Math 2.0

Combinatorics Proof

Proof of Bertrand's ballot theorem by reflection

Builds on Classical definition of probabilityBinomial coefficientNumber of k-element subsetsFactorial formula for binomial coefficientsComplement principleRule of sumBijection principleFactorial

Proof

We count the bad counting orders with André's reflection argument.

  1. Let p>q≥0p > q \ge 0 be integers and N=p+qN = p + q. A counting order gives a word w=w1⋯wNw = w_1 \cdots w_N with pp letters AA and qq letters BB. Let Ω\Omega be the set of such words.
  2. Each word arises from exactly p! q!p!\, q! orders of the individual ballots. So a uniformly random order gives each word of Ω\Omega the same probability, and we may use the classical definition of probability on Ω\Omega.
  3. A word is fixed by the positions of its qq letters BB. By the number of kk-element subsets, ∣Ω∣|\Omega| is the binomial coefficient (Nq)\binom{N}{q}.
  4. For 0≤k≤N0 \le k \le N, let dkd_k be the number of letters AA minus the number of letters BB among w1,…,wkw_1, \dots, w_k. Then d0=0d_0 = 0, dN=p−q>0d_N = p - q > 0, and ∣dk+1−dk∣=1|d_{k+1} - d_k| = 1.
  5. The word is good if dk>0d_k > 0 for all 1≤k≤N1 \le k \le N, and bad otherwise.
  6. If q=0q = 0, the only word is A⋯AA \cdots A, with dk=k>0d_k = k > 0. So the probability is 1=p−0p+01 = \frac{p - 0}{p + 0}. From now on let q≥1q \ge 1.
  7. We claim that ww is bad exactly when dk=0d_k = 0 for some k≥1k \ge 1. If dk=0d_k = 0 with k≥1k \ge 1, then ww is bad by step 5.
  8. Conversely, let ww be bad. If w1=Bw_1 = B, then d1=−1<0<dNd_1 = -1 < 0 < d_N. Let kk be the least index with dk>0d_k > 0. Then k≥2k \ge 2 and dk−1≤0d_{k-1} \le 0. Also dk−1≥dk−1≥0d_{k-1} \ge d_k - 1 \ge 0, so dk−1=0d_{k-1} = 0 with k−1≥1k - 1 \ge 1.
  9. If w1=Aw_1 = A, then d1=1d_1 = 1, and some j≥1j \ge 1 has dj≤0d_j \le 0. Let kk be the least such jj. Then dk−1≥1d_{k-1} \ge 1 and dk≥dk−1−1≥0d_k \ge d_{k-1} - 1 \ge 0, so dk=0d_k = 0.
  10. For a bad word ww, let k≥1k \ge 1 be the least index with dk=0d_k = 0. Let ρ(w)\rho(w) be the word obtained by exchanging AA and BB in w1,…,wkw_1, \dots, w_k.
  11. Since dk=0d_k = 0, the first kk letters have as many AA as BB. So ρ(w)∈Ω\rho(w) \in \Omega. Its differences are −dj-d_j for j≤kj \le k and djd_j for j≥kj \ge k.
  12. So ρ(w)\rho(w) is bad, and kk is again its least index ≥1\ge 1 with difference 00. Hence ρ(ρ(w))=w\rho(\rho(w)) = w, and ρ\rho is a bijection of the set of bad words to itself.
  13. Since k≥1k \ge 1, ρ\rho changes the first letter. So ρ\rho maps the bad words that start with AA bijectively onto the bad words that start with BB.
  14. Every word that starts with BB is bad, because d1=−1d_1 = -1. Such a word is fixed by the positions of its other q−1q - 1 letters BB among N−1N - 1 places. So there are (N−1q−1)\binom{N-1}{q-1} of them.
  15. By the bijection principle and the rule of sum, there are 2(N−1q−1)2 \binom{N-1}{q-1} bad words.
  16. By the factorial formula and N!=N⋅(N−1)!N! = N \cdot (N - 1)!, q!=q⋅(q−1)!q! = q \cdot (q-1)! from the definition of the factorial, (N−1q−1)=(N−1)!(q−1)! p!=qN⋅N!q! p!=qN(Nq).\binom{N-1}{q-1} = \frac{(N-1)!}{(q-1)!\, p!} = \frac{q}{N} \cdot \frac{N!}{q!\, p!} = \frac{q}{N} \binom{N}{q}.
  17. By the complement principle, the number of good words is (Nq)−2qN(Nq)=p−qp+q(Nq)\binom{N}{q} - \frac{2q}{N} \binom{N}{q} = \frac{p - q}{p + q} \binom{N}{q}.
  18. Dividing by ∣Ω∣=(Nq)|\Omega| = \binom{N}{q}, the probability that AA stays strictly ahead throughout the count is p−qp+q\frac{p - q}{p + q}.

