7/21/18

- Introduction
- Score Probabilities and Ranges
- Graphs
- PY: Perfect-match scoring with repeated symbols
- PN: Perfect-match scoring with non-repeated symbols
- AN: Any-match scoring with non-repeated symbols
- Comparison of Score Distributions
- Summary of Closed-form Mean Scores
- Dependency of Perfect-match and Any-match scoring
- Score Distributions of Common Games
- Five Dice Game
- Appendix: Mathematical Results
- References

Let C be the length of the secret code and of the guesses the codebreaker provides, and let N be the size of the alphabet from which each element of a code can be drawn. We use the notation (C,N) to refer to a particularly-sized game. For example, in the standard Mastermind game, each code is four colors chosen from six possibilities, so we have C = 4 and N = 6, and use the notation (4,6) to denote it.

We use the term *population* to describe all possible codes of the
specified length that can be created from the given alphabet. The symbol Q is
used to describe the size of the population.

We will consider three types of scoring: Perfect-match (P), Any-match (A) and Mastermind-type (M). With Perfect-match scoring, one point is awarded for each symbol in a guess code if and only if it occurs in the same position as in the secret code. For example, the Perfect-match score of the codes (3,4,1,1) and (3,2,1,4) is 2, corresponding to the digits 3 and 1 that occur in the first and third positions, respectively, in each code.

With Any-match scoring, one point is awarded for each symbol in a guess code
that occurs *anywhere* in the secret code. For example, the Any-match
score of the codes (3,4,1,1) and (3,2,1,4) is 3, corresponding to the digits 1,
3, 4 that occur in each code.

With Mastermind-type scoring, 10 points are awarded for every symbol of the guess code that is found in the same position as in the secret code, plus 1 point for every other symbol that is found in the secret code, but in an incorrect position. For example, the Mastermind-type score of the codes (3,4,1,1) and (3,2,1,4) is 21, with 10 points given for each of the digits 3 and 1 that occur in the first and third positions in each code, and 1 point for the digit 4 that occurs in each code, but not in the same position.

We also consider two classes of Mastermind-style games, those with symbols that are allowed to be repeated in the secret and guess codes (Y), and those with symbols that cannot be repeated (N). We can describe a game by its scoring type and whether or not repeated symbols are allowed. For example, MY refers to a game with Mastermind-type scoring and repeated symbols, and AN denotes a game with Any-match scoring and non-repeated symbols. The abbreviations PY, PN, AY, AN, MY and MN refer to the six scoring/repetition types considered here.

*Repeated symbols*

When the same alphabet symbol is allowed to appear more than once in a code, each element of the code is selected from the alphabet of N symbols (sampling with replacement). Each of the C code elements has the same N possibilities, so the total number of possible codes is

For example, in the standard (4,6) game with repeated symbols, the population size is 6
*Non-repeated symbols*

When repeated symbols are *not* allowed, we must have **N ≥
C**, in order for there to be enough symbols in the alphabet from which
to draw C different ones (sampling without replacement). The first symbol in a
code can be chosen from any of the N alphabet symbols. The second symbol
cannot be the same as the first symbol, so it can be chosen from the remaining
N - 1 alphabet symbols. Similarly, the third symbol cannot be the same as
either of the first two symbols, so it can be selected in N - 2 ways.
Continuing in this fashion for all C symbols, the number of possible codes is

Since all symbols in a code are distinct, we can remap the alphabet symbols to an arbitrary sequence, and so without loss of generality, we can take the alphabet as the first N positive integers and assume that the reference (secret code) for score computations is the sequence (1,2,...,C).

