# Beer Math for Athletes

In this analysis we examine the effects of getting hammered using our Proprietary Decisioning System [smoov.bra.in2023] to test our hypothesis: athletes who drink alcohol are not serious people

BEER MATH FOR ATHLETES 📊

A few of the guys in the office are MMA nerds. Not D1 level athletes, but still solid. Late 20’s. In their relative primes. They are decent fighters but struggle to move up belts.

This past week the topic of hitting a plateau came up.

In their own words they “do all the right things” in terms of eat/train/sleep… but they still “drink and party on the weekends” (to get chicks/have fun/blow off steam/bla bla bla).

But are the tradeoffs worth it?

Let us see.

I will explain it just as I explained it to them.

Because this is a meaningless blog and not a scientific journal, we will use quick and easy bathroom math to test our hypothesis.

Our hypothesis: athletes who drink alcohol are not serious people.

We will run the analysis using default settings in [smoov.bra.in2023]

First, we evaluate the known params:

*for the purpose of this exercise, let’s assume this is for a D1 college athlete that stays in school all 4 years*

• 4 years is ~35,000 hours or 1,460 days or 209 weeks
• Assuming we use the NCAA practice limit of 20 hours a week, that athlete will spend ~4,200 hours training during those 4 years
• This means the rest of the time (excluding games) is spent recovering/optimizing
• This leaves us a maximum of ~31,000 total recovery hours in this 4-year span
• This means the average athlete’s ratio of training time vs recovery/optimization time in a 4 year college career is 12% and 88% respectively

bla bla bla

Now for the fun part…

Now, let us consider a function of a complex variable f(z) = beer + t where we assign the amount of beers chugged, game minutes played, time spent recovering, and the amount of numbers gotten at the bar a specific weight to form a probabilistic argument to estimate the number of championship rings, cash, and glory the athlete may earn in their career.

Let us import the vars into our shitty bathroom formula:

t² [(x²) + (y²) + y(z²)] + rings
P = ——————————————
Σi (4x – num + beer²)

Solving for beer + t we can conclude with a 69.699% probability that it takes roughly 48 to 72 hours to fully recover after getting hammered to re-reach 100% performance levels (e.g., optimal physical and mental function) liver and metabolic processes notwithstanding.

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This means: athletes who get hammered every weekend (or once per week) will need to spend approximately half of their 4 years of eligibility just recovering from drinking (roughly 14,000 total hours) to operate at 100% baseline in terms of physical and mental optimality.

*for safety, let’s assume the 14,000 total recovery hours may vary 2,000 hours up or down in either direction as the genetics, drink tolerance, type of drink, drink amount, liver metabolism efficiency, etc may vary a great deal from athlete to athlete*

We can also assume that:

1. During this recovery period a significantly smaller percentage of junk is filtered from the body, metabolic processes are impeded, liver cells are damaged, fat loss is slowed while fat storage is accelerated, etc.
2. It only takes 1 drink to reach suboptimality (in terms of optimal physical and mental function for athletic performance/recovery/etc) and the danger zone is reached once the athlete reaches the 3 to 6 drink range (i.e., low tolerance rookie numbers).
3. Drinking suboptimality and inefficiency can also carry over into areas beyond athletic performance (e.g., impaired judgment, higher probability of accidents/violence/arrests, weakening of the immune system, heart attack, STDs, learning/memory/academic performance, depression, suicide, increased risk of getting sick, etc) which can lead to negative outcomes off the field

Considerations:

• Although beer math is PhD level broscience, this blog is not a scientific journal (yet) and this is not a white paper
• This anal-ysis was typed up on the toilet this morning and is probably full of typos and mathematical errors (because bathroom math is usually pretty shitty)
• This topic is beyond the scope of a twitter level bathroom analysis, but it would be interesting to analyze more variables on a deeper level and see what possible findings we may uncover

Possible vars for future shitty analysis:

1. How much talent and athletic potential may be unrealized/lost due to inefficient energy allocation (drinking recovery/training hours/athletic improvement)
2. How much possible energy can/would have been repurposed to improve from the freshman year 1 baseline skill set to reach the maximum skill set genetically possible when leaving school after year 4 if no time is spent getting hammered and recovering, if 20% of time is spent getting hammered and recovering, if 50% of time is spent getting hammered and recovering, etc
3. What is the maximum number/percentage of athletes in any recruiting class who can reach the next level (e.g., 2X, 3X, 10X, etc skill increase from year 1 to year 4) regardless of time spent getting hammered and recovering
4. What is the actual value of 14,000 academic hours (i.e., the aggregate time spent recovering for a 4-year athlete drinking once per week) in terms of tuition cost, scholarship value, future earning potential, etc

*due to the business nature of the NCAA, we can assume that every hour in the 4-year span has a dollar value and every choice made in the athlete decision tree can/will have an impact on future earnings/achievements either positively or negatively*

Conclusions:

This will need to be studied further, but holding the above to be true, we can conclude with near certainty that:

1. Athletes who get hammered will have suboptimal performance outcomes
2. Getting hammered can lead to recovery and monetary suboptimality/inefficiency
3. Getting hammered is a negative net and shows no real benefit to the athlete at all as it appears to be a steady trend line down in all cases