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| Articles >> horse-racing >> Finding different ways to evaluate performance
Finding different ways to evaluate performance
As a racing researcher, I get often find myself analyzing stats in the same old way I have always done, without actually thinking is there anything else I can do with this data? For example – when analyzing trainer data I tend to do the following:
a) get the relevant data for each trainer – eg. runs, wins, strike rate, returns, roi:
b) look at the number of wins / runs first to determine whether the data set is big enough;
c) look at the win strike rate;
d) look at the roi (return on investment);
e) if the figures look worth further investigation at this point then look at the placed stats to see if they correlate with the win stats;
f) break the figures down into years to see how consistent the data is – then go through the whole process again (points b to e) on a year by year basis.
At this point I decide whether a trainer stat is worth noting or not. Of course it is not an exact science, but hopefully experience will help determine which stats (both positive and negative) are worth noting down.
I find this method works quite well for trainer research, but of course many researchers will follow a similar path and hence most of the “interesting data” will be found by many. Hence, in a constant drive to ‘stay ahead of the crowd” I decided to see if I could find a different method to evaluate trainer performance.
Here are a couple of my ideas.
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