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Team Events: Beating the Bookmakers?!

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Written by Administrator   
Saturday, 16 March 2013
"Many of you must have heard about sports betting and odds offered by bookmakers," writes Peter Zhdanov. While he is in no way interested in making money by placing bets, the analytical aspect of the problem seemed to him to be worth researching. His article is a lesson in how to work out the odds in a team tournaments.

Originally published at ChessBase

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Team events: beating the bookmakers?!

By Peter Zhdanov


Lets say you have two teams composed of four chess players each. This is typical for the Chess Olympiad, World Team Chess Championship and other important events. How high is the probability that team A will win? How probable is a draw? Team Bs victory? Here is a relatively easy step-by-step guide. As an example we will use the match Romania-Russia from round one of the recent Womens World Team Chess Championship.
 

Step 1

Calculate the rating differences on each board and find out the expected score of the player.
 

Example: Foisor (2401) vs Gunina (2505), a rating difference of 104 points. If we consult the FIDE Handbook, we will see that it Foisor is expected to score 0.36 points per game against Gunina.

Step 2

Find out the expected probability of a win, draw and loss in each game.
 

Example: Foisor has White. The FIDE website tells us that she won 50% of her White games and drew 34% of them. Alternatively, you can use a ChessBase database. It offers even more accurate statistics. Since a win is worth 1 point and a draw 0.5, we obtain a simple equation:

0.5x+ 0.5*0.34x=0.36

x is about 0.537. The probability of a win is approximately 0.5*x=0.2685, i.e. about 26.9%. The probability of a draw is, correspondingly, (36%-26.9%)*2=18.2%. The probability of a loss is (100%-probability of win probability of draw) = 100-26.9%-18.2%=54.9%

Note: apply this formula to the weaker of the two players. Otherwise you might end up in a situation when there is no solution. For example, if the expected result is 0.9 points per game and the player draws 30% of the games and wins 40%, then it is impossible to achieve a 0.9 result while maintaining the proportion, so we should compose the equation for the second player (expected result is 0.1).
 

Step 3

Repeat steps 2 and 3 for all the four boards in the team.
 

Title ROMANIA Rating
-
Title RUSSIA Rating
IM Foisor Cristina Adela 2401
-
IM Gunina Valentina 2505
WGM Bulmaga Irina 2354
-
GM Kosteniuk Alexandra 2495
WGM Lami Alina 2353
-
IM Galliamova Alisa 2459
WGM Voicu-Jagodzinsky Carmen 2281
-
WGM Girya Olga 2440


These are the input variables:
 

Win1 Draw1 Lose1 Win2 Draw2 Lose2 Win3 Draw3 Lose3 Win4 Draw4 Lose4
0.269 0.182 0.549 0.234 0.13 0.636 0.293 0.113 0.594 0.202 0.175 0.623


Step 4

Each game can have three theoretically possible results: White wins, draw, Black wins. Hence, if we have 4 boards, there are 3^4=81 possible outcomes of the match. It is easy to calculate each probability.
 

Example 1: board 1 wins, board 2 loses, boards three and four make draws. The probability of this outcome (using data from Step 3) is: 0.269*0.612*0.034*0.204*100%= appr. 0.338%. As you can see, this is a very small probability. Example 2: and how high is the chance that Russia will win on all the four boards? 0.549*0.636*0.594*0.623*100%= appr. 12.92%.

Step 5

To calculate the probability of the match ending in a draw/victory for a certain side, we have to add up the probabilities of all the corresponding outcomes. All the 81 possible outcomes:
 

1111, 1110, 111=, 1101, 1100, 110=, 11=1, 11=0, 11==, 1011, 1010, 101=, 1001, 1000, 100=, 10=1, 10=0, 10==, 1=11, 1=10, 1=1=, 1=01, 1=00, 1=0=, 1==1, 1==0, 1===, 0111, 0110, 011=, 0101, 0100, 010=, 01=1, 01=0, 01==, 0011, 0010, 001=, 0001, 0000, 000=, 00=1, 00=0, 00==, 0=11, 0=10, 0=1=, 0=01, 0=00, 0=0=, 0==1, 0==0, 0===, =111, =110, =11=, =101, =100, =10=, =1=1, =1=0, =1==, =011, =010, =01=, =001, =000, =00=, =0=1, =0=0, =0==, ==11, ==10, ==1=, ==01, ==00, ==0=, ===1, ===0, ====
 

Where 1 stands for a win of a player on the corresponding board, = for a draw, 0 for a loss. E.g., 1111 means that all the players in the first team won their matches. 1110 means that three first players won their matches and player #4 lost.
 

