After watching UFC 309 systematically demolish my model's predictions with the precision of a prime Anderson Silva, I've spent the past week in a caffeine-fueled debugging frenzy. Between muttering obscenities at my terminal and questioning my life choices, I've had some genuine epiphanies about AI development that might save others from my particular flavor of statistical hell.
The Problem with Yes-Man AI
Large Language Models are like that friend who encourages your 3 AM business ideas. "A blockchain-based dating app for cats? Brilliant!" This becomes particularly dangerous when you're knee-deep in feature engineering and looking for validation rather than criticism.
After extensive testing, I've discovered something fascinating about GPT o1's mathematical capabilities. While most LLMs give basic statistical approaches, GPT o1 can dive deep into complex statistical problems. But the real breakthrough came from building an AI feedback loop: get statistical approaches from GPT o1, feed them to Claude for implementation (it writes cleaner code), then feed Claude's code back to GPT o1 for validation.
Even debugging has improved. When Claude's code throws an exception, feeding the error back works once. But for persistent issues, asking Claude "what do you need to debug this error?" is far more effective. It responds with diagnostic code that, once fed with real data, leads to actual fixes rather than band-aids.
This iterative process, combined with extensive prompt engineering and lots of sample data to help GPT o1 truly understand the problem domain, has led to the first major mathematical breakthrough in V5's development: our new Bayesian approach to handling fight statistics.
Bayesian Beta Binomial: The Zero Division Solution
This is only one of many many improvements in V5 but I find it super interesting so I'm writing about it and fuck you if you don't want to hear about it. Let's dive deep into how we handle the dreaded divide-by-zero problem in fight statistics. When calculating success rates (like submission accuracy or strike accuracy), we use Bayesian Beta Binomial analysis to provide meaningful priors that smoothly handle edge cases.
The approach works like this: Instead of naive division that breaks on zeros, we model each ratio as a Beta distribution where:
- α (alpha) represents successful attempts plus prior successes
- β (beta) represents failed attempts plus prior failures
- The posterior mean (α / (α + β)) gives us our smoothed estimate
For example, with submission accuracy:
submission_accuracy = (submissions + α) / (submission_attempts + α + β)We determine our priors (α, β) through empirical Bayes by analyzing the historical distribution of success rates across all fighters. These priors vary by stat type, reflecting the different base rates we see in MMA:
- Submissions: Lower α and higher β values reflecting their relative rarity
- Strikes: More balanced α and β values reflecting higher occurrence rates
- Takedowns: Intermediate values based on historical success rates
This approach elegantly handles three critical cases:
- Zero attempts: Returns the prior mean (α/(α+β))
- Small sample sizes: Heavily weights the prior
- Large sample sizes: Converges to the empirical rate
To understand why this matters, consider how submission accuracy was traditionally handled: A fighter attempting 10 submissions and landing none would be assigned 0% accuracy. This creates two problems: it skews averages downward, and when comparing fighters (fighter1_sub_acc / fighter2_sub_acc), we risk another divide-by-zero error.
Our Bayesian approach instead provides more nuanced, realistic estimates. For example:
- 10 attempts, 0 successes = 3.5% accuracy
- 9 attempts, 0 successes = 3.8% accuracy
- 8 attempts, 0 successes = 4.1% accuracy
This prevents over-punishing unsuccessful attempts while ensuring we never hit true zero. The accuracy gradually increases as sample size decreases, reflecting our increasing uncertainty with smaller sample sizes.
The V5 Redemption Arc
For V5, we're continuing to embracing AutoML (specifically AutoGluon) to eliminate the uncertainty in model optimization. Through V1-V3, I spent countless hours manually tuning gradient boosted algorithms like XGBoost and CatBoost. While I learned an enormous amount about hyperparameter optimization and various other tuning, I was never entirely confident I was meeting professional machine learning engineering standards.
AutoML removes that uncertainty. It systematically explores model architectures and hyperparameters in ways that would take me months to do manually. I still do significant tuning, but now it's faster and more reliable. No more wondering if I missed some crucial optimization or made rookie mistakes in the model architecture.
What This Means For Users
MMA-AI.net will continue hosting Model V4 predictions while V5 is under development. However, there won't be any improvements to the current model during this transition. If you want to take a step back and not ride with me for a month or two while I finish V5, I completely understand. This isn't about quick fixes - it's about building something that actually works.
The Bottom Line
Sometimes you need to lose six parlays in a row to light a fire under your ass and actually learn some math. But at least we're failing forward, and V5 will be built on more solid foundations.
P.S. To the UFC 309 fighters: those 3 AM tweets weren't personal. It was just the model and the Monster Energy talking.