Changes to formula for 2018

I am changing the computer rankings formula on to reflect margin of victory starting with 2018.  This is big change to the core beliefs of the model which have always been based on simplicity.  To this point the model only considered wins and losses with no regard to margin of victory, away/home/neutral site for game, offensive or defensive stats, or month when game was played. A model that is this simple, this mathematical, and has excellent correlation to the final AP rankings year after year should not be tinkered with lightly.

By making this change to include strength of schedule I am hoping to accomplish 2 things:

First, this change should make early season rankings more in line with human polls starting from about weeks 3-4. Currently since margin of victory does not matter the formula can not really distinguish between a 3-0 Baylor team and 3-0 Alabama team.  It is only later in the season when there is more connectedness between Baylor’s and Alabama’s opponents or opponents opponents that the model can see Alabama’s wins to be superior to Baylor’s.   Now, with margin of victory the model will be able to reward a 60-0 Alabama win vs an average Vanderbilt team earlier in the season.

The 2nd goal deals with Auburn and the final 2017 committee prediction.  After 3 very successful years of nailing the playoff committee rankings before they came out, last year was a bust for the playoffPredictor methodology when it came to Ohio State / Alabama and the final rankings. The model put Ohio State at #4 in the final rankings, when the playoff committee had them at #5.  So what happened?   A lot of it has to do with Auburn.  Even after Auburn lost to Georgia in the SEC championship game, the computer did not punish Auburn much.  Going into the game the computer had them at #11 and after the game the computer had them at #12. So they only dropped one spot in the eyes of the computer.  But the humans dropped them from #2 pre-game to #7 post game. Because the formula uses this week’s computer rankings plus last week’s average bias, Auburn’s bias was so high (9 spots between computer at #11 and committee at #2) that when the computer only dropped them from #11 to #12, it expected the committee would similarly drop them from #2 to about #3 — what happened is that the computer was right before the committee saw it.

Let’s take a closer look — here are the week 13 computer and human rankings for 2017. Week 13 is post Auburn-Alabama game (where Auburn beat Alabama) but pre SEC championship game.  Note under the old formula (which does not take in margin of victory) Auburn is #11 in the computer.  and #2 in the humans.

Now here are the week 14 computer and human rankings. Week 14 is post SEC championship game, where Georgia solidly beat Auburn by 21 points. Again, under the old formula Auburn has moved from #11 only to #12 in the computer, and moved from #2 to #7 in the humans.

Clearly Auburn did not deserve to move from #11 in the computer to say #20 just because they lost to Georgia. Yes, they had 3 losses, but the losses were to Clemson (the #1 team in the final estimation of the committee), Georgia (played for the national championship) and LSU (average team), balanced with wins against Georgia and Alabama, who both played for the national championship.  Clearly that is a team resume that should have been right where the computer said (around 10) and not around 20.  So there is no fault in the computer here — it is the fault of the committee for not seeing what the computer saw earlier.


Now let’s look at how 2017 would have played out if margin of victory was part of the computer formula all along. At week 13 Auburn is #4 in the computer. Of course they will still be #2 in the humans — so their bias will be a lot lower – only a 2 spot bias.

At week 14 with the new formula, Auburn moves to #11 in the computer.  That coupled with the more normal team bias would have put them squarely out of the final top 4 in the models calculus, accomplishing the stated goals.


The other goal that adding strength of schedule will accomplish is get a more accurate computer ranking earlier in the season.  Back to 2017, here is the old model computer rankings for week 4

and here is what it would have been with the new margin of victory components included:

and finally here is what the AP poll was at that time:


Note the details like Wisconsin is #7 in the new method, outside of the top 15 in the old.   Alabama is at #3 instead of #5. Mathematically looking at the top 10 in all 3 lists,  the average delta of old to AP is 5.0 and the average delta of new to AP is 4.1, indicating about a 25% improvement in computer to human by week 4.   The correlation of the top 15 improves from .65 to .67.

