Horse running through field

Dam Lines Unplugged

by Roger Lyons

In my last post about comparing the contemporary influence of competing dam lines, I explained that the measure of their influence (Dam Line Index, or DLI) is a function of 1) the number of SWs descending in direct female descent of the given dam (S) and 2) the average generational distance of the dam from those SWs (G). Or S / G = DLI. That function yields a measure that roughly reflects the relative influence of different dams of different eras.

I also mentioned that I go back only seven generations in search of any given dam–say, La Troienne–even though some contemporary SWs trace to La Troienne further back than that. Setting a maximum generational distance is important because it limits the extent to which the DLI is biased in favor of early-era dams. Going back eight or nine generations would increase the number of SWs descending from La Troienne more than it would increase the average generational distance.

Now, what I didn’t mention in that post is that there’s another factor related to that same bias: the historical limit that is set on the population of SWs surveyed. The tables published in my last post are based on SWs of stakes run from 1995 to the present. But, what if we lopped off the earliest five years and just used SWs from 2000 to the present?

Well, fewer of the more recent SWs would be traceable to older dams within the maximum seven-generational range. That means using only the more recent SWs tends to tilt the bias in favor of the later-era dams, just the opposite of what happens if you increase the generational distance of the survey.

Okay, I realize this is kind of wonkish, but, if you compare the rank order based on stakes run since 1995, here, with the alternate rank order based on stakes run only since 2000, here, you’ll get a sense of what is at stake in how these statistics are done.

The range of the alternate DLIs is compressed, such that La Troienne’s lead is diminished. Some of the later-era dams have displaced some of the earlier-era dams in the top spots. Helene de Troie, the dam of La Troienne and ranked 6th on the original list, is now ranked 23rd. Grey Flight, nowhere to be found on the original list, is now ranked 34th. Best in Show, the latest-born of all the top dam lines, at an average of only four generations removed, has edged up from 10th to 8th.

Even more indicative is that Urban Sea, to which I referred in my last post as ranking 362nd on the original list (not in the top-40 table), ranks 154th on the alternate list.

Basically, these two issues–how many generations are surveyed and how recent the pool of SWs–bear on the question of currency, and, to the extent that currency is valued above other considerations in the assessment of dam lines, less turns out to be more.

Measuring Dam Lines

by Roger Lyons

Every few years, around November sale time, I survey female lines to see how their rankings have shifted over time. The terms “survey” and “ranking” require definition. This time round, I pulled out the winners of stakes, as compiled by WTC, that were run from 1995 to the present and tabulated every occurrence of every dam in the female lines of these SWs going back seven generations, along with the generational distance of each occurrence.

Then I crunched the numbers as usual to find out for each dam 1) how many SWs were descended from her and 2) the average generational distance of her occurrence in the female lines of those SWs. That’s all you need in order to get a rough idea of relative contemporary influence because, if you divide the number of SWs by the average generational distance at which the dam occurred in the female lines, then you end up with an index that you can use to rank the dams in a more or less valid way–based on the number of SWs per average number of generations removed. Let’s call it the Dam Line Index (DLI).

For example, La Troienne, which ranks highest among the 70,659 individual dams represented, occurs in the female line (within seven generations) of 298 SWs (since 1995) at an average of 6.42 generational removes. That means her DLI is 298 / 6.42 = 46.42, which is the number of SWs descended from her per generation.

The average generational distance is useful because it controls to some extent for the differential opportunity of mares of different eras. Best in Show, for example, which ranks 10th, occurred in the female lines of 78 SWs and at an average generational distance of only 3.79 generations. So, the index of her influence is 20.58 SWs per generation. That’s a lot less than La Troienne, but not that much less than Escutcheon, which has the second-highest rank, at 28.21 and an average generational distance of 6.38.

Now, there’s always going to be someone who says (without thinking) that seven generations is not enough since La Troienne occurs beyond the seventh generation of some contemporary SWs. That is an untenable position, though, because, if you extend that rationale across the population of dams (not just La Troienne), there can be no generational limit that will satisfy them all.

Besides, the method already has an inherent bias in favor of the older dams. Consider that Urban Sea, dam of sires Galileo and Sea the Stars, plus other high-class sons and daughters, ranks last (362nd) among dams with a DLI of at least 7.0. She is the dam of seven SWs, but has not had the advantage of subsequent generations through which to multiply her influence. She’s almost certainly bound to be better as a tail-female influence than she ranks now, and increasing the maximum generational distance of the survey would only serve to exaggerate the bias against her.

Click here to view the alphabetical list of top-40 dam lines and here to view the same list in rank order.

More about this topic in subsequent posts.

Zenyatta vs. Joe DiMaggio

by Roger Lyons

Hands down, the greatest streak in sports is Joe Dimaggio’s 56-game hitting streak in the season of 1941. I say this advisedly–advised, that is, by an article written by Stephen Jay Gould for The New York Review of Books (August 18, 1988), which I read back then and managed to track down for this occasion in that publication’s online archive. In it, Gould cites Nobel laureate in physics, Edward Mills Purcell, who conducted probability analyses of all recorded streaks in baseball to find out which ones had been likely to occur all along and which ones were utterly improbable. He found that all “fell victim to the laws of probability,” except for Joe Dimaggio’s 56-game hitting streak.

