Olocip was invited to the World Football Summit event in September 2018, where Mario Garrido – chief business development manager- presented Olocip and several examples regarding performance predictions for the 2018-2019 season and established from our predictive models of Artificial Intelligence.

Among such predictions, highlighted the Cristiano Ronaldo, in order to evaluate and predict his arrival in the new Italian league and new team, Juventus, trying to answer to the concerns raised at that time, which were answered during the event.

“Thanks to Artificial Intelligence we can contextualize Cristiano in his new environment to know what his performance will be, and taking into account the new scenario, one more year, his adaptation to the Italian league, rapport with his teammates … We can affirm that his numbers As for goals, they will be diminished if we compare them with their last season at Real Madrid. “

In May 2019, and with the competition almost completed, from Olocip we have conducted a study analyzing the comparison between the prediction published in September, and the statistics and performance during the current season of the striker, thus determining the accuracy of the predictions and how they align with reality.

One of the most mediatic signings in the history of football has been the transfer of Cristiano Ronaldo from Real Madrid to Juventus at a rate of 100 million euros. The departure of the forward generated a high degree of uncertainty about the possible performance that would offer in the team led by Allegri. Following these unknowns, from Olocip were established Cristiano Ronaldo’s performance predictions in Juventus as well as the impact the striker’s arrival would have on the team.

One of the most mediatic transfers in the history of football has been the transfer of Cristiano Ronaldo from Real Madrid to Juventus at a rate of 100 million euros. The departure of the forward generated a high degree of uncertainty about the possible performance he would offer in the team led by Allegri. After these unknowns, from Olocip were established the predictions of Cristiano Ronaldo on his performance in Juventus, as well as the impact that the arrival of the striker would have on the team.

In the offensive area, and on the basis that in the 2017-2018 season Cristiano Ronaldo scored in the 26th league in 27 games, Olocip developed performance predictions for each of his statistics. In this way and in relation to the goal in particular, the models established that the Portuguese would achieve a goal every 118 minutes reason why his offensive contribution would be diluted in the group of scorers opposite to his previous season. Currently, he has scored one goal for Juventus every 123 minutes: 21 goals in 29 games he has played. Some figures below the records of goal scorers who arrived at Real Madrid, as established by the models of Olocip.

TCT Scout, Olocip’s tool showing performance predictions, also predicted a notable drop in the shots the Portuguese would take at the Italian club. While Real Madrid averaged 6.60 shots every 90 minutes, Juventus is recording 5.80 shots, and Olocip’s prediction of 4.92 already reflected a decrease in their accuracy in the face of goal.

Olocip also pointed out that Cristiano would lose more balls at Juventus than at the white club, where he added an average of one ball lost every 90′. The prediction was set at 1.31, registers aligned with the current ones, standing at 1.21.

Although Ronaldo would decrease in goals, Olocip’s AI models already predicted that he would offer more assists. The striker made five passes in his last campaign with the white team: one assist every 458”. Our own predictive models indicated that Cristiano would slightly increase his number as an assistant: one goal pass every 333”. At the moment, the Portuguese adds 8 assists: one every 324, which shows a slight improvement in this section.

These predictions have been developed by our solution TCT Scout, a pioneering tool in the market to contextualize the future performance of players from other teams from descriptive data to know player performance in a future context and how to adapt to the new club.

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