The first of Tomas Plekanec’s 15 seasons in the NHL came in 2005-06, which coincided with the league’s introduction of a shootout.
The shootout was not kind to Plekanec. Among all NHL players with at least 20 attempts, Plekanec’s career shootout percentage of 13% is the lowest league history. A deeper look into Plekanec’s shootout usage reveals an interesting pattern:
Seasons # Years Plekanec Shootout Attempts 2005-06 to 2014-15 10 23 2015-16 to 2018-19 5 0 Each of Plekanec’s shootout attempts occured in his first 10 seasons, and his success rate was so bad in that timeframe that he never again got an opportunity.
As part of the NFL’s technology conference held last week, I presented to club staffers various approaches on how R could be used to explore NFL data. Here’s a slightly abridged version. This specific audience featured both novice and expert R users. Given that a semester’s long course would be needed to truly cover the breadth of how R could help explore football, note that this particular document is more an overview than anything else.
How do you estimate outcomes under a set of rules that don’t (yet) exist?
In this post, I’ll walk through how the NFL league office used resampling to estimate metrics related to an offseason proposal related to overtime.
Background Back in March, the NFL’s competition committee and owners debated the merits of a rule change, proposed by Kansas City, that would allow both teams the opportunity to possess the ball at least one time in overtime.
Brian, Alex, and a host of others have done nice work recently when it comes to creating better scatter plots using images in ggplot in R. In this post, I’ll show how easy it is to use the ggimage package and do the same.
What data to use? Ron, Sam, Max, and the folks at nflscrapR have built a pretty amazing tool to analyze football play-by-play data. They’ve nicely stored csv’s that summarize several play-level and team-level characteristics relating to each play.
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:
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.
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.
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.
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.