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All comments by Nicolas Hammond
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I've published some details in my book, see http://www.detectingcheatinginbridge.com. I've got three chapters on opening leads.

There is a snippet on opening leads at http://www.detectingcheatinginbridge.com/selections.html but this shows how good the top players are on opening leads; not the effectiveness of trump leads. I do have the data, it's just not in the book. The book shows the different styles of opening leads of the top pairs.

The pair that leads trumps the most (Sementa/Duboin) leads them over 11% of the time; the pair that leads them the least is Hamman/Wolff at under 4%. I was lucky enough to chat with Bobby yesterday and asked him why he doesn't like trump leads. He gave a long explanation with examples.

The top pairs get opening leads wrong about 1 in 5 hands.

As others point out; when you first learn you are given “rules” to follow. As you play more, these “rules” should become “guidelines”.

I have over 200K records, all from top tournaments. However, trying to dissect the data to find the right circumstances is something I haven't done.

One general rule is to always lead a trump against a low level doubled contract to cut down on ruffs.
July 27
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If only we knew someone with a lot of time on their hands, say, someone who has just retired, and someone who knew the BBO format relatively well and was an excellent Bridge player who knew what stats to create…

Oh well, wishful thinking.

On a serious note… my book includes data on the elite players by name - Meckwell, HHs, Lauria/Versace. According to the lawyer these are “public figures” by the fact that they have established themselves at the top level. As they are “public figures”, I did not ask, nor need, their permission. Frankly, a lot of the graphs also show the number of boards played so it is trivial to pick these three pairs (+ Fantoni/Nunes). Strictly I don't need anyone's permission to list their names - this is statistical data therefore publishable. However, the conclusions are obvious for some data so the list of pairs in some tables has some “names withheld” because the result is too obvious.

I did ask some other top pairs below the top three if I could include their data. If either player said no I did not include them in most statistics.

I specifically asked Kit (+ Fred) if I could include them. They are an excellent reference pair.

I also include Boye with his two main partners (with permission).

There were some other top pairs that gave permission, but I cut the data for space reasons.

Without permission (but perfectly legal), I included data on same well known pairs in some charts. This is so you can see how some pairs rank compared to other pairs, including the known cheating pairs. For example, Sabine/Roy; top women players, top Seniors, top USA, top European. These are all on lists that cannot be used for cheating detection.

Back to Fred's suggestion.

There is the law of unintended consequences.

The goal of Bridge is to win.

If there are published statistics of how often you do x, y or z, then you may stop playing the game to win and work on how to improve your statistics. Think of the Lehman rating on OKB.

Lists are both good and bad.
July 25
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bridgescoreplus.com downloads all the data.

There is an option to output handrecords in various formats, including BW.

Same for EBL/WBF/BBO events.

You have my email and cell #, so email, or text. For others, PM me.
July 25
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@Barry: I'll turn the question around. If a Bridge Organization had a list of suspected cheaters, what would you want them to do with it?

The answer is probably legal based on the organization. Almost certainly there are political issues, but that is outside my domain.

I have provided the information in your last sentence in a proposal given about a year ago. I provided recommended guidelines on usage.

I would expect the likes of WBF to disinvite certain pairs.

For ACBL, there are different legal issues. IANAL.

I don't know if EBL has the ability to disinvite.

Almost certainly you would stuck cameras on highly suspected pairs and also be more active in assigning limited resources to those suspected. This is probably the biggest benefit.
July 24
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@Stefan: I have multiple algorithms.
ACBL only records the table result, so there is less data to go on; which means I need more data from the results to be statistically valid.

WBF/EBL record the opening lead. But the data can't be trusted - see the book for details.

Vugraph record bidding and play. More information can be processed. Less data is needed for the results to become statistically valid.

@all: The software detects humans cheating in Bridge. If you use Bots, you will have to train Bots to cheat in Bridge. Now you are into an area beyond most people's ability and knowledge base.

@Michal: I am happy for someone to create an independent test. I've written what I think is a fair way of doing things. I've made some predictions in the book, when (not if!) they are proved to be true, I can reveal the names.
July 24
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Here's the test:

Take all data that I'm using - raw data from ACBL, EBL, WBF web sites.

Take this same data, but change all the names. So I can't have code to check on names.

Give me this new data which has random names for the players.

Put me in a monitored setting/environment. I start with a clean database. I run the software against this data set. I show you the list of cheating players.

