I think it’s wacky and outdated that we teach school math as a process leading up to calculus. That’s a relic of the Sputnik era, when were were all going to calculate rocket trajectories. Instead, starting in fourth grade, I think we should be teaching mathematical logic, proofs, and algorithms. We should also emphasize statistics and probabilities. Every kid should be able to do Boolean algebra and formal logic, rather than getting mired in just traditional algebra. We should also teach programming languages, especially C++, but we need to make sure that kids are also comfortable with the theory and concepts of algorithms, which underpins all programming language. Also, like Ada Lovelace, we should learn that math is a beautiful thing to be visualized, and not just formulas to be memorized. When we see an equation or algorithm or logical sequence, we should visualize it just as we do a line of her dad’s poetry, such as “she walks in beauty like the night.”
playoffPredictor.com Nailed It!
As if ever there was a doubt…
Well, well — In a surprise move, the http://t.co/OKY8ULofBw machine predicts Ohio State takes the 4th spot, not TCU. FSU moves up to #3
— Neville Aga (@CiscoNeville) December 7, 2014
Couldn’t have got it more right, and got it right the instant the games went final.
In fact, the playoffPredictor.com site got every single top 4 for the entire season (with the exception of the initial poll, of course — that is needed to build the bias file between the computer ranking and the committee ranking).
playoffPredictor.com correctly predicted a lot of things the committe did this year, including:
- TCU over Alabama in the October poll
- Ohio State over TCU and Baylor in the final poll
Score 1 for big data and analytics!
Looking forward to keeping the site going next year. I’ll probably also do some other stuff now that the math is coded — such as comparing all teams in the BCS era. Stay tuned!
3rd times a charm
well, I deleted my last 2 blogs.
hopefully this has twitter integration now
#coffee. How you get grown ups to do something that otherwise would be against our human nature. # PowerPoint pic.twitter.com/EGtxpaTo6J
— Neville Aga (@CiscoNeville) December 8, 2014
yeah!
My Halloween Trick-or-Treaters 2014
274 trick or treaters at our door this year! Here are all the costumes from most popular to least:
| Row Labels | Sum of count |
| elsa | 14 |
| ninja | 12 |
| batman | 11 |
| witch | 7 |
| mouse | 6 |
| princess | 6 |
| fairy | 6 |
| spiderman | 6 |
| power ranger | 5 |
| tenage mutant ninja turtle | 5 |
| zombie | 4 |
| cat | 4 |
| pirate | 4 |
| vampire | 4 |
| ghoul | 4 |
| anna | 3 |
| skelleton | 3 |
| fantasy girl | 3 |
| ironman | 3 |
| army skeleton | 3 |
| convict | 3 |
| bumble bee | 3 |
| cowboy | 3 |
| teenage mutant ninja turtle | 3 |
| mickey mouse | 3 |
| captain america | 3 |
| dracula | 3 |
| nerd | 3 |
| grim reaper | 3 |
| guardians of the galaxy | 3 |
| sport person | 2 |
| skeleton | 2 |
| thunder players | 2 |
| minion | 2 |
| snow white | 2 |
| monster high | 2 |
| lion | 2 |
| generic princess | 2 |
| scarecrow | 2 |
| baby | 2 |
| cat girl | 2 |
| girls | 2 |
| clown | 2 |
| dorothy | 2 |
| butterfly | 2 |
| balerina | 2 |
| thor | 2 |
| indian | 2 |
| general | 2 |
| regae | 2 |
| wolfman | 2 |
| angel | 1 |
| red riding hood | 1 |
| turtle | 1 |
| greek goddess | 1 |
| sharknado | 1 |
| basketball player | 1 |
| ghost | 1 |
| groovy girl | 1 |
| alice in wonderland | 1 |
| cat lady | 1 |
| robin | 1 |
| heart girl | 1 |
| smurf | 1 |
| hippie | 1 |
| supermodel | 1 |
| hot dog | 1 |
| gi joe | 1 |
| hulk | 1 |
| waldo | 1 |
| hunter | 1 |
| purge | 1 |
| incredible hulk | 1 |
| reindeer | 1 |
| cat woman | 1 |
| butcher | 1 |
| chef | 1 |
| bad surgeon | 1 |
| jelly | 1 |
| sophie the first | 1 |
| john lennon | 1 |
| supergirl | 1 |
| johnny bravo | 1 |
| swat team guy | 1 |
| joker | 1 |
| the purge | 1 |
| cinderella | 1 |
| toga | 1 |
| clone wars | 1 |
