So I decided to try building my own NFL prediction model. Just a fun side project, you know? Started simple, like anyone would. Watched a lot of games, thought I knew stuff. First try was honestly kinda pathetic.
Step 1: The “Throw Spaghetti at the Wall” Phase
Figured, hey, it’s mostly quarterback and defense, right? Started scribbling down some obvious stuff:
- W-L records (duh)
- Quarterback stats everyone knows – like passing yards and touchdowns
- Defense numbers – points allowed mostly
- Home or away? Seemed important
- Spread numbers people bet with
Threw all that into a free spreadsheet tool. Made some very confident predictions for Week 5 based on these super complex rules in my head. Yeah… didn’t go well. My picks were worse than flipping a coin. Total disaster. Realized I needed actual data and less gut feeling.
Step 2: Wrangling the Data Beast
Okay, time to find better numbers. Scoured the web for free NFL stats sites. Dumped tons of historical stuff into my sheet:
- Points for and against
- Yards gained and given up (both passing and rushing)
- Turnovers – fumbles lost, interceptions
- Sacks and QB hits
- Third down conversion rates
Got data for like 3 seasons back. This part sucked. It was boring, took forever, and I had to keep fixing messy formatting. Lots of copying and pasting. My finger hurt. But, finally had a decent pile of numbers to poke.
Step 3: Learning to Speak “Model” (Sort Of)
Heard people talk about machine learning for this stuff. Sounded fancy. Didn’t know where to start. Found this beginner library for Python that seemed less scary. Didn’t want to write tons of code.
Started simple again. Tried feeding my historical data into this basic linear model tool in the library. Basically asked it: “Hey, based on these stats for the last few weeks, who wins the next game?” Trained it on 2021 and 2022 data, tried to predict some early 2023 weeks.
At first, it was only slightly less wrong than my first try. Like embarrassing. Saw it massively overrating one or two things, like quarterback rating, and ignoring everything else. Had to teach it better.
Step 4: Playing Matchmaker for Stats
Realized just dumping all stats wasn’t smart. The model got confused easily. Needed to pick features that actually help tell winners apart. Experimented:
- Tried using point differentials instead of raw points
- Added strength of opponent numbers – how good were the teams they beat or lost to?
- Calculated trends – is a team winning or losing their last few?
- Dropped some basic stats that weren’t adding anything useful
Took a lot of trial and error. Ran the model, looked where it messed up badly, then tried adjusting the stats I fed it. Slowly, painfully, it started getting a little less wrong. More like 55-58% right on test weeks instead of 45%. Still terrible for betting, but progress!
Step 5: The Ugly Truth – It’s Never Perfect
Got kinda excited when it nailed a big upset prediction. Then immediately got humbled the next week when it picked a team that got blown out. Things the model still totally misses:
- Injuries to key players. (Big time!)
- Sudden coaching changes.
- Bad weather days that wreck passing games.
- Team morale and locker room drama (impossible to quantify!).
So yeah, it’s a model, not a magic ball. Mine’s way better than my first silly version, but it still screws up. You need good, clean data, you gotta pick your stats carefully, tweak constantly, and always remember football is messy and human. Building it taught me way more about football stats and how numbers lie than I ever expected. Fun project, frustrating, but satisfying when it gets a tough one right.
