Noodles (NDLS) Return on Sales [ROS] (2012 - 2026)
Noodles (NDLS) posted Return on Sales [ROS] of -1.21% for Q2 2026, up 10.48 percentage points from -11.69% a year earlier but down 0.56 percentage points from the prior quarter.
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Analysis
Noodles (NDLS) Return on Sales [ROS] (2012 - 2026) Analysis & Trends
For the trailing twelve months through Jun 30, 2026, Return on Sales [ROS] at Noodles was -2.58%, up 4.16 percentage points year-over-year; for FY2025, it was -6.38%, down 0.75 percentage points from FY2024.
- Annual Return on Sales [ROS] shows a five-year change of -1.29 percentage points (FY2020 to FY2025).
- In prior years, Noodles' Return on Sales [ROS] was -5.63% in FY2024 (-4.63 pp), -1.00% in FY2023 and 1.22% in FY2021 (+6.31 pp).
- Quarterly Return on Sales [ROS] has run from a low of -11.69% in Q2 2025 to a high of 4.25% in Q3 2021 over five years.
- On a year-over-year basis, Return on Sales [ROS] has increased in each of the last three quarters, with an average year-over-year change of +1.11 percentage points over the last seven quarters.
- The strongest year-over-year quarter for Return on Sales [ROS] in the past five years was Q2 2026, with a gain of 10.48 percentage points; the weakest was Q4 2024, with a drop of 4.24 percentage points.
- According to Business Quant data, Return on Sales [ROS] for the three prior quarters was -0.66% (Q1 2026), -3.34% (Q4 2025) and -5.18% (Q3 2025).
Peer Set
Peer Comparison
| # | Company | Market Cap | Enterprise Value | Gross Profit (Qtr) | ROS (Qtr) |
|---|---|---|---|---|---|
| 1 | Mcdonalds | 164.07 Bn | 158.89 Bn | 6.42 Bn | 47.02% |
| 2 | Starbucks | 108.16 Bn | 95.79 Bn | - | 10.52% |
| 3 | Chipotle Mexican Grill | 40.91 Bn | 36.89 Bn | - | 15.70% |
| 4 | Yum Brands | 37.32 Bn | 34.20 Bn | 1.47 Bn | 30.20% |
| 5 | Restaurant Brands International | 24.36 Bn | 21.46 Bn | 1.38 Bn | 28.41% |
| 6 | Darden Restaurants | 22.52 Bn | 21.62 Bn | -113.70 Mn | 13.90% |
| 7 | Restaurant Brands International Limited Partnership | 17.01 Bn | 12.57 Bn | 1.38 Bn | 28.41% |
| 8 | Yum China Holdings | 14.14 Bn | 8.50 Bn | 537.00 Mn | 11.09% |
| 9 | Texas Roadhouse | 10.28 Bn | 9.64 Bn | - | 8.50% |
| 10 | Noodles | 82.34 Mn | 73.64 Mn | 90.30 Mn | -1.21% |
Historic Data
Download Data
Historic Data
| Date | Value |
|---|---|
| Jun 30, 2026 | -1.21% |
| Mar 31, 2026 | -0.66% |
| Dec 30, 2025 | -3.34% |
| Sep 30, 2025 | -5.18% |
| Jul 1, 2025 | -11.69% |
| Apr 1, 2025 | -5.16% |
| Dec 31, 2024 | -6.05% |
| Oct 1, 2024 | -3.91% |
| Jul 2, 2024 | -9.04% |
| Apr 2, 2024 | -3.37% |
| Jan 2, 2024 | -1.81% |
| Oct 3, 2023 | 1.59% |
| Jul 4, 2023 | -0.22% |
| Apr 4, 2023 | -1.77% |
| Sep 27, 2022 | 1.18% |
| Jun 28, 2022 | 1.43% |
| Mar 29, 2022 | -5.40% |
| Dec 28, 2021 | -3.79% |
| Sep 28, 2021 | 4.25% |
| Jun 29, 2021 | 4.94% |
API Access
Noodles Return on Sales [ROS] API
Pull this series into your own models, spreadsheets and apps with the Business Quant
Historical Metrics API. The request below matches the chart above — change the
frequency, period or values and it follows. Swap YOUR_API_KEY for your own key.
https://data.businessquant.com/historic?slug=return-on-sales-%5Bros%5D&ticker=NDLS&period=max&api_key=YOUR_API_KEYimport requests
url = "https://data.businessquant.com/historic"
params = {"slug": "return-on-sales-[ros]", "ticker": "NDLS", "period": "max", "api_key": "YOUR_API_KEY"}
data = requests.get(url, params=params).json()const res = await fetch("https://data.businessquant.com/historic?slug=return-on-sales-%5Bros%5D&ticker=NDLS&period=max&api_key=YOUR_API_KEY");
const data = await res.json();