As we are progressing into a world where sports have become a vital part of our lives, it has also become a hot market for investors to gain greater income. We can also see that there has been a surge in sports viewership which leads to more tournaments. Capitalizing on them has become a difficult task for an investor. So a programmers team have come up with a strategy to build the best team whilst keeping in mind the investor’s budget which has a limit of 1 billion Euros relying on Machine learning algorithms to detect potential recruits for their clubs and potential assets for the investor to optimize their market gains.
How does that work ?
The team have a FIFA dataset in which there are few columns named — rating, release clause, and wages. They built 2 models using Supervised Learning on Rating variables and made it a classification problem by splitting this variable into 2 classes: greater than or equal to 70 and less than 70 ratings. They chose 70 as their threshold as most of the major clubs have only players with ratings greater than 70. In the second model, the rating class was obtained from previous best classifiers instead of « actual rating » and the release-clause was combined with annual wages to determine the cost to investors.
The dataset used is FIFA 19 and FIFA 20 data which contain 18k+ unique player ratings with 100+ different attributes for each player:
- Player positions, with the role in the club and in the national team.
- Player attributes with statistics as Attacking, Skills, Defense, Mentality, GK Skills, etc.
- Player personal data like Nationality, Club, DateOfBirth, Wage, Salary, etc.
Example: Bangladesh National Team
Due to the miscoordination among the players recently, the national team of Bangladesh slipped to their worst ever FIFA ranking of 194th. The ultimate success of a team depends upon its player selection. Generally, the coach and the management team select the best 11 players for each match from a pool of players by evaluating various attributes of the players. Now, using these Machine Learning algorithms, a better player classification technique is proposed and the soccer team is formed automatically of the best player combination possible. The results achieved based on the performance attributes would help the coach and management team to build up a more successful soccer team.
The constructed 2 models that utilize Machine learning algorithms benefit investors while simultaneously select the rightfully classified players as good performers and then regress them in the budget of the investor. Ultimately, the narrowed down selection process of a player within a club is rather better than selecting at random.
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Perseverance Rover: Humanity’s newest feat
February 19th, 2021 marks a new historical event in the space exploration journey, as NASA scientists rejoice.
NASA’s 2020 Mars mission successfully landed on Mars. This mission includes the Perseverance Rover along with other experiments.
While descending to touch down on the red planet, we got a close-up picture of the Perseverance rover, the first-ever high-resolution color image to be sent back by the Hazard Cameras that is most likely to become a classic photograph in the history of spaceflight.
A 360° rotation of its mast allowed the Mastcam-Z instrument to capture its first panorama, and we also got the first-ever audio recording from the red planet thanks to a microphone on the rover.
The camera system covered the whole landing process, showing the intense ride and the so-called « seven minutes of terror » descent sequence.
“Touchdown confirmed! Perseverance is safely on the surface of Mars, ready to begin seeking the signs of past life.” Said NASA engineer, Swati Mohan, during the live stream of the landing.
This landing is a considerable achievement of engineering that took multiple years to attain and was one of the main difficulties of the mission. As a matter of fact, because Mars’s air is so thin, it was more difficult to slow the spacecraft while descending, and it required a parachute and rockets, among other equipment.
Another challenge that the rover faced was the dangerous terrain it was headed to. Jezero Crater, the new home to the Perseverance Rover, was riskier than previous missions, as it presents deep pits, high cliffs, and big rocks making it harder for the rover to touch down.
However, NASA managed to design new pieces that allowed Perseverance to scan the surface and navigate around any obstacles which was a huge success.
The main goal of this mission is to hunt for the remnants of life and to study the Martian rocks up to 3.8 billion years old.
With this new source of data, the knowledge about our neighboring planet will pave the way to the future, when humans will set foot on the Martian soil.