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.
Taking a step back Most of sports analytics involve decisions made on the margins. We debate fourth down decisions that, across a season, add up to roughly a a third of an expected win. We compare the merit of Cy Young candidates, when the relative difference of each candidate’s WAR equates to nothing more than rounding errors. We tell NHL coaches to pull their goalie a few minutes earlier, knowing that, in more seasons than not, not an extra point will be gained by going so.
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.
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.
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?
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.
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.
One of the more reliable indicators of which NHL team is likely to get the next power play has little to do with score or style of play. Instead, it’s been shown repeatedly (Ex 1, Ex 2) that referees call a substantially higher number of make-up penalties than would otherwise be expected in order to maintain an overall even number of violations on each team. Call it a biased impartiality – in order to appear impartial by game’s end, ref’s let previous decisions drive future ones.
To err is to human In the third inning during a contest a few weeks back between the Nationals and Cubs, Washington’s Brian Goodwin hit a line drive to left field with two outs and a runner on third. Despite an initial pause, Chicago’s Kyle Schwarber ran in and attempted to field the ball around his knees.
Ruled an error on Schwarber, the play gave the a Nationals run in an eventual 9-4 win.