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With sports betting now legal in several US states, I might as well give away my number one piece of advice for amateurs looking to gamble: Don’t bet. It’s an easy recommendation. Numbers implied by betting markets are too good, too close to the truth that, when accounting for the vig, it’s nearly impossible to make a long term profit. But just because your local statistician tells you not to bet doesn’t mean you shouldn’t check out betting market odds.

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The best team in baseball during the 2013 season was the Detroit Tigers. Detroit’s rotation featured the eventual Cy Young award winner (Max Scherzer), alongside both the 2011 and 2016 Cy winners (Justin Verlander and Rick Porcello, respectively). The Tigers line-up was spearheaded by Prince Fielder and Miguel Cabrera, the latter of whom would win his second consecutive MVP. Indeed, betting market rankings had the Tigers atop the sport for basically the entire season.

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In the 2016-17 season, the Washington Capitals dominated the NHL like few teams in recent history, winning the Presidents’ Trophy with 118 points. The Caps entered a 2nd-round playoff series with Pittsburgh as decent-sized favorites (58 percent), and sure enough, Washington outplayed its rivals. In each of seven consecutive games, the Capitals outshot the Penguins, finishing with 70 total more shots on goal. Unfortunately for Washington, not enough shots turned into goals, and because hockey games are decided by goals, it was the Penguins that moved onto the Eastern Conference Finals.

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One way in which professional sports are relatively fair is that, in each season, teams are almost always given an identical number of home games. This seems like an obvious way to run a sports organization, until you remember that postseason berths in NCAA hoops and football often hinge on incredibly unbalanced schedules. Playing at home is a benefit, and while the reasons for the overall advantage are somewhat up for debate, there are obvious and unique advantages that make playing at home different each sport.

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Baseball games are too slow, too long, so damned long, and, like my seven-year old daughter getting dressed in the morning, taking forever. Despite the headlines, there’s one aspect of the game that has actually worked to speed the game up: how umpires call balls and strikes. As one piece of evidence, Brian and I found that, in the bottom half of extra innings, calls tend to favor whichever team is closer to winning.

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When the Jets traded pick No. 6, No. 37, No. 49, and a 2019 pick to the Colts for pick No. 3, I broke out my trusty draft curve to see what it said about the trade. Pick Number Value Team 3 52.5 to the Jets 6 50.6 to the Colts 37 33.5 to the Colts 49 28.3 to the Colts Even when you ignore the 2019 pick conveyed to the Colts, the Jets are enormous losers.

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In yesterday’s post, I walked through the use of a state-space model to evaluate NHL team strength over the course of an NHL season. But analyzing team strength is just the start of how we can use this type of framework to analyze betting market data in sports. In today’s post, I’ll ask a different question – What team had the best home advantage? And did perceptions of any teams (cough cough, Vegas) change over the course of the season?

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A few years ago at a statistics conference, Greg, Ben and I sat down to air a few greviences about our favorite sports. They were upset about baseball, and I was irked about hockey. Why the irritation? Relative to other popular sports like basketball and football, it seemed to us at the time that the best team was winning less often in baseball and hockey. And as fans wanting skilled teams to be rewarded, it was frustrating to so often have well-constructed teams fall short of titles.

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In 2014, Greg and I won the first annual Kaggle March Madness contest. This led to really cool things happening to a pair of nondescript statisticians that, to the likely detriment of our social lives, happened to know where to find good basketball data while also remembering, on a tight deadline, to enter type = "response" in our glm code. The Kaggle victory was simultaneously awesome – like New York Times awesome – and embarassing, as Greg and I realized how lucky we were to have finished on top.

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