Tag Archives: surveillance

Roulette Analysis

•Windows 7 and above
•.Net 4.5.2 compatibility
•SQL compatibility
Pricing: Let’s talk about it!
Demo Video:
You are a casino owner, gaming manager or surveillance manager.  You have a feeling that something is going down at the roulette tables, but you can’t put your finger on it.  Looking for a method to calculate the probabilities?
Excelpunks puts the statistics into this equation with Roulette Analysis!
•Includes analyses for Single and Double Zero roulette
•Probability alerts immediately inform you of improbable results.
•Round-By-Round Analysis and Entire Game Analysis:
  • Occurrence of Specific numbers
  • Win/Loss analysis on all wager types
  • Win/Loss analysis on Specific Numbers
  • Win/Loss analysis  of the game as a whole
•Custom Analysis •Win/Loss analysis of any combination of any number of games
Let robust statistical principles do the math for you.  Think that your player is winning more than he should?  Let Roulette Analysis prove it mathematically!
Get in touch with us at excelpunks@gmail.com for details!

Casino Surveillance Reporting Suite with Integrated Baccarat Analysis


  • Windows 7 and above
  • .NET Framework
  • SQL server compatibility

Contact us at excelpunks@gmail.com for details!

Demonstration Video:


  1. User-friendly interface with drag and drop functionality
  2. Comprehensive details presented in an easily navigable format.
  3. Compatibility with Excel – retrieve and upload large amounts of information using Excel into and out of the system.
  4. Customizable user access levels define how much access each user has to report and access data.
  5. Integrated Incident Reporting platform with Baccarat Analysis for comprehensive reporting on losing shoes.
  6. Automatic report data generates upon loading – presenting the number of reports created by respective users and categories.
  7. Print reports individually or by batch
Password Protected Log-ins
  • Password protection for all log-ins.
  • Reports are uniquely identified by the user’s log-in name.

Incident Reporting

  • Standardized reporting made easy with drop-down lists for incident categories, locations, staff and subject profiles.
  • Dynamic report list generation allows you to scroll through all selected reports
Incident Report

Subject and Staff Profiles

  • Dynamic profile list generation allows you to scroll through all selected profiles
  • Attach any kind of file to the profile from images to movies and access them directly from the subject profile.
  • Easy access to all related Incident Reports and Daily Logs.

Subject Profile

Baccarat Analysis – Game Analysis

  • Examine a host of factors like wager ratios, game wins and shoe biases -all integrated with your Incident Report.

Baccarat Tracking Sheet

Baccarat Analysis – Player Analysis

  • Analyze all the games ever reviewed for specific players
  • Find out the level of probability for suspicious behavior for all reviewed games.

Player Analysis

Custom Report Generation

  • Custom report generation allows for dynamic, customized report lists for display.

Custom Data


  • Customization is available on request – we understand how unique your operations are and we will tailor the system to suit your specific needs.

Should you be in another industry, but would like a customized reporting system of your own, do let us know!

Baccarat Analysis


  • .NET framework
  • SQL server compatibility

Baccarat.  It is THE hottest game in Asia and well known for its high limits and the crowd it draws.

Excelpunks is pleased to present – Baccarat Analysis.

View the demo:

Individual Game Analysis

Baccarat Analysis (Review)

  • Analyse each game being played
  • Easy to use point and click interface
  • Systems automatically examine the results of each coup – all you have to do is enter the results and the wagers
  • WIN: Compute your player’s probability of winning for the player, banker, tie or pairs
  • WAGER: Monitor your player’s wagering pattern to see if he is wagering high on the winning hands and low on the losing hands – there’s no need to guess, let us prove the maths!
  • SHOE: Analyse the shoe itself to see if there was a bias to either the player or the banker and assess its probability
  • THEORETICAL WIN: Compare the expected theoretical win and the result and see how probable the result is
  • 1st Card: Determine the probability of 1st card knowledge

Master Your Games

  • Using binomial distributions and T-tests, study each game on 5 metrics: Win rate, Wager Ratios, Shoe Bias, Theoretical Win and 1st Card knowledge
  • 6 threat levels assess the improbability of a result occurring from confidence levels of between 75% to 99.9%
  • Multiple measurement systems analyse various aspects of play to see just what your player is doing

LONG-TERM Player Analysis – Analyse Your Players Based on All Games EVER Reviewed…that’s right, every one

Baccarat Analysis (Player Analysis)

  • A centralised database captures EACH and EVERY coup ever reviewed for quick and easy analysis
  • Review EVERY GAME ever played by your player and spot trends across all shoes played with the Player Analysis module.
  • Easily recall a previously reviewed game with a mouse-click
  • Automatic algorithms do all the math for you – you just need to type in a name and click!

Easy transfer of your existing data!

Baccarat Analysis (Database)

  • Integration with Excel allows you to transfer your existing data into the system
  • Customization is available on request

Get in touch with us at excelpunks@gmail.com for details!

The Logic Of The Losing Shoe

The Logic of the Losing Shoe

For those in the casino industry, especially for us surveillance folks, the words ‘losing shoe’ are all too familiar.

A losing shoe is a period of play, normally lasting the length of one shoe of cards (which may be from one to as many decks as the shoe can hold!), which registers a substantial loss.

Ever wondered how that loss limit was set?

In considering this question, it is useful to once again refer to our central limit theorem.  Here is the graph again.


