Sunday, December 18, 2016
Earnings-Based Metrics vs. Speed-Based Metrics
Sorry to put a teaser on here, but I'm almost ready to post my next article on Horse Racing Nation. I'll begin sharing some of the results I've found comparing the value of traditional earnings based metrics for evaluating sires vs. using speed-based metrics. Once I post the article, I'll post an update here as well, so that you don't need to keep checking for the article.
Wednesday, October 12, 2016
Average Starts: Mean vs. Median
In general, medians tend to be a better predictor of the future than means (averages), since they're not as influenced by extreme outliers. In many cases, the median value can even be a better predictor of the future mean than the mean itself. While I don't think that's the case with average starts per horse, it's worth noting that the median horse in my database has only 12 starts in their career (compared to the mean of 16). It's not surprising to see the median lower than the mean when looking at a metric that has an upper limit so far above the values for most members of the population.
Tuesday, October 11, 2016
Average Starts Per Horse By Crop
Well, this isn’t exactly what I expected to
find. I took a look at the average
number of starts per horse in each crop.
I was expecting to see a gradual steady decline over the past 15
years. However, it appears that there
hasn’t been much decrease since the 2002 crop.
For the 2011 crop, keep in mind that this
data was gathered mid-way through their five year old season, so they will
likely end up with a similar number of starts to previous crops.
These results definitely make the very high
average number of starts that I found for some of the ‘old time’ sires’
offspring even more impressive, since it appears that the game hasn’t changed
quite as much as I’d imagined.
Friday, October 7, 2016
Durability of Offspring of Big Brown and Mr. Prospector
In response to my article on Horse Racing Nation about the durability of Unbridled's Song's offspring, people mentioned Mr. Prospector and Big Brown as two other sires whose offspring might be expected to show signs of unsoundness. My data on both is limited, but I took a look anyway.
Mr. Prospector's offspring showed an average of ten career starts. That certainly suggests some issues, but may also be due to very limited sample size (less than 100 horses) in my database.
Big Brown's offspring so far are averaging eleven career starts. That sounds bad, but it's actually pretty average for two reasons:
1. I believe his first crop is only six years old now. So a very large percentage of his offspring who have raced have not yet finished their careers.
2. Other sires who began their stud careers in the 2007-2009 time period are showing similar averages. This may partly be due to the factor in #1 above, and also may reflect the tendency for horses to race less than they did even 10-20 years ago.
Mr. Prospector's offspring showed an average of ten career starts. That certainly suggests some issues, but may also be due to very limited sample size (less than 100 horses) in my database.
Big Brown's offspring so far are averaging eleven career starts. That sounds bad, but it's actually pretty average for two reasons:
1. I believe his first crop is only six years old now. So a very large percentage of his offspring who have raced have not yet finished their careers.
2. Other sires who began their stud careers in the 2007-2009 time period are showing similar averages. This may partly be due to the factor in #1 above, and also may reflect the tendency for horses to race less than they did even 10-20 years ago.
Tuesday, October 4, 2016
Unraced Sires
Just noticed another of my old posts, addressing the issue of unraced sires (particularly, sons of Storm Cat). Coincidentally, I saw the following article on Bloodhorse today: http://www.bloodhorse.com/horse-racing/articles/215550/unraced-stellar-rain-gets-his-first-winner I'm hopeful that I can do some research and provide some real analysis on the topic sometime soon.
Blood-Ex: Anybody know what happened
I had completely forgotten about this until I looked back at my last few posts from 2008. Does anyone know what happened with Blood-Ex? I seem to vaguely remember them announcing some delays to their launch. They definitely don't seem to exist currently, and a quick Google search didn't shed any light on what happened. Does anyone know?
