Okay, so check it out, today I’m gonna spill the beans on my experience messing around with some data from that Chivas vs. Leon game. It was a wild ride, lemme tell ya.
First off, I snagged the data. I won’t bore you with the details, but think web scraping and a whole lotta cleaning. Got it into a decent format, ready to rock.
Next, I dove in. I wanted to see what the heck was going on beneath the surface of the final score. Who was passing well? Who was a defensive liability? The usual stuff.
The first thing I did was look at the overall passing accuracy. I used some Python (yeah, I’m a Python kinda guy) and libraries to crunch the numbers. I calculated the passing accuracy for each player on both teams. Then, I wanted to see how the passes were distributed on the field. So, heatmaps! Heatmaps of pass origins and destinations. Got a good sense of where the play was concentrated.
Then, I moved onto the defensive side of things. I calculated the number of tackles, interceptions, and fouls committed by each player. I wanted to see who was putting in the work defensively, and who was maybe a little too aggressive (or just bad at tackling!). I even tried to map out where the fouls were happening on the field, just to get a sense of the danger zones.
After that, I started looking at individual player performance. I dug into their passing networks, who were they passing to most often? Did they tend to play safe passes, or were they trying to thread the needle with risky through balls? Used some network analysis tools to visualize the connections between players.
Then, I got a little more fancy. I tried to build a simple model to predict the probability of a shot resulting in a goal, based on factors like the distance to the goal, the angle, and the type of pass that led to the shot. It wasn’t perfect, but it gave me some interesting insights.
It wasn’t all sunshine and roses, I ran into a few snags. One time, the data was inconsistent. Had to correct some mislabeled column of player name. That took me a while. And of course, getting those heatmap visualizations to look just right…that was a battle in itself!
So, what did I learn? Well, without giving away too much (gotta keep some secrets, right?), I spotted a few key players who were seriously influencing the game. Not necessarily the guys who scored the goals, but the ones who were pulling the strings in midfield. Also, one team’s defensive strategy was clearly more effective at shutting down the other team’s attacks. The data backed it up.
Overall, it was a fun project. Messing with real-world data is always a good way to sharpen your skills and learn something new. And hey, it gave me a whole new appreciation for the beautiful game!
