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Interesting yes... Helpful maybe.


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I've been doing some pattern analysis on the SFP shoes. As the title says, this is probably in the category of interesting...but is it helpful? Maybe, maybe not...you be the judge. I've got a database of 1,647 shoes, containing all the "real" shoes y'all have entered into SFP plus a few 5 dimes shoes. Randomly generated shoes are not included.

I was curious to know, what are the top 10 most common *confirmed* event patterns, including the next result? Some of them are obvious. The 4 most common events at each length are almost always the singles but even those show some differences with the next result, see line 1 and 3 in the 3 events category. After the singles it gets a little more interesting. 

I probably should just spend my time trying to beat the kitchen table  ..but this was fun analysis project to code up, and hopefully helpful.

Number of shoes: 1647

Number of Events: 3              Confirm        Events        Confirm        Next Play
Count:999 (b) pbp (b) >> b    (b)                 pbp             (b)                 b              - Next play banker is more common for pbp
Count:981 (p) bpb (p) >> b
Count:978 (b) pbp (b) >> p    (b)                 pbp             (b)                 p              - Next play player is less common for pbp
Count:951 (p) bpb (p) >> p
Count:525 (p) bbpb (p) >> p
Count:512 (p) bpbb (p) >> b
Count:494 (b) pbpp (b) >> b
Count:488 (b) pbpp (b) >> p
Count:485 (b) ppbp (b) >> b
Count:484 (b) pbbp (b) >> p

Number of Events: 4               Confirm        Events        Confirm        Next Play
Count:461 (p) bpbp (b) >> p    (p)               bpbp            (b)                p
Count:453 (b) pbpb (p) >> p
Count:415 (p) bpbp (b) >> b
Count:409 (b) pbpb (p) >> b
Count:235 (b) pbppb (p) >> b
Count:232 (p) bpbpp (b) >> b
Count:225 (p) bppbp (b) >> b
Count:224 (p) bbpbp (b) >> b
Count:221 (p) bpbbp (b) >> p
Count:220 (p) bpbbp (b) >> b
 
Number of Events: 5
Count:206 (p) bpbpb (p) >> b
Count:201 (p) bpbpb (p) >> p
Count:196 (b) pbpbp (b) >> p
Count:175 (b) pbpbp (b) >> b
Count:120 (b) pbppbp (b) >> b
Count:114 (b) pbpbpp (b) >> p
Count:113 (p) bpbpbb (p) >> b
Count:107 (b) pbpbbp (b) >> b
Count:105 (b) pbbpbp (b) >> b
Count:104 (p) bpbbpb (p) >> b

Number of Events: 6
Count:92 (p) bpbpbp (b) >> b
Count:92 (b) pbpbpb (p) >> b
Count:91 (p) bpbpbp (b) >> p
Count:82 (b) pbpbpb (p) >> p
Count:57 (p) bpbpbpp (b) >> b
Count:55 (p) bpbbpbp (b) >> b
Count:54 (b) pbpbppb (p) >> b
Count:53 (b) pbpbppb (p) >> p
Count:51 (p) bpbpbbp (b) >> p
Count:49 (b) pbpbbpb (p) >> p
 
Number of Events: 7
Count:44 (p) bpbpbpb (p) >> b
Count:43 (b) pbpbpbp (b) >> b
Count:41 (p) bpbpbpb (p) >> p
Count:39 (b) pbpbpbp (b) >> p
Count:30 (p) bpbpbpbb (p) >> p
Count:29 (p) bpbppbpb (p) >> b
Count:28 (b) pbpbbpbp (b) >> b
Count:27 (p) bbpbpbpb (p) >> b
Count:25 (b) pbpbppbp (b) >> p
Count:25 (b) pbpbpbpp (b) >> p

Number of Events: 8
Count:20 (p) bpbpbpbp (b) >> p
Count:19 (p) bpbpbpbp (b) >> b
Count:17 (b) pbpbpbpb (p) >> p
Count:16 (p) bbpbpbpbp (b) >> b
Count:15 (p) bpbppbpbp (b) >> p
Count:13 (b) pbpbppbpb (p) >> b
Count:13 (p) bppbpbpbp (b) >> p
Count:12 (p) bpbbpbpbp (b) >> b
Count:12 (b) ppbpbpbpb (p) >> b
Count:12 (p) bpbpbpbbp (b) >> b

Number of Events: 9
Count:11 (p) bpbpbpbpb (p) >> b
Count:10 (p) bbpbpbpbpb (p) >> p
Count:10 (p) bpbpbpbpb (p) >> p
Count:9 (p) bppbpbpbpb (p) >> b
Count:8 (p) bpbppbpbpb (p) >> b
Count:8 (p) bpbpbpbpbb (p) >> p
Count:7 (b) pbppbpbpbp (b) >> b
Count:7 (b) pbpbppbpbp (b) >> p
Count:7 (b) pbpbpbpbp (b) >> p
Count:7 (p) bpbpbppbppb (p) >> p