==

Here's another test:

Give me a database from a country where I do not know the players.

I will ask the software to detect who might be cheating.

See if it matches any convicted pairs.

==

Here's another:

I provide a set of MD5 hashes on players that are cheating. I put it in a book. See how accurate the predictions are. Wait… I already did that.

==

The software is written to detect humans cheating. There are multiple algorithms, not one.

Writing an algorithm to detect bot cheating would be different. I think.

Remember… a cheating pair has unauthorized information. A smart cheating pair knows when to use that unauthorized information or not. Cheating pairs make mistakes. Just fewer mistakes in certain parts of the game in certain circumstances. Knowing what they are, and how to measure them, is what is new.
July 23
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Yes/no. (If I understand your question).

What is very interesting is that I can tell when a pair suddenly “improved” or got worse.

For example, I believe I know when F/N started to cheat; the same with the Doktors being monitored.

What is very interesting is the number of top pairs that suddenly got a lot worse after the summer of 2015. Many pairs have a dip in their performance starting in Chennai.

The book shows the big change in defense ability in European and World events post 2015.

Before the non-Americans get upset… there is little data kept in American events to run data on.
July 23
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I have a lot of stuff. Data on all top players. Data on all ACBL, EBL, WBF players.

Data is statistically significant given a large number of boards.

Using this I can work out weaknesses in someone's game.

Annoyingly I played against someone (pro pair) this tournament that I had given advice to (their opening leads were poor). Against us they promptly found all the right opening leads. They had actively worked on improving their opening leads for the last 2-3 months.

Obviously a couple of boards is meaningless, but I'll be curiously to check this pair in about a year when I have more data on them to see if their opening leads have improved.
July 23
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@Stefan: “What happened in 1983/84?”

There is suddenly less public data. If someone wants to transcribe from World Championship books, that would be great.

Would love to get the data so that the software can process it.

See https://www.sarantakos.com/bridge/vugraph.html

If not for Nikos Sarantakos there would be no data before about 2003.

Edit: clarify statement in third paragraph.
July 23
Nicolas Hammond edited this comment July 25
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I do know how often world class players get this, or similar situations, right. I also know the same but for cheating pairs. There is a difference. This is how probable cheating pairs are identified. Details in book….
July 23
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@Barry: “I'd love to see the results from a large data set of club games. I need a good laugh now and then.”

I don't do (much) club data. There are various reasons. It is possibly to accurately process some club data, but I'm not going to give the details.

The problem with club data is the wide variance of skill level. The same problem exists at tournaments, but to a lesser extent.

In the book, I show the difference between top players, then top players when they only play against top players. It is the latter where it is easier to detect cheating.
July 23
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There is much more than one algorithm. There are different algorithms for different parts of the game.

If the algorithms are fully described, it becomes easy for a player to know how to manipulate some plays to avoid being detected.

It then becomes a cat and mouse game of trying to stay ahead of the cheating pairs with each successive generation of cheating detection tools requiring more sophistication.

Also… being blunt, the software is a commercial product. There is no benefit to releasing the source code or algorithms to non-licensed customers.
July 23
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If anyone is interested in writing up hands from a pairs event, let me know. I can get you the BW format.

For example, see https://bridgewinners.com/article/view/blue-ribbon-pairs-day-3-first-session-bidding-competition/

If someone wants to write up the final round of the von Zedtwitz, copy the following into a BW article and you will have all the hands:

