Okay, so today I’m gonna walk you through how I tried to predict Mackenzie McDonald’s performance in his next tennis match. It was kinda a spur-of-the-moment thing, but I learned a lot. Let’s dive right in.
Step 1: Data Gathering – The Initial Scramble
First, I needed data. Like, a TON of it. I started by scraping match results from a couple of tennis websites. I’m talking about sites that keep records of everything: wins, losses, court surfaces, opponents, you name it. It was messy, I used some Python with Beautiful Soup, basically just went in and grabbed all the tables I could find related to McDonald’s past matches. It was a real headache cleaning up the HTML.
Step 2: Feature Engineering – Making Sense of the Mess
Raw data is useless, right? So, I had to figure out what mattered. I decided on a few key features:
- Win/Loss Ratio: Pretty obvious, how often he wins.
- Opponent Ranking: Who he’s playing against matters a lot.
- Surface Type: Clay, grass, hard court – each plays differently.
- Recent Form: Last 5 matches – is he on a hot streak or a slump?
- Head-to-Head: His record against his specific opponent.
I wrote some more Python scripts to calculate these from the scraped data. It involved a lot of `if` statements and loops, trust me.
Step 3: Model Selection – Choosing the Right Tool
I’m no AI expert, but I’ve dabbled. I went with a simple Logistic Regression model using scikit-learn. Seemed like a good starting point. I know there are fancier options, but I wanted something I could understand and tweak without getting completely lost.
Step 4: Training the Model – Feeding the Beast
This is where the magic (or, well, the slightly-less-than-magic) happens. I split the data into training and testing sets. Like, 80% for training, 20% for testing. Then, I fed the training data into the Logistic Regression model and let it do its thing. It basically learns the patterns between the features (win ratio, opponent ranking, etc.) and the outcome (win or loss).
Step 5: Testing and Evaluation – How Good Is It, Really?
After training, I ran the testing data through the model. This gave me predictions for matches the model hadn’t seen before. I then compared these predictions to the actual results to see how accurate it was. I looked at metrics like accuracy, precision, and recall. The results? Eh, not amazing. Around 65% accuracy. Definitely room for improvement.
Step 6: Prediction – The Moment of Truth
Okay, so now I had a model. Not a great one, but a model nonetheless. I gathered the data for McDonald’s upcoming match, plugged it into the model, and…it predicted a loss. Bummer.
Step 7: The Actual Match – Did I Get It Right?
He lost. So, I technically got it right. But honestly, I wouldn’t bet my life savings on this model anytime soon.
What I Learned
- Data Quality is King: Garbage in, garbage out. I need cleaner and more comprehensive data.
- Feature Engineering Needs More Thought: Maybe I missed some crucial features. Things like fatigue, weather conditions, or even McDonald’s mental state could play a role.
- Model Selection: Logistic Regression might be too simple. Maybe a more complex model like a Random Forest or a Neural Network would perform better.
- More Data is Always Better: The more historical data I have, the better the model can learn.
Next Steps
I’m not giving up! I’m planning to:
- Find better data sources.
- Experiment with different features.
- Try out more advanced machine learning models.
It was a fun project, even if the results weren’t perfect. It gave me a good excuse to mess around with Python and learn a bit more about machine learning. And who knows, maybe one day I’ll have a model that can accurately predict tennis matches. Until then, I’ll stick to watching them.
