What Does Moneyball Tell Us About Sports Betting For Geeks?

You’ve seen the 2011 movie Moneyball right? After all, pretty much everyone has. But just in case you’ve been on the moon for the last few years, the central premise of the movie is that a geek gets to carry the day. The movie tells the true story of the Oakland As who realised they needed to do something a little different to try and make any kind of decent sized dent in Major League Baseball.

So basically the general manager Billy Beane is on a scouting mission at another club and he’s discussing the signing of a player. The other team’s Head Coach glances over at a chubby geekish figure who quietly shakes his head – not giving his assent to the deal in other words.

After the meeting, The Oakland As’ boss (played by Brad Pitt) seeks out the geek called Peter Brand (played by the always excellent Jonah Hill), who’s a young Yale economics graduate with his own ideas about how to assess each player's value. To cut a long story short, Beane finds out that this guy Brand bases his decisions on a numbers-only approach ignoring all other extraneous factors.

The A's manager then starts to gradually put this stuff into practice back at the A's training camp in California. This really tees off all the professional scouting and coaching staff and the new system gets off to a decidedly dodgy start before gradually coming good – and how it does!

The Oakland As go on a record-breaking winning streak and the rest, as they say, is history.

Anyway, if you’ve seen the movie, you’ll already know all this stuff and a lot more detail – so there’s not too much point labouring the point here, unless you’re s gambler on any kind of spurt that is.

If you do enjoy trying to make it pay on sports gambling and on betting on baseball in particular, then the principles applied in “Moneyball” are highly relevant to your efforts.

So let’s explain why that is; well if you were given odds of very slightly better than even money on the toss of a coin (as long as that coin was completely true and evenly balanced) you’d be a fool not to always take the bet.

Similarly, if two dice were thrown and you were given odds of 36-1 on snake eyes, (double one) you’d also be a fool not to take the bet. That’s because the odds of a coin landing on one side or another are exactly even money, whilst the odds of a double one when two dice  are thrown is 1 in 36, or 35-1 against. So in both cases, the laws of probability state that you’d gradually come out ahead over time.

The dice example is particularly pertinent here though. So let’s imagine the Oakland As are about to play the Cleveland Indians in the World Series final (it was the Cleveland Indians from whence Beane poached Brand in real life by the way). Let’s also assume that you think the As have two chances out of three whilst the Indians have a one in three chance. So in other words, having weighed everything you can up about the series, you think that if it was played three times, the Oakland As would tend to win two of them as an overall mean average whilst the Cleveland Indians would be likely to win one out of three – all as a hypothesis obviously.

Now let’s say you look at the odds and the Indians, who you’ve deemed to have a one in three (or a “2-1 against” chance) are actually 5-2. In this case, you’d take the bet. Similarly, if you saw that the As were actually odds of 4/6 rather than the 1-2 you thought was a fair assessment given all the knowns – you’d take that bet.

Now quite how you arrived at your own assessment of value is your affair – and that’s where the stats analysis comes in. But what’s crucial here is that you arrive at that individual assessment in complete ignorance of the actual odds – before you look in other words.

Then – and only then – do you take the bet if the odds are a decent proportion better than you have deemed they “ought” to be.  In practice, of course, the same applies to the points spread – i.e. you come up with your own independent assessment of where the points spread “should” be and act on your own analysis if they’re out by a substantial percentage in practice.

Remember, it’s only by using your own judgement and going against the market at times that you can possibly make money here. If the market was always about right – well you’d have no chance.

But remember too that your quantitatively-based decisions to trade may be right – but the outcome wrong. You simply have to accept this as a statistically-based gambler. It’s the same with the dice; although you’d be foolish not to take the 36-1 odds previously referred to, you’d still lose the vast majority of individual wagers. But the point is that you’d “inevitably” come out steadily ahead if you were allowed to market the same value-based bet time and time again.

This is a neat analogy of what we’re trying to achieve in sports betting by geekishly analysing the probabilities – exactly like happened in Moneyball the movie.

You will never win them all but that’s completely not the point. The point is that you can win more than you lose by the superiority of your own analysis if you study the stats – and that really is the essence of the Moneyball type of approach.