Reviving ThoroughMetrics...Again
I've finally gotten access to a good enough database of pedigree information and racing results that I should be able to do a lot of the analysis I talked about years ago. As I work through the data analysis, I'll be posting here somewhat regularly, discussing some of the more interesting findings and issues I run into. I'm also going to be publishing a series of articles at Horse Racing Nation, looking at a variety of questions related to thoroughbred pedigrees.
Sunday, December 14, 2008
Two Steps Forward...One Step Back
It's also frustrating to take a step backwards in any project, but sometimes it's necessary. I noticed that the correlations in my earnings per start data were lower for 1999 to 2000 than the earlier year, and suspected that there was something funny going on in the data. It appears I was right. Because the data was collected sometime around late 2002 or 2003, the year 2000 crop had only a very limited number of starts in the data, and this likely resulted in more random variation and less correlation to previous years. Because the average correlations I've been reporting include this crop, I'm going to go back and recalculate them without it. What I know is going to happen is that the averages will all go up. What I don't know is whether there was any systematic bias in the results. By having one crop where the horses' four year old and up seasons weren't included, were sires whose offspring mature late being 'punished'? Would this have had any impact on the analysis I've been doing? Probably not, but there's certainly enough question that I'll eliminate the year 2000 crop so that the data represents something closer to a complete record of the performance of the included offspring.
Saturday, December 13, 2008
Experimental Design
In response to the series of posts on my research into predictive factors for sires' offsprings' earnings per start, 'Winston...not really' suggested the basic design for an experiment involving breeding multiple stallions to the same band of mares to avoid the possibility that what we're measuring when look at stallion performance is simply a result of a systematic difference in the quality of mares that are being sent to them. His suggestion is definitely a good idea...if you have a few billion dollars to spare. For anyone who doesn't, statistical analysis should be sufficient. I've mentioned before that we're going to need to make adjustments to the sire statistics to take the quality of mares into account, and its something I'd like to tackle in the near future. However, if we're going to adjust for 'quality' of mare, it may make sense to at how to measure that quality first. Racing success? Previous breeding success? Quality of bloodlines? I know that some studies on the topic have indicated that racing success is more important than bloodlines, but I'm not sure anyone has tried to measure the relative predictive value of racing success versus success of previous progency...and how the balance changes as a mare ages and we're able to gather data on a slightly larger number of her progeny.
Monday, December 8, 2008
EPS Research Update
I've continued working on the earnings per start research that I discussed recently here. Some of the new findings:
1. Looking at the overall mean earnings per start for a sire's offspring, rather than the mean of the offsprings' earnings per start, did result in a slightly better predictive result (.45, instead of .40). Put more clearly (I hope), it was better weighting the EPS by race rather than by horse. However, this still wasn't as good a forecasting tool as the median (which had a crop-to-crop correlation of .50). I definitely need to try out something called a "weighted median" so that I can try weighting the median by race instead of horse. I expect this to provide an even more accurate prediction. And in fact, I'm guessing that weighting each race evenly isn't optimal either. We learn more information about a horse's ability from its first or second race than we do from its twentieth race, and the statistics will probably reflect that.
2. Using only the median data, comparing crops that were more than one year apart (ie looking at the correlation between a sire's 1997 and 1999 crops) SEEMS to show a slight trend towards less correlation than crops in consecutive years. The effect isn't clear enough to be definitive, but it would make some sense, since over time the quality of mares that a sire is sent to may change.
3. Also using only the median data I tried using two year averages (means) of the individual year medians as a predictor of the following year (for example, the average of the 1998 median and the 1999 median to predict 2000 results). It didn't seem to work any better or worse than one year. Even a three year average didn't work any better. This surprised me a little, but what may be happening is that the benefit of greater sample size that the multi-year averages are giving us could be cancelled out by the effect I described in #2 above. There's also a good possibility that this is simply due to random chance, and over large studies, the two year averages would have a slight benefit.