Idea

A bad count must reach a tie at some moment. Swapping the votes up to the first tie turns a bad count that starts with AA into one that starts with BB, and back. Every count that starts with BB is bad, so the bad counts are twice those starting with BB, a fraction 2qp+q\frac{2q}{p+q}.

Reading path

To understand this page from the ground up, read these 72 pages in order. The foundations come first. The path takes the proofs Proof of the number of k-element subsets by induction, Proof that a composition of injections is injective, Proof that a function is bijective iff it has a two-sided inverse, Proof of the complement principle, Proof of Pascal's rule from the recursive definition, Proof that a composition of surjections is surjective, Proof of the factorial formula for binomial coefficients by induction, Proof of the rule of sum, Proof of the bijection principle.

  1. Binomial coefficient definition
  2. Cardinality of a finite set definition
  3. Combination definition
  4. Construction of the reals from Cauchy sequences concept
  5. Element definition
  6. Factorial definition
  7. Integers object
  8. Ordered field definition
  9. Proposition concept
  10. Random experiment concept
  11. Rational numbers object
  12. Proof of Pascal's rule from the recursive definition proof
  13. Set definition
  14. Ring of integers statement
  15. Conjunction definition
  16. Disjunction definition
  17. Implication definition
  18. Predicate definition
  19. Pascal's rule statement
  20. Empty set object
  21. Ordered pair definition
  22. Intersection definition
  23. Union definition
  24. Sample space definition
  25. Existential quantifier definition
  26. The rationals form a field statement
  27. Universal quantifier definition
  28. Proof of the factorial formula for binomial coefficients by induction proof
  29. Cartesian product definition
  30. Disjoint sets definition
  31. Subset definition
  32. The rationals form an ordered field statement
  33. Uniqueness quantifier definition
  34. Factorial formula for binomial coefficients statement
  35. Binary relation definition
  36. Universal set definition
  37. Power set definition
  38. Order on the reals definition
  39. Complement definition
  40. Function definition
  41. Interval definition
  42. Codomain definition
  43. Function composition definition
  44. Identity function definition
  45. Injective function definition
  46. Sigma-algebra definition
  47. Surjective function definition
  48. Left inverse definition
  49. Right inverse definition
  50. Proof that a composition of injections is injective proof
  51. Event definition
  52. Proof that a composition of surjections is surjective proof
  53. Bijective function definition
  54. Composition of injections is injective statement
  55. Probability axioms concept
  56. Composition of surjections is surjective statement
  57. Equinumerous sets definition
  58. Proof of the rule of sum proof
  59. Proof that a function is bijective iff it has a two-sided inverse proof
  60. Probability measure definition
  61. Finite set definition
  62. Rule of sum statement
  63. Bijective iff it has a two-sided inverse statement
  64. Probability space definition
  65. Proof of the bijection principle proof
  66. Proof of the complement principle proof
  67. Classical definition of probability definition
  68. Bijection principle statement
  69. Complement principle statement
  70. Proof of the number of k-element subsets by induction proof
  71. Number of k-element subsets statement
  72. Proof of Bertrand's ballot theorem by reflection proof

Where this leads

1 page builds on this one.

Further reading

  1. Web https://en.wikipedia.org/wiki/Bertrand's_ballot_theorem, Proof by reflection
About this page
TagFYQY3KVC7D
URIm2://FYQY3KVC7D
Kindproof
WritingW0 stub
MSC05A15
ProvesBertrand's ballot theorem
UsesClassical definition of probability, Binomial coefficient, Number of k-element subsets, Factorial formula for binomial coefficients, Complement principle, Rule of sum, Bijection principle, Factorial
Changed4 November 2026

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