Now, the method how I am incorporating strength of schedule is: 1 win is given for games where the final margin of victory is 16 or less points, 2 wins given for 17-32 points, and 3 wins given for a margin of victory 33 points or more.  I don’t like this, but it is a crude way to start this process and get the desired effect.  I feel there is a differentiation between a team down 16 and down 17.  At 16 points down, even late in the 4th quarter, that’s just a two score game.  Anything is possible in one play, so even if the offense has the ball and a 16 point lead, a pick six followed by a two point conversion makes a compelling game, and that is always one play away.  However, at 17 points (3 scores) down, I feel the other team will tend to give up a little bit more — you have really beaten a team when you are wining by 17 points with just 5 minutes left to play and you control the ball.   The ideal formula will take all these into consideration — If I have a 1st down, I am up by 9 points, the other team has no timeouts, and there is 3 minutes on the clock — that should all come into play.  I may use ESPNs in-game probabilities as the margin of victory component (when ESPN says team A has a 99.9% chance of winning, call the game then, and if that happens at 45:00 minutes of game time vs 59:40 minutes of game time — that is how the team earns margin of victory — but I may wait till next year to implement that.  I’m all for suggestions! Drop me a line — or at reddit under /r/cfbanalysis








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)
Canada 1962, 1966, 2017
Chile 1973, 1998, 2012
China 1978, 1996
Dominican Republic
Egypt 1959, 1965, 1999
India (lived here)
Iran (lived here)
Mexico (lived here)
New Zealand
South Africa
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
The Netherlands a.k.a Holland
Costa Rica

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


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



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:

Canada  (2016)
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)

Countries I have been to, by last decade last visited

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!

True Bid/Ask spreads on 3xETF, 9+ months out, out-of-the money calls

Most people like to sell premium and collect money.  Me, I like to buy premium in anticipation of a melt-up.

Today I placed an order to buy TQQQ 76.67C, Jan 2019, and another order to sell the same call option. I did this to see what the true market bid/ask spreads are.

In the morning, Schwab was publishing bid@1.40, midpoint@2.60 and ask at 3.80.   There had been no volume on this contract for several days, and the market has been up over the last few days/weeks.

I started placing orders to buy at $1.40, going up in 20c increments until the bid dropped again when I removed my order. The marketmaker bids rose and stayed elevated to $2.50, at which point my bid became best when my order was in, and the bid dropped to $2.50 when I removed my order.  I got filled at $3.50. I bought 10 contracts.

Then I sold  1 contract. Started at $3.50, got filled at $3.30.

So the real spread was $3.30-$3.50 (about 6%), and not the $1.40-$3.80 (46%)  that the platform said at the beginning of the day.


Compare to QQQ options for the same date (Jan 2019) — the same % out of the money (7% for QQQ, 21% for TQQQ) is 190 strike. Before starting, bid is $3.28 to ask of $3.35  (2%) . Using the same methodology buying 32 contracts and selling 2 I got filled buying at $3.29 and selling at $3.28 (0.3%).

Above is the view before starting QQQ trade

Above is the view after completing both QQQ trades (buying and selling).  Notice I am all of the volume. Started at 405, I bought 32 and sold 2, ending volume is 439.


So, the final analysis is as following:



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.

My IRA option, 2017

2017 was a great year for stocks.   I want to use this column to detail one particular trade I made in 2017 — my call option on TQQQ.

So first, why options?  I have been trading stocks since in my early twenties, but trading options are new to me, something I have only done for a couple years now. Back in 2016 I spent a lot of time watching Robert Shiller’s OpenYale class on Financial Markets. In that class he talks about the development of options and futures markets. Options and futures are labeled as derivatives markets, but when you step back and think about it the options market IS the real market. Think about a soybean farmer in Texas. On December 1 she will not particularly care about the spot price of soybeans on that day- her land is all harvested and ready for next year’s planting. But she would be very interested in the price of soybeans in October of the following year, when she could bring a crop to market if she decides to plant it now. Same for a company: Delta Airlines does not care so much about the spot price of jet fuel today, but they do care very much about what it will be 1-24 months from now, and getting a predictable price so they can plan their capital expenditures and fare prices accordingly. In a lot of ways the options market is what drives real business and spot prices are not nearly as important. So from that view options are not merely gambling.

I trade almost exclusively in an IRA account, and you can trade options there — just no margin, which is fine for me.  In my IRA I am granted level 1 options access, which means I can buy calls and puts. I have been comfortable with owning QQQ (Nasdaq 100) for 10+ years, one day in about 2013 I saw a ticker “TQQQ” pass on the bottom of the CNBC screen. I looked it up and it was 3x the QQQ return. I knew interest rates were ridiculously low then and I naively thought this fund achieved 3x by borrowing cash at low rates and using that to actually buy things like Apple and Cisco. So I went in – and it has done fantastically, returning about 1000% over that period.

