Nerdy Boring Statistical Analysis: How to Profitably Bet on Lewis vs. Hunt

This is part of a series of threads.
Thread 1
Thread 2


1. Introduction
Several days ago, I defined a statistical model to estimate win probability for MMA bouts.
I have since made several critical refinements.
Credit to @razmataz1314 for questioning the temporal linearity of athletic decline, leading to an important change in the model.

The science behind this model has grown increasingly esoteric and diverse, pertaining to integral calculus, continuous interest rates (believe it or not), and combinatorial statistics.
If anyone wants to learn more about its derivation, they may post a request on my profile page and @ me.
(BTW, @wookie brawl , I hope you got what you needed from me. Of course it's somewhat obsolete now).

However, since this model is likely profitable, it behooves me now not to release the software or the most intricate details of its derivation.

With recent improvements and a data sample size of 52 bouts, it may now be accurate enough to be profitable for legal betting purposes.

This probability model (now expanded to include financial statistics) is the basis for the betting guides which I will post.




2. WARNING: Hedging Your Risk
Investors have a saying: Hedge your risk by diversifying your portfolio.
Statisticians have a similar saying: Reduce your confidence intervals by increasing your sample size.

This means that if you only place very few bets based on this model's recommendation, you're not guaranteeing a profit. There is always risk, but it can be reduced by placing multiple small bets.

The table below shows you the relation between risk and sample size.
View attachment 237075

As you can see, there is a very high risk of losing money if you only place, say, 2 bets.
You may mitigate the risk by placing 9, though.
In fact, your risk is almost always lower for an odd number of bets than for an even number.
There are three reasons.
1. Combinatorial Statistics
2. Unfair Betting Lines
3. The Model's Accuracy
Basically, the model has a 73% success rate at picking the winning fighter.
However, if you place an even number of bets, then for half of the possible outcomes, you'll lose half of your bets.
Since the oddsmakers' odds are unfairly skewed against you (your returns are generally less than 100% of what you bet), you'll end up losing money in all of these cases.




3. Two Betting Styles
My model includes two betting approaches:


3a. High Risk (not recommended)
The high-risk method has a historical 44% profit margin.
No, I did not make that up.

However, these high returns come with grave risk.
Remember how risk is reduced by placing multiple bets? Well this method disregards that wisdom to some degree.

Bet spreads are heavily weighted towards particular fights based on their individual expected returns.
For example, my model's highest win was a colossal bet on David Teymur to beat Lando Vanatta.

Why did the model bet so much on this fight?


The high-risk betting method prefers weighting bets where it perceives a high expected return, and this happens when there's a great disparity between the oddsmakers' expectations and the probability model's.
Basically, the oddsmakers' were convinced Lando would win that fight, and my model was convinced that David would win. This gave it a +300 line on what it perceived to be a sure-fire win.
The high-risk betting model salivated at this opportunity.

David did win, resulting in massive gains.

However, its greatest loss came when the oddsmakers' were sure that Pedro Munhoz would defeat Damian Stasiak. My model ardently disagreed, and tried to exploit the tantalizing +425 on Damian.

It backfired. Badly.
Unless you're either
1. able and willing to lose a lot of money or
2. able and willing to place many dozens of bets to mitigate the risk,
don't use it. It's simply too dangerous.


3b. Low Risk (recommended)
The low-risk method has a historical 35% profit margin.
Obviously this is still very high, so I recommend this approach over the high-risk approach.

The low-risk method is characterized simply by
I. distributing your wager evenly across all bets, and
II. always betting on the fighter that the model predicts will win regardless of betting lines.

Again, refer to this chart for risk evaluation.
View attachment 237075
Again, place an odd number of bets to minimize risk.
Note that this chart absolutely does not apply to the high-risk method.




4. Gambling Guide for... UFC Fight Night: Lewis vs. Hunt
I was only able to make recommendations for 9 of the bouts on this fight card.
Disperse your wagers according to the instructions below.

High Risk (not recommended)
View attachment 237091
View attachment 237105

[TLDR]
Low Risk (recommended)
View attachment 237131
View attachment 237103
I did not understand any of this, but I respect the amount of work you put in.

That being said, I still just see a sloppy brawler in Lewis, and Hunt eats those guys up. Plus they are in New Zealand. I don't see how he can win. Hunt won't get taken down either. This is a stand up fight, and Hunt has like 10 times the experience.
 
Too complicated to lay out my entire strategy for tonight. However, the three things I disagree with re. your model probabilities:

- Lewis being so favoured. If he can't get top position he's going to be in trouble IMO. I have it 50/50.
- Nguyen being favoured after what Smolka did to him.
- Kunimoto being favoured when he's coming off a long layoff and hasn't looked good in any of his fights other than the quick win over Sarafian.

Win 2/3 of those and I'll be impressed.
Kunimoto was robbed HARD.
I'll take credit for those two picks.
 
I'm not gonna lie TS I read this and have no fucking clue what's going on
 
Profit Margin Tonight:
-1%.

Notwithstanding, if Kunimoto had been given the W, the profit margin would have been
+35%.

One fight can make all the difference...
 
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