4. The most interesting result of the new work I did was that the average of the 1996 and 1997 medians was a GREAT predictor of the average of the 1998 and 1999 medians. Even without refining the 'predictor' to use a weighted median and to adjust for quality of mares, the correlation of the sires' earnings per start medians between these two year sets of data was .77.
Obviously these results have suggested all sorts of productive follow-up research. One of the things I'll need to look at is how the multi-year studies I did for median would perform for mean...since that's really what people are looking at when they look at a sire's career AEI.
1. Looking at the overall mean earnings per start for a sire's offspring, rather than the mean of the offsprings' earnings per start, did result in a slightly better predictive result (.45, instead of .40). Put more clearly (I hope), it was better weighting the EPS by race rather than by horse. However, this still wasn't as good a forecasting tool as the median (which had a crop-to-crop correlation of .50). I definitely need to try out something called a "weighted median" so that I can try weighting the median by race instead of horse. I expect this to provide an even more accurate prediction. And in fact, I'm guessing that weighting each race evenly isn't optimal either. We learn more information about a horse's ability from its first or second race than we do from its twentieth race, and the statistics will probably reflect that.
2. Using only the median data, comparing crops that were more than one year apart (ie looking at the correlation between a sire's 1997 and 1999 crops) SEEMS to show a slight trend towards less correlation than crops in consecutive years. The effect isn't clear enough to be definitive, but it would make some sense, since over time the quality of mares that a sire is sent to may change.
3. Also using only the median data I tried using two year averages (means) of the individual year medians as a predictor of the following year (for example, the average of the 1998 median and the 1999 median to predict 2000 results). It didn't seem to work any better or worse than one year. Even a three year average didn't work any better. This surprised me a little, but what may be happening is that the benefit of greater sample size that the multi-year averages are giving us could be cancelled out by the effect I described in #2 above. There's also a good possibility that this is simply due to random chance, and over large studies, the two year averages would have a slight benefit.
4. The most interesting result of the new work I did was that the average of the 1996 and 1997 medians was a GREAT predictor of the average of the 1998 and 1999 medians. Even without refining the 'predictor' to use a weighted median and to adjust for quality of mares, the correlation of the sires' earnings per start medians between these two year sets of data was .77.
Obviously these results have suggested all sorts of productive follow-up research. One of the things I'll need to look at is how the multi-year studies I did for median would perform for mean...since that's really what people are looking at when they look at a sire's career AEI.
Thursday, December 4, 2008
Changes In Behavior
The first study I did for Thoroughmetrics compared the success of horses that had been sold at yearling auctions against the success of those that were sold at two year old in training auctions. What I found was that there was a drastic difference. Not only did those in one of the types of auctions perform better overall (and at all price levels), but the performance was more predictable too...there was a much stronger relationship between price level and performance. Really valuable information for anyone spending hundreds of thousands of dollars (or more) per year at auctions of unraced horses.
That said, it recently occurred to me that there's a real problem with studies which look at any human behavior that changes drastically over time. In this case, the conclusions of the study will only be valid as long as buyers use the same criteria to make their decisions about what a given horse is worth, and as along as sellers enter the sames types or quality of horses in each type of auction that they did at the time of the study.
A similar problem exists with any data on the return on investment (ROI) of any handicapping system. Data gathered on the ROI will only be predictive as long as handicappers as a group don't change how they make decisions about how to allocate their money to horses with various characteristics. So although ROI is what the handicapper ultimately cares about, it is also one of the least stable measures to look at.
For handicappers, the solution is to actually create a 'line' based on the key characteristics of the horse. If you know what the odds on a given horse should be, you only need to compare those to the actual odds to know if a bet has a positive expectation. So instead of thinking something like "lone speed in a 6F race on average returns $2.30 for every $2.00 bet, so I'll bet on #4", handicappers should be thinking along the lines of "lone speed in a 6F race increases a horse's chance of winning by 15%...let's see if that makes #4 a good value at his current odds".
