I Built a Betting Model. It Took 6 Months and $200. Here's What I Learned.

For years, I bet based on intuition and research. Then I decided to build an actual predictive model. Here's the truth about what that involved.

The time investment:

Learning Python (I came from a non-technical background): 2 months, maybe 50 hours Gathering and cleaning data: 1 month, 40+ hours Building the initial model: 1 month, 30 hours Testing, refining, and validating: 2 months, ongoing

Total: roughly 150 hours over six months.

The financial investment:

Sports data subscription: $15/month × 6 = $90 Computing resources: basically free (Google Colab) Books and courses: about $100 Total: roughly $200.

What the model does:

My model predicts point spreads for NFL games based on team statistics, situational factors, and historical performance. It outputs a predicted spread, which I compare to the actual betting line. When the difference is large enough, that's a bet.

What I learned building it:

Data is everything. The model is only as good as the inputs. I spent more time cleaning data than actually building algorithms. Garbage in, garbage out is absolutely real.

Simplicity beats complexity. My first models were overly complicated, trying to capture every possible variable. They overfit to historical data and performed terribly on new games. My current model uses about 15 variables. Less is more.

Backtesting is seductive and dangerous. Any model can be tuned to perform well on historical data. The real test is out-of-sample performance. My backtest results were great (57%!). My live results are more modest (54%).

Models don't eliminate emotion—they help manage it. Having a model gives me confidence to bet even when my gut says otherwise. It also stops me from betting when my gut says yes but the model says no.

My model performance:

After one full NFL season of live betting: 54.2% win rate on sides bets. Profitable, but not by the margin my backtests suggested.

Was it worth 150 hours? Honestly, yes—but not purely for the profits. I now understand sports analytics at a much deeper level. I can evaluate other people's models and claims. And I have a framework for continuous improvement.

But if you're expecting to build a model in a weekend that prints money, you're going to be disappointed.