Friday, 7 December 2012

UEFA Champions League Knockout Phase Draws: Monte Carlo Simulation with R

Draws for the knockout phase of the 2012–13 UEFA Champions League will be held in Nyon on the 20th December 2012. The rules of the draw are simple and are as follows:
  • 8 Group winner teams will be seeded.
  • 8 Group runner-up teams will be unseeded.
  • Teams coming from the same group and from same association can not be drawn against.
One can compute the probabilities of the likely outcomes by using ensemble average, a standard way of finding the frequencies where we identify whole sample space. Here we take a different approach and use time averages. What we do is to run large number of draws (20 million times) using randomly generated draws. I have called this exercise a Monte Carlo simulation, rather a simple sort while there is no fancy sampling of any sort, only rejection of pair up or the given draw if the last remaining pair does not satisfy the rules. A statistical language GNU R is used to generate the likely outcome. We use Mersenne Twister RNG, a default random number generator which is known to have very large cycle.   In result we basically count the likely pair-ups in a matrix (matrix of pairs) and find the frequency of each likely pair up. Simulation takes 3-4 hours on a single CPU Intel Xeon with 16 GB RAM running Ubuntu GNU/Linux. Well, to be honest I haven't optimized the code.

Qualified teams, their groups and associations definitions, the simulation and results reporting R code are as follows (note that we use xtable for html outputs, for other functions we use, see below codes after tables):


winnerTeams               <- c('Paris-Saint-Germain', 'Schalke-04', 'Malaga', 'Borussia-Dortmund', 'Juventus', 'Bayern-Munich', 'Barcelona', 'Manchester-United');
winnerAssociation         <- c('FR', 'DE', 'ES', 'DE', 'IT', 'DE', 'ES', 'ENG');
runnersUpTeams            <- c('Porto', 'Arsenal', 'Milan', 'Real-Madrid', 'Shakhtar-Donetsk', 'Valencia', 'Celtic', 'Galatasaray');
runnersUpTeamsAssociation <- c('PT', 'ENG', 'IT', 'ES', 'UA', 'ES', 'SCO', 'TR');
countMatrix <- matrix(0, 8, 8)
row.names(countMatrix) <- winnerTeams;
colnames(countMatrix) <- runnersUpTeams;
many <- 20e6;
system.time(drawMany(winnerTeams, winnerAssociation, runnersUpTeams, runnersUpTeamsAssociation, countMatrix, many))
countMatrix <- countMatrix/many;
print(countMatrix);

Simulations results can be interpreted based on frequencies (probabilities) of pairings, rather intuitively while probabilities are not that far off . For example if we consider Barcelona, Milan and Arsenal score the largest 0.23 and 0.21.  So my guess based on these frequencies, which I select maximums first ,then the second maximum and so on. If all ties I select the highest count using the second table.  Here are the predicted pairs, (ordered with highest probability):

Barcelona - Milan
Malaga - Arsenal
Bayern Munich - Real Madrid
Borissia Dordmund - Valencia
Manchester United - Celtic
Juventus - Galatasaray
Schalke 04 - Porto
PSG - Donetsk

Note that predicted pairs are quite depending on your selection strategy.
Table for the frequencies:


Porto Arsenal Milan Real-Madrid Shakhtar-Donetsk Valencia Celtic Galatasaray
Paris-Saint-Germain 0.00 0.13 0.14 0.18 0.12 0.18 0.12 0.12
Schalke-04 0.12 0.00 0.15 0.19 0.12 0.19 0.13 0.12
Malaga 0.19 0.23 0.00 0.00 0.19 0.00 0.20 0.19
Borussia-Dortmund 0.13 0.14 0.15 0.00 0.13 0.19 0.13 0.13
Juventus 0.13 0.15 0.00 0.22 0.00 0.22 0.14 0.13
Bayern-Munich 0.13 0.14 0.15 0.19 0.13 0.00 0.13 0.13
Barcelona 0.18 0.21 0.23 0.00 0.19 0.00 0.00 0.18
Manchester-United 0.13 0.00 0.16 0.22 0.13 0.22 0.14 0.00