Number of Events: 10
Count:7 (p) bpbpbpbpbp (b) >> p
Count:6 (b) pbbpbpbpbpb (p) >> p
Count:5 (b) pbpbpbpbpb (p) >> p
Count:5 (p) bppbpbpbpbp (b) >> b
Count:5 (b) ppbpbpbpbpb (p) >> b
Count:5 (p) bpbppbpbpbp (b) >> b
Count:4 (b) pbpbpbpbbpb (p) >> p
Count:4 (b) pbpbppbpbpb (p) >> p
Count:4 (p) bpbpbpppbpbp (b) >> b
Count:4 (p) bppbpbpbpbp (b) >> p
 

Edited by XDotNet
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At this point it's raw data but...take this 3 Event series for example

Count:525 (p) bbpb (p) >> p = The 5th most common pattern for the 3 event patterns.....BUT

(p) bbpb (p) >> b    is not even in the top 10

Pretty interesting....is it reliable enough to be exploitable? It does come from the real data in SFP....so maybe?

It might be interesting to run it through some shoes to see if betting player after this pattern is an advantage play.

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5 hours ago, kachatz1 said:

It's binary code for Baccarat.

GEE Thanks Kevin...That helps a lot...I've got it now...NOT!...LOL:lol:

Now that you have all had your fun at Oz's expense...if anyone can explain WTF that all means to someone in an age group that thinks it would have been a real bonus for the world if Justin Beiber had been drowned at birth...go for it.:confused:

Edited by ECD
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1 hour ago, ozscouser1 said:

GEE Thanks Kevin...That helps a lot...I've got it now...NOT!...LOL:lol:

Now that you have all had your fun at Oz's expense...if anyone can explain WTF that all means to someone in an age group that thinks it would have been a real bonus for the world if Justin Beiber had been drowned at birth...go for it.:confused:

As soon as the boss (my 14 month old) takes a  nap, ill post an example.

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This example will be using the "3 & 4 Event Length Pattern" groups of data below.

"4 Event Length Pattern (4ELP)" = A pattern made up of 4 confirmed events. Examples:  bpbp(b1,1,1,1) OR pppbbpb(p3,2,1,1) OR pbppppbbbb(P1,1,4,4)

Number of Events: 3              |    Number of Events: 4
Count:999 (b) pbp (b) >> b    |    Count:461 (p) bpbp (b) >> p
Count:980 (p) bpb (p) >> b    |    Count:453 (b) pbpb (p) >> p
Count:978 (b) pbp (b) >> p    |    Count:415 (p) bpbp (b) >> b
Count:951 (p) bpb (p) >> p    |    Count:409 (b) pbpb (p) >> b

How to read this mess :P  Count:461 (p) bpbp (b) >> p

BaccStats1.png.f31d00c096e567f228eb95ad9c5af562.png

 

This next screen shot explains 1 anomaly i've found in the SFP database, there may be more, hopefully several more.

I scanned all the SFP "real" shoes (approx 1,650 hand entered casino shoes, the rest are computer generated). Then I counted how many times each pattern showed up with that patterns next bet and ranked them from the most common to the least common.. The screenshot below shows the top 4 patterns for 3 & 4 ELP with the patterns next bet. As you would expect the single events dominate the ranking.

*1st Screenshot below* - Using the 3 event data

PBP - With the next bet of banker showed up in the SFP database 999 times.

PBP - With the next bet of player showed up in the SFP database 978 times

If we analyze the single events (pbp/bpb) in the 3 ELP category, we see nothing exciting...Normal baccarat statistics, banker is slightly more common than player.

But look at the 4 ELP events.... in the SFP database, player is the more common event than banker and has an advantage? That's not normal statistics. Have a close look....

BaccStats.png.5c6e3a9d1dd0290f54768957d3fa4cef.png

Weird....banker should have a slight advantage but the SFP database shows for  bpbp and pbpb that player does ??? Wierd....

Final notes

  1. Is this the HG ... HELL NO!
  2. Is it helpful HELL YEeee....  maybe :) depends on how you use it.
  3. Will there be shoes/streaks of shoes where the stats above don't work...yep..that's baccarat.

So above...we've discovered that in the SFP "real" shoes that 4 single events are opposite normal baccarat stats. Whatever your betting system is, this might be helpful to confirm or cancel the bet you're system has chosen. When your in a shoe you might mentally note that this pattern was statistically opposite in SFP...

**Final final note - I'm not advocating using this to bet...i'm just reporting something interesting I've mined from SFP. Personally, i'm going to try to mentally notice what happens when pbpb or bpbp comes along. This is such a different kind of betting statistic/trigger/helper that we should be cautious until we can see it in action over many many shoes.

Questions are welcome....

 

 

Edited by XDotNet
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