15 Years of the Good Food Awards | #PodSaveChocolate Ep 134
Episode 134 of PodSaveChocolate explores the 15-year history of the Good Food Awards in the Chocolate and Confections categories. Insights into the results, as well as into what it took to compile and clean the data. (TL;DR – extracting and cleaning the data was NOT pretty.)
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Episode 134 Overview
I received an email from the Good Food Foundation (“GFF”) recently telling me about the 2025 Awards winners. Rather than running the regular competition, this 15th round of awards was based on GFF community members voting on favorites!
I noticed some issues with the description on the 2025 Winners page, which I brought to the attention of the organizers. The page has been updated. Well, at least in part, because as I write this there are no links from the winning company names to their websites.
Because I have done analysis exercises with the International Chocolate Awards and the Academy of Chocolate Awards, I forged ahead with downloading the data from the website ⋯
The Source
⋯ and immediately ran into problems! It should come as no surprise to anyone that the data on the page, even though it seems to be orderly, is not. I used ChatGPT, Gemini, and Claude to extract the data in a consistent format.
Consistency is sometimes referred to as the “hobgoblin of small minds.” But when it comes to data analysis projects like this LLMs can still be very literal and fail to recognize two words or phrases that are misspelled or mistyped. The technical terms are “normalization” which means the same “type” of data are in one column and one column only, and “regularization” which means that the same word or phrase is used (and spelled) the same way every single time it appears.
Ideally, I wanted to hand the LLM a single URL; the Winners page and have it do all the work. When that didn’t work I generated a list of URLs (one for each page, Chocolate and Confections, for each year) and pasted that into the prompt hoping the LLM could process the page using a prompt I evolved over many iterations. Each of the LLMs failed for one reason or another. One limit I faced over and over again is that on my plan, many queries were to large for Claude to process.
I even asked Claude to generate a mini-DTD that told it what the data looked like, to reduce the processing load, but that failed, too.
In the end, I was able to get Claude to download the data for both categories for each year, but only by copying the data into the prompt. This was slow. It required more than a dozen queries, copying and pasting the results into a spreadsheet.
The next step was cleaning up the data. For example, I needed to ensure the company names were spelled the same each time so they weren’t double-counted ⋯ a maintenance task that’s challenging for the publishers, even when you don’t take into account that a company may have changed its name. I tried to automate this with Claude and Gemini, but that failed, too.
Link rot was another tedious task to tackle. I copied just the list of URLs then asked Claude to generate a unique list of URLs. These were then submitted to a URL checker. When a URL generated an error, I manually went and checked whether it was still valid, and if not why; for many errors I asked ChatGPT if the company was still in business.
All of this is in an updated spreadsheet that I plan to send to the organizers. Because, the website is less useful to the winners if the link rot is not addressed (SEO). It’s also more user-friendly to the visitors.
With that all said and done ⋯ following are some stats.
The Top Line
There are 412 winning products in the Chocolate and Confections categories in the 15 years that prizes have been awarded. (The Confections category was not added until 2013.)
| Year | Chocolate | Confections |
|---|---|---|
| 2011 | 7 | 0 |
| 2012 | 8 | 0 |
| 2013 | 16 | 10 |
| 2014 | 13 | 16 |
| 2015 | 13 | 15 |
| 2016 | 12 | 16 |
| 2017 | 12 | 19 |
| 2018 | 18 | 20 |
| 2019 | 13 | 21 |
| 2020 | 16 | 21 |
| 2021 | 11 | 20 |
| 2022 | 17 | 22 |
| 2023 | 19 | 17 |
| 2024 | 17 | 16 |
| 2025 | 3 | 4 |
| Total | 195 | 217 |
Companies With Three or More Awards
The following table needs to be updated to reflect the fact that some companies appear to have merged. For example, when you go to the BatchPDX website, you are redirected to Creo Chocolate. Should the awards count be combined? I can see it both ways.
Also, deduping the list was not complete. I see that “Fruition Chocolate” and “Fruition Chocolate Works” are separate entries, and that needs to be fixed in the source data from which these summary tables were generated. (There may be other such errors (I fixed one already) – please let me know in the comments.)
It’s also important to recognize that these awards (like all such programs) are self-selected. While we can infer that Alan McClure of Patric Chocolate is a highly-skilled maker (he is), his presence on the list also reflects two realities: 1) He probably entered at least 10 of the 15 competitions; and 2) He entered more than one product into one or both categories each year he entered.
