# 2019 OKC Marathon in the books!

The 2019 OKC Marathon is in the books. This year Shelli, Austin and I all did the half-marathon. Results are back, and here is how I stacked up:

2 hours and 18 minutes to run 13.1 miles for an average pace of 10:33.  My goal at the beginning of the year was to run in under 2 hours — well, that was just not going to happen.  As much as I wanted to I couldn’t commit to the training needed to make it happen.  Still, I’m happy with 10:33. I placed right in the middle for my division – place 156 out of 310 total runners male 45-49.

Since I’m into statistics now, I also calculated my Z score to see how I did against a normal distribution. The average finish pace for my division was 10:59 with a standard deviation of 2:28. That puts me at a Z-score of -0.18, which has a 1-probability of 57% . (In other words, I placed in the 57th percentile for my division).   Not bad!

Here are the top, 50% and bottom finishers in my division:

# Never enough… red skittles

Red skittles are scarce. At least that’s always been my impression every time I open a mini pack we get as halloween candy. Too many yellows, too many greens. Never enough reds.  Possiblywrong recently published some data on 468 packs of skittles looking for duplicate packs. I wanted to use his research data to answer a different question: Are red skittles scarce compared to the other colors?

Using his data and the statistical analysis tools given to me by my MBA professor (Robert Dauffenbach) we can answer this question.  First the raw data- in 468 packs of skittles there were 5583 reds, 5499 oranges, 5688 yellows, 5301 greens, and 5669 purples.  Averaged out it is real close to 12 skittles of each color in a pack.

Close to uniform, but not exactly uniform.  Let’s analyze this further:

From his raw data the standard deviations of number of candies by color is about 3.2.  Here is the compiled data:

So, true population averages appear to be 12 candies of each color with a standard deviation of 3.2 per color (in one bag). Now the central limit theorem will help us here- even though the underlying distribution is uniform, a distribution of sample averages will be normal, assuming n is sufficiently large, and n=468 definitely satisfies this requirement (n>=30 is probably all we need to get from uniform parent to normal sampled means).   Now if we make the assumption that the population means and standard deviations are 12 and 3.25 respectively, we can answer the question if the difference in Yellows (12.15) to Reds (11.93) is statistically significant.  The standard error of the mean is the standard deviation over the square root of the number of samples = 3.25/sqrt(468) = 0.15.   That means if we have have 12.15 yellows, that’s one standard error of the mean from 12, a result we should find 68% of the time.  In other words not statistically significant.   (Busted — yellows are not more common).  Reds with a z-score of -0.47 are also within 1 standard deviation of the standard error of the mean meaning reds are plentiful — they are not held back, regardless of what I think.

However, the data does point to one outlier — greens. At an average of 11.33 that is -4.52 standard deviations below what’s expected.  The probability associated with a z-score of -4.5 is about 1/100,000 — meaning if it was a daily possibility you expect to find it 1 day in 275 years.  That is statistically significant and the null hypothesis that greens are filled at 12 per pack is rejected.  The alternative hypothesis is accepted, and that implication is that skittles intentionally under-fills green in order to keep packs at 59 per pack and not at 60.

Who would have thunk it? Greens!!

# Values for owner-api.teslamotors.com for Tesla_Client_Id and Tesla_Secret

TESLA_CLIENT_ID=81527cff06843c8634fdc09e8ac0abefb46ac849f38fe1e431c2ef2106796384

TESLA_CLIENT_SECRET=c7257eb71a564034f9419ee651c7d0e5f7aa6bfbd18bafb5c5c033b093bb2fa3

# Book Review: Get in the Boat

Recently I finished reading the book Get in the Boat, by Pat Bodin.

The point of this book is that technical people are not in the boat with corporate leaders, because they speak a different language and have different priorities and risks.

Well, that’s nothing new.

However, the part of this book I found particularly enlightening was the treatment between technologists and IT.  That hit me like a 2×4 square to the jaw.  I got into this field 20+ years ago because coming out of college I saw with wonder the way Cisco Systems was connecting and changing the world. It was clear to me, even back in 1992, that people in the wake of Cisco Systems were the movers and shakers of the world, and only good things came from being associated with this company of strength. At that point in time central IT did not exist, not like today anyways, and central IT was being elevated — from a cost center to a strategic focus for the business. Very relevant.  Somewhere along the way IT became a burden — divorced from the leading-edge technology that changes business for the better and gives each business who properly digests technology a competitive advantage against their peers and now married into a me-too table-stakes of basic uptime and SLA fulfillment.

