Absence of Proof is not Proof of Absence!

Francis Bacon (1620) “For a man always believes more readily that which he prefers.” If we leave aside the contentious issue of whether or not it is possible to be a profitable sports trader using a wholly fundamental approach to handicapping, we can readily identify three evidence-based views of ‘form’:

• Evidence of Presence (EoP),
• Evidence of Absence (EoA), and
• Absence of Evidence (AoE).

The natural tendency to confirm our initial opinion leads us to look for supporting evidence (EoP) in both the historical record and in statistical trends with the obvious danger of the logical error of affirming the consequent. By contrast, evidence that eliminates a contender (EoA) is the recommended approach with its natural link to the logically correct method of denying the consequent. Finally, lack of evidence (AoE) neither confirms nor denies the status quo but many neophytes seem to confuse it with EoA.

Even-Equivalent Probability of Ruin

Epstein (2009) provides a detailed, mathematical analysis of the risk of ruin within the broader context of the basic theorems of gambling. Without loss of generality, it is worth providing a layman’s interpretation of how the probability of ruin impacts the choices made by the average handicapper.
In the classic treatment of ruin, there is a working assumption of unit bets at even-money in order to make the calculation of the risk of ruin more tractable. To avail ourselves of this analytical solution, we need to transform our real-world bets into their even-money equivalents, see Krigman (1999).

For example, if you are in the fortunate position outlined above with a 42% strike rate at an average price of 2.50 then your advantage is the equivalent of having a win probability of 52.02% at 2.00 with both the same expected value and standard deviation (0.25, 6.169) as the original bet and with a probability of ruin equal to 24.07%. Obviously, it is also possible to estimate stake size given a specific price and preferred risk of ruin.

Gamblers Ruin at Casino Incroyable

A nice mixture of mathematics and humor - Gamblers Ruin at Casino Incroyable with passing reference to "Bold Play", "Minimum Gain", and "Gamblers Ruin".

ZEER: Zero Evens-Equivalent Rate

When comparing handicapper performances across different circuits, it is better to zero the tax rate at evens using ZEER:
ZEER = (StrikeRate * (Odds + Tax - (StrikeRate * Tax))) / 2
Edge = ZEER - (1 - ZEER)

For example, comparing handicappers (see above) at Aqueduct, Gulfstream, and Santa Anita gives ZEERs of 52.94%, 54.04%, and 53.00% respectively.
This yields equivalent edges for the three handicappers of 5.89%, 8.08%, and 6.00%, identifying the Gulfstream handicapper as the most successful.
In summary, ZEER allows a group of handicappers to compare their performances on a common scale (evens at zero tax level)!

Kelly Horse Race or Ziemba Roulette

In the spirit of the New Year, I offer the following thought-provoking argument:
Betting is often described as a competition between you and the "Crowd" (i.e. Pari-Mutuel market) on which you can better estimate the true distribution of odds in a particular sporting event. In that context, a good starting point is to ask Bill Benter's fundamental question of handicapping: what additional variables (if any) explain a significant proportion of the variance in results to date that is not already accounted for by the public odds (Wisdom of Crowds)? By keeping records, it is possible to determine whether or not you have been successful over time in so doing. However, it is not possible to know (in advance) if you have an overlay in an upcoming event. This is the fundamental flaw of handicapping and, eo ipso, the flaw of fundamental handicapping.
The alternative approach is technical trading and an often underestimated strategy is "Bold Play" and its variants. Bold play is recommended for subfair games (i.e. p < 0.5, assuming even money bet) and, given the high-level of taxation, one can argue that horse-racing qualifies. Without going into the mathematical details and assuming a little "poetic licence", bold play reduces to an algorithm comprising two rules:
__a) Bet amount to reach target bankroll in single event (e.g. one race); or
__b) Bet amount in current event to reach bankroll level from which it is possible to attain target bankroll in next event.
You will no doubt immediately recognize that it is possible to iterate rule b) over many events (e.g. complete race card) but, ideally, you want to minimize the number of iterations. This technical trading approach only requires information on current bankroll, target bankroll, number of remaining events, house limits, and access to the public odds.
In summary, the advice to use fundamental handicapping to find overlays which you then exploit using edge-based staking (Kelly, 1956) assumes a superfair game but if, in fact, the game is essentially subfair then technical trading is recommended using bold play (Ziemba, 2002) or one of its variants in as few iterations as is feasible given whatever constraints are in place (e.g. maximum stakes).

Session Handicapping

From an economics viewpoint (expected utility theory), the prescribed way to approach a series of bets is to focus on expected utility (for example. wealth). This leads invariably to Kelly staking and maximizing the long-term expected growth rate of one's bankroll. With this approach, the handicapper is advised to play every race where he has an edge (namely, "bet your beliefs"). By contrast, from a psychology viewpoint (prospect theory), the prescribed way to approach a series of bets is to focus on loss aversion (pain of losses far outweighs joy of wins). This leads invariably to session handicapping and minimizing regret.
If for example, we define session handicapping as a day's wagering, then it is possible to adapt Belgian mathematician Thomas Bruss’s Odds Algorithm (http://www.ems-ph.org/journals/newsletter/pdf/2006-12-62.pdf) to determine when is the optimal time to stop betting to enhance the probability of ending the day in profit. In other words, the odds algorithm works out after which race during a day's session you should quit assuming you are ahead!

