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If you are the account owner, please submit ticket for further information. Prize Money The Faxon Law New Haven Road Race is again home of the 20K Championship and proud to be the longest running USATF National Championship. New Haven has been hosting the Men’s Championship since 1993 and the Women’s Championship since 2006. If you are an elite athlete interested in competing with us on Labor Day, click here. Qualifying times must be run no sooner than 18 months prior to event. First female, male and mixed gender relay teams receive awards. Netflix has decided its million-dollar-prize competition was such a good investment that it is announcing another million-dollar challenge. Netflix prize winners, from left: Yehuda Koren, Martin Chabbert, Martin Piotte, Michael Jahrer, Andreas Toscher, Chris Volinsky and Robert Bell.

Adding details announced Monday about the extremely close finish to the contest. Netflix, the movie rental company, has decided its million-dollar-prize competition was such a good investment that it is planning another one. The company’s challenge, begun in October 2006, was both geeky and formidable: come up with a recommendation software that could do a better job accurately predicting the movies customers would like than Netflix’s in-house software, Cinematch. To qualify for the prize, entries had to be at least 10 percent better than Cinematch. The winner, formally announced Monday morning, is a seven-person team of statisticians, machine-learning experts and computer engineers from the United States, Austria, Canada and Israel. The group — a merger of teams — was the longtime frontrunner in the contest, and in late June it finally surpassed the 10 percent barrier. That, in turn, prompted a wave of mergers among competing teams, who joined forces at the last minute to try to top the leader. Netflix publicly said the finish was too close to call. But the race was even closer than had been thought, as Netflix’s chief executive, Reed Hastings, explained for the first time at a press conference in New York on Monday.

Then, just before time ran out, The Ensemble made its last entry. The two were a dead tie, mathematically. The Netflix contest has been widely followed because its lessons could extend well beyond improving movie picks. The researchers from around the world were grappling with a huge data set — 100 million movie ratings — and the challenges of large-scale predictive modeling, which can be applied across the fields of science, commerce and politics. The way teams came together, especially late in the contest, and the improved results that were achieved suggest that this kind of Internet-enabled approach, known as crowdsourcing, can be applied to complex scientific and business challenges. That certainly seemed to be a principal lesson for the winners. 1 million challenge share their strategies for designing a program that will know your movie taste. Yet the sort of sophisticated teamwork deployed in the Netflix contest, it seems, is a tricky business. Over three years, thousands of teams from 186 countries made submissions. Yet only two could breach the 10-percent hurdle.

Out of thousands, you have only two that succeeded. The data set for the first contest was 100 million movie ratings, with the personally identifying information stripped off. Contestants worked with the data to try to predict what movies particular customers would prefer, and then their predictions were compared with how the customers actually did rate those movies later, on a scale of one to five stars. The data set of more than 100 million entries will include information about renters’ ages, gender, ZIP codes, genre ratings and previously chosen movies. Unlike the first challenge, the contest will have no specific accuracy target. Comments are no longer being accepted. This sounds like an interesting project, but they ought to emphasize acquiring more movies for their online streaming than telling people what to watch. This is good stuff on multiple levels. Contests should be used more often. Contests with large prizes inspire the type of collaboration and problem solving that result in advances that are applicable in multiple arenas.

Netflix should be congratulated for this idea. TIED with Bellkor, but submitted their entry 20 minutes after Bellkor did. Maybe this is the future of work. Maybe the real reason companies are offshoring and hiring H1-B visa holders to perform the type of work described in this article is because the projects they have going take a long time and there is high probability of failure. Any money paid in salary is probably money out the window. My two cents — if they wanted better efficiency in the collaborations, they needed to make the prize bigger. I’ve been a Netflix member for some 6 years, and anything will be an improvement over what they have been using. This is a great story, I remember the 2008 story and found it fascinating. That said, I’ve rated hundreds of movies on netflix and I’ve recieved no more that 10 recomendations ever. So, I hope they implement this new software quickly.

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