Legendary (but fragile) turf champion Da Hoss, trained by Mike Dickinson managed to win the Breeders Cup Mile on the Turf after a two years layoff!
Here you can watch the race:
Although 15 years have already passed since then, it still stays vivid in my memories as does the fact that the champ was the very first horse to eliminate when I was handicapping for the Cup the night before!
Many years later this race was used by one of the handicappers on DRF seminar DVDs to make his case about how horses are more likely to win on turf than on dirt when they are coming from a layoff.
Recency is one of the fundamental Type 1 factors.
Exactly as I did when handicapping Da Hoss, one of the first things we are going to use when we are handicapping a race is the recency of each starter.
The most typical approach is to categorize a starter as coming from a layoff, second or third of the layoff, deep form cycle, long layoff and first time out. Note that this categorization is covering all the starters of the race something that we will see its importance later.
For now I will just select an example using recency to explain the procedure I follow in general and in a next posting I will extend this to a more generic concept that can be applied in many cases.
The factor I am going to analyze is for horses coming from a layoff. I define as such any horse who has not race for the last 45 days but his last race was not more that 120 days ago. If the horse is a second time out the second condition is not used.
I will use as a secondary factor the surface of the race which can either be turf or dirt (including synthetic surfaces).
The reason I am selecting surface as the secondary factor is because it is a common belief among handicappers that it is easier for a horse coming of a layoff to be ready to win on turf than it is for dirt. I still remember I heard such a comment in one of the DRF seminars (sold on DVD) and I also remember that the speaker used as an example the legendary (but fragile) turf champion Da Hoss who trained by Mike Dickinson managed to win the Breeders Cup Mile on the Turf after a two years layoff!
Let’s see if this opinion is correct.
The tool we are going to use to reach this type of conclusions is the chi square hypothesis test. At this point I will not describe in detail this method, for more detailed description you can read this thread started by an expert poster on this field posting under the nick TrifectaMike.
You can also find a lot of related material on the Web. A very good introduction that I found on youtube is the following:
For starters let me just say that this method starts with a hypothesis that certain random events have the same probability and using a statistical method based in the chi square distribution tries to either confirm or reject it. The outcome of the experiment will either be that indeed the original (null) hypothesis is correct or it will be a percentage of confidence that the hypothesis is wrong. For all my testing I will be using as level of confidence the 0.05 threshold. Meaning if I will reject the null hypothesis I will be sure by 95% that I am correct about my rejection.
Note that for this case we will only consider the favorites of the race, in other words we will only consider races where the favorite was coming of a layoff, any other race will not be considered for our test. Our conclusions will only apply to favorites and for all other horses I will do a similar experiment later.
Using compare factors and recency factors python modules I was able to query my database getting the following results:
BE CAREFUL WE ARE USING FAVORITES ONLY
Factor | Winners | Loosers | Win% | ROI |
layoff on turf | 291 | 592 | 33% | 0.83 |
layoff on dirt | 707 | 1155 | 38% | 0.88 |
Chi Square calculations
degress of freedom | 1 |
chi2 | 6.5 |
critical value | 3.84 |
We can immediately see that the null hypothesis is rejected and indeed there is a significant difference between the two sets.
To our surprise though, it is not what we expected! Please note that the dirt starters are winning with a higher frequency!
So far we have proved that the common belief of turf starters coming of layoff are winning more than dirt starters is completely wrong when we are considering only the favorite of the race.
We have not finished yet though…
The next and final step is to calculate how well this discrepancy is reflected on the pools. This is what is really important, because if the public is aware of this irregularity then we do not have anything to gain out of it..
For this, I am rerunning the chisquare test but now I am using as the expected value the one that is suggested by the odds of the horse (adjusted for the take out). If the crowd is betting perfectly then I am expecting the null hypothesis to hold true. The results were as following:
Calculating Chi Square using expected values based on the odds
degress of freedom | 1 |
chi2 | 4.17 |
critical value | 3.84 |
Good news! As you can see the null hypothesis is rejected and the condition is significant.
What this means is that the DRF seminars and other sources of handicapping like books have done a good job, completely misleading the public to constantly misjudge horses starting on turn while coming of a layoff and starting as the favorite of the race. Note that the ROI for this starters is only 0.83 while for dirt is 0.88
One note here, the fact that just the ROI is higher for the dirt does not necessary means that the condition is significant, this is exactly why we need to calculate the chi square.
I will continue with more similar examples as I think this is a very fundamental starting point to create a winning betting strategy….