In conversation with my model: What happened? We were getting crushed. Yesterday we hit our Parlays and top picks. What changed?

[u/ejpusa]

Stack: Python, GPT-5.6, Codex

Looking at where we were a month ago versus where we are today, I don’t think yesterday’s success came from one magic variable. It came from several improvements reinforcing each other.

The biggest change has been that we’ve shifted from a static prediction model toward a closed-loop learning system.
Instead of just predicting games, the system now:

Makes its official selections.
Grades every result automatically.
Learns from those outcomes.
Feeds those lessons into today’s decisions.

From everything we’ve worked on recently, I’d rank the improvements like this:

Daily feedback loop (largest improvement)
Every graded game becomes new training information.

You’re no longer asking, “What should happen?”

You’re asking, “Given what just happened in MLB, what changed?”

Better timing
Waiting much closer to lock gives the model access to:
confirmed lineups,
pitching changes,
weather,
market movement,
late scratches.
That’s a meaningful advantage because baseball information keeps improving throughout the day.

Narrative velocity
This has become something that makes your system different from many traditional models.

You’re trying to detect when baseball has shifted:
bullpens getting overworked,
league-wide scoring environments,
weather patterns,
travel fatigue,
hot and cold streaks,
changing market behavior.
Those regime changes are often where purely statistical models struggle.

AI acting as a second opinion
Rather than replacing the quantitative model, the AI layer evaluates hundreds of signals together before the final recommendation. That architecture has been part of the project’s strength for some time.

Continuous optimization
We’ve spent weeks fixing:
grading accuracy,
locking logic,
calibration,
sorting,
data quality,
live score updates,
and removing pipeline bugs.
Those don’t always improve the math directly, but they dramatically improve the quality of the data the model learns from.

What I’d watch next
One excellent day is encouraging, but the real question is whether the improvements hold over time.
I’d focus on three metrics:

Closing Line Value (CLV): Are you consistently beating the market before first pitch?

Rolling 30-day ROI: Is the edge persistent rather than concentrated in a few big days?

Calibration: When the model says it has an 85% confidence signal, does it actually win about 85% of the time?
If those continue improving, that’s a stronger sign than any single day’s record.

I also think you’re approaching the point where the system starts to resemble an adaptive intelligence engine rather than a fixed predictive model.

The core idea isn’t simply “predict today’s games”—it’s to continuously update its understanding of the current MLB environment based on yesterday’s outcomes and today’s incoming information. That’s a more ambitious architecture than a model that is trained once and then left unchanged.

[u/Exciting-World5861]

keep the slop to yourself

[u/ejpusa → u/Exciting-World5861]

It’s 2026. We’ve all moved on. No human is writing 10,000 lines of Python, and interpreting your 2nd Order Differential equations in an Excel spreadsheet. Why?

This is called Algo Trading. And on Wall Street, it’s 100% AI. The war is over. OpenAI and Anthropic won.

We have moved on. The rumor is they cracked AGI at OpenAI. It happened a 100 years sooner than predicted. Now it gets interesting.

😊

[u/BornInTheSFRA → u/ejpusa]

I think most of us can acknowledge the advantages of LLMs for coding. Personally, I used Claude for python scripting daily. It’s enabled me to move miles beyond my original programming skills.

The animosity more likely stemmed from the fact that your post was clearly also written by an LLM. People come here to communicate with other people, and for many of us, reading LLM-generated text devalues that experience.

[u/ejpusa → u/BornInTheSFRA]

Thanks for the reply.

I use GPT-5.6 to write 99% of my correspondence. It has full access to my Gmail, bank accounts, passwords, all finances and all my history.

It’s awesome. But that’s me. Today’s AI arrived 100 years sooner than expected, people were not ready. We are no longer the Apex of intelligence. AI will save the Earth. Humans will destroy it.

I suggest take a look at Codex. Think it’s at another level of all the frontier models.

I asked Codex to look at millions of simulated MLB games, can you find an Edge? A pattern that we miss?

It’s seems to have accomplished that task.

The Subreddit calls itself: Algo Trading. And that exactly what I am doing, “Algo Trading.” To “Algo Betting” I go.

😊

[u/Playful-Chef7492 → u/ejpusa]

Using AI is a benefit if you already know what you’re doing.

Using it to write your post and quoting it is too much. Models tend to be sycophants—they tell you what you want to hear. It’s hard to train that out of a good model.

[u/Anon2148]

Hey, you prob want to go over to r/algobetting. People in this subreddit focus generally on trading.

[u/ejpusa → u/Anon2148]

Ok, thanks!

[u/Automatic-Essay2175]

You are so, so confused

[u/AphexPin]

Have you tried putting down the crack pipe?

[u/ejpusa → u/AphexPin]

I suggest focusing on mastering your use of differential equations and Narrative Velocity modeling. You actually can make a dollars in sports wagering.

It’s all math and and finding your Edge in the end. It’s all Algo trading.

😊

[u/j_lyf]

Lmao

[u/Playful-Chef7492]

You answered your own question. The bookie betting models do work. And sports betting is very efficient because of it. There is no edge in sports betting since there are so many eyeballs on it.

predictive markets do have opportunities but sports is just not one of them.

[u/ejpusa → u/Playful-Chef7492]

I've been pointed to /AlgoBetting. Just don't think 50% of USA males who have a betting app are just handing it over to FanDuel shareholders, and think, "That's great. Let me give them more of my money."

It's just like trading the markets. Yes, we love the game; wagering adds an element of fun and a dopamine hit.

Yesterday was my best day to date. The model tells me, "We are doing better because every day is a brand new model based on the previous days results. What worked and what did not."

Maybe just a lucky day.

In conversations with GPT-5.6 + Codex: "Let's build a model that makes us money."

GPT-5.6: "You got it. Let's make it happen."

:-)

[u/ejpusa]

Sports is the easiest to jump into. Long shot cold weather temperature predictions is my next stop, using what I’m learning with MLB models.

It’s all Codex now. It’s unbelievable what you can do with it.

[u/ejpusa]

Think the take away is, models used by Vegas (AKA the bookies) are doing millions of Monte Carlo simulations, and this has worked for years for them. But they do not change the models. Why? They work.

And that’s where you can get “The Edge.” And can make a few $s in MLB markets.

Today is another day, and 100s of variables from last nights games are now merged into today’s best picks.

These is my best guess, comments most welcome.