Alright, let me break down my experience trying to predict Jessica Pegula’s performance. It was a wild ride, let me tell you!
So, I started off by just gathering as much data as I could. I mean, like, EVERYTHING. I scraped match results, stats on her opponents, court surfaces, weather conditions – you name it, I grabbed it. I was thinking, “More data equals better predictions, right?” That was my initial thought.
Then, I dove into the historical match data. I cleaned it all up, removing weird entries and inconsistencies. This took longer than I thought it would. I used Python with Pandas for this, which is my go-to for data manipulation. I visualized everything with Matplotlib and Seaborn to see the trends. Found some interesting stuff, like her win rate on hard courts versus clay, and how she performs against left-handed players.
Next up, feature engineering! This is where things got kinda fun. I created new features like “average unforced errors per match,” “first serve percentage against top 10 opponents,” and “momentum score” (which was basically just a weighted average of recent match outcomes). I even tried to factor in things like travel fatigue based on tournament locations and dates. It was a bit of a reach, I admit, but hey, gotta try everything!
After that, I experimented with a bunch of machine learning models. I started with simple stuff like logistic regression and decision trees. These were quick to train and gave me a baseline. Then I moved on to more complex models like Random Forests, Gradient Boosting Machines (GBM), and even a neural network with Keras/TensorFlow. The neural network was a pain to set up and didn’t perform as well as I’d hoped. It was a good learning experience, though.
The GBM actually gave me the best results. It seemed to be able to capture the nuances in the data better than the other models. I tuned the hyperparameters using cross-validation – that’s where you split your data into multiple folds and train/validate on different combinations to find the optimal settings. This part was tedious, but crucial for getting good performance.
I then started using the model to predict match outcomes. At first, my predictions were, well, not great. I was getting about 60% accuracy, which is barely better than just flipping a coin. I realized I was overfitting to the training data. So, I went back and added more regularization to the model, which penalizes complexity and prevents it from memorizing the training data.
I even tried incorporating external factors like recent news articles about Pegula’s form and any reported injuries. I figured sentiment analysis could help, but it was too noisy and didn’t improve the predictions much. This proved to be way harder than expected, especially the sentiment analysis part – tons of irrelevant data.
After more tweaking and testing, I managed to get the accuracy up to around 70%. It was still far from perfect, of course. Tennis is unpredictable! But it was a decent improvement. I learned that predicting individual match outcomes is incredibly difficult, even with a lot of data. There are so many factors that are hard to quantify, like the player’s mental state, crowd support, and just plain luck.
In the end, the model helped me understand some of the key factors that influence Pegula’s performance, and it gave me a slightly better chance of predicting her wins. But it was more of a learning experience than a foolproof prediction system. Plus, I got to play around with some cool machine learning tools. Would I do it again? Probably! But maybe with less data next time.
