Anmeldung » at to capture cross-team player analysis

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The San Francisco 49ers and Oakland Raiders are giving it a try, too. https://www.shoesukonline.com/ . You can watch the game live on TSN2 and TSN Mobile TV at 9pm et/6pm pt. Jonathan Huberdeau and Quinton Howden are expected to make their debuts for Team Canada.In recent years, the explosion of advanced stats has changed the way we understand the game of hockey. For the coming season, TSN has launched TSN Hockey Analytics, a team focused on employing a series of emerging stats (such as Corsi, Fenwick, zone starts, and many more) to analyze elements of the game that arena€?t always apparent to the naked eye. One of the principal contributors, Travis Yost, has been on the forefront of the advanced stat movement in hockey. We talked to Travis about how he first got into hockey analytics, how ita€?s changed his understanding of the game, and what the rest of us can learn from crunching some numbers on the leaguea€?s best players. How long have you been working in the field of hockey analytics? Ia€?ve been doing it for close to five years now. I started doing a lot of the stat stuff at HockeyBuzz.com. Once [hockey analytics] became popular and emerged out of these really small areas on the internet, thata€?s when I jumped from HockeyBuzz to NHL Numbers, which is part of the Nations Network. From there I started writing for The Sporting News and the Ottawa Citizen, and that got me to where I am now with TSN. As a hockey fan, what first drew you to working with advanced stats? Ia€?ve been a big sports fan my whole life, and I was familiar with how impactful statistical analysis was to sports like baseball. I [said to myself], a€?I watch a lot of sports, I know what Ia€?m talking about.a€? But your eyes lie to you so much, and in 2009 I really started getting into data stuff. I guess I was part of the second wave of people who got into hockey analytics. The first wave included writers like Vic Ferrari, Tyler Dellow, and Sunny Mehta, who was hired by the Devils this summer, along with a handful of people out of Edmonton called the Oilogosphere [http://en.wikipedia.org/wiki/Oilogosphere] who started back in 2006 or 2007. When sites like Vica€?s Hockey and Objective NHL started, hockey analytics sites were almost impossible to find. This was before the Twitter age. Youa€?d only come across really smart hockey analytics writing in the far corners of the internet. But their initial flushing out of ideas impressed me to the point where I said, a€?You know, I think a lot of this stuff matters.a€? So I just started working on it on my own. Obviously over the last two years ita€?s exploded, but people dona€?t realize ita€?s not a new thing. Wea€?re coming up on the ninth season this stuff has been happening, and ita€?s a massive thing, but it took a long time to get people on board. What do you think precipitated the massive growth of hockey analytics in recent years? There have been case studies every year, whether ita€?s the Dallas Stars, or the Minnesota Wild in 2012, or Toronto last year. And much more often than not, hockey analytics have [separated] the paper tiger teams from the teams who are ready to take that next step. People point to last yeara€?s Toronto Maple Leafs as the big reason hockey analytics exploded into the mainstream. Because everyone who had been looking the data said, a€?This team is doomed to fail.a€? And lo and behold, they missed the playoffs last year. Previously, the other big case study was the 2012 LA Kings. They came in as an 8th seed, but they were winning something like 59% of the shot share heading into the playoffs. Anyone who was reading smart analytics stuff at that time was hearing, a€?This team is a juggernaut and theya€?re going to explode at any moment. Fake Shoes UK. a€? And they rolled through the postseason, and that was big, because it sold a lot of people on how valuable this stuff is with respect to predictability. I think that was when people started assigning real value to it. What traditional hockey stat do you believe is the most misleading? It seems laborious to even talk about it, because I think everyone knows plus/minus is misleading now. Therea€?s no value in it. Goals are such a random occurrence, and the attribution of the stat is problematic to begin with. I dona€?t think plus/minus tells you anything about the player: maybe therea€?s a chance his linemates shot 57% that season, or maybe that his goaltender stopped 87% of shots when he was on the ice. To be honest, no one really uses it anymore, and the few people who do, I cana€?t imagine theya€?ll be using it much longer. Can you break down one advanced stat you use and explain it to us? I think one of the best metrics is Relative Corsi, or CorsiREL. We know that good players post good Corsis and bad players post bad ones, but team effects cana€?t be understated. For example, Phil Kessel, who is one of the best players in the world, cana€?t possibly breach 44% Corsi because Toronto as a team is so bad. Whereas very average players on elite teams like Los Angeles or Chicago are posting Corsis of 56 or 57%. The biggest misconception is that the Kings or Blackhawks player must therefore be better than Phil Kessel, and thata€?s not that case. What I like to use is Relative Corsi, which captures a playera€?s ability to drive play compared to his teammates. Last year, Kessel posted a +2.02%, suggestive of Torontos ability to control play at even-strength more favorably with him on the ice as opposed to off. Another perfect example of this is the Sedin twins, who for years have posted insane Relative Corsi rates. Regardless of whether Vancouver is good or bad, the Sedins still look exceptional. Last year, while the rest of their team was middling, Vancouvera€?s possession numbers dropped as a team. But the Sedins still posted exceptional numbers when they were on the ice. Theya€?re so elite at what they do in generating shots and generating control of the puck that wea€?re able to say, a€?These are elite players.a€? So thata€?s one metric we look at to capture cross-team player analysis. How have hockey analytics changed your understanding of the game? I watch hockey totally differently, and I dona€?t say that just to make a point. I used to spend so much time just watching the offensive zone, because thata€?s where the goals are scored, and thata€?s what we care about. When I first got into hockey analytics, I kept my focus on the offensive zone, because thata€?s where the shots happen, and shots are much more meaningful. Over the years, Ia€?ve realized that ita€?s not about the shots themselves, but how those shots are being generated. Now I spend a lot more of my energy watching the neutral zone. If youa€?re casually watching the game with your friends, the neutral zone seems to be where a lot of nothing happens. But really, much of what leads up to offensive success is how teams come through or defend the neutral zone. Because we know how valuable neutral zone possession is, as opposed to dump and chase. So Ia€?ve started paying a lot of attention to tracking neutral zone performance, because while it seems like a dead area, ita€?s really the most important area on the ice. ' ' '