Each of the six scoring/repetition types can be considered to have a discrete
probability distribution of scores over all of the Q^{2} possible pairs
of codes. We can use the two-letter scoring/repetition abbreviation to denote
a random variable taking on the possible scores for that particular type of
game. For example, the probabilities for the (4,6) PN game can be determined
by tabulating how many of the 360^{2} = 129,600 pairs of codes have each
of the scores between 0 and 4. It can be determined that 46,080 pairs have a
score of 1, so we have

Pr{PN = 1} = 46,080/129,600 = 35.5% for the (4,6) gameA Mastermind-type score of 10(C-1) + 1 (e.g., 31 in the standard game) is impossible since having C symbols correctly chosen with exactly C - 1 in the correct locations implies that the final symbol would also be in the correct position, producing a score of 10C instead. Therefore, regardless of whether repeated symbols are allowed or not, we have

Pr{MY = 10C - 9} = Pr{MN = 10C - 9} = 0In Mastermind-type scoring, the sum of the tens and units digits of the score can between 0 and C, inclusive, as can be the tens digit. Therefore, when the tens digit is i, the possible units digits are 0,...,C-i (C-i+1 values). Since the one score 10(C-1) + 1 is impossible, the number of Mastermind-type scores is by [1]. For example, in the standard (4,6) game, Mastermind-type scores can take any of the following 4(4 + 3)/2 = 14 values:

Depending on the relative sizes of C and N, some impossible scores can be predicted when repeated symbols are not allowed. If N < 2C, then every two subsets of C distinct symbols drawn from a population of N symbols have at least 2C - N symbols in common. This fact establishes certain zero scores for Any-match and Mastermind-type scoring:0,1, 2, 3, 4,10, 11, 12, 13, 20, 21, 22, 30, 40

Pr{AN = s} = 0 whenever s < 2C - NFor example, in the standard (4,6) game, we have 2C - N = 2, soPr{MN = 10A + B} = 0 whenever the sum of digits A + B < 2C - N

Pr{AN = 0} = Pr{AN = 1} = 0and

Pr{MN = 0} = Pr{MN = 1} = Pr{MN = 10} = 0 (indicated in bold in the list above)Finally, we note that in the particular case N = C, the entire alphabet is consumed in every code, and so it is impossible to have C - 1 perfect matches without the last one too:

Pr{PN = C-1} = 0 if N = CAlso, since every code is a permutation of the secret code:

Pr{AN = C} = 1 if N = C

To assign pixels to codes, let the pixel index range from 0 to N^{C} -
1, and let the alphabet consist of N consecutive digits beginning with 0. Then
each code is the base-N representation of the pixel index; for example, in the
standard (4,6) game, the code associated with index 0 is 0 0 0 0, with index 5
is 0 0 0 5, with index 6 is 0 0 1 0, with index 216 is 1 0 0 0, and with index
1295 is 5 5 5 5.

With the origin at lower left, the intensity of the pixel at position (x,y) is the score associated with code indexes x and y. A black pixel corresponds to a score of 0, and a white pixel is used for the maximum possible score (which depends on the scoring type). Proportionally-varying shades of gray are used for intermediate scores. The diagonal of the plot is all white since the score of a code combined with itself is always the maximum score.

There typically are lighter diagonal lines and squares throughout the graph where the pixel indexes differ by a constant amount due to the associated codes having multiple digits in agreement; for example, points (x,y) where |x - y| is a multiple of N. More detail can be shown in close-up using a bitmap display application such as Microsoft Paint. Following are 1296 x 1296 pixel graphs for the standard (4,6) game:

__Perfect-match scoring__ (relatively few gray levels due to only 5 possible
scores):

Click image for full resolution

__Mastermind-style scoring__ (more details resulting from 14 gray levels):

Click image for full resolution

__Any-match scoring__ (only 5 gray levels, but more complexity and lighter
tones apparent since higher scores are more likely):

Click image for full resolution

The corresponding pixel graphs for other game sizes have a similar appearance.

