Jockeys..Do They Have An Impact? Two Jockeys Make A BIG Difference!

One night, while hanging out in the chat room, the conversation centered around jockeys. Do jockeys really make a difference? One assumption made was that jockeys made no difference. Jockeys that had high win percentages started the game with some good performances and gained attention of trainers, who then tried to gain the jockeys for rides. With multiple horses to choose from, these “preferred” jockeys had their choice of better horses which further enhanced their win percentage over time. Basically, this argument assumes that all jockeys are created equal and their above average win percentages are manufactured by the good horses the game offered to them.

The other argument simulates real life. There are good jockeys and bad ones. By getting a good jockey to ride your horse you have a better chance to win than one who doesn’t fare well. This argument contends that jockeys do have an effect on an outcome of their horse.

So I decided to try to determine one way or the other which argument is true. For nearly two months I’ve spent time (off and on) looking at many races and got overwhelmed by the number of jockeys out there. So I had to narrow things down and I picked 40 jockeys that have been around for a long time with various winning percentages and have had hundreds of races in the past year in an attempt to make a determination one way or the other.

Horses were randomly selected and the race results I used were over the past 6 months. I did not want to go back further as any programming changes and tweeking of the game may have an impact on the results. Certain variables in the crunching of numbers had to be addressed and these are the major variables I used.

VARIABLES:

1) Track Surface: The surface (dirt, grass, poly) had to be the same between the races compared for a horse.
2) Race Distance: Race distances often fluctuate for horses between race starts. To be included in the analysis the two compared races had to be within 1 furlong of each other.
3) Track Conditions: Track conditions had to be the same for each race. A fast track in one race needed to be matched with a fast track in the next race.
4) Field size: Field sizes also change from race to race. This was hard to tackle. A minimum of 6 horses in each race was required to be included in the analysis and both races compared had to be the exact size. This is also part of the reason I reviewed 6 months of data as it was difficult to find back-to-back races with the same field size matching the above conditions.
5) Travel: Only races that were in the same region were included in the sampling to eliminate/reduce the effects of traveling distances.
6) Race Level: Races had to be within the same level, such as allowance or claiming. Within each level a tolerance was granted, for example, in claiming the horse had to be within $10,000 in the claiming tag of the prior race. Thus a horse racing in a $5,000 claim race would be considered in the sample if the second race was no greater than $15,000 claim price.
7) Race Conditions: If a race had conditions attached to it, such as NW4L, the horse had to be in a similar condition in the second race. A tolerance was allowed for a notching of 1 level in either direction. For example, a horse racing in NW4L could be eligible and included in a race for NW5L.
8) Time Between Races: Races had to be within a 21-31 day range of each other.

Other variables were not factored in and were an unknown when doing the analysis. First, the horse’s condition: were they sharp, ready to go, or other? It would be nice to have included this. Second is the race track. It’s not known if horses perform better on different tacks. Third is post position. Again, no way to tell if this has an impact on horses within the game programming.

RESULTS

The results were definitive with a pair of surprises. Two jockeys were found to have improved results each and everytime they took a mount away from a jockey with a winning percentage change of 7% or more between jockeys. More startling was that they both had a win percentage of over 55% when taking these rides for the first time and an ITM percentage of about 85% for each of them.

Before I get to those two unique riders I want to display some of the supporting data. The results do show and support that switching from a poor jockey to a better jockey has a very good chance in improving results. Roughly 2/3rds of changes to better jockeys lead to improvement in results, the bigger the winning % change, the bigger the improvement. Just to clarify, improvement in results is measured by finishing a race better than the prior start. A horse could do this by finishing 4th after finishing 7th in the prior start. Horses finishing last in the comparison race were not used as these would skew the results more favorably as there is no opportunity to finish worse.

As shown below, jockey K. Ledwith has a winning percentage of about 32%. In races where the prior jockey had a winning percentage of 1-5% below Ledwith’s, the results were rather marginal. A fair amount of improvement, but nothing spectacular. This is a small % grouping, pretty much jockeys of equal talent to K. Ledwith so the results don’t change significantly. Stepping up to the 5.1%-10% grouping, a significant variance becomes apparent. 11 horses improved in performance, 4 stayed the same and 2 declined. Even more impressive is the change from jockeys that under-perform him by 10.1% or more in the winning percentage as 13 out of 18 improved in results.

