Sportpunter

When Philipp Kohlschreiber defeated the French Open Champion Rafael Nadal at the Gerry Webber Open at big odds of almost 7.00, it was one of the biggest upsets over the past month. Nadal, was clearly tired after winning the French Open but the punters still backed him to win.

Of course, it was a massive win for Kohlschreiber, who had never defeated a number one seeded play since he defeated Marin Cilic back in 2010. So perhaps he was in a rare stint of form. It was his third win on the trot, and faced fellow compatriot, ageing, yet in form Tommy Haas.

Kohlschreiber was listed as favourite at 1.66, whilst Haas had to settle for the slight outsider odds of 2.40. Least to say that Sportpunter rated Haas at 47% chance to win, so there was some value there on Haas.

If you don’t already know, Haas defeated Kohlschreiber in straight sets. This got me thinking. How often does the market over compensate for a player who had a massive win the week after? It is possible that the chances of a player winning the week after are less than what the public perceive?

Perhaps Kohlschreiber had already won his grand final after defeating Nadal, or was too happy and excited to hold his nerve for the next match. Perhaps Haas had to focus more knowing that his opponent had just taken out one of the world’s best.

Well I decided to test this theory, and using data going back to the start of 2005, looked at all matches where a player had won at odds of greater than 5.00 (not including retirements) and how they went in their next match, so long as it was in the same tournament.

Since 2005, this has occurred 328 times. In 117 (36%) of them, the player who won previous at odds of 5.00 or more won their next match, whilst 64% of the time, or almost 2/3rds of the time – they lost.

Had we bet to win $1000 on a player who previously won at 5.00 the game before, we would have bet $254,305 (average of $775) and lost a total of $30,130. Our precent return on investment would therefore be -11.8%.

Clearly, without doing any form whatsoever, we have shown that there is no value in betting on a player who had previously won at odds of 5.00. However interestingly, is how we would have done betting against a player who had won at 5.00 or greater the match before.

Since 2005, we would have bet $992,724 and made a profit of $33,591. This represents a return of investment of 3.6%

So just by this analysis alone and not even performing any form analysis, one would have made 3.6% ROI and $33,000 by backing against a player who previously just had a big win.

So clearly, the market overreacts based on a big victory the previous game. This, I can assure you, happens in every sport.

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The opening match between Greater Western Sydney Giants and the Sydney Swans looks to be a very interesting betting game indeed. No one is really sure how to rate the Giants, except for the fact that the Swans will make them look like midgets.

But interestingly, the amount that the books believe that the swans will give the giants a hiding for is greater than any match played by Gold Coast last year. The current line for the Swans Giants game is -90.5. Meaning that the Swans have to win by at least 91 points to cover the line.

The biggest line offered last year for newly formed Gold Coast in 2011 was -88.5 when they ventured down to Simmonds Stadium to play would be premiership team Geelong. The average line that the Gold Coast had last year was -49, but here we are opening with a lot bigger line.

So are GW Sydney tipped to be a worse team in 2011 than Gold Coast in 2012? Yes I think so. But is that really warranted? Let’s compare pre season results to determine that:

Gold Coast pre season 2011:

So a fairly average opposition with teams playing against the Gold Coast finishing 4^th, 7^th and 15^th. The average losing margin was 59 points. Let’s compare this to GW Sydney in 2012:

So a similar opposition as well, with top 2 favourite Hawthorn, vast improver Richmond and lower of the ladder Gold Coast. The average losing result here is 55 points.

So the preseason records of both teams is comparable.

But everyone remembers that opening (round 2) game of Carlton vs. Gold Coast last year where Carlton spoiled the AFL party to win by 119 points when the line against Gold Coast was just +26.5. It was a very underdone Gold Coast, with all of their senior players being very underdone. Brennan and Brown could only muster 22 possessions between them and Ablett had not played a pre season match and looked match unfit compared to normal. However this week GW Sydney’s main gun Scully is out with a cheekbone fracture.

Perhaps there is a case, that new formed teams, like Gold Coast and GW Sydney will start poorly and then improve as the season goes on. This happened to Gold Coast didn’t it? Let’s find out.

Gold Coasts average losing margin was 54 points over 2012, and the graph below shows their progress. Many will have forgotten that they actually won 2 of their first 6 matches, and the trend for their performance did improve over the season as shown by the linear regression line (but keep in mind that Hawthorn did play only half a side in the final round of the season)

What can be more interesting is finding out how Gold Coast went against the line in 2011. The average line for Gold Coast was +49 with the smallest being +17.5 against Brisbane in round 21. But how they went against the line is interesting as shown in the graph below:

So it seems, even against the Line, Gold Coasts results in 2011 did increase, although as pointed out below, this is minimal if you don’t include the last match versus hawthorn:

What is also interesting is how my ratings for Gold Coast went in 2011. Shown below is Gold Coasts ratings over the span of the year:

So not a huge improvement, but clearly the form of Gold Coast improved as the season went on.

So what does this all mean? Well I guess is means that we could possibly expect the same from GW Sydney. They might well start off slow, but then improve with a few games under their belt. Perhaps the bookies have the Giants with the correct line of +90.5 considering this. But that said; remember it is bigger than any line last year against the Gold Coast.

Make sure you read up about our previous article about how well we did betting on the Gold Coast last year, as well as checking our betting history over 13 years, and subscription options.

