Monthly Archives: May 2012

“Fairness” in sports

My brother Conor (when he’s not blogging about political theory) does some excellent writing about Barcelona’s dominant football team. A couple weeks ago, he took up the age-old topic of fairness in sports in the context of European soccer. In most European leagues, there are no salary caps, revenue sharing agreements, or redistributive drafts. Rather than coddling the worst teams, leagues bust them down a division. Conor defends the uncontrolled European league structures with a call to the benefits of an aristocratic class:

There’s no escaping it. [Barcelona’s] degree of perfection requires an unequal distribution of talent and resources. This concatenated brilliance is probably unjust when measured against nearly any standard of fairness—but it’s also as close as anyone has yet come to fulfilling that specific style of play. FC Barcelona are but one example. For instance, recent Chelsea squads have flirted with perfection of a wholly different style of play. They are no less aristocratic simply because they have refined different aspects of their squad. Their strengths may be different, but they are no less refined for that. Every coat of arms is different—the aristocratic task for each is to live up to their particular identity. Undemocratic though they are, no one will mistake them for ordinary.

For whatever else they do to The Game As A Whole (or As A Spectacle), aristocratic clubs elevate the stakes and—more often than not—the peaks of athletic achievement. If Barcelona regularly administers whippings to clubs in La Liga’s middle and lower echelons, their clásico jousts with Madrid have periodically taken both teams yet closer to the pinnacle of sport.

I find this topic endlessly interesting, especially the comparison between United States leagues and European leagues.  The United States redistributes less income proportionally than many other Continue reading

More NBA spatial data

Adrian the Canadian — my designated Deadspin trawler — sent me an interesting graphic by Kirk Goldsberry and Matt Adams showing the highest percentage shooters from various regions of the court. You might recall that Goldsberry presented similar work at the Sloan Sports Analytics Conference in March (runner up for the research award). My take on this work is that, while interesting and impressive in terms of data, much of the spatial variation in shooting could be explained by factors other than location-specific shooting ability (this will sound familiar if you read my post yesterday on player tracking data).

First, random chance is an issue, especially when trying to identify the best shooters at each location. I think Goldsberry requires a certain number of shots for inclusion at each spot, but he doesn’t do the statistical analysis to determine whether the differences he presents are statistically significant (i.e., large enough such that they are probably not due to chance variation). His big surprise — Rondo leading the league in one mid-range zone — is likely based on a fairly small sample of shots.

Second, defensive position is missing from the analysis. A big red flag for this one is that Durant, at only 40% shooting, leads in the three point zone just to the shooter’s right at the top of the key. Every other three point zone has a guy over 50%. Unless there’s something challenging for right handers Continue reading

Player tracking in basketball: not a silver bullet

For anyone who follows quantitative sports analysis, player tracking cameras are not news. Along with the NBA, soccer teams use them (even in the MLS) and rugby teams use them. They give x-y-z coordinates for each player at a high frame rate, which can be processed into a variety of statistics. Many think that this approach will revolutionize sports analysis. I stumbled across an article at ESPN today spreading this view to the masses.

Tracking data can help with many things, but it won’t save analysts from themselves. Here’s a point-counterpoint from the article linked above.

Point: “Paul Pierce averaged 4.5 assists this season, which is pretty good for a scoring wing. But that number doesn’t tell the whole story. According to SportVU, Pierce’s teammates shot a higher percentage after his passes than any other player in the NBA. This shows Pierce is passing at the right time — he’s giving his teammates mostly layups and open shots.”

Counterpoint: Pierce might be making great passes, but it’s just as likely that Pierce plays with better than average shooters or better than average cutters/floor spacers, or that Pierce commands a strong defender Continue reading

Scrabble riddle responses

Last week, I posted a Scrabble riddle posed by my buddy Tony. Here’s the riddle again:

Specify a word that cannot be played under any circumstances in a Scrabble game.

If you still want to take a stab at the riddle, follow the link above to see the complete rules. Now, SPOILER ALERT – answers are below!

Continue reading

Scrabble riddle

This comes from my buddy Tony:

Specify a word that cannot be played under any circumstances in a Scrabble game.

Feel free to look up Scrabble rules if you need a refresher (or ask me). Googling “unplayable Scrabble words” is not allowed, of course. Send your answers to me privately and I’ll post them in a few days — there’s more than one right answer. Also, no words allowed that are clearly excluded by Scrabble rules (proper nouns, foreign words, etc.).

If you’d like to see some of the possible answers, check out the next post.

Why are the hockey playoffs so unpredictable?

The NHL playoffs have many more upsets than the NBA. Adrian the Canadian tells me that this is ruining their product, since the most exciting teams often get unlucky and bow out early. I can’t help but agree — I stopped watching this year after my favorite team (the Red Wings), my local team (the Bruins), and probably the best team (the Penguins) got bounced. The NHL wasn’t always so unpredictable — the Canadiens, Islanders, and Oilers won 13 of 15 cups between 1975-76 and 1989-90. Adrian’s theory is that the the rise of the butterfly goalie has increased save percentages, which makes outcomes more random.

It’s pretty easy to show that increased save percentages do indeed muddy up the result. I generated 1,000 simulated games for three sets of parameters. First, the 1980s (before the butterfly):

  • Both teams: 89% save percentage
  • Team A: 32 shots per game on average
  • Team B: 28 shots per game on average

Then, for the late 90s/early 2000s (butterfly goalies, slightly fewer shots on average perhaps due to popularity of the neutral zone trap): Continue reading