When codes are allowed to have repeated elements, the score distribution is based on sampling individual symbols with replacement, and a binomial probability distribution is applicable. We assume that the alphabet of symbols in the game in question is the positive integers. Considering the scoring of one symbol at a time as a Bernoulli trial, the probability of a success (1 point earned) is simply the chances of a randomly-selected number from the alphabet 1,...,N matching its ordinal position in the code pattern being constructed, or 1/N. The score probabilities therefore follow the binomial distribution

The mean of this score distribution is C/N [2].For example, in the standard (4,6) game with repeated symbols and Perfect-match scoring, the scores have the following probabilities:

(4,6) PY

Score Probability 0 625/1296 48.23% 1 500/1296 38.58% 2 150/1296 11.57% 3 20/1296 1.54% 4 1/1296 0.08% Mean score: 4/6 = 0.67

where f

(1)

ffor n ≥ 0 (OEIS A114488)._{0}(n) = 1f

_{1}(n) = nf

_{2}(n) = n^{2}+ n + 1f

_{3}(n) = n^{3}+ 3n^{2}+ 5n + 2f

_{4}(n) = n^{4}+ 6n^{3}+ 17n^{2}+ 20n + 9f

_{5}(n) = n^{5}+ 10n^{4}+ 45n^{3}+ 100n^{2}+ 109n + 44f

_{6}(n) = n^{6}+ 15n^{5}+ 100n^{4}+ 355n^{3}+ 694n^{2}+ 689n + 265

If g_{m}(n) denotes the mth forward difference of the factorial numbers:

gthen f_{m+1}(n) = g_{m}(n+1) - g_{m}(n), g_{0}(n) = n!,

fIt follows that f_{m}(n) = g_{m}(n)/n!

Interestingly, in the probabilities above, the polynomial functions f

f _{m+1}(n) = (n+1)f_{m}(n+1) - f_{m}(n), f_{0}(n) = 1.(2)

Now assume for the moment that N = C. Then all alphabet symbols are consumed in creating each of the C! possible codes, and the number of codes with a given score s in the range 0,1,...,C is simply the number of code elements occurring in their ordered position, known as the partial derangement

where !(C-s) is the subfactorial of C-s. The associated score probability is the ratio of R(C,s) to the number of possible codes: Noting that N!/(N-C)! = C! in this case, we can combine (1) with the immediately preceding results to obtain from which we can see that fLemma 3 shows by a simple induction argument that the probabilities (1) sum to unity. An analogous approach is used in Lemma 4 to determine that the mean of the score probability distribution is C/N, which is the same mean as in the repeated-symbols case PY.

For example, in the standard (4,6) game with non-repeated symbols and Perfect-match scoring, the scores have the following probabilities:

(4,6) PN

Score Probability 0 181/360 50.28% 1 128/360 35.56% 2 42/360 11.67% 3 8/360 2.22% 4 1/360 0.28% Mean score: 4/6 = 0.67

The mean of this distribution is C^{2}/N [3].

For example, in the standard (4,6) game with non-repeated symbols and Any-match scoring, we have max(2C-N,0) = 2 and the scores have the following probabilities:

(4,6) AN

Score Probability 2 6/15 40.00% 3 8/15 53.33% 4 1/15 6.67% Mean score: 16/6 = 2.67

In contrast to the grayscale representations shown earlier, the score probability distribution function (pdf) can be plotted in the usual way. The following graph shows the score pdfs for the standard (4,6) game with Mastermind-type scoring. There is some skew toward higher scores for non-repeated symbols (blue) as compared to repeated symbols (red).

Symbols: Repeated Non-repeated Scoring: Mastermind type

(T)Perfect Match

(CF)Any Match

(T)Mastermind type

(T)Perfect Match

(CF)Any Match

(CF)Score: 0 7.24% 48.23% 7.24% 50.28% 1 18.66% 38.58% 32.59% 35.56% 2 17.15% 11.57% 42.75% 23.33% 11.67% 40.00% 3 4.89% 1.54% 16.33% 24.44% 2.22% 53.33% 4 0.28% 0.08% 1.09% 2.50% 0.28% 6.67% 10 13.93% 11 17.49% 13.33% 12 6.82% 20.00% 13 0.34% 2.22% 20 8.10% 3.33% 21 3.09% 6.67% 22 0.39% 1.67% 30 1.54% 2.22% 40 0.08% 0.28% Mean score: 7.71 0.67 1.71 8.67 0.67 2.67 (T): Tabulated

(CF): Closed form

The level of pdf skew between the two types of repetition varies for other game sizes and scoring types.