{handviewer b=1&d=n&v=0&n=S2HT542DJ9752CQJ4&e=SAKQ7HJ9DKT64CK75&s=SJT965HAKQ7D83C63&w=S843H863DAQCAT982}
{handviewer b=2&d=e&v=n&n=SQ632HAJ9DAQ62CK9&e=STHKQ642D83CA8752&s=SAK4H73DKT97CQJ43&w=SJ9875HT85DJ54CT6}
{handviewer b=3&d=s&v=e&n=S654HKT7DKQ53C765&e=SAJT93HA98DJ74CA9&s=SQ8HQ6432D96CKQ43&w=SK72HJ5DAT82CJT82}
{handviewer b=4&d=w&v=b&n=SQT963H6DK95CKJT5&e=SJ85HAT5DAT4CQ986&s=SAKHQJ9743DQ76CA3&w=S742HK82DJ832C742}
{handviewer b=5&d=n&v=n&n=SAQ7HT863DQJT6CQ5&e=S652HAKQ4D92CAKJ2&s=SJ4HJ752DK843CT76&w=SKT983H9DA75C9843}
{handviewer b=6&d=e&v=e&n=SKT65HQT3DT76CT62&e=SJ72H65D42CQJ9743&s=SQ4HAK982DAJ98C85&w=SA983HJ74DKQ53CAK}
{handviewer b=7&d=s&v=b&n=SQJ643HKJ93DA9CAJ&e=S8HA2DKQJ64C76542&s=S9752HQT4DT8CKT98&w=SAKTH8765D7532CQ3}
{handviewer b=8&d=w&v=0&n=SQ973HK7DKT53CK96&e=SA2HQJ982DQ86CT43&s=SKJ54HAT654D72CA2&w=ST86H3DAJ94CQJ875}
{handviewer b=9&d=n&v=e&n=SA7HT9863DQ6CA853&e=ST652HAQJDAKJ92C4&s=SKQ8HK42DT53CT762&w=SJ943H75D874CKQJ9}
{handviewer b=10&d=e&v=b&n=SAJT7642H95DQJC32&e=S83H762DKT8763CT4&s=SKQ95HKJ43D92CK76&w=SHAQT8DA54CAQJ985}
{handviewer b=11&d=s&v=0&n=ST4HQ64D984CAQT86&e=SJ9853HJ9DQJ5C752&s=S7HKT8752DAK72CJ9&w=SAKQ62HA3DT63CK43}
{handviewer b=12&d=w&v=n&n=ST754HKT5D742CAK5&e=S9863HA92DA63C742&s=SAKQJHQJ643D9CT63&w=S2H87DKQJT85CQJ98}
{handviewer b=13&d=n&v=b&n=SJ75HKJT9864DCQ65&e=SA83HQ7DT76CAKJ94&s=ST96H532DQ42CT873&w=SKQ42HADAKJ9853C2}
{handviewer b=14&d=e&v=0&n=SKJ7HAKQDT65CAJ96&e=S5432H8532DK42CT4&s=ST986HJT6DA3CQ732&w=SAQH974DQJ987CK85}
{handviewer b=15&d=s&v=n&n=SAH84DKJ7542CQJ84&e=ST975HAKQ976DAC65&s=SJ62HJT2DQT93CT93&w=SKQ843H53D86CAK72}
{handviewer b=16&d=w&v=e&n=SK3HT93DQ7642CA97&e=SQJTHKQ72DAJ8C832&s=S9764HJ654D93CQ65&w=SA852HA8DKT5CKJT4}
{handviewer b=17&d=n&v=0&n=SQ4HKQ65DA973C862&e=SK53HJ874DKQ5CKT4&s=SAT9876HA2DJCQ975&w=SJ2HT93DT8642CAJ3}
{handviewer b=18&d=e&v=n&n=ST986HAKQ83DCK876&e=S4H9DQ98653CAT954&s=SAJ3HT7642DAKTCQ2&w=SKQ752HJ5DJ742CJ3}
{handviewer b=19&d=s&v=e&n=SAK54HK76D972CAJ6&e=S97HA5DAKJ85CQ872&s=SJT32HQJ43D63CKT5&w=SQ86HT982DQT4C943}
{handviewer b=20&d=w&v=b&n=SJT763HKQJ76DJC74&e=S42H94DA8643CK953&s=SAQ85H85DQ952CT62&w=SK9HAT32DKT7CAQJ8}
{handviewer b=21&d=n&v=n&n=SATHA96DAJ753CAQ4&e=SKJHJ743DKQ9CKT76&s=S864HQ82DT8642CJ2&w=SQ97532HKT5DC9853}
{handviewer b=22&d=e&v=e&n=SJT42HQ653DKT6C65&e=SA98HJ9742DA98CA9&s=SQ763HTDJ5432CKT3&w=SK5HAK8DQ7CQJ8742}
{handviewer b=23&d=s&v=b&n=SKQ97HT94DK53CKQ3&e=SAT83HK75DJ98CAT5&s=SJ65HAQ2DT76C9742&w=S42HJ863DAQ42CJ86}
{handviewer b=24&d=w&v=0&n=S4HK2D53CAJT98653&e=SAK9HAQ8DAKQJ76CK&s=SQJ87HJT4D982CQ74&w=ST6532H97653DT4C2}
{handviewer b=25&d=n&v=e&n=SKT875H74DJCAJ973&e=SA4HAT862DQ762CQ8&s=S963HKQ9DT84CKT54&w=SQJ2HJ53DAK953C62}
