Getting ready for machine learning - cleaning up free NHL game and odds datasets
Before beginning any feature engineering or ML, it’s necessary to clean up the data first. In this article we work through a real-life example
Rating: 2/5 ⭐⭐
While doing my own exploration and analysis, I decided to read some literature to get inspiration and read from other’s mistakes. I picked up Matt Rudnitsky’s Smart Sports Betting: How to Shift from Die-hard Fan to Winning Gambler. As it is a short read (~100 pages) I read it within a few hours.
As the book is short, I’ll keep this review short as well. In summary, I think the books subheading ‘How to Shift from Die-hard Fan to Winning Gambler’ is a bit incorrect. A more accurate subheading would be ‘How to Shift from a Total Idiot to just kind of an Idiot’.
Roughly half of the book is focused on basics, such as:
The next half of the book is focused on strategies but only discusses the NFL. To be fair, the cover of the book features a football, but nowhere in the title/subheading does it specify that only NFL advice will be given.
The strategy section is pretty light and no concrete information is given, only general guidance. The main theme of the section is that it is very difficult to get an edge on the closing line (efficient market theory) and so it’s best to try and find an edge on the opening line. Even though the opening line limits are low, unless you’re a professional it probably doesn’t matter. So, look past the record of the teams and leverage fancy stats to try and get an edge.
Although the content wasn’t specific enough, I did appreciate how the author provided resource links, and even provided his gmail address for readers to give him additional links. It was also a really quick read. I’d recommend this book to those who have very little knowledge of sports betting (if most of your bets come from parlays, you should probably read this) and are interested in the NFL. Otherwise, I’d recommend The Everything Guide to Sports Betting which I’ll review next.
Before beginning any feature engineering or ML, it’s necessary to clean up the data first. In this article we work through a real-life example
Discovering several profitable trends that consistently produce positive returns yearly
Leveraging historical performance and spread data to predict what team will cover the spread
昨日はカナダの選挙でした。人気ではありませんでした。
Comparing at the over/under line from 2010 with weather, team ratings, weeks, etc
A review of Matt Rudnitsky’s ‘Smart Sports Betting: How to Shift from Diehard Fan to Winning Gambler’
64% percent of stocks underperform the market and only 6.1% will outperform by 500%+. What makes these outperformers unique?
Using standard QB stats from 2016-2019, teammate ratings, and strength of schedule to predict 2020 fantasy points.
Using standard QB stats from 2016-2019 to compare predicted 2020 fantasy points vs actual performance.
雪が降っていて、桜はアイスクリームみたい
来週、ユダヤの祝日のハヌカーです。
毎年、北米で人気のゲームFantasy Footballをプレイしています。
Available on iOS, Android, BlackBerry, and Web, Red Sea Rescue is a passover themed game using tilt controls to avoid obstacles.
EZ4X is a graphical, automated forex paper trader that allows users to choose techincal indicators and risk tolerance to automatically execute trades on a cu...