Lessons on Data Analytics from Benter who made $1 Billion on Horse Betting
Early this month, Bloomberg reporter, Kit Chellel, interviewed Bill Benter, revealing for the first time in 20 years how he made almost $1 billion from horse betting.
How he did it? Data Analytics.
Benter explains he got his ideas from an academic paper “ Searching for Positive Returns at the Track: A Multinomial Logit Model for Handicapping Horse Races.” In the paper, a horse’s probability of winning a race depends on various factors like straight line speed, size, winning record, jockey, etc. These factors carry different weights which could be determined probabilistically.
So, Benter started his journey by learning advanced statistics and programming. In 1984, he hired two ladies to help him build his database containing results from thousands of races. During that time, he spent his time studying regressions and writing algorithms.
With his self-made model, he developed his own odds and compared them with the public odds. For example, if he calculated the odds of winning to be one-in-three while the public odds is one-in-four, he is then able to risk arbitrage and make profits.
While he lost about $120,000 in his first year, 1988, he continued to improve on his modelling so that by 1990-91 season, he won about $3 million.
Imagine what if we apply Benter’s success to all businesses and industries…
What Bill Benter did and achieved during a time when big data or data analytics was less in fashion was truly remarkable. Bill Benter applied data analytics on horse racing and his model had been consistently successful for the last twenty years. Today, data analytics is applied across all industries and professions - telco, transport, audit, marketing, just to name a few. These days, “big data”, “data analytics” and “machine learning” are buzz words that every company wishes to lay its hands on.
In 2014, Singtel set up a subsidiary, DataSpark, to offer data analytics and intelligence. Dataspark COO, Ying Shao Wei, says that data analytics allows businesses “to find out where and when the crowds go including their home and work locations; see in-depth profiles of their customers, not just numbers of people; and discover patterns in customer location behaviour.”
Even government agencies are using data like commuters’ travel patterns to better calculate train frequency, adjust directions of escalators at MRT exchanges to facilitate movements of commuters.
Other than an accounting geek, I am a big fan of technology, big data, data analytics and machine learning. As businesses collect more data in a data-driven world, the potential of data analytics to solve business problems will just keep growing.
Probably in the next decade, data science and coding will no longer just be relevant for computer geeks. It will be the new norm for aspiring business leaders like you and me.