For example, I know a VP at a leading higher education institution who has a main job of approval of emails that have to go out campus wide.   Think about that for a minute- when did IT go from creating a project where a person in London could color-match a car manufactured in Berlin to  bureaucratic email approver?  And this is a vice-president.  Makes you wonder how relevant the lowlife IT individual contributor is to the president of the University?

The book does a great job of understanding value-chaining: How can your actions at the Red level impact Blue, and impact Green.  Don’t understand those colors? Read the book! It reinforces the basic message we already know inside – You are not relevant because of what you do, but because of how you affect other people.

When you talk to green about “technologists” they equate that word with blue people – lines of business who practice “shadow IT”.  When you talk to Red about “technologists”, they will think IT.  Big mistake.  Even the way we as IT talk about Blue people is in a way to delegitimize and dirty them (again, shadow IT).  We have to be those Blue people, not bash them!

For anyone who has read the Phoenix Project, this book is a great 2.0 read to that book. A lot of the principles and messaging connect.

This is a fantastic read for anyone who works in my field of technology, especially those who work or sell into information technology.

In my line of work (Higher Education) – we may have 5,000 full time equivalents in an organization.  Of those, Id say Red is about 200 (IT people), Blue is 4,795 (faculty / staff) and Green is about 5 people.  5 People that’s all. A lot of titles like VP of applications may seem to the untrained eye to be Green, but they are blue.  A teacher who is leading edge and consumes technology in her classroom in a way years ahead of her peers and gets better grades for students – Blue, not Red.    I think of Red as lone-wolves in centralized IT. Period.

Get in the Boat is available on Amazon for \$17.95.

# When does an economist recognize inflation (CPI)

I’ve always wondered… and want a real economist to tell me the answer.  I am about to head to Vegas and have a burger, fries and shake at Shake Shack.  For \$18.   At the same day, no doubt, the BLS will release some nutty data that inflation measured at the CPI level grew only by 2% this year.  The McDonalds hamburger, fries and shake that I bought in ~2010 (for \$5) to the \$18 Shake Shack equivalent is clearly not 2% each year, its more like 20% each year.

OK, So I get that an economist would see the Shake Shack burger and the McDonald’s burger as different items, so inflation would not apply.  This got me to thinking — how would an economist view this logic:

Baseline: In a 1 town global economy with 100 people and 1 restaurant (a McDonalds).  They sell a quarter pound burger for \$1.00. All 100 residents eat one of these burgers every year.   Year 1 CPI=100, which also equals the GDP.

At the beginning of year 2 this hypothetical economy gets a new restaurant – a Shake Shack.  It charges \$2.00 for a quarter pound burger. However, they have no sales for the year. All 100 people still eat one burger at McDonalds every year. Year 2 CPI = 100, GDP=100.  <- no inflation in this economy.

During year 3, ten people switch eating their annual hamburger from McD to Shake Shack. GDP = 110 (90 from McD, 20 from SS).  However, CPI = 100, since the burger at SS is considered a “different product” or has “productivity gains” or some other such garbage.  After all, if they were interchangeable products no rational consumer would pay \$2 for something they could get for \$1 down the street <- no inflation in this economy.

Year 4, all people stop eating at McD and eat at SS.  GDP = 200.  CPI remains at 100, since in theory, these 100 consumers could have eaten at McD. <-still no inflation in this economy

Year 5, the McDonalds closes down. GDP=200, CPI=100.  Even though people are still eating a burger, that is now twice as expensive, and there are no other options, there is still no inflation since theoretically someone could open a McD?  <–?

Year 6, McDonalds corporate buys out Shake Shack in a hostile takeover. They remodel the Shake Shack restaurant, bringing back all McDonalds decorations and “classic” recipes for the burger.   However, they keep the price at \$2 each.  GDP=200, CPI =100.

Note that in year 6 you have the exact same conditions as year 1, same product, 2x as expensive, however there has been no inflation at all in this scenario.

How would an economist react to this line of thought?

# Idea – model hourly (ex open/close) stock performance as random variable

Idea that hit me today while driving — there is a lot of timing bias in the behavior of an individual stock due to the fact 1) humans are on a daily cycle and 2) opening prices gap from yesterdays close / close positioning.  There is also the fact of after market hours news to move prices.

Thing that I am looking for — model a stock performance as a random variable that is *normally distributed*  <- we find that modeling the daily return of \$AAPL or \$MSFT is not normally distributed (because of things like October 1987 <- that is an event that is so many standard deviations off the curve that it olny had a 10^-79 probability event, but it happened anyways).   Hypothesis:  We know daily price movements are NOT normally distributed, but perhaps the price movements from, say 11am to 1pm ARE normally distributed.

