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5. Conclusion: We are now able to optimize a variable. But when optimizing a factor, we should see the bigger picture. Will maximizing something lead to a destructive effect on other factors?
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4. The natural cycle of nurturing players was disrupted, as young players had limited opportunities to prove themselves. So, when that generation finally retired, it was the beginning of the end for the club.
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3. The prediction accuracy was 84%. Using this system, players such as
#Maldini and#Costacurta were able to play even in their 40s. But this system had a huge flaw; it was designed to optimize the career of a player, not a club.Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
2. With his hefty price tag, Redondo lost two seasons. The club decided using a
#NeuralNetwork to predict injuries. They could find if a player they are about to sign is injury prone. And the manager could omit a player from the matchday squad if he was about to be injured.Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi -
1.
#ACMilan turned to#MachineLearning about 20 years ago. The system were hugely successful, contributing to two#UCL triumphs. But why did they turn to#ArtificialIntelligence? They bought Fernando Redondo from#RealMadrid, but he got injured almost as soon as he joined Milan.Prikaži ovu nitHvala. Twitter će to iskoristiti za poboljšanje vaše vremenske crte. PoništiPoništi
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