{handviewer b=26&d=e&v=b&n=S7HQ987632DK5CT43&e=SJ43HT4DJ862CJ986&s=ST92HKDAT974CKQ72&w=SAKQ865HAJ5DQ3CA5}
{handviewer b=27&d=s&v=0&n=SA65HJ64DK6CAQ643&e=SJ4HT873DQJ954CT8&s=SKT732HAKDAT7CK92&w=SQ98HQ952D832CJ75}
{handviewer b=28&d=w&v=n&n=SQJ952H862D954CQT&e=SA74HQ973DK6C8765&s=ST8HAJT4D72CAK943&w=SK63HK5DAQJT83CJ2}
{handviewer b=29&d=n&v=b&n=S83H532DKT853C843&e=SKJ752HJT9D9CAQJ5&s=ST4HAQ7DQJ6CK9762&w=SAQ96HK864DA742CT}
{handviewer b=30&d=e&v=0&n=SAKT82HKQJDKJ82CJ&e=S9753HTDA753CQT87&s=SJ4HA853DQT96CK95&w=SQ6H97642D4CA6432}
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{handviewer b=32&d=w&v=e&n=SKQ87HKJ864D64C52&e=S92H75DT98753CAQ6&s=ST653HQ92DQJ2CJT8&w=SAJ4HAT3DAKCK9743}
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{handviewer b=34&d=e&v=n&n=SKQJ9HK82DAJ9CJ83&e=SA72H9DQ83CAT9764&s=ST854HAQT4DKT65CK&w=S63HJ7653D742CQ52}
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{handviewer b=36&d=w&v=b&n=SK872HJT5DT96C842&e=SAH72DKQ843CKJT93&s=SQ9543HA943DAJC65&w=SJT6HKQ86D752CAQ7}
July 23
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{handviewer b=1&d=n&v=0&n=S2HT542DJ9752CQJ4&e=SAKQ7HJ9DKT64CK75&s=SJT965HAKQ7D83C63&w=S843H863DAQCAT982}
{handviewer b=2&d=e&v=n&n=SQ632HAJ9DAQ62CK9&e=STHKQ642D83CA8752&s=SAK4H73DKT97CQJ43&w=SJ9875HT85DJ54CT6}
{handviewer b=3&d=s&v=e&n=S654HKT7DKQ53C765&e=SAJT93HA98DJ74CA9&s=SQ8HQ6432D96CKQ43&w=SK72HJ5DAT82CJT82}
{handviewer b=4&d=w&v=b&n=SQT963H6DK95CKJT5&e=SJ85HAT5DAT4CQ986&s=SAKHQJ9743DQ76CA3&w=S742HK82DJ832C742}
{handviewer b=5&d=n&v=n&n=SAQ7HT863DQJT6CQ5&e=S652HAKQ4D92CAKJ2&s=SJ4HJ752DK843CT76&w=SKT983H9DA75C9843}
{handviewer b=6&d=e&v=e&n=SKT65HQT3DT76CT62&e=SJ72H65D42CQJ9743&s=SQ4HAK982DAJ98C85&w=SA983HJ74DKQ53CAK}
{handviewer b=7&d=s&v=b&n=SQJ643HKJ93DA9CAJ&e=S8HA2DKQJ64C76542&s=S9752HQT4DT8CKT98&w=SAKTH8765D7532CQ3}
{handviewer b=8&d=w&v=0&n=SQ973HK7DKT53CK96&e=SA2HQJ982DQ86CT43&s=SKJ54HAT654D72CA2&w=ST86H3DAJ94CQJ875}
{handviewer b=9&d=n&v=e&n=SA7HT9863DQ6CA853&e=ST652HAQJDAKJ92C4&s=SKQ8HK42DT53CT762&w=SJ943H75D874CKQJ9}
{handviewer b=10&d=e&v=b&n=SAJT7642H95DQJC32&e=S83H762DKT8763CT4&s=SKQ95HKJ43D92CK76&w=SHAQT8DA54CAQJ985}
{handviewer b=11&d=s&v=0&n=ST4HQ64D984CAQT86&e=SJ9853HJ9DQJ5C752&s=S7HKT8752DAK72CJ9&w=SAKQ62HA3DT63CK43}
{handviewer b=12&d=w&v=n&n=ST754HKT5D742CAK5&e=S9863HA92DA63C742&s=SAKQJHQJ643D9CT63&w=S2H87DKQJT85CQJ98}
{handviewer b=13&d=n&v=b&n=SJ75HKJT9864DCQ65&e=SA83HQ7DT76CAKJ94&s=ST96H532DQ42CT873&w=SKQ42HADAKJ9853C2}
{handviewer b=14&d=e&v=0&n=SKJ7HAKQDT65CAJ96&e=S5432H8532DK42CT4&s=ST986HJT6DA3CQ732&w=SAQH974DQJ987CK85}
