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Market Bias in 5f Handicaps
Market Bias in 5f Handicaps by David Renham
In this article I am looking to see whether market forces are the same at different course and distances. The focus is 5f turf handicaps (excluding 2yo nurseries) and I have concentrated on races with 7 or more runners. I have decided to split the betting market into thirds – as I do when I analyse draw bias. Of course there is not always an even split, but it should balance out as the table below shows:
Hence, with 10 runners, the middle ‘third’ gets the extra runner, while with 11 runners the top and bottom ‘thirds’ get the extra runner. This idea continues for all other groups of three (eg 13, 14 and 15 runners). Hopefully therefore, we will get a fairly accurate reflection of market bias overall.
The data has been taken from 2002 to July 2009 and for all 5f courses the handicap market bias stats are as follows:
No surprises that the top end of the market have produced the majority of winners – essentially the top ‘third’ of the market have produced the winner 4.6 times more often than the bottom ‘third’ of the market. However, let us see what happens when we look at the splits with different sets of number of runners:
As the number of runners increases, the better it seems for the top ‘third’ of the market. It seems to back up the old adage the bigger the field, the bigger the certainty.
Let us break down the stats by course. There is quite a variance between courses – I have ordered them initially alphabetically:
Beverley tops the list for the top ‘third’ of the market with 76.1%; Salisbury has the lowest on 31.8%. A huge difference between them – now let us put the courses in order of best performances for the top ‘third’ of the market:
The question that needs to be addressed at this juncture is how valid are these course figures? In many cases my hypothesis is that they are fairly accurate. The figures for each course cover a fair number of races – Musselburgh for example has had 111 races, Beverley 92. Hence, in most cases we are dealing with decent sample sizes. Also, as a punter who has tried to specialize in sprint handicaps, many of the courses with low or lowish top ‘third’ percentages are courses I have really struggled at – York, Salisbury and Newmarket are three such examples. The percentages for such courses indicate that results have not been as market biased as one would expect, so in other words these races have been far more open contests – my losses at these courses can vouch for that!!
For me, the question now is, do I use these figures in the future when analyzing 5f sprint handicaps? The answer is a simple “yes”. I think the information that has been collated is going to prove useful. I will now think twice about backing an outsider at certain courses such as Beverley, Bath, Lingfield, Newbury, Pontefract and Warwick; whereas the reverse will be true at the outsider biased C&Ds at Ripon, York, Salisbury and Goodwood.
The beauty of this type of research is that it can be extended to all race types, all distances, etc, etc. My next port of call will be 6f handicaps to see if the course stats for 6f correlate with those for 5. If they do not, then maybe I will have to go back to the drawing board.
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