In a previous post, we looked at daily temperature readings 1949-2018 from an observation spot in Stockholm archipelago, and noticed a very clear increasing trend.
Here, we’ll look at the same spot & same period, but instead of temperature, we’ll focus on wind speed. What do you think, has the wind speed increased, decreased or hasn’t it changed at all in these 70 years….?
The same clear trend as for temperatures – wind speed is increasing. Personally, I’m not surprised, since if temperatures are raising, that means that there’s more energy in the atmosphere, and that should result in more wind.
with a bit of skill, domain knowledge and not least guts, it’s fully possible to double, triple or increase initial stake by ecen larger factor, in just a few weeks. Of course, as always in any investment, these is a possibility of significant losses, but with some careful preparations and follow-up, you can indeed 100-fold hour money in 10 weeks.
Ever thought about how bookies set the odds for events…? The problem at hand for a bookie is to set the odds in such a way that he makes a profit, regardless of what outcome the event has.
The way bookies achieve this feat is twofold:
– moving odds, i.e. The odds for the event change depending on how the punters place their bets.
– adding a markup to the odds, i.e. lowering the odds just a tiny fraction.
Below a simulation where random punters place random amounts of bets on one of two outcomes, A or B. The solid red and blue lines show the cumulative bets over 10 betting rounds, while the red & blue lines with ‘x’ shows the money at risk for respective outcome. The orange line shows the total anout of income, i.e sum of bets.
In the figure, the blue x- lime is above the orange line, which means that should the outcome of the event be A, then the bookie will lose money. To guard against this loss, the bookie will ‘markup’ the odds for event A, by lowering the odds a bit, typically 5-10%.
A quick test shot with KERAS, inspired by this tutorial using the MNIST dataset of more than 60000 images of hand written digits. Task at hand: correctly identify as many as possible of these 28 x 28 images, looking like this:
I’m flabbergasted….! In less than 70 lines (seven-zero!) of Python/KERAS-code, and after a training session of about 20-30 min on my laptop, the CNN is able to correctly identify 99.2% of the images….!
These really powerful libraries are way cool – almost to the point where they take the fun & challange out of it…! 🙂
Posted in Big Data, Complex Systems, Data Analytics, KERAS, Machine Learning, Neural networks, Numpy, Python
Tagged AI, Data Analysis, KERAS, Machine Learning, Matplotlib, Neural networks, Numpy, Python
32 teams, 64 games. 3 different ranking models tested, FIFA’s official, a “wisdom-of-crowds” (static), and a dynamic version of the wisdom-of-crowds model.
Prediction results: 67% of game outcomes correctly predicted.
Betting results best strategy (max probability), with uniform betting on all 64 games : 32% profit (as measured over overall stake)
Notable: all three medalists, France, Croatia, Belgium are “outliers”, i.e. outside of a confidence interval of 89%, in all three ranking schemes.
Posted in Bayes, Data Analytics, FIFA, Gambling, Probability, PYMC, Python, Statistics
Tagged Bayes, Gambling, Probability, PYMC, Python, sports
In order to figure out how the NMEA-WiFi Gateway deals with clients, e.g. if it expects any “handshake” or any other communication setup protocol, I decided to write a simulator mimicing the gateway, and then using iRegatta 2 from Zifago to verify that it can read the simulated NMEA messages sent by my “soft” gateway.
So, in the video above, my laptop (on the right) is pretending to be the NMEA-WiFi gateway, constantly broadcasting UDP packages containing NMEA sentences onto the network, and on the left my iPad running iRegatta 2 is collecting them and displaying the information obtained from the sentences.
With the communication between the Gateway and its clients now figured out, I’m able to collect full race data, including multiday races, from all the instruments onboard onto my laptop for after race “post mortem” race performance analysis.
Posted in Data Analytics, Maritime Technology, Nautical Information Systems, NMEA, Numpy, performance, Python, Simulation, TCPIP
Tagged Communication, Data Analysis, iRegatta2, Network, NMEA, Numpy, performance, programming, Python, Simulation, sports, TCP/IP, Technology