Great read! Very interesting to see how teams are using a combination of machine learning and human intervention to dynamically price tickets. It makes sense that these teams have the goal of removing the human element here, but I wonder if they can really get to a state where no human intervention is needed. As we see with airlines, ticketing is a complication practice and still requires a tremendous amount of human interaction, especially when things go wrong. The final question you pose is a great one. As a consumer of sports, music, and entertainment, I generally see secondary markets as causing more harm that good. With tickets selling out in seconds and scalpers inflating the price on these markets, it does feel like some policing is necessary.
It’s been very interesting to see how teams and organizations have responded to concussion and CTE issues surrounding football. Some colleges, including, Harvard and the entire ivy league, now prevent tackling during football practice to reduce the number of hits players are exposed to. It is disappointing to see that the NFL, who had revenues of $8B in 2017, has only put $1.3M towards the HeadHealthTECH challenges. It is also surprising to hear that helmet manufacturers are resistant to change and innovation. The concussion and CTE issues have been so prominent in the media, and is such a major concern for parents who have children playing football, that I would have expected these manufacturers to have placed significant investments in helmet safety product development. In my opinion, the NFL should be funneling more money to this issue, and investing in more partnerships to help solve it.
This is a very interesting application of machine learning. I had no idea this was possible today. It does appear that these advancements in additive manufacturing bring a lot of opportunities as well as challenges to an industry very much set in its ways. In the near term, I could very much see a backlash coming from labor unions, safety organizations, and other manufacturers who will be resistant to the changes 3D printing brings. Despite this, you bring up some fantastic advantages to 3D printing. It does sound like a lot of innovation is still required for 3D printing to become common place in building construction.
This is a very interesting approach. In some ways, Volition is relying on market research to build it’s product roadmap. I see both merits and drawbacks to this approach. It’s great to give customers what they want, but the fundamental assumption here is that customers actually know what they want. My concern for Volition is that a purely customer driven product development process can lack direction and clarity. Great call on identifying Volition’s need to keep it’s innovators engaged with the product, and these innovators are a fundamental part of Volition’s overarching strategy. I am curious how Volition plans to maintain these relationships when they are not actively developing new products.
It’s cool to see how Spotify has been able to grow their product via acquisition, and how they have been able to effectively integrate these aqui-hired teams into the larger Spotify organization. I do worry about the ultra competitive industry in which Spotify operates. While Spotify has been the market leader in using these algorithms to curate music, it seems like competitors such as Google, Amazon, and Apple have even more data than Spotify. For instance, Google Play can leverage youtube and search data when making recommendations to listeners. With so much content out there today, I do think that the company that is able to sift through the noise and give users the best curated experience will ultimately win.
Interesting to see the interplay between hardware and software used at Benfica. Collecting this data seems like a rather complicated undertaking. This model does indeed sound ideal for a club like Benfica, which is known for developing young players and selling them off to larger clubs in more respected leagues. I can definitely see analytics and machine learning helping Benfica identify the most talented players early on, and in a way weed out those who are not cut out for the top leagues. However, can algorithms really identify top talent potential? Given the fact that these algorithms make decisions based off of historical data, are they not inherently biased to past performance, therefore making it difficult for them to accurately predict the future? If anything, we see athletes in sports like Michael Jordan and Tom Brady, who initially showed average potential, but ended up being some of the great in their respective sports.