Mean scores are shown in the plot below for the AN case (green) and the PY and PN cases (blue). Values of N range from 2 to 26. Values of C range from 3 (bottom curve) to 9 (top curve). The red line indicates the mean score 9/N that is shared when C = 3 in the AN case and C = 9 in the PY or PN case. Some tabulated values are shown in black for the AY case. In all cases, the mean score decreases with the alphabet size N and increases with the code length C. Results for Mastermind-type scoring are excluded since its score ranges are disjointed and mean values less meaningful.

Abbreviation Scoring Symbols Population size Mean AN Any match Non-repeated N!/(N-C)! C ^{2}/NPY Perfect match Repeated N ^{C}C/N PN Perfect match Non-repeated N!/(N-C)! C/N

Pr{Mastermind-type score = 10A + B} = Pr{Perfect-match score = A and Any-match score = A + B}In general, the score distributions of Perfect-match and Any-match scoring are not independent, so the probability distribution of the compound event in Mastermind-type scoring cannot be determined by multiplying the corresponding marginal probability distributions [4]. A couple of simple examples for the standard (4,6) game provide proof. For non-repeated symbols, we have

Pr{PN = 1} = 128/360 = 35.556%Similarly, for repeated symbols, we have

Pr{AN = 4} = 24/360 = 6.667%

Pr{MN = 13} = 2880/129600 = 2.222% ≠ 2.370% = 3072/129600 = Pr{PN = 1} Pr{AN = 4}

Pr{PY = 3} = 20/1296 = 1.543%Due to the dependence of Perfect-match and Any-match scoring, there is unfortunately no convenient method of combining them to produce generalized Mastermind-type score distributions. The practical alternative is computer-aided tabulation of Mastermind-type scores for all pairs of codes.

Pr{AY = 4} = 18306/1679616 = 1.090%

Pr{MY = 31} = 0 ≠ 0.0168% = 366120/2176782336 = Pr{PY = 3} Pr{AY = 4}

Score Mastermind

(4,6) MYBulls and Cows

(4,10) MNSuper Mastermind

(5,8) MYJotto

(5,10) PYFive Dice

(5,6) AY0 7.2392% 7.1429% 4.8198% 59.0490% 2.1904% 1 18.6614% 28.5714% 16.1431% 32.8050% 15.2093% 2 17.1539% 25.0000% 19.4595% 7.2900% 36.5820% 3 4.8868% 5.2381% 9.3007% 0.8100% 34.5615% 4 0.2840% 0.1786% 1.5195% 0.0450% 10.8214% 5 0.0482% 0.0010% 0.6353% 10 13.9318% 9.5238% 8.2602% 11 17.4897% 14.2857% 16.1769% 12 6.8158% 4.2857% 10.1997% 13 0.3429% 0.1587% 1.9276% 14 0.0720% 20 8.1019% 3.5714% 4.8299% 21 3.0864% 1.4286% 4.4460% 22 0.3858% 0.1190% 1.1516% 23 0.0401% 30 1.5432% 0.4762% 1.1482% 31 0.3204% 32 0.0267% 40 0.0772% 0.0198% 0.1068% 50 0.0031% Mean

score7.7144 5.2000 7.6417 0.5000 2.3852

In many game implementations, the total of pips on the five secret dice is provided, which considerably reduces the number of feasible patterns at each move, as well as increases the average score (as would be expected since more information on the secret pattern is provided). The sum of the dice ranges from 5 to 30, or in the general case, from C to CN. For each dice sum, there is a separate distribution of scores ranging from 0 to C. However, a score of C-1 will never occur since knowing all of the dice values but one, as well as the sum of the dice, implies that the remaining die value is also known.