{handviewer b=15&d=s&v=n&n=SAH84DKJ7542CQJ84&e=ST975HAKQ976DAC65&s=SJ62HJT2DQT93CT93&w=SKQ843H53D86CAK72}
{handviewer b=16&d=w&v=e&n=SK3HT93DQ7642CA97&e=SQJTHKQ72DAJ8C832&s=S9764HJ654D93CQ65&w=SA852HA8DKT5CKJT4}
{handviewer b=17&d=n&v=0&n=SQ4HKQ65DA973C862&e=SK53HJ874DKQ5CKT4&s=SAT9876HA2DJCQ975&w=SJ2HT93DT8642CAJ3}
{handviewer b=18&d=e&v=n&n=ST986HAKQ83DCK876&e=S4H9DQ98653CAT954&s=SAJ3HT7642DAKTCQ2&w=SKQ752HJ5DJ742CJ3}
{handviewer b=19&d=s&v=e&n=SAK54HK76D972CAJ6&e=S97HA5DAKJ85CQ872&s=SJT32HQJ43D63CKT5&w=SQ86HT982DQT4C943}
{handviewer b=20&d=w&v=b&n=SJT763HKQJ76DJC74&e=S42H94DA8643CK953&s=SAQ85H85DQ952CT62&w=SK9HAT32DKT7CAQJ8}
{handviewer b=21&d=n&v=n&n=SATHA96DAJ753CAQ4&e=SKJHJ743DKQ9CKT76&s=S864HQ82DT8642CJ2&w=SQ97532HKT5DC9853}
{handviewer b=22&d=e&v=e&n=SJT42HQ653DKT6C65&e=SA98HJ9742DA98CA9&s=SQ763HTDJ5432CKT3&w=SK5HAK8DQ7CQJ8742}
{handviewer b=23&d=s&v=b&n=SKQ97HT94DK53CKQ3&e=SAT83HK75DJ98CAT5&s=SJ65HAQ2DT76C9742&w=S42HJ863DAQ42CJ86}
{handviewer b=24&d=w&v=0&n=S4HK2D53CAJT98653&e=SAK9HAQ8DAKQJ76CK&s=SQJ87HJT4D982CQ74&w=ST6532H97653DT4C2}
{handviewer b=25&d=n&v=e&n=SKT875H74DJCAJ973&e=SA4HAT862DQ762CQ8&s=S963HKQ9DT84CKT54&w=SQJ2HJ53DAK953C62}
{handviewer b=26&d=e&v=b&n=S7HQ987632DK5CT43&e=SJ43HT4DJ862CJ986&s=ST92HKDAT974CKQ72&w=SAKQ865HAJ5DQ3CA5}
{handviewer b=27&d=s&v=0&n=SA65HJ64DK6CAQ643&e=SJ4HT873DQJ954CT8&s=SKT732HAKDAT7CK92&w=SQ98HQ952D832CJ75}
{handviewer b=28&d=w&v=n&n=SQJ952H862D954CQT&e=SA74HQ973DK6C8765&s=ST8HAJT4D72CAK943&w=SK63HK5DAQJT83CJ2}
{handviewer b=29&d=n&v=b&n=S83H532DKT853C843&e=SKJ752HJT9D9CAQJ5&s=ST4HAQ7DQJ6CK9762&w=SAQ96HK864DA742CT}
{handviewer b=30&d=e&v=0&n=SAKT82HKQJDKJ82CJ&e=S9753HTDA753CQT87&s=SJ4HA853DQT96CK95&w=SQ6H97642D4CA6432}
{handviewer b=31&d=s&v=n&n=SAQ432HJDAT9CK932&e=ST76HQT72DKQJCA75&s=SJ5HAK5D8652CJT84&w=SK98H98643D743CQ6}
{handviewer b=32&d=w&v=e&n=SKQ87HKJ864D64C52&e=S92H75DT98753CAQ6&s=ST653HQ92DQJ2CJT8&w=SAJ4HAT3DAKCK9743}
{handviewer b=33&d=n&v=0&n=SAQ842HT976DAKCT4&e=SJT6HK5DJ2CAKJ832&s=S97HAQJ2DQT7654C5&w=SK53H843D983CQ976}
{handviewer b=34&d=e&v=n&n=SKQJ9HK82DAJ9CJ83&e=SA72H9DQ83CAT9764&s=ST854HAQT4DKT65CK&w=S63HJ7653D742CQ52}
{handviewer b=35&d=s&v=e&n=SAT752HA6DT532CA4&e=S9HJ7432DJ8CKQJ95&s=SKQJ84HQ985DQ764C&w=S63HKTDAK9CT87632}
{handviewer b=36&d=w&v=b&n=SK872HJT5DT96C842&e=SAH72DKQ843CKJT93&s=SQ9543HA943DAJC65&w=SJT6HKQ86D752CAQ7}
July 23
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Yes. But there is less data on Seniors and Womens. I cover pre-2015 and post-2015.
July 22
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And there I was thinking all these people were queuing to get into the Bridge events…

