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Lay the Draw Really Work?

Betting on cricket across the countries that play the sport is huge.
Betting scandals involving Indian bookies named John, and players such as Hansie Cronje, Salim Malik and Mohammed Azharruddin reinforce just how big the market really is. The Indian market is all about trading and was in existence well before betting exchanges shook up the way we bet on cricket. Most of the hundreds of Indian bookies are simply aggressive traders like you’ll find working the stock markets. Tight margins, their clients can bet for or against a result, and the bookie is looking to make his earn in the spread between prices. This gives us some understanding of why the draw is so popular – siding with one team or the other is difficult without information and leaves you open to risk. The draw will always fluctuate – the only times it moves in just the one direction are if the weather sets in immediately, the team batting first collapses early or predictions of the pitch being a ‘road’ ring true.

‘Crushing’ a price sets off tremors in the market – as it shortens, more
and more punters get involved. Bookmakers lay the draw, then hedge with other bookies, shortening the price around the world with essentially the same money going around and around. This practice can happen trading shares, currencies, racing or cricket – the concept is the same, but the volume of money required will vary significantly.

Those that rate themselves as decent odds compilers on Test cricket will almost inevitably come up with ‘the draw’s too short’ before the game. Certainly during Australia’s glory era around the turn of the century, the draw was rarely a viable option. With extra hours of play available to make up for time lost combined with Australia’s aggressive game plan, even prices generated by reams of historical data were still big unders.

So if the draw is too short, what happens if we lay it every time? Do we it lay it and cheer for a result, or do we look to trade out when the
price drifts in-running?

Trawling through some historical data from Betfair (available for anyone to download via http://bdp.betfair.com/bdpforum/index.php), I pulled a small sample of Test matches and tested a few theories.
Data from the full calendar year of 2005 was used, removing all matches which did not deserve Test status in my opinion – namely the ICC Super Series matches (abnormal length (six days) and how serious were they anyway?) and matches involving Bangladesh or Zimbabwe. That left a grand total of 36 matches which is too small to make any serious findings, but at least it gives us a base to work from.

There are numerous premium racing services who will come up with all
sorts of backfitted data to support their sales pitch, and surprise,
surprise, their world-beating system fails dismally once your cash gets
involved. With that in mind, rules were put in place for extracting the
data.

Data Guidelines

  • Four Draw prices were recorded from each match – lowest price before play, lowest price in-play, highest price before play and highest price in-play.
  • Each price must have been matched for at least 200 pounds.
  • No punter will ever know what the lowest or highest price in-play will be unless it’s 1.01 or 1000 and the result is known. Hence prices which could be requested as soon as the market re-opened in-play – half the lowest draw price, and double the highest – were sought.
  • Commission is not taken into account

I could have probed a lot deeper into the historical data, but I’ll
leave that to someone far more talented than me working with databases and spreadsheets.

Theory 1 – lay the draw pre-match and then bet it back in-play.

On the conservative side, the top pre-play price matched (worst result) was used, and then looking to bet back at double those odds for half the stake.

Using lay stakes of £100 (offering someone else £100 at the price) and betting back with £50, the net results from 36 matches was a loss of £263. In 25% of matches, the draw price never climbed to the price
required, and in four of those matches, the in-play price never even
went above the highest pre-match price, so we’d have been stuck with the lay. These were matches you could probably use discretion as weather played a significant part in each of them. All things considered – if you managed to lay a price lower than the highest traded pre-game, and you kept out of games with obvious bad weather, then you should be able to take out a profit via this system.

Theory 2 – back the draw pre-match and then lay it back in-play.

Lowest pre-match price taken for the back price, and seeking to lay it back at half the odds and double the stake.

Using back stakes of £100, and laying back for £200, the net result from the sample was minus £500. A total of 15 matches, 41.7%, never saw a short enough draw price to trade out, including a span of six straight matches through May – August.

Theory 3 – lay the draw pre-match with no hedge trade.

Laying to win £100, the net result from the sample was +£291. A small profit, but also one which could have been wiped out by one more draw. Better timing on placing the initial bet would make a difference to the bottom line, you’d be stiff to have laid the draw at top price in every match!

Theory 4 – lay the draw in-running at half of the lowest pre-match price traded.

Laying to win £100, this theory turned a profit of £258. This is the
most exact of the hypotheticals, as you would set your price as soon as the game starts, using the prices traded data. It would also be the
least risky as only 11 of the prices sought were odds-against (greater
than 2.0).

So after all that, what can we conclude? We have the basis of four
systems to work on, but as mentioned above, a sample of this number of matches cannot be taken as long-term trends.

The only method I question whether it would turn a profit over time with a more hands-on approach is Theory 2. A green-top pitch producing wickets in the first session would push the draw price out higher and higher with no turning back. Keeping an eye on the weather, pitch and light conditions plus the price fluctuations would make Theories 1 and 3 more appealing, while more conservative punters working off a smaller bankroll would be better suited by Theory 4 with its lower risk profile.

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