Okay, so yesterday I was messing around with some football data, right? I wanted to see if I could put together some player ratings for that Ud Las Palmas vs Real Madrid game. It was a bit of a headache, but I think I got something decent in the end. Here’s how it all went down.
First things first, I needed the raw data. I started by trying to scrape some stats from a few sports websites. It was a proper grind, jumping between different sites, because some had more detailed info than others. I’m talking about things like shots on target, passes completed, tackles, fouls committed, all that jazz. Some sites were easy to scrape, others were a total pain, with weird layouts and stuff. Eventually, I managed to pull everything I needed into a CSV file. That alone took me like, two hours.
Then came the fun part: cleaning the data. You wouldn’t believe the state it was in. Missing values everywhere, inconsistent formatting, spelling mistakes… the works! I used Python with Pandas to sort it out. I had to fill in the missing data, usually with the average for that position. Fixed all the formatting so everything was consistent, and got rid of any duplicate entries. Basically, I spent a good hour just tidying everything up.
Next, I started working on the rating system. I decided to keep it relatively simple. I gave points for positive actions – goals, assists, key passes, successful tackles, interceptions – and deducted points for negative actions – fouls, yellow cards, missed shots, getting dispossessed. I weighted each action based on how important I thought it was. So, a goal got way more points than a completed pass in your own half, for example. Getting the weights right was tricky; I had to tweak them a few times to get ratings that seemed realistic.
After that, I ran the data through my rating system and got the initial player ratings. But they didn’t look quite right. Some players who had a quiet but effective game were rated too low, while others who had one or two flashy moments were rated too high. So, I added some positional adjustments. For example, defenders got a bonus for clean sheets and a penalty for errors leading to goals. Strikers got a bonus for converting chances. This made the ratings much more balanced.
Finally, I put together a simple table with the player names, positions, and ratings. I looked at a few highlights of the game to see if my ratings made sense. For the most part, they did! There were a few players where I disagreed with my own system, but overall, it seemed pretty solid. I even compared my ratings to what some of the sports news sites were saying, and they were surprisingly similar.
Here’s a quick summary of the key steps:
- Data Collection: Scraped data from multiple sports websites.
- Data Cleaning: Used Pandas to clean and format the data.
- Rating System: Developed a points-based system with weighted actions.
- Positional Adjustments: Added bonuses and penalties based on player position.
- Validation: Compared ratings to game highlights and sports news sites.
It was a fun little project, even though it took way longer than I expected. I learned a lot about data manipulation and building a basic rating system. I might try to refine it further in the future, maybe adding more advanced stats or a more sophisticated weighting system. But for now, I’m pretty happy with the results.
