Through Math for America, I am part of an on-going collaboration with the New York Times Learning Network. My latest contribution, a Test Yourself quiz-question, can be found here:
https://learning.blogs.nytimes.com/2012/01/25/test-yourself-math-jan-25-2012/
This question is based on the rising number of weight-loss surgeries performed each year in the U.S. How many are being performed on patients under 21 years of age?
As the books close on the 2011 NFL regular season, it’s time to revisit my pre-season prediction that the new kickoff rule would result in a slight decrease in per-game scoring.
The pre-season predictions on the number of touchbacks turned out to be fairly accurate. In 2011, about 43% of kickoffs (922 out of 2151) resulted in touchbacks; in 2010, only 16% of kickoffs (359 out of 2221) resulted in toucbacks (thanks to NFL.com for the data).
Did the increase in touchbacks reduce overall scoring in 2011, as hypothesized? No. In 2011, around 44.4 points were scored per game in the NFL; in 2010, around 44.1 points were scored per game. Per-game scoring actually increased slightly this year !
One issue worth mentioning, however, is the disproportionate effect the top three scoring teams have on the data. During the 2010 season, New England was the highest scoring team in the league with 518 points total points; this was nearly 80 points more than the second highest scoring team. In 2011, the Packers, Saints, and Patriots all scored over 500 points! If we remove the three highest-scoring teams from each season, scoring for the rest of the league actually drops about 0.7 points per game.
It’s been fun drilling down into the data this year, and many other interesting questions popped up along the way. And off-season changes always create new opportunities for analysis.
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This is an fascinating article about how scientists at Arizona State University’s Center for Social Dynamics and Complexity are using mathematical modeling to study and evaluate basketball teams.
http://asunews.asu.edu/20110406_nbadynamics
Through analyzing ball movement on offense, a graph, or network, is created to model the team’s play. By studying and comparing these networks, ASU mathematicians and scientists are able to start asking, and answering, interesting mathematical questions.
For example, the LA Lakers were identified as a “high entropy” team. This means that the Lakers’ ball movement on offense is less predictable than a “low entropy” team like the Utah Jazz.
Furthermore, analysis suggests that a player’s shooting percentage may not be the most important quantitative characteristic on offense; the shot by itself may be less important than the sequence of plays that led up to it.
Another fascinating and exciting story at the intersection of math and sports!
My latest contribution to the New York Times Learning Network is a Math Lesson designed around exploring loan repayment and risk assessment in the context of the European Debt Crisis.
In this lesson, students interact with some cool infographics, collect debt data, run the numbers on possible loan repayment schedules, and explore an elementary notion of “risk” in finance.
As the Detroit Lions prepare for their first compelling Thanksgiving Day game in 15 years, I thought I would revisit my pre-season hypothesis that scoring in the NFL would be down in 2011 due to the new kickoff rule.
A quick recap of my argument: the new kickoff rule will result in more touchbacks, which will reduce overall starting field position, which will result in fewer points being scored. An elementary analysis suggested that per-game scoring would be down by about 2 points per game.
The first two weeks of the season saw record-setting offensive production: scoring was actually up by 2.5 points per game! But now, with more than half the 2011 NFL season in the books, the average points-per-game is 44.07. During the 2010 NFL season, the average points-per-game was around 44.16.
A TV analyst recently suggested that scoring decreases as the season progresses, due to factors like weather and injury. Not only does this give me another idea for a math and sports analysis, it also gives me hope that perhaps my pre-season prediction may still come true!
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