Here are the 31 possible outcomes that result in Team 1s victory:

1111, 1110, 111=, 1101, 110=, 11=1, 11=0, 11==, 1011, 101=, 10=1, 1=11, 1=10, 1=1=, 1=01, 1==1, 1===, 0111, 011=, 01=1, 0=11, =111, =110, =11=, =101, =1=1, =1==, =011, ==11, ==1=, ===1
 

Outcomes that result in draws (19):

1100, 1010, 1001, 10==, 1=0=, 1==0, 0110, 0101, 01==, 0011, 0=1=, 0==1, =10=, =1=0, =01=, =0=1, ==10, ==01, ====
 

The remaining 31 outcomes represent Team 2s victories. You dont really need to calculate them, but here is a list anyway:

1000, 100=,10=0, 1=00, 0100, 010=,01=0, 0010, 001=, 0001, 0000, 000=, 00=1, 00=0, 00==,0=10, 0=01, 0=00, 0=0=,0==0, 0===,=100, =010, =001, =000, =00=,=0=0, =0==, ==00, ==0=,===0
 

Step 6

Calculate the probabilities of all the outcomes of Team 1s victories (see Step 4) and add them up. Similarly, calculate the probabilities of outcomes that result in a draw and sum them up.
 

Note: I am using an Excel sheet for this so that I dont have to perform this calculation manually. Just input the variables from Step 3 into the sheet. The software will do the rest.
 

Example: in the example above (Romania-Russia) the chance of Romanias triumph is about 12.91%; the probability of a tie 17.55%. The remaining 69.54% stand for Russias victory.

Step 7

Translate from Math language into bookmakers jargon. If Romanias chance to win is 12.91%, it means that the bookmaker is supposed to offer odds of about 100/12.91=appr. 7.75 for Romanias victory. In the reality the coefficient will probably be lower, because the bookmakers charge a certain commission.
 

Similarly, the odds for a draw should be about 5.7 (100/17.55) and for Russias win 1.44 (100/69.64). I have compared these coefficients to a line offered by one of the bookmakers:
 

Romania 11, Draw 4.5, Russia 1.31
 

We can see that, according to our model, it didnt make much sense to bet on Russia and/or on a draw, because the offered odds were below the expected values. On the opposite, Romania was somewhat underestimated by the bookmakers.
 

I have considered a few other lines, and in most cases the coefficients obtained using our model were relatively close to the ones used by bookmakers. However, there were some notable exceptions too.
 

Remark 1

Unlike the attention-getting title of the article suggests, this model was not designed for beating the bookmakers. It is intended for those inquisitive minds who have always wanted to know how high the chances are for their team to succeed. Naturally, the method described above works for any number of boards, so it is not restricted to 4 vs. 4 competitions. Initially, I have been experimenting with 2 vs. 2 models for the sake of simplicity.
 

Remark 2

No model is perfect. This one has an underlying assumption that players perform according to their FIDE ratings and similarly to their previous results, e.g., a drawmaster makes more draws than an aggressive player. Obviously, other parameters can be considered. For example, the personal score between two people; tournament standings; current shape of the players, etc.
 

Or, another interesting idea, use not FIDE Elo, but estimates of FIDE Elo with White and with Black. Obviously, most people perform stronger with White than with Black, so it does make a difference.
 

Remark 3

Even if you discover that the model suggests the odds to be 20, while the bookmaker offers 40, dont be in a hurry to bet. While this strategy should be beneficial in the long run, your chances to succeed in a particular case are very slim. For example, Romania succumbed to Russia 0.5-3.5:
 

Title ROMANIA Rating
½-3½
Title RUSSIA Rating
IM Foisor Cristina Adela 2401
0:1
IM Gunina Valentina 2505
WGM Bulmaga Irina 2354
½:½
GM Kosteniuk Alexandra 2495
WGM Lami Alina 2353
0:1
IM Galliamova Alisa 2459
WGM Voicu-Jagodzinsky Carmen 2281
0:1
WGM Girya Olga 2440


So if you had bet on Romania (11 is a higher coefficient than 7.55, see Step 7), you would have lost your money.
 

Acknowledgements

This article would not have been written without the support of my best friend and avid math & chess fan Nikolai Smirnov, with whom we discussed the idea in general and the potential application of the model in real life during the Chess Olympiad 2012.
 

Peter Zhdanov is an IT project manager, expert and author of two books on parliamentary debate, BSc in Applied Mathematics & Computer Science and a PhD student in Sociology. In chess he is a Russian candidate master, author, manager of grandmaster Natalia Pogonina and editor of the Pogonina web site.




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Last Updated ( Saturday, 16 March 2013 )
 
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