(Source: http://schools-wikipedia.org/)

The central limit theorem proposes that up to 99.9% of all occurrences happen between -3 to -1 and 1 to 3 σs from the average or mean.  σ extends into the positive (meaning 1 to 3 σ) and negative (meaning -1 to -3 σ).

In order to derive any sort of boundary in a casino game, one has to calculate the following:

  1. Probability Of Events Occurring (for more complex calculations, refer to: https://excelpunks.com/concepts-8-combinatorial-analysis-counting-possible-outcomes-and-creating-your-own-casino-game/)
  2. Mean Or Average Probability Of An Event Occurring (this is to determine the average expected result)
  3. Standard Deviation Of The Event Occurring (this is to determine σ)

Probability Of Events Occurring

The probability of events refers to the mathematical probability of an event.  This can be calculated by determining the possible outcomes of what you are trying to measure and then dividing that by the total possible outcomes for an event.

A simple example would involve a single deck of cards.  The probability of drawing an Ace of Spades is 1/52, since there is only one Ace of Spades in the deck.  The probability of drawing an Ace of any suit is 4/52, since there are four Aces in the deck.

Mean Or Average Probability Of An Event Occurring

For this, we turn to binomial distribution.  Binomial distribution calculates the means and standard deviations of occurrences that have one of two outcomes.

Example: Baccarat

Baccarat is a good example, where the outcome is either a Player or Banker.  For simplicity, let’s disregard coups ending in a tie for now.

Formula for the mean (binomial distribution):

N x P = Number of trials of an event x probability of the event occurring

We generally know that the Banker has a probability of 0.458597 while the Player has that of 0.446247, with Ties making up the difference.

For a shoe where the player wagered exclusively on Banker for 40 coups, the mean win for him would be:

40 x 0.458597 = 18.34388 (this means that he would be expected to win 18.34388 coups)

Formula for the standard deviation (binomial distribution):

SQUARE ROOT(N x P x Q) = SQUARE ROOT (Number of trials of an event x probability of an event occurring x probability of an event NOT occurring)

The standard deviation for that kind of play as described above would be:

SQUARE ROOT(40 x 0.458597 x (1-0.458597)) = SQUARE ROOT(9.931431) =3.151417

*the greater the difference between P and Q, the smaller the standard deviation!

Standard Deviation Of The Event Occurring

Here is a table calculating the number of coups a player wagering exclusively on Banker would win from a 40 coup shoe in terms of σ.

Total Coups Mean σ -3 σ -2 σ -1 σ 1 σ 2 σ 3 σ
40 18.3438 3.1514 18.3438 – (3 x 3.1514) = 8.8896 coups 18.3438 – (2 x 3.1514) = 12.041 coups 18.3438 – (1 x 3.1514) = 15.1924 coups 18.3438 + (1 x 3.1514) = 21.4952 coups 18.3438 + (2 x 3.1514) = 24.6466 coups 18.3438 + (3 x 3.1514) = 27.798 coups

Now, to determine the expected result in dollars, we will multiply the player’s wager (assuming he wagered on ALL coups) by the coups he is expected to win or lose.

Determining Whether You Have a Losing Shoe

I have included 2 values, assuming average wagers of $1 and $5.  You can add zeros to the backs of the average wagers as you please!

Percentage of Players Winning at that level 2.50% 13% 34% 50% 34% 13% 2.50%
Average Wager Standard Deviations -3 σ -2 σ -1 σ Mean 1 σ 2 σ 3 σ
$1 Winning Coups 8.8896 12.041 15.1924 18.3438 21.4952 24.6466 27.798
Losing Coups 31.1104 27.959 24.8076 21.6562 18.5048 15.3534 12.202
Expected Result ($22.67) ($16.52) ($10.37) ($4.23) $1.92 $8.06 $14.21
Percentage of Players Winning at that level 2.50% 13% 34% 50% 34% 13% 2.50%
Average Wager Standard Deviations -3 σ -2 σ -1 σ Mean 1 σ 2 σ 3 σ
$5 Winning Coups 8.8896 12.041 15.1924 18.3438 21.4952 24.6466 27.798
Losing Coups 31.1104 27.959 24.8076 21.6562 18.5048 15.3534 12.202
Expected Result ($113.33) ($82.60) ($51.87) ($21.15) $9.58 $40.30 $71.03

You can see that on average, players are expected to lose to the house.  But when they start to win, the losing shoe is called – but at what point?

Notice the percentage levels above each σ.  This means that for -2 σ, 13% of players would achieve a result of -$16.52, wagering at $1 a coup.  At the mean, 50% of players would achieve a loss of -$4.23, wagering at $1 a coup.

To put that in perspective, a player wagering on Banker for 40 coups at $5,000 a coup would be expected to lose -$21,150.  If that player starts to win more than $9,580, you might want to watch him more closely.  A win of $40,300 would be unlikely and a win of $71,030, even more so.  You would definitely want to call a losing shoe at that point!

The Z-Table

Now, the -3 to 3 σ measurement didn’t just pop out of nowhere.  These measurements are derived from the Z-table.

Notice that the Z-table shows 0.0 to 3.0 down by the left and 0.00 to 0.09 on the top.  This is the measurement in terms of σ, from 0.00 to 3.09.


You can determine the probability of σ by following the row on the left where the first 2 digits of your σ result appear to where it meets the column titled by the 3rd digit of your σ.


0.16 σ would be 0.5636

1.82 σ would be 0.9656

The table is true for positive and negative σs, meaning that reading for -1.56 σ would be the same as 1.56 σ, which would be 0.9406.

You can set your own σ levels depending on your appetite for risk.

Good luck!