Trade Selection (Trailing Stop Loss / Take Profit)

I would like to propose a betting wizard enhancement for betting exchange tools that would be of great value to both Pre-Event Scalpers and In-Play Traders. The new wizard, Trade Selection, would include options to set both trailing stop loss and take profit thresholds for all bets, as standard.

Sample Size: Confidence Interval and Confidence Level

There are two key factors to consider when estimating sample size for testing a betting rule: confidence interval and confidence level. Confidence interval refers to the range within which you expect the correct strike-rate to fall and confidence level refers to how certain you are that this range holds the true value. For example, with a confidence interval of +/-5% and a confidence level of 99%, using these values and without going into the exact calculations [=POWER(PRODUCT(PRODUCT(NORMSINV(PRODUCT(SUM(ConfLevel,1),1/2)),1/SQRT(2)),1/PRODUCT(SQRT(2),ConfInterval)),2)] gives a sample size of approximately 664 (663/664). What this means is that testing a betting rule on 664 races drawn randomly from, for example, 2008 (i.e. different population from that used to discover betting rule), you can be 99% certain that the strike rate your betting rule generates from this new random sample will be within +/- 5% of the true strike rate.

Negative Expectation Misconception

Some handicappers believe incorrectly that, if they play a negative expectation game, they will lose a percentage of their initial bankroll equivalent to that expectation. This is incorrect.
For example, assuming that the expectation of randomly betting favorites at Track X is -17% and that Joe Punter starts with a bankroll of $1,000, he expects to lose (randomly betting favorites at Track X) $170 (17% * $1,000) over a season. In fact, he can expect to lose 17% of his total bets (turnover), not of his initial bankroll. Suppose he makes 250 flat $30 bets on favorites giving him a total turnover of $7,500 ($30 * 250), he can expect to lose $1275 (17% * $7,500) - not $170! In other words, he stands to lose (on average) $275 more than his initial bankroll!

Calculating Initial Bankroll

The first decision you make as a professional sports trader is to calculate the size of your initial bankroll.
This task is like tackling a 'chicken and egg' problem. On the one hand, you need to know your average strike rate in order to calculate a starting bankroll. On the other hand, this is difficult to accurately assess before you begin to trade. However, assuming you can generate a conservative estimate of the strike rate, Peter Webb provides a simple formula:
  LLR = ROUND(LN(NumBetsPerYear)/-LN((1- 
       StrikeRate%)),0)
for calculating the longest expected losing run relative to strike rate. Using this value, initial bankroll can be calculated, as follows:
  IB = ((LLR * 2) * UnitStake)
For example, with a projected 25% strike rate and an average number of bets per year of 1000, you can expect a longest losing run of approximately 24 so, given a unit stake of $25, the recommended initial bankroll is (24 * 2 * $25) = $1,200.

Fundamental (Weighing) or Technical (Voting) Analysis

In essence, there are two major schools of analysis among traders in the world's stock markets - fundamental and technical. Fundamental analysis is a weighing machine that measures the intrinsic value of a company. By contrast, technical analysis is a voting machine that measures the historical trends in the company stock price
With sports trading (e.g., horse-racing), there are two markets available on most betting exchanges (e.g., Betfair): pre-event and in-play. The pre-event market is similar to the standard bookmaker or pari-mutuel equivalent with respect to the back side (i.e. punting) of the equation. However, the addition of a lay option generates a highly liquid market for technical traders as they negotiate the ebbs and flows of money for the market leaders in the last fifteen minutes before the start of the event. Though there is some scope for a solely technical or fundamental approach to the in-play market, it actually requires a form of fusion analysis (i.e. blend of fundamental and technical) because, unlike stock markets, there is only one winner and many losers at the end of a finite time interval (i.e. end of race). Thus, even though the sports trader can punt (i.e. back OR lay) a single selection, the recommended approach is to trade (i.e. back AND lay) one or more selections to either "green up" (i.e. guarantee return regardless of outcome) or "red down" (i.e. spread liability across all selections to minimize loss regardless of outcome) the trader's overall position.

Back Story: As Time Passes Things Change

A simplistic interpretation of the Second Law of Thermodynamics is that as time passes things change. Though fundamental to our world, change is very difficult for most adults. In that respect, I am no different from my peers. According to my career script, I expected to become a successful, self-employed, consultant and to retire in late middle-age to the west coast and study mathematics. Actually, I achieved the initial career goal relatively quickly and lived the lifestyle I had anticipated for a short while only to be trumped by entropy. Facing an uncertain market for my expertise and experience, I decided on a complete change of direction. This is the continuing story of that journey...