Recently one of my potential clients pointed out a website that claims to debunk the Kelly criterion. For those that don’t know the Kelly criterion is a money management strategy that works out how much you should bet on a sports outcome based on the probability of the team winning, the odds that the team are at and your bank size.

It is not the first time I’ve noticed the article on http://professionalgambler.com/debunking.html as the article has been up there for quite a while. Several years ago another of my friends said it was “embarrassing for the bloke” that the article was still there. But considering that I highly use the criterion in my betting, I hate to have potential clients find articles that criticise the method that I use. Well, they have every right to criticise me, but when they get it completely wrong, that’s where I come in.

So this is my article, debunking the debunking of the Kelly criterion

PG, for short, suggest that we get a couple of decks of cards and do a test. That’s so old school. I’ll do the same thing with a statistical simulation. Using exactly his test, but I might do it say, 50,000 times instead. Computer programming is handy you know.

And guess what? The results actually favour PG’s argument. When betting with the Kelly criterion the average bank size after 100 bets was $49,320.70, up quite a bit from the original $10,000. When working out how much each bet was using the average size bet of the Kelly criterion, the average finishing bank size over 50,000 simulations was $53,763.75. In fact 65.7% of the results favoured PG’s flat betting strategy.

So perhaps PG is correct, we should all be flat betting no matter what? Not so. You see PG decided in his article to compare the average size of all the Kelly bets and bet the same amount flat betting. That sounds all very good, but in reality you have no idea what your average bet size is several years into the future.

To show why this method doesn’t work, I will do another simulation where instead of just betting 100 bets (which one could easily do in a day or two), but with 1000 bets.

The results are incredible and obviously not realistic, but they are there to prove a point. The average finishing bank using the Kelly criterion was just over $100 Billion, whilst the average flat betting strategy was just over $1,000 Billion, or 10 times the size.

Seems like the flat betting wins again yes? No. Because the average size bet for flat betting was $288 Million, which considering that you only have a $10,000 bank is pretty impressive.

The comparison is assuming that even with a $10,000 bank, that you are going to bet $288 Million each bet, and if you lose your original $10,000 (which you could do on the very first bet), you just keep going betting $288 Million at a time.

Quite clearly, the comparison is not legitimate.

You see PG doesn’t seem to understand what exactly the Kelly Criterion is. The Kelly criterion suggests that you bet more when the probability is greater, you bet more when your advantage over the bookmaker is greater, and you bet more when you bank size is greater.

He incorrectly labels this a progressive betting technique when it is far from it. Lets look at each criteria in the Kelly betting system.

You bet more when the probability is greater. If something was a 99% chance to win, you surely would bet more than if something was a 1% chance to win. Id hate to be risking 5% of my bankroll on a 200/1 shot. PG probably has never thought of this in this case, because like a lot of Americans, he only bets on the line where something is around a 50-60% chance to win each time.

You bet more when your advantage over the bookie is greater. Surely if you rated a team a 90% chance to win and they were paying $2.00, wouldn’t you bet more than if you rated a team a 50% chance to win and they were paying 2.01?

It seems that the only thing that PG doesn’t like about the Kelly criterion is the fact that as your bank grows larger (or smaller) then you bet more (or less). Incorrectly calling this a progressive betting system, the reason why one should adjust their bet size based on their bank size is given by a few examples.

Remember when you were young child and you would save all week to go down to the corner shop and buy a $1 bag of mixed sweets? Would you do the same now? Save all week to buy some sweets? Of course not, now that you are more wealthy, you spend more. You could save all week to buy a TV or a dining room table that would cost you $1,000, and if you did, here you have spent a percentage of what you are worth.

When I first started betting, I put $100 into a bookmaker and bet $10 a time. As I realised my edge was significant, and my bank kept increasing, I increased by bet size. Imagine me now betting $10 a bet. What would be the point? Some might say that of course you change your bet amount at the end of the season or after each month, but isn’t that just adjusting your bet size to your bank? Why not do it after every bet?

The truth is, to accurately compare the Kelly Criterion to flat betting one has to keep constant the percentage of the bank that one bets. With PG’s card game example, the average bet size using the Kelly criteria based on odds of 1.95 is 14.47%.  So lets do the simulation example, comparing how the Kelly Criterion went compared to flat betting with each bet at $1,447 (14.47% * $10,000).

Whilst the average Kelly bet bank $49,320.70 over the 50,000 simulations, the average bet size when flat betting was only $29,623.16. That’s a $39,000 profit compared to $19,000 profit – Less than half the profit. In fact Kelly betting had a greater profit 32% of the time.

But what happens if we compare with say, 1000 bets like previously? Kelly betting finished, as previously, with an average bank size of just over $100 Billion, whilst flat betting finished with an average bank size of $205,966.82. That’s 500,000 times smaller. Ouch!

In fact flat betting only had a higher bank than Kelly betting 0.6% of the time.

PG finally digs himself a hole by stating that it is “futile” to try to estimate the probability of a team winning because there are so many “countless variables involved” and hence  “you can never know what your winning expectation might be”

Quite clearly, PG is from the old school of professional gambling. One who hasn’t woken up to the power of mathematics. I wish J. R. Miller all the best of luck of course, but in the mean time, consider his article on debunking the Kelly System, debunked.