In honor of this Sunday’s Super Bowl, Episode 38 is a special, bonus “gambling” podcast. We welcome mystery guest, E.V. Better, which is an alias for “Expected Value Better.”
Meb starts by asking E.V. how he got to this point in his career. E.V. had a traditional finance background, working at a long/short hedge fund for 5 years, but realized he could apply certain predictive analytics that work in the financial world to the sports betting world. He helped create a basketball model at Dr. Bob Sports and enjoyed it so much that he made the jump from traditional finance.
Next, Meb requests a quick primer for the non-gamblers out there; for instance, how the various types of bets works, the “lines,” the most popular bets, and so on. E.V. gives us the breakdown.
The conversation then drifts toward examples of “factors” when it comes to gambling (such as “value” or “momentum” is in the stock market). E.V. tells us there are really two schools of thought in traditional investing – fundamental and technical investing. When it comes to gambling, there are similarly two schools of thought; you have the strength of a team that’s measured by traditional stats (for example, net yards per pass) or technical factors (having been on the road for 14 days…having suffered 3 straight blow-out losses). When you combine these two factors, you better a better idea of which way to go with your wager.
These leads to two questions from Meb: One, how many inputs go into a multi-factor model? And, two, how do you replace older factors that don’t have as much influence or predictive power as they used to? E.V. gives us his thoughts.
Meb asks about “weird” or interesting factors that are effective. E.V. points toward “travel distance,” though the effect has diminished over time as travel has become easier. He also points toward “field type.” This leads into a discussion about betting against the consensus (contrarian investor, anyone?). And this leads into a common investing mistake – recency bias. For example, because the Broncos won the Super Bowl last year, people expected them to be great again this year…and they didn’t even make the playoffs (Meb is still bitter).
Meb steers the direction away from the NFL. Whether basketball, baseball, or whatever other sport, you’re simply trying to find an edge over the house. Meb brings up “variability” (the more games the better if you have a slight edge), and asks how this changes over different sports.
E.V. says duration of season is a huge factor. Also, the level of data available for analysis is key (for example, the amount of data in baseball is amazing). But overall, E.V. says the goal is reduce the variance to make thing as simple and predictive as possible to find your edge.
Meb asks about underrepresented sports (curling, or NASCAR) offering more, or better opportunities (think “small caps” versus the “Apples” of the investing world). E.V. says the issue is finding a counter-party. You might be a great curling modeler, but have fewer market participants from which to profit.
This leads into how to quantify an edge, and what a good edge value should be. E.V. says a 10%+ edge would be fantastic, but it’s important to be conservative in your estimate of just how big your edge is. After all, you won’t have a consistent edge every game. Meb makes an interesting correlation to investing you’ll want to hear.
Next, Meb asks about gambling as an asset class. Where would gambling fit into a portfolio and how would it work together? E.V. says sports is a unique alternative asset class that’s uncorrelated to other markets. This quality makes gambling an interesting addition to a portfolio.
Next, Meb moves to “quick hits” – shorter questions, many of which came from listeners via Twitter.
- What’s the worst bad beat you’ve seen?
- Have you looked at “intra-game” gambling, or