In 2017 I decided to try TQQQ as an option. I’m not even sure that an option should be allowed on such a product as it is an option to being with. Sort of an option on an option.  The thought was buy an out of the money call. My goal was to buy an instrument that worked as follows — if the market went up 20-30% in 2007 this option would return +700%. That is a significant return on a sizable investment that can potentially be somewhat life-changing. At least enough to buy a new car or along those lines. If the market went up less, say 10%, this option would return -100%. Fortunately I have an overall portfolio where I can stand to lose a few % if the option did not pan out and I was comfortable defining my risk this way so I decided to pull the trigger.

On 3/31/17 the TQQQ option book looked like this:

The underlying spot price was 88.21. I knew I wanted to give myself some time – to me a short term option is more like gambling but a longer term option is a call on the market. So I picked January 2018. (The actual expiration is 1/19/2018). I also knew I wanted an out-of-the-money call. The only real question was how out of the money? I felt there was a chance the Nasdaq could return 25%, which would imply a TQQQ 1 year return of about 75%. How strong was my conviction? A 75% return on an 88.21 price is 154.3 Doing analysis with those number you get the following:

All the numbers above assume an initial investment of $10,000.

If I picked a strike of 130 I would make 1,250%. A strike of 90 would result in a profit of 400%. In either case, if the market was down for 2017 I would have lost -100%.

In the end I debated hard between the 100 and 120 strikes. I really wanted to pull the trigger on the 120 strikes, but I felt there was too much risk there. For example, if the market had returned 10% last year the returns would have looked like this:

It would have been a good year, The market would have been up for most, but that 120 strike option would have returned -100%. A total wipeout in an excellent year for stocks was too painful for me to contemplate, so I pulled the trigger on the 100 strikes.

I bought 16 calls of TQQQ strike 100 / 1/19/2018 on April 11, 2017. The purchase price was $6.00 each contract.

Often when you have a big winner you sell too early. I did a that this time, but I don’t regret it. My goal was to let the $10k bet ride to Jan 18, 2018 and take what it was worth then. Instead after the position doubled (which turned out to be 1 month later on 5/11/2017) I sold half the position. I sold 8 contracts at $12.60, getting back my $10k investment. I would then let the rest of it ride to expiration.

Except I didn’t. On 12/4/2017 I sold another 4 of the contracts, knowing the end of the option period was near and I did not want to lose all my profit. I sold that lot at $36 each.

I let my 4 remaining options ride to the end. Since they were now deep in the money calls near expiration, they trade at almost the exact difference between the underlying price and option strike price – (Delta of 1). I sold those for whatever the market would bear on market open on 1/17/2018, which turned out to be $63 each.

So my profit as it stands today is:
debit of $6 * 16 * 100 = -$9,600
credit of $12.60 * 8 * 100 = +$10,080
credit of $36 * 4 * 100 = +$14,400
credit of $63 * 4 * 100 = $25,200
Total of $40,080

So a return of 417% in 9 months. That will do pig, that’ll do.

What if I had waited and not sold at all? Based on the absolute final trade of TQQQ on 1/19/2018 expiration (167.94) – that would have been 1032%.

What if I had done the 120 strike? There the return becomes a fantastic 2200%. The calculations to here have been using the ask price of the options, not the midpoint. Another problem with these options are the spreads. Look at the 120 strike- $2.8 to buy and $1.3 to sell. That means as soon as you execute your order your $10,000 position gets cut in half. Even though that would have worked wonders in 2017 it is still a hard call to pull that trigger.

What’s next?
So fo 2018 I am going to keep 10% of my portfolio in options. My thinking is to ladder 4 options, with strikes of 3,6, 9 and 12 months and $5,000 position each. As for the underlying I am sticking with TQQQ – If we are in the last throes of a bull market, that one will go parabolic before crashing — I would not be surprised to see NASDAQ 8600 by the end of March 2018.  If that were to happen, that implies TQQQ would be at $260 / share.   If you took a position in March 16, 2018 call @ 200 strike which you can buy now for $2.00 each and you could sell them for $60 each, that’s a return of 2,900%.  Insane? Yes. Improbable? Yes. Impossible?  We’ll know in 54 trading days.

I’ll detail how that works out in 2019.