Multi-Game Session Simulator
Most casino visitors don't stick to one game all night. They might start at the blackjack tables, move to slots, try their luck at roulette, and finish at the craps table. This simulator uses Monte Carlo methods to model how switching between games with different house edges affects your overall session outcomes.
Why Multi-Game Sessions Matter
Each casino game has a different house edge and variance profile. When you combine multiple games in one session, the mathematics become more complex. A player who splits time between low-edge blackjack (0.5%) and high-edge slots (8%) experiences a blended house edge that determines their expected loss. This simulator runs thousands of sessions to show you the range of possible outcomes.
Build Your Casino Session
Games in Your Session
Add games in the order you plan to play them. Allocate time (in minutes) to each game.
Simulation Results
Based on 5,000 simulated sessions:
Your Session Timeline
Total: 60 minBankroll Trajectories (Sample of 50 Sessions)
Breakdown by Game
| Game | Time | Bets Placed | Total Wagered | House Edge | Expected Loss |
|---|
Outcome Distribution
How often you can expect each outcome:
Understanding Multi-Game Sessions
According to the American Gaming Association, the average casino visitor plays 2-4 different games during a typical visit. Understanding how this affects your expected outcomes requires understanding the mathematics of each game's house edge and how they combine.
How the Simulation Works
This tool uses Monte Carlo simulation, a computational technique developed during the Manhattan Project that uses random sampling to model complex systems. For each simulated session, the tool:
- Calculates bets per game: Based on decisions per hour for each game type
- Simulates each bet: Using random numbers weighted by true probability
- Tracks bankroll: Recording how your balance changes through the session
- Checks stop conditions: Stopping if you hit stop-loss or bust
- Aggregates results: Compiling statistics across thousands of runs
The variance parameter models how "swingy" each game is. Slots have high variance (big wins and losses), while baccarat has low variance (outcomes cluster near expected value). Research from the UNLV International Gaming Institute has extensively studied these mathematical properties across casino games.
The Blended House Edge
When you play multiple games, your effective house edge is a weighted average based on how much you wager on each game. If you bet $1,000 on blackjack (0.5% edge) and $500 on slots (8% edge), your blended edge is:
Game Properties Used in This Simulator
| Game | House Edge | Decisions/Hour | Variance Level |
|---|---|---|---|
| Blackjack (Basic Strategy) | 0.5% | 70 | Low |
| Blackjack (Average Player) | 2.0% | 70 | Low |
| Roulette (European) | 2.7% | 40 | Medium |
| Roulette (American) | 5.26% | 40 | Medium |
| Craps (Pass Line) | 1.41% | 100 | Low |
| Baccarat (Banker) | 1.06% | 80 | Very Low |
| Slots (Low Volatility) | 4% | 600 | High |
| Slots (High Volatility) | 8% | 600 | Very High |
| Video Poker (Perfect Strategy) | 0.5% | 400 | Medium |
| Keno | 25% | 20 | Extreme |
House edge figures are based on data from the Nevada Gaming Control Board and academic research published in gaming mathematics journals. Actual returns vary by specific game rules and player skill level.
Strategic Insights
While no strategy can overcome the house edge in the long run, understanding these mathematics helps you make more informed decisions about entertainment spending:
Time vs. Money Trade-offs
Games with lower decisions per hour (like roulette) expose less of your bankroll per hour than games with rapid decisions (like slots). However, this doesn't change the percentage you're expected to lose—it just stretches the entertainment value over more time.
The Variance Factor
High-variance games like slots mean your actual outcomes will deviate wildly from expected value. You might walk away a big winner or lose your entire bankroll quickly. Low-variance games like baccarat produce outcomes that cluster closer to the expected loss.
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