This type of change in thinking applies to ownership as well. Instead of doing studies that focus on general measures that change (like which type of auctions provide the best value), we're better served by looking at more stable measures. For example, if we find that offspring of AP Indy on average receive Beyer ratings 2 points higher in their races than offspring of Silver Charm, that's not affected by changes in human behavior (unless you think that there's a systematic change in how Beyers are being subjectively adjusted over time). So instead of simply knowing which type of auction tends to provide better value, we could go into any auction and evaluate what each horse is worth based on their Beyer ratings, and whether they're a good value based on the bidding.
I realize that in essence I'm saying that most people have the right approach, and that while the findings of my original study were valuable, they will become less valuable over time as behavior changes. However, where most people go wrong is that they judge each horse's value subjectively, or using flawed statistics that don't have much predictive value. It doesn't help to judge individual value, if you don't have a valid model for valuation.
That said, it recently occurred to me that there's a real problem with studies which look at any human behavior that changes drastically over time. In this case, the conclusions of the study will only be valid as long as buyers use the same criteria to make their decisions about what a given horse is worth, and as along as sellers enter the sames types or quality of horses in each type of auction that they did at the time of the study.
A similar problem exists with any data on the return on investment (ROI) of any handicapping system. Data gathered on the ROI will only be predictive as long as handicappers as a group don't change how they make decisions about how to allocate their money to horses with various characteristics. So although ROI is what the handicapper ultimately cares about, it is also one of the least stable measures to look at.
For handicappers, the solution is to actually create a 'line' based on the key characteristics of the horse. If you know what the odds on a given horse should be, you only need to compare those to the actual odds to know if a bet has a positive expectation. So instead of thinking something like "lone speed in a 6F race on average returns $2.30 for every $2.00 bet, so I'll bet on #4", handicappers should be thinking along the lines of "lone speed in a 6F race increases a horse's chance of winning by 15%...let's see if that makes #4 a good value at his current odds".
This type of change in thinking applies to ownership as well. Instead of doing studies that focus on general measures that change (like which type of auctions provide the best value), we're better served by looking at more stable measures. For example, if we find that offspring of AP Indy on average receive Beyer ratings 2 points higher in their races than offspring of Silver Charm, that's not affected by changes in human behavior (unless you think that there's a systematic change in how Beyers are being subjectively adjusted over time). So instead of simply knowing which type of auction tends to provide better value, we could go into any auction and evaluate what each horse is worth based on their Beyer ratings, and whether they're a good value based on the bidding.
I realize that in essence I'm saying that most people have the right approach, and that while the findings of my original study were valuable, they will become less valuable over time as behavior changes. However, where most people go wrong is that they judge each horse's value subjectively, or using flawed statistics that don't have much predictive value. It doesn't help to judge individual value, if you don't have a valid model for valuation.
Wednesday, December 3, 2008
Earnings Per Start
I finally got my hands on some data that will allow me to do research on the predictive value of various measures of sires' success. I have about ten years worth of basic data on the performance of offspring of the top one hundred sires at a point in time several years ago. The data has a number of minor flaws for my purposes, but it's still a treasure trove of valuable information and will allow me to start answering some of the questions I'm interested in.
I started playing around with it today, and took a look at some data for the 82 sires that had offspring in every crop from 1996 to 2000. So far I've found that the average correlation for consecutive years of the mean of earnings per start of the sires' offspring is .40. The average correlation for consecutive years of the median of earnings per start of the sires' offspring is .50.
What does that mean? Actually, we can't really draw any conclusions about the absolute levels of the correlations until we make some adjustments. It's very possible that a large part of the correlation is due to the relative quality of the mares being sent to each stallion. I have the data I need to make some adjustments for that, but it's not in 'machine readable' form. If anyone with access to a copy of American Produce Records wants to volunteer to help me enter the CI values for about 20,000 mares, I'm taking volunteers! In the meantime, my findings do show one valuable piece of information - that the median earnings per start correlates better from year to year than the mean of the offsprings' earnings per start does.
