# Probability, statistics and football Franka Miriam Bru ckler Paris, 2015.

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1 Probability, statistics and football Franka Miriam Bru ckler Paris, 2015 Please read this before starting! Although each activity can be performed by one person only, it is suggested that you work in groups of three: one performing one part of each experiment (eg, rolling one die), the other performing the second part of the experiment (eg, rolling the second die) and the third keeping track of the results It is also suggested that you discuss and agree on answers to questions before continuing with the tasks Note that at each workplace you have pencils and calculators: throughout the tasks, you will be required to fill in some answers and to do some calculations 1 Level 1 Discovering probability Introduction Probabilities of events are described by numbers between 0 and 1 (ie percentages between 0 % and 100 %: you obtain the percentage by multiplying the decimal value with 100, eg, 0,25 is 25 %) The larger the probability, the oftener the event is expected to happen in a series of trials with equal conditions At your workplace you can find a 12-sided (dodecahedral) die and two regular 6-sided dice Guess the answers to the following questions: 1 The probability of rolling a with a regular die is 2 The probability of not rolling a 3 The probability of rolling and with a 12-sided die it is with a regular die is with two dice is 4 The probability of rolling a total of 5 with two regular dice is 5 The probability of rolling a total smaller than 5 with two regular dice is 6 If you roll the pair of normal dice (and check the totals) a number of times and then the same number of times roll the 12-sided die, do you expect that the corresponding numbers will appear approximately the same number of times? 1

2 Roll all the tree dice and make notes of the totals obtained on the two regular dice and the numbers on the 12-sided die Repeat at least 30 times Here is the table to help you keep track of the results: Roll sided die 2 dice Roll sided die 2 dice Roll sided die 2 dice Now, determine how many times appeared which result on the two regular dice, and how many times appeared which result on the 12-sided die Result appeared on sided die: 2 regular dice: Try to represent your results graphically in such a way that someone not seeing the tables above could understand which results you obtained in your experiment! You have rolled the dice o = times; this is your total number of trials The fraction of the occurrences of a result and o is called the results (relative) frequency Calculate the frequencies for your results; you can express them as fractions, decimally or as percentages, it is up to you! (Relative) frequency of result On 12-sided die: On 2 regular dice: Use the relative frequencies to answer the following sentences and compare your answers to your guesses from the beginning: 2

3 1 The with a regular die had frequency 2 A result different from 3 The result and with a 12-sided die frequency with a regular die appeared with frequency with two dice appeared with frequency 4 The total of 5 with two regular dice appeared with frequency 5 A total smaller than 5 with two regular dice appeared with frequency 6 The frequencies of same results on the two dice and the 12-sided die are (not) similar (Cross the not if you think the results are similar) The previous activities show that relative frequencies, particularly when calculated from a large number of trials, can be used as estimates of theoretical probability In season 2014/15, the Ligue 1 teams had the following relative frequencies of goals per shot:1 One may imagine that rolls of a die represent a team s shots and that rolling a specific result, eg a 1, represents the team scoring a goal If you know that there exist dice with 4, 6, 8, 10, 12, 14, 16, 18 and 20 sides, complete the table above by entering the number of sides of the die you would use to model shots! ooo Conclusions ooo The more trials you make, the nearer the calculated (relative) frequency of an event is to its (theoretical) probability If the probability of an event happening is p, the probability of it not happening is 1 p = 100% p If some events cannot happen simultaneously, the probability of at least one of them happening is the sum of their probabilities 1 Data from 3

5 2 Level2 Football penalties: Introduction to probabilistic modeling Introduction It has been noted that in most leagues and championships about 70 % to 80 % of penalties result in a goal A common notion on penalties is that they are a kind of lottery, both for the shooter and the keeper By simple probabilistic modeling one can support the reasonableness of the mentioned percentages if this lottery assumption is true By the way, the numbers can also be supported by geometric and physical arguments, but this is not our concern here In the table below you can find the data on penalties for the last three years of the French Ligue 1 and the last three World Cup Finals Calculate the corresponding frequencies Ligue 1 14/15 Ligue 1 13/14 Ligue 1 12/13 WC2014 WC2010 WC2006 Penalties awarded: Penalties scored: Frequency (in %) Imagine the following scenario: The keeper holds the shot if he guesses rightly into which part of the goal the shooter sends the ball, otherwise the penalty results in a goal For example, one could consider the goal divided into 4 areas, so the keeper has probability p = to guess correctly In other words the probability of scoring a goal in this simple scenario is Now, are the assumptions of the scenario realistic? What is missing? Think about that before reading further Is it sensible to consider the goal to be divided, from the keeper s point of view, into just 2 or 3 parts? And into 100 parts? Write the range of numbers of parts you think reasonable to dived the goal into: Choose a number o = from that range At your workplace you can find plastic pearls in two colors: 60 in color 1 and 6 in color 1 Take o 1 pearls in color 1 and one in color 2 and put them in the first box Eg, if you decide to divide the goal into 4 parts, you use 3 pearls in color 1 and one in color 2) The pearl in color 2 represents the keeper saving the goal If you would close your eyes and take a pearl from the box, what is the probability of drawing a pearl in color 1 (ie, the goal being scored)? o = Besides the keeper holding the ball, what other reason can prevent the scoring of a goal? Think of Zlatan Ibrahimović or David Trezeguet Obviously, in professional football it is not very probable that a player misses the goal Choose a percentage from the first row of the table below and take pearls accordingly Put them in the second box (this box represents possible misses by the The pearls in color 2 represent the misses Chosen percentage of miss 2 % 4 % 5 % 6 % 8 % 10 % Take pearls in color Take pearls in color