| giraffe | 1 |
| kit kat | 1 |
| wig creepy clown | 1 |
| batgirl | 1 |
| elmo | 1 |
| little red riding hood | 1 |
| raggadey ann | 1 |
| luigi | 1 |
| busted con— | 1 |
| marty mcFly | 1 |
| riddler | 1 |
| mermaid | 1 |
| santa claus | 1 |
| axe man | 1 |
| schoolthing | 1 |
| minecraft | 1 |
| shiner | 1 |
| minie mouse | 1 |
| fireman | 1 |
| cow | 1 |
| football player | 1 |
| black/brown guy | 1 |
| buzz lightyear | 1 |
| cross dressing girl | 1 |
| sully | 1 |
| mummy | 1 |
| superhero | 1 |
| muscle man | 1 |
| surper girl | 1 |
| deer | 1 |
| genie | 1 |
| doctor | 1 |
| texas chainsaw massacre | 1 |
| nothing | 1 |
| baseball player | 1 |
| optimus prime | 1 |
| tinkerbell | 1 |
| ou fan | 1 |
| transformer | 1 |
| panda | 1 |
| two faced ghost | 1 |
| peanut butter | 1 |
| vampire gypsy | 1 |
| piglet | 1 |
| whinnie the poo | 1 |
| pink lady | 1 |
| cat america | 1 |
| boba fet | 1 |
| jsaon | 1 |
| 20′s girl | 1 |
| killer | 1 |
| Grand Total | 274 |
Meraki Easter Eggs
So the Meraki dashboard is awesome. In addition to doing things like manage wireless and wired networks, it can do several other things. I know of 3. Add more in the comments if you know them:
In the make-a-wish field at the bottom of the dashboard type:
“unicorn”
“make me a sandwich”
“make me pink”
So who is the #1 IT company anyways?
John Chambers has given us a consistent vision over the past couple years – transforming Cisco into the #1 IT company worldwide.
Awesome!
During the breakouts I started reflecting on this vision in a little more depth. The first obvious observation is that if our goal is to be the #1 IT company worldwide then we can’t be #1 yet.
So if we are not #1 who is? And how far away are we from that position? How will we know when we get there?
Turns out there actually is a Wikipedia entry dedicated to this question. Their criterion is annual revenue, which is a good start, but I don’t think greatest revenue equates to #1 IT company. In my mind the #1 IT company would be a leader in annual revenue, market cap, and number of employees. Market cap in particular would be the most compelling singe statistic to me.
So how do we (Cisco) rank among our IT peers in market cap?
As of August 2014 Cisco is the #41 largest market cap corporation in the S&P. That is #41 of all companies, not just IT companies.
This begs the question “Of the 40 companies with a larger market cap, which companies are IT companies?” As we have articulated all companies are IT companies now (Ford is an IT company that sells cars, Coke is an IT company that sells soda) – and that is very true. There are no companies large or small that can survive this market without a strategic vision enabled by technology. However, for the purposes of this article let’s just agree that IBM is an IT company and Exxon is not an IT company, OK?
So pulling out the Exxons, Proctor&Gambles and Pfizers of the world I get that we are currently the IT #10 company by market cap. This is based off of S&P data – so only US companies considered here. Here is the list:
OK – so how long reasonably would it take us to get from #10 to #1? Well, let’s say the Internet of Things is that vision of every tree and cow is an internet connected tree and cow and the growth engine for us to get to say ~15% revenue growth a year for the next 5 years . On top of that say we can grow total earnings a year by 20% and say we even get P/E multiple expansion from where it is now at 15 to 20 – the math comes to
So that gets us to $431B market cap. Note that projection is waaaaaay optimistic. Everything would have to go right and then some to get to those kinds of numbers. Consider that 5 years ago in August 2009 we were at $130B market cap and $36B in sales – so in the last 5 years we have grown market cap by donuts and sales by a total of 25%. To think we will grow sales 100% in the next 5 years when we only did 25% in the last five is definitely stretch thinking.