My next step (which I'll get to in the next few days) is to look at the actual mean earnings per start (in other words, counting each race once, instead of counting each horse once). I believe that will be a closer approximation of the popular AEI, so it will be interesting to see if that performs any better than the measures I've already tried. After that, I'm very interested to see how the different measures do at predicting eachother. In other words, is next year's crop's mean earnings per start better predicted by this year's crop's mean earnings per start or this year's median earnings per start. It would not be a huge surprise if the median turns out to be the best predictor, since it is less impacted by a single huge success (like Curlin). It would certainly be an indictment of using AEI to evaluate sires' quality if it can be outperformed by something as crude as the median earnings per start.
I started playing around with it today, and took a look at some data for the 82 sires that had offspring in every crop from 1996 to 2000. So far I've found that the average correlation for consecutive years of the mean of earnings per start of the sires' offspring is .40. The average correlation for consecutive years of the median of earnings per start of the sires' offspring is .50.
What does that mean? Actually, we can't really draw any conclusions about the absolute levels of the correlations until we make some adjustments. It's very possible that a large part of the correlation is due to the relative quality of the mares being sent to each stallion. I have the data I need to make some adjustments for that, but it's not in 'machine readable' form. If anyone with access to a copy of American Produce Records wants to volunteer to help me enter the CI values for about 20,000 mares, I'm taking volunteers! In the meantime, my findings do show one valuable piece of information - that the median earnings per start correlates better from year to year than the mean of the offsprings' earnings per start does.
My next step (which I'll get to in the next few days) is to look at the actual mean earnings per start (in other words, counting each race once, instead of counting each horse once). I believe that will be a closer approximation of the popular AEI, so it will be interesting to see if that performs any better than the measures I've already tried. After that, I'm very interested to see how the different measures do at predicting eachother. In other words, is next year's crop's mean earnings per start better predicted by this year's crop's mean earnings per start or this year's median earnings per start. It would not be a huge surprise if the median turns out to be the best predictor, since it is less impacted by a single huge success (like Curlin). It would certainly be an indictment of using AEI to evaluate sires' quality if it can be outperformed by something as crude as the median earnings per start.
Tuesday, December 2, 2008
True Nicks
One of the more popular (and controversial) factors that many people use in breeding decisions is the past success of 'nicks' between various bloodlines. Some people wouldn't consider a mating where the nick has been unsuccessful in the past, while others think that the entire theory is pseudo-science with no predictive power. One of the most popular ways of using nicks is to use the ratings provided by Truenicks. Their ratings are provided free for stallions whose farms have 'sponsored' them, while they are sold for around $20 per hypothetical mating for other stallions. The Truenicks people use the following statistics to back up their claim that their ratings are a powerful predictive factor in the success of matings:
"1. While only 13% of the entire Thoroughbred population earn “A” rankings (A to A++), 37% of the stakes winners rate as “A’s.”
2. Horses rated “B” or better (B to A++) represent just 30% of the entire population, yet 3 out of 4 (77%) stakes winners rank “B” or better.
3. Almost half of Thoroughbreds in general–44%–are on the low end of the scale (rated “C” through “F”), yet only two in 25 stakes winners (8%) have these lower rankings."
There's just one problem. As I've discussed in the past, this type of analysis is "cheating". Those who develop automated trading systems to predict movements in financial markets are all too familiar with the pitfalls of 'overoptimizing' your theory to fit past data. The systems developed do a great job of predicting the past, but have little predictive power going forward. The problem here is that the ratings themselves are based on the same data that is being used to demonstrate their success. To do a valid study of the predictive power, we'd need to look at rating in some point in time and then track the success of horses with various rating going forward.
