- Finance - Analysis of financial securities and discussion on strategies to outperform the markets.
- Sports Analytics - Looking into the statistics behind sports (NFL, NHL, NBA, MLB) and applying them to fantasy and sports betting.
- Japanese - As a frequent traveller to Japan for personal and business trips, I am studying the language and write on various topics for the Japanese audience.
- Travel - Avid global traveller, I write about my experience and provide location-specific advice.
Recent Posts
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
Identifying profitable statistical trends against the NFL spread
Discovering several profitable trends that consistently produce positive returns yearly
Building a machine-learning model to predict NFL spreads with Gradient Boosting
Leveraging historical performance and spread data to predict what team will cover the spread
カナダの選挙２０２１年
昨日はカナダの選挙でした。人気ではありませんでした。
Analyzing historical NFL over-under data to identify trends
Comparing at the over/under line from 2010 with weather, team ratings, weeks, etc
Smart Sports Betting by Matt Rudnitsky - Book Review
A review of Matt Rudnitsky’s ‘Smart Sports Betting: How to Shift from Diehard Fan to Winning Gambler’
Picking stocks is statistically likely to underperform the market
64% percent of stocks underperform the market and only 6.1% will outperform by 500%+. What makes these outperformers unique?
Can we use historical stats to predict a QB’s fantasy ranking - Using strength of schedule and OL strength
Using standard QB stats from 2016-2019, teammate ratings, and strength of schedule to predict 2020 fantasy points.
Can we use historical stats to predict a QB’s fantasy ranking?
Using standard QB stats from 2016-2019 to compare predicted 2020 fantasy points vs actual performance.
桜と雪
雪が降っていて、桜はアイスクリームみたい