallas Mavericks. Irresponsibly. Haralabos „Bob“ Voulgaris ist ein kanadisch-griechischer Pokerspieler und professioneller Sportwetter. What Haralabos Voulgaris' New Front Office Job In Dallas Says About Sports Betting And The NBA. Veteran sports bettor and poker player.
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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.
Voulgaris chose the right moment to start building a predictive model for NBA games. 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.
The relationship got off to a rocky start. 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.
According to Voulgaris, this is not really a big advantage as the data can be found by regular players if they do the right research, at least for NFL games.
In other words, a player can see the ownership data on Thursday and the patterns will likely still be the same on Sunday. Voulgaris does explain that this type of data would have a bigger advantage in MLB daily-fantasy-sports games since the games are played and completed in a single day instead of over a five-day window.
But this doesn't mean the average player isn't getting screwed. I think that's the part where there is some impropriety. The Hendon Mob Poker Database.
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