Table for the counts (actually all are integers):

Porto Arsenal Milan Real-Madrid Shakhtar-Donetsk Valencia Celtic Galatasaray
Paris-Saint-Germain 0.00 2589164.00 2897024.00 3658337.00 2356581.00 3658892.00 2494247.00 2345755.00
Schalke-04 2348458.00 0.00 2924099.00 3735314.00 2371245.00 3743246.00 2517610.00 2360028.00
Malaga 3781019.00 4506559.00 0.00 0.00 3845872.00 0.00 3997679.00 3868871.00
Borussia-Dortmund 2500221.00 2819591.00 3098797.00 0.00 2542013.00 3863888.00 2646626.00 2528864.00
Juventus 2659773.00 2983255.00 0.00 4393444.00 0.00 4392158.00 2892103.00 2679267.00
Bayern-Munich 2500835.00 2818331.00 3098181.00 3866890.00 2542742.00 0.00 2647340.00 2525681.00
Barcelona 3620075.00 4283100.00 4684023.00 0.00 3721268.00 0.00 0.00 3691534.00
Manchester-United 2589619.00 0.00 3297876.00 4346015.00 2620279.00 4341816.00 2804395.00 0.00

This is the main simulation function:


drawMany <- function(winnerTeams, winnerAssociation, runnersUpTeams, runnersUpTeamsAssociation, countMatrix, many) {
 for(i in 1:many) {
  repeat {
     dr          <- drawOneKnock(winnerTeams, winnerAssociation, runnersUpTeams, runnersUpTeamsAssociation,0);
     if(sum(dr) > 0) break;
   }
  updateCount <- mapply(incMatrix, dr[,1], dr[,2])
 }
}

A single draw can be generated as follows:


drawOneKnock <- function(winnerTeams, winnerAssociation, runnersUpTeams, runnersUpTeamsAssociation, names=1) {
    k=1
  repeat {
    k=k+1;
    if(k > 1000) return(-1);
    blockWin = 1:8     ; # tracking for draw
    blockRun = blockWin;
    winners  = c(); # Draw results
    runners  = c();
    for(i in 1:7) {
         kk =1;
      repeat {
        kk=kk+1;
        if(kk > 1000) return(-1);
        winner <- sample(blockWin, 1);
        runner <- sample(blockRun, 1);
        if(!(runner == winner) && !(winnerAssociation[winner] == runnersUpTeamsAssociation[runner])) {
          break;
        }
      }
      blockWin <- blockWin[-which(blockWin == winner)];
      blockRun <- blockRun[-which(blockRun == runner)];
      winners  <- c(winners, winner);
      runners  <- c(runners, runner);
     }
      winner <- blockWin;
      runner <- blockRun;
      # check if last remaining is ok, otherwise invalidate draw
      if(!(runner == winner) && !(winnerAssociation[winner] == runnersUpTeamsAssociation[runner])) {
        winners  <- c(winners, blockWin);
        runners  <- c(runners, blockRun);
        if(names)  dr <- cbind(winnerTeams[winners], runnersUpTeams[runners]);
        if(!names) dr <- cbind(winners, runners);
        break;
      }
   }
  dr
}

And counting the pair-ups is performed by a simple function:


incMatrix <- function(i, j) {
   countMatrix[i,j] <<- countMatrix[i,j]+1;
   return(0);
 }

Monday, 3 December 2012

Function closures and abstraction in MATLAB/Octave

Recently a short idea on how function closures can be build in R is shown [link]. It is also discussed in detail by Darren Wilkinson's blog [link]. Here we will mimic the approach presented there. Recall that function closures are basically a reference to a function with the attachment of its lexical environment. Note that, it is not a function pointer while it also return the values of its arguments if they are defined in the given scope. This sort of language construction can be very handy when some of the inner details need to be parametrized, so to speak to prevent re-writing the function over and over again.