Well, even at $431B five years from now, that still 30% smaller than Apple is today. Could Apple decline significantly? Sure. The iPhone is getting older and who know if the iPhone phablets will be a big bust? So sure they could decline over the next 5 years.
How about Google, currently in the #2 position right now? $431B would be bigger than Google right now, but it is real hard to see how Google would decline over the next 5 years. In fact, 5 years from now there will be more ad spending on digital media than TV media. Could they lose advertising dollars to Facebook? Sure, but the point is someone will win – weather it is Apple vs Microsoft or Google vs Facebook. So the short answer is no, even in a best case scenario we will not be the #1 IT company in 5 years.
These aren’t the droids you are looking for. If you look at the list closely, I’d argue that the top ones on the list are all consumer companies (Apple, Google, Facebook), leaving less enterprise challengers in our path (IBM, Oracle, Cisco). So, if you take the vision is to be the #1 Enterprise IT company in the world (which actually I think is the vision JC has) then things get more possible.
Here is the list again with the enterprise focused companies called out in green
I think IBM and Oracle are well within our sites. There is no reason we can not have an Advanced Services arm like IBM and I don’t see what propels names like IBM, Oracle and HP forward over the next 5 years. We have Internet of Everything and growth in mobility. They have— old technology.
So yes, even at a more reasonable 5% annual revenue growth and 10% annual earnings growth with no P/E expansion, that math gets us to
So yes, if IBM and Oracle stagnate and we execute, in 3-4 years we will be a bigger market cap player than either of those.
Microsoft however is a different story. We would need the optimistic scenario to play out to overtake them. If they grow at expectations and we grow at others’ expectations of us then we will not surpass Microsoft in the next ten years.
So here’s to double digit revenue growth and 20% earnings growth! I personally am looking forward to the journey of becoming #1!
If you have made it through the article to this point then tweet me back (@nevilleaga) I am curious to see who you think is the #1 IT company right now and who we are going to take down when we achieve our vision.
Why I’m changing my Twitter handle
So I have decided to change my handle from @nevilleaga to @CiscoNeville. Why? I tend to push work related content on twitter and I tend to do personal content on Facebook. What can I say? Scott Hanson inspired me!
So I probably will not change my style — that is– still tweeting only about a couple times a week and trying to back up my tweets with more in-depth analysis on this blog.
Will I be tweeting about IT Security? Cisco Security? Probably just a little bit. After 6 years now in a generalist role I have lost a little of the pure play around security, so my tweets tend to be everything Cisco sells (which is quite a lot). But I’ll always have the Security first perspective things (it touches every point of the network) and that’s where my hat lies.
Why not @CiscoSecurityGeek? Too many characters. Twitter only allows 15 
So catch me at @CiscoNeville
Volatility is much lower today than it was when I was born…
This was so non-intutive to me that I deemed it worthy of a quick blog post. Take historical prices from yahoo finance (or another website) and look at the daily high and low quoted prices of the S&P 500 during any handful of days from 1972 and compare them to a handful of days from 2014.
For all 1972-1975 the lowest daily volatility was 1.05% (October 9, 1972). The highest was 6.47% (interestingly October 9 again, this time 1974) and the median was 1.85%. An average day during that period would be Feb 26, 1973 where the S&P500 high was 113.26 and the low was 111.15 for a daily volatility of ~1.86%.
Contrast that to 2014. Over the last 90 days the lowest volatility has been 0.31% (April 23) and the median has been ~ 0.7%. In fact, 80% of the trading days had volatility below 1%, where as pointed out in the paragraph above zero trading days from 1972 to 1975 were below 1%
Conclusion: Even with all the internet trading and cheap commissions, daily volatility has gone down significantly over the past 40 years. You would think the free-er flow of capital would increase volatility, but it has not.
Non Stupid
The movie critic reviews Non-stop, and finds it lacking. Nonstop is the story of a flight from New York to London with a terrorist and federal air Marshall on board. The intent of the film makers is to draw the audience in by developing several characters among the passengers and have the audience try to figure out who the terrorist is. Is it the cop traveling to see his sister? The Arab? The flight attendant? The chatty passenger? The pilot? The air Marshall himself? The better question is who cares?
