Speed-Stamina Course Profiles And Juvenile Races

In an earlier posting, Speed-Stamina Fingerprints, we outlined an approach to "hoof-printing" racecourses in terms of their speed-stamina profiles based on the best times for various distances using a power-law equation, approximately of the form: Time=Speed*Distance^Stamina. This approach also allows us to effectively project performances by inexperienced 2yo and 3yo horses, as follows.

For example, supposing a 2yo colt turns in an eye-catching performance in a novice race and is entered in a graded stakes race 10 days later. Historical data tells us that the median winning time of this future event is 73.57s. The question we want to ask: Is the maiden winning colt likely to be competitive in the graded stakes event? One way of answering this question is to translate the maiden performance into an equivalent performance at the graded stakes course and distance. Using our hoof-printing technique produces a time of 73.40s telling us that this promising colt is likely to be an above average contender for the graded stakes event. Fast-forward to race day and our selection wins in a time of 73.51s (just 0.10s slower than projected). (Note that we were not restricted to choosing performances at the same distance as the future event).

Obviously, this technique has restrictions in terms of producing realistic projections. It works best with:

• Maiden 2yo and 3yo races at sprint distances;
• Races on “Good” or “Good-To-Firm” going; 
• Recent “In-The-Money” races;
• Projections from one graded stakes racecourse to another (e.g. Newmarket to York); and
• Horses that race prominently and do not require "luck in running".

That said, it has potential for projecting future times based on performances at racecourses with vastly different configurations, a goal which cannot be achieved by speed figures!

Speed-Stamina Fingerprints

In 1981, Peter Riegel formulated an equation for the relationship between distance and time of athletics world-records. In 1982, Steve Roman adapted Riegel's equation to try and resolve the Secretariat Preakness controversy.
In a similar vein, we can generate unique speed-stamina, power-law fingerprints for racecourses (and horses) based on the best times for various distances. The simplest interpretation of these racecourse fingerprints is to confirm our expectations of the demands imposed by similar distances for different course configurations (Epsom is faster than either Ascot or Newmarket (lower y-intercept); Ascot and Newmarket have similar stamina profiles (same slopes)). Another possible insight might be how these course fingerprints reflect the potential impact on horses with different pace profiles (early speed at Epsom). A further analysis might be on how to better baseline and equate speed figures at different racecourses (use standard course with own speed-stamina equation). Finally, more controversially, using speed-stamina fingerprints for classic-generation (3yos) horses to match with course fingerprints in the lead-up to Group 1 contests (Epsom Derby) or for comparing performances from different classic generations (Frankel vs Sea The Stars).

Note the graph only shows best times and power-law equations for five, six, and seven-furlong races at Ascot, Epsom, and Newmarket and are for illustrative purposes only.

Time Distance And Fatigue

Notwithstanding the strengths and weaknesses of the dosage approach to handicapping, it is worth reviewing the excellent article by Steven Roman on speed-stamina profiling. Looking forward to the Breeders Cup at Santa Anita, one could consider generating dirt and turf course profiles (based on track records at different distances) to provide insights into the challenges faced by European shippers. Going further, one could profile those shippers (based on best performances at equivalent distances in Europe) to identify live-longshots for exotic plays. Obviously, I am glossing over the difficulties of generating meaningful numbers using winner final times, beaten lengths, varying track configurations, and qualitative track conditions. Nevertheless, our goal should be to look for live contenders to fill tickets and not on demanding mathematical accuracy!