Original idea is from this GitHub Repo. I did an in-depth analysis and visualization on the FIFA 2018 dataset. The end goal is to predict the best possible top 10 international squad at the upcoming World Cup.
The data is scraped from the website SOFIFA by extracting the Player personal data and Player Ids and then the playing and style statistics.
| 0 | |
|---|---|
| Unnamed: 0 | 0 |
| Unnamed: 0_x | 0 |
| ID_x | 158023 |
| Name | L. Messi |
| Age | 30 |
| Photo | https://cdn.sofifa.org/sm/18/players/158023.png |
| Nationality | Argentina |
| Flag | https://cdn.sofifa.org/flags/52.png |
| Overall | 94 |
| Potential | 94 |
| Club | FC Barcelona |
| Club Logo | https://cdn.sofifa.org/xs/18/teams/241.png |
| Value | €118.5M |
| Wage | €565K |
| Special | 2161 |
| Position | CF|ST|RW |
| Unnamed: 0_y | 0 |
| Acceleration | 92 |
| Aggression | 48 |
| Agility | 90 |
| Balance | 95 |
| Ball Control | 96 |
| Composure | 97 |
| Crossing | 77 |
| Curve | 90 |
| Dribbling | 97 |
| FK Accuracy | 92 |
| Finishing | 95 |
| GK Diving | 6 |
| GK Handling | 11 |
| GK Kicking | 15 |
| GK Positioning | 14 |
| GK Reflexes | 8 |
| Heading Accuracy | 71 |
| ID_y | 158023 |
| Interceptions | 22 |
| Jumping | 67 |
| Long Passing | 87 |
| Long Shots | 88 |
| Marking | 13 |
| Penalties | 75 |
| Positioning | 93 |
| Reactions | 95 |
| Short Passing | 88 |
| Shot Power | 85 |
| Sliding Tackle | 26 |
| Sprint Speed | 87 |
| Stamina | 73 |
| Standing Tackle | 28 |
| Strength | 59 |
| Vision | 92 |
| Volleys | 86 |
