Casino Enterprise Management

Today, several good computerized systems are available for tracking live game players. Yet, years ago computerized tracking systems were very inaccurate, somewhat inflexible, and operated from truncated entities separate from slot tracking systems and other systems of the resort/casino handling hospitality and point-of-purchase requirements.

In the past, sometimes a customer’s playing history was separate from systems that accounted for complementary management, with “integrated systems” relying on information input through a keyboard interface. As a result, many player tracking accounts were updated hours—if not days—later, leaving complementary decisions to educated guesswork based on outdated playing history.

Today, this situation has changed drastically. Information is available in real time and usually incorporates all departments involved in the customer’s “play and stay.” Most systems allow the casino executive to view the customer’s entire casino play, regardless of the location of his or her wagering, including the location of non-gaming purchases and expenditures.

Some of the major gaming industry leaders have established enterprise-wide systems that cull information between all host properties located in different areas of the country. Even though player tracking systems have made decision-making easy and practically flawless, every system inevitably grapples with its own Achilles heel, which is the quality of input information and the correctness of the calculation variables.

To that end, player tracking must be re-evaluated to determine if these factors have been taken into consideration, and whether or not these factors have been correctly established by the creators and end-users of the player tracking and reinvestment systems.

Player Rating System Flexibility
Flexibility of variables is as important in live games as it is in slot/video player tracking. In the real world, the amount of wagers, the different mathematical advantages the house experiences based on the different wagers, and in strategy related games such as blackjack, the establishment must have accurate metric variables.

Without accurate variables, tracking systems will incorrectly produce players’ theoretical earning value, affecting bottom line results. A ten percent error in the player’s favor, while calculating reinvestment, could cost an average size casino in Nevada as much as $160,000 annually (see Nevada Gaming Abstract, 2005).

That’s $160,000 in player reinvestment that could be eliminated or utilized more wisely. The following rating factors need to be taken into consideration and/or reexamined to guarantee that a system is not providing over-inflated theoretical player win information. Remember, a variable error of ten percent, or a combination of variable errors equaling ten percent in favor of the customer will contribute to the $160,000 figure—or more.

Speed Rating Games
It’s important to establish an accurate default for the number of rounds dealt per hour. If a system is programmed to use 60 rounds an hour as the default and the actual number of rounds dealt are 54, then the system will produce a ten percent error in theoretical win. As one would assume, a ten percent error in theoretical win, results in a pass-through error of ten percent in player reinvestment calculations.

Each game type needs to be analyzed and assigned a realistic number for rounds per hour. For example: the game of pai gow poker is a much slower game than blackjack. While a standard hand shuffled blackjack game with an average of four to five players may reasonably obtain a total of 60 rounds per hour, a dealer at pai gow poker may only deal 30 to 40 rounds per hour. Using a default of 60 rounds an hour across the board on all card games will cost your casino unnecessary reinvestment expense.

In addition, your system needs to have the flexibility to evaluate game speed based on the number of average players on the game. Games with one or two players are much faster while full tables of six to seven players will slow down to a grind. In order to optimize your player rating system, you need to be able to input fast and slow game situations.

Qualifying Empirical Data
The number of rounds per hour of an average game, as well as any variation such as slow or fast games, need to be supported by empirical data, which is collected from actual observations of games on a casino’s floor.

It’s important to use real case numbers gleaned from casino game observations, and not from gaming articles or someone else’s casino numbers. The calculation of rounds per hour is not complicated. Observe several average dealers and track the number of actual rounds they deal to an average of three to four players for one hour. Look for normal table play. Reject any samples that have extensive fills or games stoppages from unusual occurrences such as drink spills. A more detail description for calculating rounds per hour can be found in my CEM April 2006 article titled, “Metrics of Live Game Speed.”

Establishing Accurate House Advantages
Establishing the house advantage for specific games is fairly easy to accomplish, as in roulette, for example. Other than one exception, all bets placed on the layout are subject to a 5.26 percent house advantage. In this situation you would establish the house advantage default multiplier at 0.0526.