The great news is that we have the data available to do that! Because of the free ratings available for many stallions, we can create a list of ratings for several hundred (or more) horses now, and then track their performance on the track over the coming months and years. It will be several years before we have the final results of the study, but the amount of work involved isn't overwhelming, and it will be a fun study to follow as the results begin to come in. I'll be talk more in future posts about the specific study design I'll be using for this, and how I'll deal with a few of the issues or pitfalls that it will face.
"1. While only 13% of the entire Thoroughbred population earn “A” rankings (A to A++), 37% of the stakes winners rate as “A’s.”
2. Horses rated “B” or better (B to A++) represent just 30% of the entire population, yet 3 out of 4 (77%) stakes winners rank “B” or better.
3. Almost half of Thoroughbreds in general–44%–are on the low end of the scale (rated “C” through “F”), yet only two in 25 stakes winners (8%) have these lower rankings."
There's just one problem. As I've discussed in the past, this type of analysis is "cheating". Those who develop automated trading systems to predict movements in financial markets are all too familiar with the pitfalls of 'overoptimizing' your theory to fit past data. The systems developed do a great job of predicting the past, but have little predictive power going forward. The problem here is that the ratings themselves are based on the same data that is being used to demonstrate their success. To do a valid study of the predictive power, we'd need to look at rating in some point in time and then track the success of horses with various rating going forward.
The great news is that we have the data available to do that! Because of the free ratings available for many stallions, we can create a list of ratings for several hundred (or more) horses now, and then track their performance on the track over the coming months and years. It will be several years before we have the final results of the study, but the amount of work involved isn't overwhelming, and it will be a fun study to follow as the results begin to come in. I'll be talk more in future posts about the specific study design I'll be using for this, and how I'll deal with a few of the issues or pitfalls that it will face.
Monday, December 1, 2008
How Much For The Holy Grail?
Ok, it may not be the 'holy grail' of pedigree analysis. In fact, it's really just a starting point, not a final destination. But after my realization a few days ago that sire data by crop simply isn't available for free, no matter how much manual work I'm willing to do, it's beginning to feel like it.
To recap, what I'm hoping to do is evaluate the predictive power of existing measures of sire success such as AEI and AEI/CI, and ultimately to come up with something that works better. In order to do that, I need to get data on multiple crops of offspring for a reasonable large group of sires. Realistically, I'd say I'll need about 50 sires included to have full confidence in my results.
Looking at the BRIS and Equineline web sites, it looks like I can get the data I need for $18 per sire. So it turns out that the holy grail costs about $900. Based on the sample reports on the BRIS site it looks like the data is human readable, but not in a good format for computer processing, so I'll probably have a long, boring data entry project to put it into Excel or Access, but that's something I can live with. If anyone with $900 to spare wants to invest in this project, I'd be happy to share the full results with you, reimburse your investment as I generate sales of the research, and throw in some free advertising on my site (if you run racing partnerships).
To recap, what I'm hoping to do is evaluate the predictive power of existing measures of sire success such as AEI and AEI/CI, and ultimately to come up with something that works better. In order to do that, I need to get data on multiple crops of offspring for a reasonable large group of sires. Realistically, I'd say I'll need about 50 sires included to have full confidence in my results.
Looking at the BRIS and Equineline web sites, it looks like I can get the data I need for $18 per sire. So it turns out that the holy grail costs about $900. Based on the sample reports on the BRIS site it looks like the data is human readable, but not in a good format for computer processing, so I'll probably have a long, boring data entry project to put it into Excel or Access, but that's something I can live with. If anyone with $900 to spare wants to invest in this project, I'd be happy to share the full results with you, reimburse your investment as I generate sales of the research, and throw in some free advertising on my site (if you run racing partnerships).
Wednesday, November 26, 2008
Or Maybe Not...