Check the correlation of \$XXX from daily performance to 11am-1pm performance.   Are they correlated for something like \$AAPL?    What is the 1 year return of \$AAPL using only 11am-1pm vs full day performance?    Need to test this and report the findings here later.

# Countries visited by Hoshi and Nergish Aga

My parents have been to 52 countries. Here is the list:

Argentina 1973, 1998
Australia 1989, 1997, 2014
Austria 1963,
Bahrain 1979
Bolivia 1973 Landed at worlds highest airport La Paz
Brazil 1972, 1973, 1997 (lived here)
Chile 1973, 1998, 2012
China 1978, 1996
Dominican Republic
Egypt 1959, 1965, 1999
Estonia
Finland
France
Germany
Greece
Iceland
India (lived here)
Iran (lived here)
Ireland
Israel
Italy
Japan
Kenya
Malaysia
Malta
Mexico (lived here)
Monaco
Morocco
Nepal
New Zealand
Norway
Oman
Pakistan
Russia
Singapore
South Africa
Spain
Sweden
Switzerland
Thailand
United Kingdom
United States 1959-1963, 1965-1972, 1973-1977, 1979 to present (lives here)
Venezuela 1972 at Caracas airport on way to Brazil
Yemen 1959, 1965
Zambia
Zimbabwe
Bahamas
The Netherlands a.k.a Holland
Panama
Nicaragua
Costa Rica

They have also been to 3 other places that are not UN member states:
Hong Kong
Falkland Islands
Tahiti

For further reading on the subject, pick up a copy of “Such a Wonderful Journey” by Hoshi Aga. It is available on Amazon.com

# Countries I have been to

Today during the OU PMBA icebreakers someone stated they have been to 34 different countries. I confidently said, “yeah, I’ve been to at least 34”. I decided to count them up today, with a map for the last year I was in said country.

I was wrong, I have only been to 32. Here is my list:

USA (2018)
Mexico (2018)
Haiti (2010)
Brazil (1973)
Argentina (1973)
UK (1998)
France (1998)
Germany (2014)
Austria (2014)
Switzerland  (2014)
Italy (2014)
Greece (2016)
Bahrain (1980)
Iran (1979)
India (1986)
Singapore (1980)
China (1978)
Japan (1978)
Hong Kong (1978)
Australia (2009)
Fiji (2009)
Monaco  (1973)
Bolivia (1973)
Venezuela (1973)
Oman (1998)
Peru (1973)
Pakistan (1978)
Egypt (1986)
Saint Martin (2012)
Sint Maarten (2012)
Bahamas (2001)

Green = 2010s
Light green = 2000s
Yellow = 1990s
Orange = 1980s
Red = 1970s

So sorry classmate who has been to 34 (or did you say 36) — you are the real globetrotter!

# Larry Kudlow

Sad news that Larry Kudlow suffered a heart attack. Wishing him a speedy recovery.

Larry Kudlow has been one of my favorite TV personalities for years. My favorite is Kudlow and Kramer, when they had their run in the 2000-2010 timeframe. I always appreciate Larry’s optimism and his true, core belief that “free market capitalism is the best path to prosperity”.    Get well quick, Larry.

# Wow, the new tax bill is really a substantial reduction

Looking at the proposed new marginal tax rates and brackets on the senate website (https://www.finance.senate.gov/imo/media/doc/12.2.17%20HR%201.PDF)   from the current 2017 rates on wikipedia (https://en.wikipedia.org/wiki/Income_tax_in_the_United_States#Marginal_tax_rates_for_2017) how big a tax break can you expect?

Quick math for a family with \$150k annual taxable income with the old (2017) method:

•      10% on \$9,325                           =   \$932.50
• +  15% on (\$37,950 – \$9325) =   \$4293.75
• +  25% on (\$91,900 – 37,950) = \$13,487.50
• + 28% on (\$150,000 – \$91,900) = \$16,268.00
•                                                                ———————–
•               total (2017)                               = \$34,981.75

and now with the new (2018) method:

• 10% on \$19,050                           =    \$1,905
• 12% on (\$77,400-\$19,050)  =     \$7,002
• 22% on (\$140,000-\$77,400) =   \$13,772
• 24% on (\$150,000-\$140,000) = \$2,400
•                                                     ———————
•           total (2018)                           =\$25,079

So basically \$10k less in tax, or an overall reduction of around 30%

Of course that does not take into account changes to itemized deductions, but at least its a start to wrap your mind around the new tax bill.