Someone will need to post a few photographs so that those not there can see what you mean.

The line must have been 200-300 people long. Combined they were wearing the same amount of clothing as one typical bridge player.

There is a secret way to get into the party from the Bridge event. I know this because I took my short cut and came back out into the main part of the Cosmo past the line and security checks. Security seemed very puzzled why I was headed the “wrong” way.
July 21
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@Stefan: There are multiple algorithms, not just one. For example, looking for cheating on the opening lead is different than looking for cheating during the play of the hand.

I have tried to simplifying it by creating a single function that merges all the data. This makes it much easier for presentation purposes.

Assuming I have algorithms that generate a high value for cheating pairs, and a low value for honest pairs then I can state, for most algorithms,

1. If your value is high, this does not necessarily mean you are a cheating pair.

2. If you are a cheating pair, then your value is high.

When I look at multiple algorithms, then if you appear high in all/most of them, the conclusion is that you are a cheating pair.

Statistics in Bridge is complicated. You have to be able to look at the numbers behind the numbers.

I was showing some of the data to a World Class player a couple of days ago. I was showing how they compared to other world class players. Showing their weaknesses - relative to their peer group - in their game and their partners.

I was showing a table which ranked based on one of the cheating algorithms. They appeared high on the list. Not at the cheating level, just much higher than they might think. But, for this table, you need the data behind the data. There is a cross-check value I use. For this pair, they had played in some tournaments with weaker players. This will affect their “ratings”. This explained their ranking; so it was not a number associated with cheating. For the other pairs on the list, the cross-check value was very high, indicating that this result was through cheating.

The book has necessarily over-simplified presentation of the data.

If I am asked (by a Bridge organization, not a player), if someone is cheating, there are multiple factors to look at. Not just a single number. Of much more interest, and cannot be shown well in the book, is trend analysis. How/if players suddenly improved. I mention some of this in the book. I can detect when certain pairs started cheating, or more accurately, when their cheating methods became effective.
July 21
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@Stefan: “”Forquet on lead against 2♠. What did he lead from ♠8 ♥K106532 ♦Q1092 ♣K2 and what do you think his partner had?"

Auction was 2 on his right by dealer. All pass. No-one vulnerable.

The answer to the lead is in the book.
July 21
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@Tony: “Good to know my leading is bad enough to avert suspicion. I assume Robson is better than me, given the angle of your post Nic. I suspect someone who led trumps more often (not my style) would give less tricks away, but would it be effective in the long run. Anyway, well done on the research.”

As you could tell by the phrasing of the question…. but here's the answers from the data I have. It's Vugraph data because I need the opening lead:

1. When you play with Andrew, he finds a safe lead 83.3%, you are 81.2%. Both are above average for World Class players. Combined you are better than all the top ten pairs in the world except for pairs that have the same first initial in their last name.

