ThierryHenry14

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Big Data in Sports: How S.L. Benfica is Using Machine Learning to Build a European Football Powerhouse
ThierryHenry14
Posted on November 13, 2018 at 10:36 pm
This article explores the use of machine-learning to optimize talent development and team management in European football.

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On November 17, 2018, ThierryHenry14 commented on Big Data in Sports: How S.L. Benfica is Using Machine Learning to Build a European Football Powerhouse :

Good questions. I fear that by licensing out their model, Benfica may eliminate the comparative advantage they have in talent development. That said, they could potentially make up for it in licensing fees.

On November 15, 2018, ThierryHenry14 commented on Brooks Running: Personalized Running Shoes for the Mass Market? :

As a runner, I can certainly appreciate the mass-market potential of 3-D printing applications in end-to-end running shoe production. Given the level of customization required to truly create bespoke products for consumers at the point-of-sale, I do wonder how cost-effective this model can ever be. Can companies like Brooks pass-through the incremental cost to consumers? To what extent would competitors such as Nike, Adidas, etc. capitalize on Brooks’ higher price points if so? And given their scale, what stops larger shoe manufacturers from rolling out these end-to-end capabilities while absorbing the incremental cost of customization in order to cannibalize Brooks’ market share?

On November 15, 2018, ThierryHenry14 commented on A Tactical Advantage? Additive Manufacturing in the US Military :

It is interesting to think about the potential 3-D printing has to revolutionize the supply chain of institutions as vast and complex as the military. My key concern is the extent to which the quality of supplies that military contractors and other OEMs guarantee can be replicated by these 3-D printers. Given the mission-critical nature of many of these parts and suppliers, there is an extremely high cost of failure. The proliferation of additive manufacturing should be accompanied by constant innovation and technological improvement that allows printers to mimic the structural complexity of the products they are designed to make.

User deleted this comment on November 16, 2018
On November 15, 2018, ThierryHenry14 commented on A Tactical Advantage? Additive Manufacturing in the US Military :

It is interesting to think about the potential 3-D printing has to revolutionize the supply chain of institutions as vast and complex as the military. My key concern is the extent to which the quality of supplies that military contractors and other OEMs guarantee can be replicated by these 3-D printers. Given the mission-critical nature of many of these parts and suppliers, there is an extremely high cost of failure. The proliferation of additive manufacturing should be accompanied by constant innovation and technological improvement that allows printers to mimic the structural complexity of the products they designed to make.

On November 15, 2018, ThierryHenry14 commented on Volition: Crowdsourcing Innovation in the Beauty Industry :

Fascinating article Tasnia! I do agree that a key challenge in sustaining this model of innovation is keeping crowdsourced innovators engaged. Also, at what point does it make sense to bring “star innovators” under your proprietary umbrella? While Volition Beauty may enjoy a competitive advantage as the first mover in this space, how sustainable is this advantage? Could other beauty/skincare companies simply poach the key contributors to Volition’s R&D process? Finding the right balance of crowdsourcing from a broad user base and creating the right incentives for that user base to remain engaged in the process will be critical going forward.

On November 15, 2018, ThierryHenry14 commented on Get Em While They’re Hot: Dynamic Ticket Pricing in Major League Baseball :

Great piece! It would be interesting to see how dynamic pricing models adapt to usage in price discrimination strategies (such as pricing season-tickets vs. regular tickets). Completely agree that using DTP will inevitably increase stadium utilization by ensuring that ticket prices are fully reflective of customers’ willingness to pay (e.g. a customer may buy a ticket for a less competitive game simply because it is priced so low, whereas they previously wouldn’t have if the ticket was priced regularly). I do worry that the DTP may lead teams to completely outprice certain extremely competitive games. Stadiums typically fit 40,000 + spectators. And if a small set of outliers bids up the ticket price significantly, it may limit the team’s ability to sell out the remaining seats. I wonder how responsive DTPs can be to daily swings in demand, in order to rectify this issue?

On November 15, 2018, ThierryHenry14 commented on Big Data in Sports: How S.L. Benfica is Using Machine Learning to Build a European Football Powerhouse :

Very valid points and completely agree with all of them, especially the point on future talent indicators. Another comment mentioned the fact that several star-athletes in basketball and other sports were mediocre in their youth, and only started to blossom later in their careers. The model, as it currently stands, would fail to recognize that kind of talent early on.

On November 15, 2018, ThierryHenry14 commented on Big Data in Sports: How S.L. Benfica is Using Machine Learning to Build a European Football Powerhouse :

I like your idea about partnering with other clubs to share data anonymously. I think the reason they don’t have this technology in their home stadium is that they sell the broadcast rights for each game to the league/competition hosting the game. Therefore, they relinquish the right to record matches using their sensor-laden cameras. There’s also the issue of recording and obtaining the other teams’ data, which may open up some legal questions. But it will be interesting to see what new innovative methods they come up with for harnessing more relevant data.

On November 15, 2018, ThierryHenry14 commented on Big Data in Sports: How S.L. Benfica is Using Machine Learning to Build a European Football Powerhouse :

Completely agree. More teams should adopt similar models – Leceister City apparently has since 2014.

On November 15, 2018, ThierryHenry14 commented on Big Data in Sports: How S.L. Benfica is Using Machine Learning to Build a European Football Powerhouse :

Thanks for your comment, and completely agree with (2). With (1), my only concern is that it may be difficult to capture the data during the match and still maintain its proprietary nature. Much of the data is captured using high-tech cameras. So I am not sure whether there is a way to single out Benfica players using such a camera, without capturing the opposing team’s data. Also, domestic leagues pay clubs for the exclusive right to record competitive games, so these cameras would have to be embedded in the League’s infrastructure (and not Benfica’s)

On November 15, 2018, ThierryHenry14 commented on Big Data in Sports: How S.L. Benfica is Using Machine Learning to Build a European Football Powerhouse :

Completely agree with the inherent bias point. The data machine does indeed rely on precedent indicators of success, which – as you said – are not necessarily foolproof.