House advantages for most other games are fairly easy to determine as well. Even games that require player strategy decisions like Caribbean stud poker can be established at a specific house advantage percentage since the strategy is basically the same for every hand. This is also noted in the game of baccarat. Due to the third card rule and the closeness of house advantage between the two primary wagers—player and banker—the house advantage can be defaulted to 1.2 percent (0.012).

Some game types may require a second house advantage level. Three card poker (TCP) has a base bet (ante) and a bonus bet (pairs plus) that have an approximate one percent house advantage difference. A player who only wagers on the (ante/player) using a “queen-ten” or better play strategy can be calculated as a 3.37 percent (0.337) house advantage player. However, if their original wagering includes the “pair plus” bonus option (see Table 1 at right) for the same wager amount, the average house advantage gets pulled down to approximately 2.85 percent (0.0285).

It may also be prudent to establish a third level of advantage calculation if it is noted that some TCP players wager the ante, the pair plus, and blindly wager the “player bet” prior to receiving their cards.

Other tiered games are craps (based on wagering variations) and blackjack (based on the basic house advantage and the effect of the player’s skill level). Blackjack might require several separate game designations because of the diversity of game types. The rule of “six to five” blackjack comes to mind. Since the rule change adds 1.4 percent to the basic game house advantage, has management changed their game default percentage to reflect this severe change?

By readjusting the house advantage to reflect this rule, the casino can more accurately market to players wishing to play against a blackjack game with a true house advantage on the same par as pai gow poker or three card poker.

Flexible Blackjack Skill Rating Level
One of the biggest mistakes the gaming industry makes in regards to reinvestment waste is to establish a generic house advantage for blackjack. How many systems are defaulted at 1.5 percent for all the casino’s blackjack games?

This inflexible number fails to take into account not only game variation, but the skill level of the player. Take, for example, a poor player who takes insurance on whims and fails to double on soft hands while always standing short on “busting” hands; should you reward him or her as you would your average player that plays a much better strategy? What about the customer who plays close to perfect basic strategy? Is this customer worth the reinvestment you expend on the average blackjack player?

Although a basic strategy player will never have a long-term positive expectation over the house, an inflexible tracking system may be creating an overall positive player expectation. In some cases the advantage player can achieve a long-term return of 0.25 percent to 0.5 percent on his or her dollar while flying below the casino’s game protection radar because the system is too generous with the estimate house advantage.

Stress Accurate Ratings
If you use a multi-level rating system to more accurately determine the effect of game speed and varying true house advantage, be sure to give the floor supervisors who rate the play knowledge to make these decisions.

All personnel rating pit customers’ play need to be on the same page, or the system will not work. Be sure to hold meetings for your floor staff explaining the parameters of the different game speed and play skill levels. The evaluators need to know when a player is rated as a basic strategy player or poor player, and when the game pace dictates a deviation from an average or standard rating category.

Also, I used to tell my floor staff that every players starts play assuming they are subject to an average game pace and playing at an average house. Everybody is “Mr. or Ms.” average unless there is an indication otherwise. Using this thought process, any mistakes made during player rating will have a minimal negative effect.

Also, stress to pit and shift managers that it is their responsibility to apply correct rating levels. By occasionally spot-checking customer ratings, management can quickly detect the poor rating and correct the discrepancies.

System Testing
After you have established your new metrics and rating levels, be sure to check the system’s accuracy by comparing theoretical win to actual win in your database. This will verify the system’s effectiveness in determining optimal player reinvestment.

 Several years ago, while I was at the Aladdin Hotel and Casino, we introduced a multi-tier evaluation system. At the end of the year, I compared the theoretical win with the actual win, and noted that the theoretical win was somewhat higher than the actual.

After investigating different possible reasons, it was determined that I had used too high a house advantage when determining the average blackjack player’s win value. After adjusting my average house advantage from 1.5 percent to 1.2 percent the system correctly reflects the theoretical win as compared to the actual win. After the second year, the positive effect of my adjustment was confirmed when the two figures were within an acceptable comparative range.

It goes without saying, the most important game rating metrics involve game types that provide the majority of the revenue and affect the level of player reinvestment the most. Since blackjack is the king of the live game casino, it is extremely important that you establish accurate game speed and house advantage for each blackjack game variation. The number of decks used, as well as the different game rules, needs to be calculated to determine each blackjack game’s basic house advantage.