Argh! The calculations I discussed in my last post may have been based on some faulty assumptions about how sire statistics are reported. Can anybody answer the following question...When BRIS presents statistics on 'leading second crop sires', are they including data only on the horses that are actually part of the sire's second crop, or data on both the first and second crop children of sires whose second crop are currently racing? I had assumed that it's the former, but after looking over the data some more I'm thinking it must be the latter since it doesn't seem like ANY horses have more starters and winners in their 'second crop' year than their 'first crop' year. If so, that's incredibly frustrating and is almost certainly going to mean that research in this area will involve paying for a substantial amount of data and doing a lot more work than I had anticipated. Why does it always seem like BRIS and Equineline go out of their way to present their data in a way that makes it useless for any serious research?
Finally Getting Somewhere
As my regular readers (all four of you) know, one of the research projects I'd like to tackle is an evaluation of the existing measures of breeding success, and ultimately coming up with some statistics that do a better job of predicting future success. As I mentioned yesterday, the main challenge has been finding the data I need to study it. This morning I had a very minor breakthrough. Although BRIS is the data provider for The Bloodhorse, they don't always present the data in the same format. It turns out that BRIS has a version of the leading freshman, leading second crop, and leading third crop sires where they include SPI. As far as I can tell, SPI appears to be the same thing as AEI, although I haven't yet checked the numbers to see that the calculations are being done exactly the same.
I took list of leading first crop sires of 2007 and the list of leading second crop sires of 2008, both ranked by SPI. Of the 75 names on each list, there were 59 in common. The correlation between the SPIs was .65. Removing the horses with very few starts (less than 20 starts in the 2007 list and less than 30 starts in the 2008 list) didn't make much difference. The remaining 34 horses had a correlation for their SPIs of .63. On the surface, these correlations look pretty high, and indicate consistent performance by sires across crops. Of course, the problem is that a lot of that consistency may be coming from the fact that some sires are covering better mares than others, so we'll need to make an adjustment for that before we can have any confidence in the results we're seeing.
I took list of leading first crop sires of 2007 and the list of leading second crop sires of 2008, both ranked by SPI. Of the 75 names on each list, there were 59 in common. The correlation between the SPIs was .65. Removing the horses with very few starts (less than 20 starts in the 2007 list and less than 30 starts in the 2008 list) didn't make much difference. The remaining 34 horses had a correlation for their SPIs of .63. On the surface, these correlations look pretty high, and indicate consistent performance by sires across crops. Of course, the problem is that a lot of that consistency may be coming from the fact that some sires are covering better mares than others, so we'll need to make an adjustment for that before we can have any confidence in the results we're seeing.
Tuesday, November 25, 2008
Data...Looking For Help
The single greatest impediment to doing statistical research for the thoroughbred industry is the lack of quality data. That may seem surprising, considering that racing is a very data intensive sport. But what I've found is that virtually every study I'm interested in doing requires data that either needs to be painstakingly entered into spreadsheets or databases manually, or simply isn't available. In many cases, the data is out there, but not in a form that's usable for statistical research. For example, I've got a copy of the American Produce Record...but the data in there can only be searched one horse at a time...not exactly ideal for statistical research where I'm looking for patterns over samples of hundreds or even thousands of horses. I've talked about some research I'd like to do evaluating the predictive value of AEI and AEI/CI...someone with direct access to the BRIS or Equineline databases could do the study in about ten minutes. Unfortunately, neither organization thinks that's how we should be accessing their data, so I would apparently need to pay for the data per horse. Again, not exactly ideal for statistical research.
If any of you have data that you can share that you think might be useful for me, I'd love to hear about it. I'd be happy to use it for research that you might find useful or interesting too.
If any of you have data that you can share that you think might be useful for me, I'd love to hear about it. I'd be happy to use it for research that you might find useful or interesting too.
Saturday, November 15, 2008
Vacation
I'll be away from Sunday until Friday, so there won't be any new posts until after I return.