Who wants Warren Buffet’s $1Billion?
Last week Warren Buffet offered up a $1 Billion prize for anyone picking a perfect NCAA tournament bracket this March. Just exactly how difficult could it be to pick a perfect bracket? I set to find out that answer mathematically. After a couple days of analysis and Monte-Carlo simulations I have come up with an answer:
1 in 500,000,000,000 (1 in 500 billion)
For comparison, the odds of winning powerball are about 1 in 175,000,000. So for every person out there with a perfect NCAA bracket there would be 2,860 lottery winners. Daunting, but do-able.
How could I come up with such a number? Read on.
To answer this problem you have to first know how the NCAA brackets work. Not counting the first 4 teams in (which usually don’t factor into the bracket anyways) 64 teams are slotted into a 6 round, single elimination tournament. In order to get from 64 teams down to 1, precisely 63 teams must lose 1 game. So, if you assume that eachgame is a 50/50 crapshoot, then the math is simple- the odds are 1 in 2^63, or about 1 in 9.2 quintillion (9.2 * 10^18). Now that indeed would be an impossible task. To put that number in perspective, for each correct bracket with those odds there would be 52 billion powerball winners. Another way to wrap your mind around that number is to visualize that there are 8*10^17 square inches on the surface of earth (including all oceans, etc), so if you placed 11 brackets on every square inch of earth and they were all unique (no repeated brackets), you would have precisely one winner. Odds are that one square inch of real estate is not in your yard.
But each game is not a 50/50 crapshoot, and there lies the tantalizing probability of bringing those odds way, way down in your favor. For instance a #16 seed has never beaten a #1 seed. They have been playing this bracket format since I was a kid and it has not happened yet. Similarity a #2 seed rarely loses to a #15 seed. And if a #15 seed wins a game, it rarely wins a 2nd game. So we can use the fact that some teams are better than others to weight down the probabilities around the whole bracket.
To model this I used a Monte-Carlo simulation. Label each team A1-A16 (first region) through D1-D16 (last region). Assign “ping-pong balls” to each team based on rank. A #1 seed gets 99 ping pong balls (or their virtual equivalent). #2 gets 95, #3 gets 90 all they way down to #16 getting 1 ball. When 2 teams play, assign a random number between 0 to 1 for each team and then multiply that number by their ping-pong ball rating. The team with the higher final number wins.
Using this method, a #1 seed will beat a #16 seed about 99 times out of 100. Similarly, a #1 seed will beat a #3 seed about 99 times out of 99+90=189 (just over 50%). We can actually adjust these weights based on historical brackets. Here is the actual perl script I used to do this simulation.
Now, getting a perfect bracket is hard enough. If this year a bunch of #16-#13 seeds make runs deep into the tournament that scenario would be impossible to predict with the weights I have assigned to the teams. My weights make it much more likely that the top teams will make the deepest tournament runs, which models reality. To test the model I used the 2008 bracket as the model bracket I want the computer to come up with. Why 2008? That was a less-madness year, where all 4 #1 seeds made the final four, and there were few upsets throughout the brackets.
My script simulates a whole tournament, multiple times. I wanted to see how many times the computer would have to simulate the tournament to get all 63 games right (compared to the 2008 perfect bracket).
The first time through, the computer got 34 games right (out of a possible 63). The second and third tries yielded less than 34 correct games, but the 4th try improved on the result and correctly predicted 45 games. From there it takes a lot of iterations to get more games accurately predicted. In fact, here are the numbers I got.
Number of correct picks (compared to 2008 perfect bracket)
Iterations the computer needed to achieve this result
34 1
45 4
47 236
48 6,144
49 9,688
50 19,770
51 101,360
52 212,544
53 351,630
54 2,162,574
55 33,794,131
56 43,477,602
63 500,000,000,000 (est)
As you can see, the numbers go up very slowly past 50 correctly predicted games. My computer can crunch about 10,000 brackets a second, but that still takes a long long time to get to 500,000,000,000. So then, how do I arrive at 500,000,000,000? Graph the known data points on a logarithmic scale, and extrapolate where 63 lands. A decently straight line to me shows about 500,000,000,000.
So, tell me. What are you going to do with your billion?