7 Type of coefficient Value Decimal value Probability Decimal 5,10 5,10 Fractional 9/2 Moneyline +533 In the last season of League 1, 88 of the 380 matches played ended in a draw this makes % A year before it was 107 of 380 matches or % These numbers is not unusual: in professional football, about 20 % to 30 % of all matches (ie, 20 to 25 of 100 of matches) end in a draw Imagine that you are offered to bet on a match to end in a draw, but you do not know which teams play that match Choose 20 %, 25 % or 30 % as the probability of you winning that bet: This corresponds to odds 2 : What would be the fair coefficient? o= Betting houses usually offer coefficients lower than fair in order to make sure they earn money Now you will use the 20-sided die found on your workplace to test how much you would win or loose if you take o as the betting coefficient and how much if the coefficient had value o 0,4 Your chosen probability of bet was = 20 This means that of 20 numbers on the die, the first numbers can model the cases when matches really end in a draw For example, if your probability was 5/20, the numbers 1 to = 5 would represent a draw Now roll your die 30 times and count the number of times and note your results: Roll Result Roll Results So you obtained numbers from 1 to times in 30 rolls (ie, of the 30 matches ended in a draw) If you had betted a value of 1 on each roll (match), how much you would have won or lost with the betting coefficient o? And with o 0,4? ooo Conclusions ooo Decimal betting coefficients represent the reciprocal values of the corresponding probabilities By decreasing the true value of coefficients betting houses ensure profit for themselves ooo 2 If you are not sure if you solved part above correctly: the odds are the number of results in the favor of your bet : the number of the remaining possible results 7

8 3 Level 3 Poisson (not fish) in football Introduction When making random experiments or observing results of non predictable events (eg, results in football matches), one will often discover typical patterns how specific results appear in a given number of events Such patterns are called distributions: a distribution consists of pairs of type result - number of appearances of the result and is most conveniently represented by a bar chart For example, it has been noted that in most championships and cups the numbers of matches with 0, 1, 2, goals approximately follow a specific pattern, known as the Poisson distribution The specific form of this pattern is determined by the average number of results considered, in our case by the average number of goals per match in a championship A typical example would be described by the diagram below The French Ligue 1 has 20 teams Each pair plays twice, so in a whole season there are matches In season 2014/15, each of the results appeared as many times as indicated by the table below Fill in the correct numbers in the last two columns (legend: R = result; F = frequency (number of matches with given result); N = number of goals in a match; T = total number of matches with N goals) In the whole Ligue /15 season there were scored in total Thus, the average number of goals per match was o= Below you can see the bar-chart which would be calculated for Ligue /15 from o using the Poisson distribution (the calculated number of matches with 0 goals would be 31, those with 1 goal 78, etc) Next to the printed bars draw the bars corresponding to true data from the table above and compare with the calculated results! 8

9 ooo Conclusions ooo For repeated independent experiments, eg football matches, that can have 0, 1, 2, successes, eg goals, the distribution of numbers of experiments (matches) with a given number of successes (goals) is often more or less Poisson distribution A Poisson distribution is determined by just one parameter, and that is the average number of successes per experiment (goals per match) ooo Introduction to prediction of results Introduction If the average number of events of a specific sorts (eg, goals per match) is m and if you assume that the Poisson distribution is appropriate for your situation, then the probability of a trial with a specific number of events (eg, a match with a specific number of goals) is given by Nr of events m m 2 m 3 Probability e m e m 2e m 6e m Approx prob 0,3679 m m 0,3679 m m 2 0,1839 m m 3 0,0613 m Nr of events m 4 m 5 m 6 Probability 24e m 120e m 720e m Approx prob m 4 0,0153 m m 5 0,003 m m 6 0,005 m In the table above, e denotes a mathematical constant with approximate value 2,718 To simplify your calculations we have entered a third line with approximate formulas for the probabilities Can you detect the general rule for the Poisson probability of k events in a trial? 9

10 For predicting football results, ie determining the betting coefficients, betting houses use variations of Poisson distribution with estimates of m based on previous data and other data available Here you will see the essential idea of the approach Below you can find all the results from Ligue 1 in season 2014/15 Each team played 38 matches Choose a team: your team has scored 0, 1, 2, goals: Goals scored In matches Relative frequency Fill in the data on how many matches In total, your team has scored goals in 38 matches, which gives an average of m = per match Remembering that decimal betting coefficients are reciprocals of probabilities, use the formulas for the Poisson distribution to determine the probabilities and the decimal coefficients for your team scoring a specific number of goals: Goals scored Probability Decimal coefficients Compare your results to the true ones by drawing simultaneous bar charts for the obtained true relative frequencies and calculated probabilities! 10

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