Academic Handicapping

Most serious horse players have heard of Tom Ainslie, Andrew Beyer, Steve Davidowitz and Bill Quirin, all of whom are respected authors who changed the Sport of Kings in both obvious and subtle ways. Ainslie gave race analysis, or “handicapping,” credibility; Beyer brought speed figures to the masses; Davidowitz made the “key race” and “track bias” part of the gambler’s lexicon; and Quirin provided statistical insights never before seen in racing texts.

Yet the contributions of these turf luminaries pale in comparison to those made by R. M. Griffith, William Ziemba, Randall Chapman and others… whose names provoke only blank stares and a collective “who?” from most racetrack patrons.

Bill Benter, considered by many to be the most successful horse bettor of all time, credits Chapman with inspiring the program that — literally — changed his fortunes.

In researching various betting strategies, Benter ran across Chapman’s 1986 paper “Searching for Positive Returns at the Track: A Multinomial Logit Model for Handicapping Horse Races” and was captivated.

“It was really a breakthrough for me,” Benter told Contingencies magazine. “If I hadn’t found that paper, I might’ve given up. The problem was so brilliantly analyzed. The approach he outlined in the paper was theoretically absolutely sound.”

“I still credit (Chapman) with my great success,” Benter continued. “I met him several times over the years, and I thank him profusely. It’s good for guys like that to know they can write a paper and change the world. It appeared in some obscure academic publication, but such a brilliant idea spawned a whole billion-dollar professional horse racing industry.”

Although no precise figure has ever been given, it is estimated that Benter and his associates made in the vicinity of $37 million a year during their heyday.

Makes Beyer’s “My $50,000 Year at the Races” sound like chicken feed, doesn’t it?

So why is Beyer the equivalent of Ringo Starr and Chapman the equivalent of Pete Best’s valet? Primarily it’s because Chapman, like Griffith and Ziemba, is a scholar — a guy sharing his thoughts and theories in, as Benter noted, “some obscure academic publication.”  

True, Ziemba published “Beat the Racetrack” for the masses, but it was highly theoretical and mathematical — not once did Ziemba write about punching a hole in the Gulfstream Park press box or dropping to his knees and proclaiming himself “king of the world,” as Beyer did.

But what guys like Griffith, Ziemba, Chapman and many, many other academics have done — in the murky shadows — is provide the framework for numerous well-known racetrack truisms… and a few myths as well.

Because academic research on horseracing is limited, it is often dated… and that allows Regular Joe’s to compete with the Bill Benter’s of the world… kinda, sorta.

For example, William McGlothin expanded on Griffith’s theory of a “favorite-longshot bias” (the still valid observation that the pari-mutuel crowd as a whole overvalues longer-priced horses and undervalues shorter-priced ones) by noting that favorites in the last race on a card are particularly underbet.

McGlothin studied 9,605 thoroughbred horse races, primarily from California tracks, and found that shorter-priced horses performed best (in terms of ROI) in the eighth race on the card, which at the time of the study, was typically the day’s finale.

The problem is McGlothin collected his data from 1947 to 1953 — long before simulcasting, home computers and online betting. Hence, I was curious as to whether or not this last-race “favorite-longshot bias” still existed today... because many handicapping tomes that I've read still accept McGlothin's findings as gospel.

I began by gathering data on favorites as a whole. To keep things as simple as possible, I eliminated races with more than one top betting choice (co-favorites, entries, etc.). Here are those digits:

Number: 14,505

Winners: 5,409

Rate: 37.3%

Return: $24,400.60

ROI: -15.89%

IV: 2.76

OBIV: 0.85

Next, using the same criteria (focusing only on races with a sole favorite), I examined the last race on each card:

Number: 1,596

Winners: 551

Rate: 34.5%

Return: $2,659.30

ROI: -16.69%

IV: 2.95

OBIV: 0.83

Clearly, if my data can be trusted (and I have no reason not to trust it), there is nothing to the argument that favorites offer greater value in the final race of the day. While the impact value on favorites in the last race is marginally higher than the IV for favorites as a whole, both the odds-based impact values (see below) and the ROI figures indicate that such horses are hardly overlooked in the wagering.

Cue the music, another racetrack myth bites the dust.