4 Steps to Finding the Optimal Number of Betting Positions at a Gaming Table
An Illustrative Example of 5 Spot vs 6 Spot Blackjack
We often have clients ask questions around how many positions they should offer on table games: is there an optimal number, and why not have all games at 5 spots?
In the same way that optimal utilization or occupancy varies by game type and segment, deciding on the optimal number of betting positions also requires a deep dive into the specifics of the game: house edge, game pace, average wager, and time on device.
Despite what the science and math says, there is always the counter-argument of the art of customer service, guest experience, and elbow room. However, do you actually know what that extra elbow room is costing your property’s top and bottom-line?
In this blog post, we illustrate the steps for calculating both revenue and profit impact for games with a varying number of betting positions, using Blackjack games as an example. This approach can be extended for any game type.
The short answer:
– If the property is constrained by table capacity or dealer supply, reducing the total number of betting positions at the property will likely reduce profitability, i.e. reducing the number of boxes on a table, without adding more tables or open hours.
– On the contrary, if the property has surplus table capacity and dealer supply and has insufficient demand, then reducing the number of betting positions will not make a significant impact to profitability. Opening the right number of tables at the right times will make a bigger impact on profits.
Let’s dig into the math.
Background: Calculating Net Contribution per Patron Visit
The goal of yield management is to maximize the profit per patron’s visit to the property. In an ideal world, the optimal experience for the patron will maximize the profit for the property. In the real world, operators deal with physical constraints including floor space, number of gaming tables (licenses), number of dealers, fixed dealer shifts and so on. The goal of yield management is to find the right balance between all these variables in order to maximize profitability.
The net profit or net contribution per patron visit is calculated as follows:
Net Contribution per Patron Visit = Theo Revenue per Patron Visit – Gaming Taxes – Comps – Labor Cost
Theo Revenue per Patron Visit = Game Pace x Average Wager x House Edge x Play Time
It is important to note:
– Game pace and playtime (or time on device) both vary by occupancy and type of game
– Gaming taxes and comps are simply a percentage of revenue
– The labor cost represents the costs associated with serving the patron, i.e. labor cost = hourly loaded labor cost x playtime (or time on device)
Step 1: Assemble the Fixed Game Inputs
Here are all the inputs and data points for a $25 Blackjack game. Every segment and operation may have different inputs.
Step 2: Determine Variable Inputs: Game Pace and Play Time (or Time on Device) by Table Occupancy
Game pace varies by product type and number of positions at the table. Often ignored, the playtime plays a critical role in the calculations and can be extracted from table management systems through rated play.
The table below is what we will use for the $25 Blackjack segment. As expected, with more players at the table, each player gets fewer hands per hour. However, playtime peaks at an occupancy of 4 at 80 minutes, and then reduces. This could be due to the game being too slow for this patron segment or just not enough elbow room, which could be one argument to why operators may prefer 5 spot layouts to 6 spot layouts.
Step 3: Calculate Net Contribution by Table Occupancy
The net contribution per player visit can be calculated for each table occupancy. The resulting output is what drives all the calculations.
Net Contribution per Patron Visit = Theo Revenue per Patron visit – Gaming Taxes – Comps – Labor Cost
From the table above, the property makes the highest revenue per patron visit when there are 1-2 players per table. Intuitively, this makes sense if labor is free and the goal is revenue maximization.
In reality, after accounting for all the taxes and labor costs, the highest profit per patron visit occurs when there are ~3 players per table.
It is also important to note that when every single spot is occupied, the property makes the least revenue and profit per patron visit.
Step 4: Running the Simulations
Now that we have the profit models built, we can put it into practice. Let’s compare these four casinos, each with a different strategy on how many betting positions to offer for a $25 Blackjack game and how many tables to open during off-peak and peak times.
Casino A vs Casino B vs Casino C vs Casino D
Casino A has the fewest betting positions since they are all 5 spot games. Casino B and Casino C have the same number of betting positions, but a different number of tables and Casino D has the most number of betting positions.
Scenario 1: Saturday Night, Peak Time
All casinos are expecting 220 $25 BJ Patrons
All casinos know that they have to open to full spread on the busiest night of the week. The key difference here is the number of patrons each casino can serve at capacity and how many patrons will go unserved and leave.
Now let’s calculate the same KPIs on a Saturday night when we are expecting more patrons than the total number of betting positions available in any one of these four casinos:
When comparing Casino A and Casino B, Casino A has sacrificed the ability to serve 32% of the patrons by offering only 5 positions. Casino B is able to generate an incremental 10% in revenue and 12% in profit by just offering the sixth spot.
Similarly, Casino D is able to generate 10% more revenue compared to Casino C that is only offering 5 spot games and is able to serve almost all the players.
In summary, during peak times, if the casino is constrained by tables and positions, the higher cost to the casino is not being able to serve the patron demand. This is where the ability to offer multiple price-points allows the casino to help segment demand.
However, since specialty games (e.g. poker derivatives, etc.) are typically limited by capacity, only a subset of price points can be offered at any given time. This is where the additional sixth or seventh spot can drive more revenue and profit to the casino during peak periods.
Scenario 2: Mid-Week, Off-Peak
All casinos are expecting 60 $25 BJ Patrons
Each casino has a different strategy on labor. Casino A is able to schedule more tables than any other casino while Casino D has a focus on cost minimization with only half as many tables open as Casino A.
Let’s calculate the total net contribution (or profit) for each one of these casinos:
In this scenario, all casinos are able to meet the demand. The difference now is cost minimization vs profit maximization.
Casino A has the highest revenue and profit because they are able to service the demand at an optimal utilization of three players per table, on average. Casino D will have the lowest operating costs with the least number of tables open but are also sacrificing revenue and profit with it.
In summary, when labor is available, the bigger impact to profitability is opening the right number of tables to meet the optimal utilization that will ensure the highest profit per patron visit.
By digging into the math and applying it to two common scenarios for a $25 Blackjack example, we can conclude that during peak times, if the casino is constrained by tables or dealers, having fewer spots on a table can actually hurt profitability. It is the trade-off between not serving a customer at all or serving all the customers at a reduced profit. For the $25 Blackjack segment, there is a 12% reduction in profit during peak times from missed revenue.
We also illustrate that during off-peak times, the bigger impact on profitability is actually having the right number of games open, not the number of spots on the table. In the extreme case, it would mean up to 27% in missed profit.
These four steps allow the operator to quantify the missed opportunity or incremental revenue by adjusting the number of spots on a table and can be extended to any game type on the floor – as long as the data is available!
As one of the founding executives, Maulin currently serves as the President of Tangam Systems. With over thirteen years of experience helping operators improve business performance with data science, Maulin has overseen Tangam’s growth to the global leader in Table Games analytics. He received his Computer Engineering Bachelor’s and Master’s from the renowned McGill University in Montreal, specializing in Artificial Intelligence.
As the Senior Manager of Analysis and Optimization at Tangam Systems, Patrick has been helping clients for over a decade to understand and solve their business challenges and drive performance improvements for their table games operation. He earned a Bachelor of Computer Science at Ryerson University and a Master of Science specializing in Computer Vision and Artificial Intelligence at one of the top programs at York University.