PODCAST - The key with investing via analytics!
40 Minute Listen
Your advice today is coming from Ted Richards. Six Park's Director of Business Development, Ted is a former AFL premiership player with the Sydney Swans, where he earned accolades not only for his football skill but his leadership, toughness and sportsmanship. He was a board member of the AFL Players Association from 2012-16. Ted has a Bachelor of Commerce and a Masters of Applied Finance and has previously worked for Citigroup and Airlie Funds Management.
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This episode we're talking Moneyball.
What's Moneyball? Well, in baseball, people often talk about the “intangibles” that players may or may not have. He or she has the "it" factor. It's a gut feel that has usually been accepted and often embraced by recruiters and teams.
The Moneyball concept questioned this widely accepted “gut feel” and how accurate it really is. Most famously, Billy Beane and his analytics team at the Oakland A’s went out on a limb when assembling their 2002 team by using data to make baseball recruiting decisions, rather than gut instinct or the “it” factor.
The idea of an algorithm or computer supporting important sporting recruiting decisions was initially laughed at. But since the Moneyball story became well known it’s now not only accepted, but embraced. This approach quickly spread into other sports too. In this episode I’m joined by Chris Keane, the senior recruiting analyst at the Sydney Swans who, as you may have guessed, has an analytics background, not a football background.
So why are we chatting about this right now?
The AFL draft is coming up so it's quite timely if you follow it or another football code that has a draft.
Most importantly, there are huge crossovers with other parts of life such as the business world and, you guessed it, investing.
The effect of bias
We’re all susceptible to biases, they are a part of our make-up, and Chris lists some of the biases that recruiters can be susceptible to:
Recency bias: Recruiters could overstate the significance of playing well in a recent game. This is especially relevant for young players who play well in the Under 18 National Championships or in a grand final, but the reality is it’s a very small sample size on which to base a judgment.
Basing judgments on individual examples and vivid anecdotes. For example, the recruiter could like a kid's personality because he or she has a great sense of humour. In contrast, the recruiter might not like someone because of something trivial like a weird running gait. These sorts of factors can all influence someone's behaviour, whether they’re aware of it or not.
Chris’ role at the Sydney Swans is to use data to question and challenge assumptions that may result from these kinds of biases.
The role of analytics in sport is predicted to get bigger and bigger. In the AFL, the quality of data is reportedly getting better and better each year, and algorithms will continue to be optimised to improve their accuracy.
On top of this, big changes to the upcoming AFL draft will require more emphasis on the ability to make quick, accurate decisions. These changes will bring the draft closer to American models. For example, this year will the first time that teams will be able to trade picks in real time (e.g. in between picks on draft day). Teams will be given a maximum of five minutes to trade with other teams, which will be entirely new for the league. In the podcast we discuss the new opportunities and risks clubs will face with this new feature on draft day.
Big League Advance
I also touch on a fund in the US called Big League Advance, which invests in minor league baseball players. Created by CEO Michael Schwimmer, the fund allows baseball players to ‘sell’ a portion of their future earning capacity for a price, which the fund ‘buys’. About 75% of the players Big League Advance signs aren't in the top 300. It has now invested in 129 players and I'm interested to see how this goes. Watch this space. In particular, check out their Chief Strategy Officer Jason Rosenfeld and his resume (for a kid who looks just 22 - seriously impressive background!)
New approaches to traditional methods can sometimes be perceived as wrong, when in fact they may improve the decision we’re trying to make. Whether it’s recruiting the next best AFL player, or making your next investment decision, we’re increasingly seeing data as a way to improve our decision making.
I want to leave you with a great quote from Daniel Kahneman, who started this all. He and Amos Tversky were the initial inspiration behind the Moneyball strategy. Kahneman won a Nobel Prize for his work in behavioural economics and he once said:
“Algorithms beat humans half the time, and they match humans the other half.”