That streak is so improbable, Purcell concludes, that “to make it likely (probability greater than 50%) that a run of even 50 games will occur in the history of baseball up to (then), baseball rosters would have to include either four lifetime .400 batters or 52 lifetime .350 batters over careers of one thousand games.” Nobody has ever hit .400 lifetime, and, then as now, only three major league players have had lifetime batting averages over .350–Ty Cobb, Rogers Hornsby, and Joe Jackson. Joe Dimmagio hit only .325 lifetime.

So, there you are. The question is, how many consecutive races would Zenyatta have to win in order to run up a streak as improbable as that?

I wouldn’t know how to get the right answer, but let’s look at it this way. The nearest any player has approached Dimaggio’s 56-game hitting streak was Pete Rose’s 44-game streak in 1978, not counting the old record of 45 games set by Willie Keeler in 1896–a different era. Dimaggio’s streak was 1.27 times longer than that of Pete Rose.

But we know from Purcell’s study that a 44-game hitting streak has at least a 50% probability. Every once in awhile someone is going to achieve it even though nobody except Pete Rose has done so lately (kind of like the Triple Crown). Given past racing streaks, I think it’s fair to assume that winning 18 consecutive races–she’s won 19 already–is probable enough to be comparable to a 44-game hitting streak. If so, it would mean Zenyatta could equal Dimaggio’s streak by winning 23 consecutive races.

But it can’t be that simple. She has the very great advantage of entering the starting gate at a chosen time and place, and that depends to some extent on how she’s training. Dimaggio had to step up to the plate every day, wherever the Yankees happened to be playing. Again, I don’t know the technically correct way to do this, but it seems to me Zenyatta would have to win, say, 25–maybe 26–consecutive races in order to be in the same ballpark as Joltin’ Joe.

Greenspan to the Rescue

by Roger Lyons

A recent Blood-Horse blog post, posted on September 27, drew the usual parallel between the performance of the stock market and demand in the commercial market for thoroughbreds. The post was occasioned by a USA Today report of an Alan Greenspan speech in which he argued that, as the blog post put it, “a sustained stock rally would be the ‘most effective’ stimulus for the sluggish economy.” As the post goes on to explain, “a rising stock market makes people feel richer, it inspires confidence, and it signals optimism in the future.” The point was to suggest that what’s good for the stock market is good for commercial breeders.

Feel-good macroeconomics has its place, but I’ve never felt that good about it. Maybe it’s because, around the time I first started hearing about the trickle-down theory–while studying at the University of Kentucky and doing odd jobs in the thoroughbred industry–I got my first Form 1099 ever, and it soaked up about everything that had trickled down to me. That was within the first year or two of the Reagan administration. I imagine he had economists who thought public policy that lavishly enriched the investor class would unleash such a mighty torrent on the rest of us that they needed to build a dam to stop the deluge (for our own good!), but, instead, the trickle just dried up.

And that’s when they started talking about supply-side economics–the if-you-build-it-they-will-come theory of macroeconomics. It was a reversion to the classical doctrine that supply creates its own demand–because, if people are employed in making stuff, they will be able to buy it. Only this time round, economic policy was in thrall to Alan Greenspan’s Ayn Rand fantasy, in which the invisible hand of the market doles out to all what they deserve–if only the policy makers just stay out of the way.

To some, it might have seemed a Yogi Berra moment when Dwight D. Eisenhower said, “things are more like they are now than they ever were before,” but his fellow Kansans knew what he meant. For nearly three decades after the war, the economic universe was expanding, and it was a patently Keynesian universe. It truly was how things should have been all along: driven by demand, wages high, unemployment low, leisure not just a retirement pipedream, and the top marginal tax rate–91%. That was the universe, by the way, in which American racing had its heyday.

But, when demand is high, it’s apparently the doom of policy to assume that supply is everything. What we have now, as a result, is the widest income disparity between the rich and the rest of us since 1928. That means, perforce, that unemployed, underemployed, and working- and middle-class Americans have no money to spend; and, at the same time, huge amounts of money are being committed to the very kinds of speculative investment that expose the economy to calamity.

The thoroughbred industry’s problem is a lot bigger than just a lack of demand for racehorses and breeding stock. Yes, commercial breeders experience it as such, and it’s painful, but, as I’ve argued before, it’s a mistake to think of thoroughbred sales as even a proximate function of effective demand. Those buyers are, in fact, investors in the system of production. Through them the system distributes young horses for training and channels breeding stock into more efficient use. They are suppliers, not consumers. I emphasize this point because, the more commercial breeders dwell on the faux-demand feature of what is really a supply function, the more they lose sight of effective demand, the consumer.

The consumers are the ordinary folks who used to watch and play the races–you know, the ones who’ve been impoverished by the cockamamie economic policies of the last 30 years. The economy of the thoroughbred industry is in the same demand trough as the larger economy and for the same reasons. It’s about unemployment, declining real wages, and diminished expectations. No matter how rich the rich are, it makes no sense to invest in thoroughbred production during a time when the ordinary people who would otherwise comprise effective demand for the product–as an object of beauty, grace, and wager–are losing their homes and livelihoods. That’s why anyone who cares about racing should raise a holy clamor whenever somebody whines, “If only the rich were richer.” Alan Greenspan?!. . . . Please!