LeetCode #3707 — EASY

Equal Score Substrings

Build confidence with an intuition-first walkthrough focused on core interview patterns fundamentals.

The Problem

Problem Statement

You are given a string s consisting of lowercase English letters.

The score of a string is the sum of the positions of its characters in the alphabet, where 'a' = 1, 'b' = 2, ..., 'z' = 26.

Determine whether there exists an index i such that the string can be split into two non-empty substrings s[0..i] and s[(i + 1)..(n - 1)] that have equal scores.

Return true if such a split exists, otherwise return false.

Example 1:

Input: s = "adcb"

Output: true

Explanation:

Split at index i = 1:

Left substring = s[0..1] = "ad" with score = 1 + 4 = 5
Right substring = s[2..3] = "cb" with score = 3 + 2 = 5

Both substrings have equal scores, so the output is true.

Example 2:

Input: s = "bace"

Output: false

Explanation:

No split produces equal scores, so the output is false.

Constraints:

2 <= s.length <= 100
s consists of lowercase English letters.

Step 01

Brute Force Baseline

Problem summary: You are given a string s consisting of lowercase English letters. The score of a string is the sum of the positions of its characters in the alphabet, where 'a' = 1, 'b' = 2, ..., 'z' = 26. Determine whether there exists an index i such that the string can be split into two non-empty substrings s[0..i] and s[(i + 1)..(n - 1)] that have equal scores. Return true if such a split exists, otherwise return false.

Baseline thinking

Start with the most direct exhaustive search. That gives a correctness anchor before optimizing.

Pattern signal: General problem-solving

Example 1

"adcb"

Example 2

"bace"

Step 02

Core Insight

What unlocks the optimal approach

Use brute-force

Interview move: turn each hint into an invariant you can check after every iteration/recursion step.

Step 03

Algorithm Walkthrough

Iteration Checklist

Define state (indices, window, stack, map, DP cell, or recursion frame).
Apply one transition step and update the invariant.
Record answer candidate when condition is met.
Continue until all input is consumed.

Use the first example testcase as your mental trace to verify each transition.

Step 04

Edge Cases

Minimum Input

Single element / shortest valid input

Validate boundary behavior before entering the main loop or recursion.

Duplicates & Repeats

Repeated values / repeated states

Decide whether duplicates should be merged, skipped, or counted explicitly.

Extreme Constraints

Upper-end input sizes

Re-check complexity target against constraints to avoid time-limit issues.

Invalid / Corner Shape

Empty collections, zeros, or disconnected structures

Handle special-case structure before the core algorithm path.

Step 05

Full Annotated Code

Source-backed implementations are provided below for direct study and interview prep.

// Accepted solution for LeetCode #3707: Equal Score Substrings
class Solution {
    public boolean scoreBalance(String s) {
        int n = s.length();
        int l = 0, r = 0;
        for (int i = 0; i < n; ++i) {
            int x = s.charAt(i) - 'a' + 1;
            r += x;
        }
        for (int i = 0; i < n - 1; ++i) {
            int x = s.charAt(i) - 'a' + 1;
            l += x;
            r -= x;
            if (l == r) {
                return true;
            }
        }
        return false;
    }
}

// Accepted solution for LeetCode #3707: Equal Score Substrings
func scoreBalance(s string) bool {
	var l, r int
	for _, c := range s {
		x := int(c-'a') + 1
		r += x
	}
	for _, c := range s[:len(s)-1] {
		x := int(c-'a') + 1
		l += x
		r -= x
		if l == r {
			return true
		}
	}
	return false
}

# Accepted solution for LeetCode #3707: Equal Score Substrings
class Solution:
    def scoreBalance(self, s: str) -> bool:
        l = 0
        r = sum(ord(c) - ord("a") + 1 for c in s)
        for c in s[:-1]:
            x = ord(c) - ord("a") + 1
            l += x
            r -= x
            if l == r:
                return True
        return False

// Accepted solution for LeetCode #3707: Equal Score Substrings
fn score_balance(s: String) -> bool {
    let mut right = s.bytes().fold(0, |acc, b| acc + (b - b'a' + 1) as i32);

    let mut left = 0;
    for b in s.bytes() {
        let v = (b - b'a' + 1) as i32;
        left += v as i32;
        right -= v as i32;
        dbg!(left, right);
        if left == right {
            return true;
        }
    }

    false
}

fn main() {
    let ret = score_balance(String::from("abdcd"));
    println!("ret={ret}");
}

#[test]
fn test() {
    assert!(score_balance("abdcd".to_string()));
    assert!(score_balance("adcb".to_string()));
    assert!(!score_balance("bace".to_string()));
}

// Accepted solution for LeetCode #3707: Equal Score Substrings
function scoreBalance(s: string): boolean {
    let [l, r] = [0, 0];
    for (const c of s) {
        const x = c.charCodeAt(0) - 96;
        r += x;
    }
    for (let i = 0; i < s.length - 1; ++i) {
        const x = s[i].charCodeAt(0) - 96;
        l += x;
        r -= x;
        if (l === r) {
            return true;
        }
    }
    return false;
}

Step 06

Interactive Study Demo

Use this to step through a reusable interview workflow for this problem.

Custom checkpoints (one per line)

Press Step or Run All to begin.

Step 07

Complexity Analysis

Time

O(n)

Space

O(1)

Approach Breakdown

BRUTE FORCE

O(n²) time

O(1) space

Two nested loops check every pair or subarray. The outer loop fixes a starting point, the inner loop extends or searches. For n elements this gives up to n²/2 operations. No extra space, but the quadratic time is prohibitive for large inputs.

OPTIMIZED

O(n) time

O(1) space

Most array problems have an O(n²) brute force (nested loops) and an O(n) optimal (single pass with clever state tracking). The key is identifying what information to maintain as you scan: a running max, a prefix sum, a hash map of seen values, or two pointers.

Shortcut: If you are using nested loops on an array, there is almost always an O(n) solution. Look for the right auxiliary state.

Coach Notes

Common Mistakes

Review these before coding to avoid predictable interview regressions.

Off-by-one on range boundaries

Wrong move: Loop endpoints miss first/last candidate.

Usually fails on: Fails on minimal arrays and exact-boundary answers.

Fix: Re-derive loops from inclusive/exclusive ranges before coding.