Consider a dice pattern having sum T. Each die's value is of the form 1 +
a_{i}, where the integers a_{i} satisfy

0 ≤ aand which can be written Therefore, at most T - C of the a_{i}≤ N-1, i=1,...,C

Analogously, each die is also of the form N - b_{i}, where the integers
b_{i} satisfy

0 ≤ band which can be written Therefore, at most CN - T of the b_{i}≤ N-1, i=1,...,C

In general, the minimum score is

For the (5,6) Five Dice game, the 26 possible score distributions are listed horizontally as follows:Some of these distributions are plotted below, along with the Mastermind Any-match score distribution in red:

Score Mean

scoreSum 0 1 2 3 5 5 100.0000% 5.0000 6 100.0000% 5.0000 7 44.4444% 55.5556% 4.1111 8 8.1633% 48.9796% 42.8571% 3.7755 9 3.0612% 10.2041% 57.1429% 29.5918% 3.4286 10 0.3149% 1.8896% 14.2353% 64.4999% 19.0602% 3.1916 11 0.2380% 5.4729% 14.9911% 63.0577% 16.2403% 3.0583 12 0.6450% 5.4824% 19.3496% 62.0263% 12.4966% 2.9274 13 0.8787% 8.9569% 18.0839% 61.5646% 10.5159% 2.8240 14 1.3032% 8.7963% 22.4280% 58.6420% 8.8306% 2.7373 15 0.8636% 13.4780% 19.2166% 57.8762% 8.5656% 2.6837 16 1.2958% 12.6059% 21.4540% 56.8097% 7.8347% 2.6512 17 1.0930% 13.3629% 21.4826% 56.5746% 7.4869% 2.6349 18 1.0930% 13.3629% 21.4826% 56.5746% 7.4869% 2.6349 19 1.2958% 12.6059% 21.4540% 56.8097% 7.8347% 2.6512 20 0.8636% 13.4780% 19.2166% 57.8762% 8.5656% 2.6837 21 1.3032% 8.7963% 22.4280% 58.6420% 8.8306% 2.7373 22 0.8787% 8.9569% 18.0839% 61.5646% 10.5159% 2.8240 23 0.6450% 5.4824% 19.3496% 62.0263% 12.4966% 2.9274 24 0.2380% 5.4729% 14.9911% 63.0577% 16.2403% 3.0583 25 0.3149% 1.8896% 14.2353% 64.4999% 19.0602% 3.1916 26 3.0612% 10.2041% 57.1429% 29.5918% 3.4286 27 8.1633% 48.9796% 42.8571% 3.7755 28 44.4444% 55.5556% 4.1111 29 100.0000% 5.0000 30 100.0000% 5.0000

The probability distribution of the dice sum itself is known in piecewise form; see [5] and this reference. The dice sum distribution is symmetric about its mode(s), and its mean is C(N+1)/2, which is the midpoint of the minimum and maximum possible sums. Since each of the N die values is equiprobable and is determined independently of the other dice (repeated symbols), the dice sum distribution approaches the normal as C increases, by the Central Limit Theorem [6].

Proof by induction.

so the formula holds for n = 0. Assume the formula holds for n. Then using Euler's recurrence relation (OEIS A000166), we haveLet x = N - C ≥ 0 in (1) and let S(C,x) denote the sum of score probabilities

after reversing the summation on k. When x = 0 (N = C), we have For proof by induction, assume S(C,x) = 1 for all C. ThenThe first term in braces by the induction hypothesis. Similarly, the second term in braces in (3) so

(3)

An analogous approach to Lemma 3 is used to determine the mean of the probability distribution S(C,x). Again let x = N - C ≥ 0 in (1). Then

[2] Hoel, Port & Stone, Introduction to Probability Theory (Boston: Houghton Mifflin, 1971), p. 83.

[3] Ibid., p. 52, 90.

[4] Ibid., p. 19.

[5] Uspensky, J. V., Introduction to Mathematical Probability (New York: McGraw-Hill, 1937), pp. 23-24.

[6] Hoel, Port & Stone, Introduction to Probability Theory (Boston: Houghton Mifflin, 1971), p. 185.

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