If two or more blackjack game types, such as double deck and six deck games, have a calculated basic house advantage within 0.02 percent or less, it should be all right to group them together. Combining a double deck game with a basic house advantage of 0.4 percent, with a single deck game that offers six to five on blackjack with a basic house advantage of 1.6 percent, would not be wise unless the combined game house advantage is set low and the casino is not interested in properly rewarding the six to five customer.

It’s also extremely important that management correctly establish a house advantage percentage effect for the players who misplay hands. Utilize software that detects card counters and advantage players, or can be obtained through random player observations. In most cases the average player will misplay enough hands to give the casino an additional 0.5 percent to 0.8 percent over basic house advantage (see Table 2 at left).

Increase Player Reinvestment
One of the arguments concerning the creation of a better live game player tracking system is the anticipated increase in player reinvestment that will be realized by players who haven’t received full awarding due to a lacking system.

On the contrary, it is easy to see that an improved player rating system can increase expenses from properly tracking customers who play on fast games and/or possess poor playing strategy skills. Wouldn’t the big player who plays alone on a table be credited with more hands played and a greater theoretical win? Doesn’t this factor drive other unrecorded costs upward that limit, if not eliminate, the gains created by the purpose of the more accurate system?

While these last two statements serve as a basis for a good argument, they are not valid in a real business world situation. First, there is an overwhelming opportunity to save reinvestment money. Because most present systems use a much higher house advantage than necessary, the saves will be unbelievable once that metric is brought back to earth.

Second, correct calculation of player reinvestment is the optimal goal. Knowing your true cost and how you can reinvest or save wisely is the key. What about the big players who ask to play alone on a table? Do they have to be penalized? What happens when they find another casino that rewards them correctly for their win potential? Management doesn’t need to spend additional perks, but in the meantime have they lost a good player? How often has this happened due to improper reinvestment metrics? Would management really know if losing inadequately compensated players is a bona fide negative factor in their player reinvestment strategy?

Third, a more accurate player tracking system will provide management with the information they need when establishing reinvestment limits. Maybe it’s more operationally sound to adjust reinvestment percentage down from 35 percent to 30 percent. It might even be beneficial for management to consider a tiered reward system if one hasn’t been established already.

In this manner, they can reward better players with a higher reinvestment percentage, while parrying additional costs of lower limit players, who just happen to prefer playing with a limited number of other players during slow hours of operation.

Don’t become myopic with the benefits of cost cutting. There are advantages to better allocating reinvestments, which will help retain your better players. These same advantages will also help attract other quality players that are not familiar with your casino.

Effect of Variable Mistakes
A mistake in the rating system by ten percent in any one of the rating variables (rounds dealt per hour, average wager, or house advantage) will lead to a ten percent increase or decrease in the cost of customer reinvestment (comps). Although mistakes made when determining average wager have just as much an effect as the other two variables, average wager mistakes affect ratings on an individual basis. Errors establishing rounds dealt per hour, or house advantage, are “system-wide” and affect every rating equally. The effect of a ten percent error, in anyone of these variables, is noted in the following example.   

By using column one as the baseline for a standard player tracking system you can see that a true rounds-per-hour of 54 indicates a true reinvest to the player, in this situation $14.18. This amount is about $1.57, or ten percent less than the system has been programmed to award. The same ten percent effect is witnessed when the house advantage variable has been established too high at 0.15 percent then the actual house advantage used in column three. The above table takes into consideration the effect of only one variable being off by ten percent.

This effect magnifies when several variables error on the high side at the same time. The different variables are multiplied together to calculate theoretical win. It was previously stated that the annual cost of over-rewarding live game players in a medium size casino could arguably be as high as $160,000. I would imagine that there are several larger or “mega” resorts that are wasting millions of dollars in unnecessary customer reinvestment due to improperly established rating variables.

Bill Zender is a former Nevada Gaming Control agent, casino operator, professional card counter and present gaming consultant. He has been involved in various areas of gaming and hospitality since 1976. He can be reached at wzender@lastresortconsulting.com.