The mantra of youth sports where “everyone gets a trophy” is permeating professional leagues. These days every team can claim some semblance of winning. In the bygone era of the NFL, two teams made the playoffs and that consisted of one game, the Super Bowl. Today six teams from each conference advance, and there is talk of adding more. In MLB, it used to be that the league leaders won the pennant and then went to the World Series; now, five teams in each league make the playoffs. In the NBA and the NHL, meanwhile, more than half of all teams make the post-season.
As the definition of post-season success broadens and winning becomes a commodity, a team’s performance isn’t enough to stand out in the $750 billion sports industry. And at a time where traditional revenue streams are under pressure and the competition for money, media, and sponsors remains stiff, sports organizations have to be more innovative.
So, what should they be doing to drive revenue? How can they use technology to attract and interact with fans? And, in the Age of Big Data, what’s the best use of analytics to increase ticket sales? These are some of the questions on the table at the 2015 MIT Sloan Sports Analytics Conference.
I recently joined 25 of my MIT classmates on an MIT Sloan Technology Trek to Seattle.
Technology is a hot area at MIT Sloan — 26 percent of the graduating class last year went into high technology jobs — so technology treks are very popular.
They are an important tool for students to learn about company cultures. However, we’re not just looking at how many hours we’ll work or how comfortable the lifestyle is. We want to feel like we’re making an impact on others in real ways, and we want to know that our MBA education is truly adding value at the organization.
Treks are a unique opportunity to ask questions and learn from employees, who are frequently alumni.
In addition to learning more about the roles of MBA grads, I also wanted to see if I could deal with Seattle’s climate. Growing up in India and living in New England for the last nine years, I knew that the Northwest would be quite different.
The current crisis in higher education has three characteristics: it’s overpriced, out of touch (with society’s real needs), and outdated (in its method and purpose). But the solution, a true 21st-century model of higher education, is already emerging: it’s free(or accessible to everyone), it’s empowering (putting the learner into the driver’s seat of profound personal, professional, and societal renewal), and it’s transformational(providing new learning environments that activate the deepest human capacities to create — both individually and collectively).
Today I would like to share some preliminary insights from our ongoing experiment, “U.Lab: Transforming Business, Society, and Self” (Watch a 7-minute video about it here), a Massive Open Online Course (MOOC) developed with MITx and delivered through edX.org.
A frequent criticism directed at MOOCs is that the learning that happens in them is not as effective as the learning that happens in a classroom. That’s why, in the U.Lab, we didn’t try to replace the classroom. Instead, we decentralized it, then took the learning out of the classroom altogether.
The price of oil has fallen nearly 60% since peaking in June, and lately there’s been a lot of ink and pixels devoted to the question of whether oil prices will plunge even further or whether they will shoot right back up. An even bigger issue is whether prices will stay at these very low levels.
While I doubt oil prices will fall much more — how much further could they reasonably tumble? Perhaps another $20 or so? — history suggests we can expect prices to remain low for the foreseeable future. What’s playing out right now in the oil market is likely the same supply-demand dynamic we’ve seen over and over: several years of extremely high oil prices followed by decades of low prices. The twin oil shocks of the 1970s, for instance, resulted in 20 to 25 years of low prices.
Of course, things are different today — but not that much different. Over the past six or seven years, oil has been relatively expensive, often trading at over $100 a barrel. During that time, both the supply and demand sides of the equation have responded.
For all the advances in both medicine and technology, patients still face a bewildering array of advice and information when trying to weigh the possible consequences of certain medical treatments. But a hands-on, data-driven tool I have developed with some colleagues can now help patients obtain personalized predictions for their recovery from surgery. This tool can help patients better manage their expectations about their speed of recovery and long-term effects of the procedure.
People need to be able to fully understand the possible effects of a medical procedure in a realistic and clear way. Seeking to develop a model for recovery curves, we developed a Bayesian modeling approach to recovery curve prediction in order to forecast sexual function levels after prostatectomy, based on the experiences of 300 UCLA clinic patients both before radical prostatectomy surgery and during the four years immediately following surgery. The resulting interactive tool is designed to be used before the patient has a prostatectomy in order to help the patient manage expectations. A central predicted recovery curve shows the patient’s average sexual function over time after the surgery. The tool also displays a range of lighter-colored curves illustrating the broader range of possible outcomes.