A trivial example of adding a number to a given number can be formulated.  Let's say x is a number and we need to add 4 to x.

function add4(x)
  x+4

end

So far so good, but do you really want to re-write an other function to add 7. Not really! So instead we can generate a function that generates addition function for 7 (Sounds like an onion.). This is quite standard in functional languages and noting new actually!

>> addx = @(x) @(y) x + y ;
>> add4=addx(4);
>> add7=addx(7);

>> add4(5)

>> add7(5)
12

So function add4 and add7 are generated by the function addx. We just utilized function handles. This might be quite trivial. Let's try the technique in a little more realistic setting. Imagine we need to take a median of each vector, which has different sizes stored in a structure.  Let's generate one

>> rng(0.42,'v4'); % reproduce numbers
>> myStr.vec1 =  rand(11,1);
>> myStr.vec2 =  rand(5,1);
>> myStr.vec3 =  rand(20,1);
>> myStr.vec4 =  rand(8,1);

So a function that takes a structure like this and returns the median value of the named member

>> medianVec= @(str, memName) median(str.(memName));

For example for vec1

>> medianVec(myStr, 'vec1');
 0.5194

If we need this procedure for vec1 repetitively, we don't need to re-write the second argument all the time. So

>> medianVecN = @(memName)  @(str) median(str.(memName));
>> medianVec1 = medianVecN('vec1');

>> medianVec1(myStr); % Vala!
0.5194 

This might still look trivial but hopefully it would give you an idea of the technique.


MATLAB/OCTAVE crash tutorial for programmers: Useful bits

In this post, I would like to give you a crash tutorial on the basics of  MATLAB ™ - GNU Octave (MO). They are not fully compatible but quite close in grammar. For differences see here. We will concentrate on the basic data structures and object manipulation that would help you to write useful code quickly.
  • Vectors/matrices and Indexing
    Vectors or vectorial operations are the core of MO
    >> A = [ 3 6 7 8 2];
    >> A(1:3); % range indexing will print values from index 1 to index 3
    >> A(4:end); % Last two values of this vector
    >> A > 4 % logical indexing; this will return logical vector, components satisfying the condition
    0 1 1 1 0
    >> A(A > 4)  % one can use this logical indexing directly to extract elements that satisfy the condition
    6 7 8

    A([2 3 4]) % positional indexing
    logInx = A>4
    A(logInx)
    6 7 8
    Let's define 3 by 3 matrix
    >> M = [ 1 3 4 ; 5 6 9 ; 2 3 4]
    >> M(:,1:2) % first two columns

  • Structures and Cells
    A structure definition can be performs as follows
    persons=struct();
    Fields can be defined after .
    persons.age=12; Further index can be assigned dynamically
    persons(2).age=32;
    persons(3).age=40; 
  • Functions
    Return vector with  function definition must be in a seperate file. For example here pov.m (in the working directory)

    function [ pov2, pov3] = pov(x)
      pov2= x^2; pov3 = x^3;
    end


    (pwd will tell you which directory you are working in. A return vector here is [pov2, pov3].

  • Function handles and apply functions
    To define new function ff with function handle
    it takes a structure xx and returns its field age value

    ff = @(xx) xx.age

    We can apply this function to each element of the structure,
    returning an array (vector)

    arrayfun(ff, persons)

  • Example Task:
    Lets align two vectors, meaning that we found matching indices and delete non matching ones. Here is the solution:

    p1 = [2    11    15    19    28    41    45    47    48    51];
    p2 = [2    11    31    36    41    45    51    60    68    71];
    [~,indexp1] = intersect(p1,p2);
    % if you use ~, it means don't assign anything
    [~,indexp2] = intersect(p2,p1);
    p1 = p1(indexp1);
    p2 = p2(indexp2);
(c) Copyright 2008-2017 Mehmet Suzen (suzen at acm dot org)

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