Friday, November 14, 2008
Dehere
In the True Nicks Blog at The Bloodhorse today they discuss how the entire future of Dehere as a sire was changed when his daughter Arrested Dreams lost the 1998 Matron by a nose. The loss cost Dehere the chance to be the leading freshman sire, and ultimately probably resulted in his being exported to Japan (and not getting to cover the same quality of mares that he would have) before his value as a sire was recognized several years later. I can't really comment on whether this analysis of events is accurate, but if so it would show how unbelievably superficial a lot of the pedigree analysis in the thoroughbred industry, and how bad the tools that many use for major financial decisions are. Does anyone REALLY believe that an evaluation that comes up with a substantially different answer depending on which horse finishes a nose ahead in one race has any possible predictive value for the future?
Tuesday, November 11, 2008
2009 Kentucky Derby
Yes, I know it's early to be thinking about the Derby, but I am anyway. Like breeding and owning horses, handicapping can offer some nice opportunities based on people's inability to correctly take sample size into account in their analysis. The top two two year old colts in training in the US (Vineyard Haven and Midshipman) are being sent to Dubai to prepare for the 2009 Derby. The common thinking is that because this "has never been successful", it can't work. On the surface, this makes sense. The two year olds that have followed this path before have always either missed the Derby entirely or not performed well. My instinct upon hearing that two more top colts were being shipped out was something along the lines of "shoot, there go two more horses we'll never heard from again". On the other hand, when everybody has the same strong emotional reaction, it's worth thinking about logically to see if their may be an opportunity. Here are a few reasons to think that the trip to Dubai may not be the kiss of death that most people think:
1. Limited sample size. How many horses have actually followed this path? Ten? Fifteen? In any case, the number is small enough that on average, we'd expect maybe one Derby winner out of the group and one or two other good performances. Godolphin may just be suffering a run of bad luck.
2. Vineyard Haven and Midshipman are far more accomplished than most of the other two year olds that were sent to Dubai. Most (such as Etched and Numaany last year) have simply been impressive maiden winners. The two colts this year are both multiple Grade 1 winners.
3. Conditions have changed. The first colt I remember following this path was Worldly Manners. When he won the UAE Derby, I'm fairly certain I remember hearing that the entire field was owned by the ruling family of Dubai. That's got to make you wonder how tough a prep race it was back then. Now it's a world class race against Southern Hemisphere three year olds (four year olds by US standards). So it's likely that horses following the Dubai route to the Derby now are getting much better preparation than ten years ago, and the sample of valid comparisons is even smaller.
Based on all this, I'd suggest keeping an eye on Vineyard Haven and Midshipman for an opportunity to get good value betting on them. If they're part of the 'pool' for Derby futures, that could be your chance. Of if they make it to the starting gate after performing well in the Dubai prep races, they may go off at higher odds than they deserve.
1. Limited sample size. How many horses have actually followed this path? Ten? Fifteen? In any case, the number is small enough that on average, we'd expect maybe one Derby winner out of the group and one or two other good performances. Godolphin may just be suffering a run of bad luck.
2. Vineyard Haven and Midshipman are far more accomplished than most of the other two year olds that were sent to Dubai. Most (such as Etched and Numaany last year) have simply been impressive maiden winners. The two colts this year are both multiple Grade 1 winners.
3. Conditions have changed. The first colt I remember following this path was Worldly Manners. When he won the UAE Derby, I'm fairly certain I remember hearing that the entire field was owned by the ruling family of Dubai. That's got to make you wonder how tough a prep race it was back then. Now it's a world class race against Southern Hemisphere three year olds (four year olds by US standards). So it's likely that horses following the Dubai route to the Derby now are getting much better preparation than ten years ago, and the sample of valid comparisons is even smaller.
Based on all this, I'd suggest keeping an eye on Vineyard Haven and Midshipman for an opportunity to get good value betting on them. If they're part of the 'pool' for Derby futures, that could be your chance. Of if they make it to the starting gate after performing well in the Dubai prep races, they may go off at higher odds than they deserve.
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