Switching to Jockeys with HIGHER win %
INCREASE: 1-5% 5.1-10% 10.1% and up Total Percentage
K. Ledwith (32% win)
Results Improved 3 11 13 27 65.9%
Results Equaled 2 4 3 9 22.0%
Results Declined 1 2 2 5 12.2%
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Although not all-inclusive, here are some of the other top winning jockeys with their results that further support that top jockeys can improve a horses race results:

Switching to Jockeys with HIGHER win %
INCREASE: 1-5% 5.1-10% 10.1% and up Total Percentage
T. Ace (31% win)
Results Improved 4 14 8 26 61.9%
Results Equaled 2 4 3 9 21.4%
Results Declined 3 2 2 7 16.7%
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A. Jolly (29% win)
Results Improved 6 14 7 27 64.3%
Results Equaled 3 3 2 8 19.0%
Results Declined 4 2 1 7 16.7%
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F. Duffy (28% win)
Results Improved 5 13 12 30 63.8%
Results Equaled 4 3 1 8 17.0%
Results Declined 5 2 2 9 19.1%
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On the flip side the under-performing jockeys were similarly reviewed. Please note that the results shown are the opposite. These show where they rode a horse in a race that followed a better performing jockey. In this case, a horse went from a good jockey to a not-so-good one. The results are similar to the above shown winning jockeys. For example, G. Post wins 11% of his races. Where he rode a horse after a jockey with a better winning percentage of 1-5%, he improved 3 times, equaled the results 4 times and 7 times performed worse. Again, this pretty much reflects jockeys of equal talent. Where the jockey was 5.1% to 10% better in the win column, the results were worse with 12 of 18 finishes being worse than the prior start, under a better jockey. Same was true with those jockeys 10.1% or better than G. Post with 11 of 14 getting worse rides.

Switching to Jockeys with LOWER win %
DECREASE: 1-5% 5.1-10% 10.1% and up Total Percentage
G. Post (11% win)
Results Improved 3 2 1 6 13.0%
Results Equaled 4 4 2 10 21.7%
Results Declined 7 12 11 30 65.2%
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Below is a sample of other jockeys that yielded similar results.

Switching to Jockeys with LOWER win %
DECREASE: 1-5% 5.1-10% 10.1% and up Total Percentage
S. Bull (10% win)
Results Improved 3 2 0 5 11.6%
Results Equaled 3 3 2 8 18.6%
Results Declined 5 12 13 30 69.8%
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T. Perkins (9% win)
Results Improved 3 1 1 5 12.2%
Results Equaled 3 3 2 8 19.5%
Results Declined 5 7 16 28 68.3%
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R. Romero (11% win)
Results Improved 3 1 0 4 10.3%
Results Equaled 3 3 2 8 20.5%
Results Declined 5 8 14 27 69.2%
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As I mentioned earlier, I did look at 40 jockeys and it would be difficult to show all of them within this article. However, results were consistent throughout. Jockeys with higher win percentages do show that a horse has a better chance of improvement when racing within similar conditions as the prior race.

Now on to the two jockeys that seem to have some uncanny way of improving a horse when the switch is made from a jockey that has a lower win percentage of 7% or more than their percentage. Since my sample is small, only 40 jockeys reviewed, these 2 findings reflect a 5% of the sample. The game has thousands of jockeys and my personal opinion is that there are other jockeys in the game that probably have this unique ability, but finding them is a needle in a haystack. I doubt that 5% of all jockeys do this. To the contrary, there are probably some jockeys out there that will produce worse results everytime there is a drop in winning percentages of 7% (or possibly some other percentage). I just did not cross paths with these.

One of these found jockeys is displayed below. The data is displayed differently than above, as it concentrates solely on the 7% improved change between the jockeys in two side-by-side races. In this case, I found 34 races and each time, the horse improved with their finish. 20 times the horse won the race with April aboard. Roughly 58% of the races and 30 times ITM or 88% or the time with the jockey switch variable. Please note that there were some finishes where a horse improved, but was not ITM (such as finishing fifth after finishing ninth.)

P. April (19% win)
Number of races where prior jockey’s
win % was at least 7% below in prior race 34
Number of races where horse improved
with finish 34
Number of times horse won 20
Number of times horse finished ITM 30

The other jockey is displayed below. Very similar to the one displayed above, winning 55% of the races when taking a horse from a jockey of lesser capacity of 7% or more. The ITM percentage of 86% is extremely impressive. So one question that obviously comes to mind: Why am I sharing these with the racing world and not keeping it to myself? A perfectly good question and one I would be asking if I had been reading this article. I want to make players aware that these situations do exist in the game and it only takes some time and effort to find them. As you are reading this article, I am already searching for other jockeys that are lying out there in the game. When I find them, I will not be sharing them. Consider these two to be a gift from me to you.

D. Fools (17% win)
Number of races where prior jockey’s
win % was at least 7% below in prior race 36
Number of races where horse improved
with finish 36
Number of times horse won 20
Number of times horse finished ITM 31

 

WRITER’S NOTE (4/2/14):  None of the above is factual.  All information has been fabricated.  Note the last two jockeys listed and thier last names:  APRIL FOOLS!!!!