Can AI replace talent scouts in sports?

30

September

2021

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It’s probably the ultimate goal of every professional sports club, whether it is about hockey, football or baseball: get the best players in your team to create a strong team that can run for championship.

The question has always been and will always be how to scout new and young talents, get the best players in your team and create this winning team? This requires a lot of information.

In the earlier days this information was collected by human scouts, roaming around countless different clubs watching players play and make analyses based on human decision making. With digitalisation many opportunities arise in this field. Take the example of Moneyball where the manager of club with one of the smallest budgets in the Major League achieved huge successes by using different scouting techniques. Rather than simply defining success based on the amount of homeruns, new statistical and innovative technologies could better predict the quality of players. An example is the slugging percentage which measures the total productivity of a batter. This unconventional way of scouting and thus replacing human scouts with technology was already a disruptive way of thinking back then (Kars, 2017).

Nowadays, digitalisation has become even more important throughout the whole scouting process. Sport clubs cannot operate without business analysts in their scouting department (Torgler, 2020). There are platforms collecting enormous amounts of data about players by watching worldwide competitions and games. These platforms offer such a valuable amount of data as no human scout could ever have captured. For example, the platform Wyscout uploads over 2000 football games every week. Specific actions can be searched for because Wyscout will provide related footage for this. Analysts working at clubs can use this enormous pool of data to make extensive analysis and provide extensive analysis of players, thus discovering new, unhidden or the most valuable talents

These platforms, however, still require human beings to eventually make the decision which player to buy. This raises the question whether scouting needs this human touch or that maybe AI can completely take over this process in the future?

Sources:

Torgler, B. (2020). Big Data, Artificial Intelligence, and Quantum Computing in Sports. 21th Century Sports. Pp. 153-173. Accessed at: https://link.springer.com/chapter/10.1007/978-3-030-50801-2_9

Kars, J. (2017). Moneyball: Hoe stats en tech honkbal veranderde. Metronieuws. Accessed at: https://www.metronieuws.nl/lifestyle/tech/2017/10/moneyball-hoe-stats-en-tech-honkbal-veranderde/

https://wyscout.com/

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Personalization is key to online survival: a case study of Fashion E-commerce

21

September

2021

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E-commerce is booming. Currently, there are over a 2.14 billion people worldwide that shop online and there are approximately between 12 to 14 million online shops in 2021 [1]. This raises the question how to attract customers to your online company by differentiating yourself amongst all those other choices that customers have nowadays.

Take the case of Netflix. It is clear Netflix has come up with an innovative business model by selling unlimited subscription fees and by doing so providing customers all over the world direct access to a wide variety of films and series. However, one of the key success factors and trivial for the dominance of Netflix in the entertainment industry is the personalized experience by using recommendations algorithms [2]. It allows Netflix to offer multiple products, namely one product for each customer [3].

Personalization cannot only be applied in the online entertainment industry, but in many other online industries and thus in the fashion E-commerce. For this particular industry it’s not a mere possibility, it is an important key success factor. Personalization is the way to attract customers to your online shopping platform and even more important lock them in. In the world of E-commerce there are many choices for customers. Furthermore, customers are able to search very easily across different platforms and switching costs are low [4].

In fashion e-commerce personalization by using algorithms is not yet exploited fully, despite the fact that it provides big opportunities for companies such as Amazon and Zalando. Recommendations can be used in many forms. One of the most basic forms is to simply show recommendations based on the browsing history of the customer. Research has shown that when 24 million of these recommendations were implemented on a platform, another 1.6 million clicks were made. Another form might be that the homepage of each individual could be altered based on previous searches [5].

In conclusion, personalization enhances the customer shopping experience. Therefore, it should be implanted in the long-term strategy of fashion E-commerce companies.

References:

[1]  https://digitalintheround.com/how-many-online-stores-are-there
[2] Amatriain, X. & Basilico, J. (2015). Recommender Systems in Industry: A Netflix Case Study. Recommender Systems handbook. P.385-419.
Retrieved from: https://link.springer.com/chapter/10.1007/978-1-4899-7637-6_11
[3] https://research.netflix.com/business-area/personalization-and-search
[4] Hwangbo, H.,  Sok Kim, Y. & Jin Cha, K. (2018). Recommendation system development for fashion retail e-commerce. Electronic Commerce Research and Applications. P. 94-101, 28.
Retrieved from https://www.sciencedirect.com/science/article/pii/S1567422318300152
[5]  https://www.shopify.com/enterprise/ecommerce-fashion-industry

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