2. When Andrew plays with Alexander, the roles are reversed. Alexander is 81.3%, Andrew is 77.8%. Alexander is above average for WC players. In this partnership Andrew is below average. I can give him some lessons if he wants.

Perhaps a little unfair to mention A/A, but they are presumably a reference pair you know. The data says Andrew is a phenomenal player (page 151) - this is from data that is nothing to do with cheating.

But…. a “safe” lead is not the best way to measure the skill level of a Bridge player in team games. At least in my opinion. There are better ways. I'll let you figure out what they might be; some of them are in the book.

You asked about trump leads. Page 132 in the book. Data from top 100 pairs. Sorry, I didn't list all 100 pairs.

Your perception is, “I suspect someone who led trumps more often (not my style) would give less tricks away, but would it be effective in the long run”

The computer says…

You/Andrew rank #17 out of the top 100 pairs for leading trumps. You are ranked 99/200 players for leading trumps - 10% of the time you will lead a trump. You may not think it is your style, but you are just above the median for top players leading trumps. Andrew (14.5%) ranks #15 when playing with you.
July 21
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@Steve: Addressing each question:

1) how was the analytics/code validated?

Tough question. How can you validate it? Only way is to see if it detects the known cheaters. See Chapter 1 of the book. There is a snippet here: http://www.detectingcheatinginbridge.com/selections.html but it does not have all the chapter.

I show what happens when one of the formulas is applied to the data and it lists seven pairs from the top 66 pairs with the most data. Six of the pairs are known cheaters. From the book, “The statistical chance of a random formula placing these six pairs in the top seven out of a selection of 66 is 1 in 13 million.” The other pair is discussed. Their data is shown in the book. Draw your own conclusions. They defend better than B/Z over a large number of boards.

For those with software background: for some of the early work where I needed to double check the results, I would write two complete different routines. Or do one in a programming language and also process the results in Excel to make sure the same results were being generated. This helps catch bugs in the data analytics code. I still does this at times, particularly if new or important code is added.

2) does the analysis have the power to separate signal from noise with acceptable confidence? Even when data can be highly sparse due to sporadic behavior and avoiding glaringly obvious abuse?

Yes. I think it does. BUT, you must have a large sample. There is a table of contents in the snippets URL above. I cover this, and other errors in the underlying data, in Chapter 12.

Everything works on the concept of the Law of Large Numbers (LLN). Chapter 7.

3) how does the analysis deal with correlation and cause? Issues like correlation to a third variable, reverse correlation, and other biases must be clearly avoided.

Beyond the level of readership of the book!

If you are in Las Vegas, I'll explain personally; but too long for a BW post.

If you cheat, you are better on defense than your peers. This can be measured. That is the short answer.

4) how does the analysis avoid confirmation bias?

I use all data, not selected data. This is the most common problem with presentations on cheating, just using selected hands. I don't try and explain away false/positives.

To give an example: There are chapters on Opening Leads - 29, 30, 31

I pose the question: how would you detect cheating on the opening lead?

5) how does the analysis identify superior skill, analysis and methods at the table. That is, how is genuine skill differentiated from illicit behavior? Can the same be said for blunders by opponents?

Take the best non-cheating pair in the world, your opinion not mine. Look at the data behind them. Compare to cheating pairs.

I show that the top pairs, with large amounts of data, clump together on a scatter plot. You can only be so good. You will make mistakes. I know the mistakes frequency of top pairs. I know that top pairs will make a bad lead 19% of the time. I can show you data on pairs that make bad leads 15% of the time. Do they cheat or are they better Bridge players? Answer is in the book.

And…. I know your data. I can show where you are relative to the top players/pairs.

The question deserves a little more respect as it is possible to distinguish between your mistakes and your opponents blunders. I show how this is done. This is one of the more sophisticated checks in the book. See ACDF3 on page 51.

“how is genuine skill differentiated from illicit behavior”. Page 146 describes a test on data from before 2015 and after 2015. Before 2015, there are ten pairs (includes five known cheating pairs) from the top 150 pairs with the most data that had a “skill rating” above a certain level. Post 2015, for pairs with the same amount of data, there are none.

It is comparative type tests like this that show cheating.

I make some statements about the known cheating pairs about areas of the game they are cheating in, above and beyond what is known. Someone will eventually find the players codes from the videos to prove these statements and validate the work.
July 21
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