So, not only did Alan enter products that the judges liked, he also entered consistently. While not inexpensive, entry fees, when considered as a part of an overall annual marketing budget, are modest and do increase access to distribution and sales.
| Company Name | Chocolate | Confections | |
|---|---|---|---|
| 1 | Patric Chocolate | 21 | 5 |
| 2 | Batch PDX | 0 | 13 |
| 3 | Fruition Chocolate | 10 | 1 |
| 4 | Dandelion Chocolate | 4 | 5 |
| 5 | Rogue Chocolatier | 9 | 0 |
| 6 | Goodnow Farms Chocolate | 6 | 2 |
| 7 | Maverick Chocolate Company | 8 | 0 |
| 8 | Sapore della Vita | 0 | 8 |
| 9 | Videri Chocolate Factory | 4 | 4 |
| 10 | Creo Chocolate | 7 | 1 |
| 11 | Askinosie Chocolate | 6 | 1 |
| 12 | Seahorse Chocolate | 6 | 0 |
| 13 | Escazu Artisan Chocolates | 4 | 2 |
| 14 | Kakao Chocolate | 0 | 6 |
| 15 | Dick Taylor Craft Chocolate | 5 | 1 |
| 16 | Fat Toad Farm | 0 | 5 |
| 17 | French Broad Chocolates | 2 | 3 |
| 18 | Chocolatay Confections | 0 | 4 |
| 19 | Fera'wyn's Artisan Chocolates | 0 | 4 |
| 20 | Monsoon Chocolate | 3 | 1 |
| 21 | Table Mountain Farm | 0 | 4 |
| 22 | Ritual Chocolate | 4 | 0 |
| 23 | Big Picture Farm | 0 | 4 |
| 24 | Bakery Nouveau | 3 | 1 |
| 25 | Xocolatl de David | 1 | 3 |
| 26 | Honey Mama's | 0 | 4 |
| 27 | Alma Chocolate | 0 | 3 |
| 28 | Serendipity Confections | 0 | 3 |
| 29 | Stone Grindz | 3 | 0 |
| 30 | Vermont Amber Organic Toffee | 0 | 3 |
| 31 | White Label Chocolate | 3 | 0 |
| 32 | Wildwood Chocolate | 0 | 3 |
| 33 | Theo Chocolate | 2 | 1 |
| 34 | JARDÍ Chocolates | 0 | 3 |
| 35 | Moku Chocolate | 3 | 0 |
| 36 | Mayana Chocolate | 0 | 3 |
| 37 | Madre Chocolate | 3 | 0 |
| 38 | Madison Chocolate Company | 0 | 3 |
| 39 | Little Apple Treats | 0 | 3 |
| 40 | JEM Organics | 0 | 3 |
| 41 | Gotham Chocolates | 1 | 2 |
| 43 | Fruition Chocolate Works | 3 | 0 |
| 43 | Fran's Chocolates | 0 | 3 |
| 44 | Feve Artisan Chocolatier | 0 | 3 |
| 45 | Dark Forest Chocolate | 3 | 0 |
| 46 | Charm School Chocolate | 3 | 0 |
| 47 | Castronovo Chocolate | 3 | 0 |
| 48 | Bibamba Artisan Chocolate | 2 | 1 |
| 49 | Xocolatl Small Batch Chocolate | 3 | 0 |
Winners by Percentage in the Chocolate Category
| Percentage | Count | Percentage of Total |
|---|---|---|
| 70 | 53 | 46.09% |
| 75 | 8 | 6.96% |
| 72 | 6 | 5.22% |
| 74 | 5 | 4.35% |
| 67 | 5 | 4.35% |
| 65 | 5 | 4.35% |
| 62 | 4 | 3.48% |
| 60 | 4 | 3.48% |
| 55 | 3 | 2.61% |
| 80 | 2 | 1.74% |
| 68 | 2 | 1.74% |
| 73 | 2 | 1.74% |
| 45 | 2 | 1.74% |
| 56 | 1 | 0.87% |
| 69 | 1 | 0.87% |
| 78 | 1 | 0.87% |
| 81 | 1 | 0.87% |
| 46 | 1 | 0.87% |
| 66 | 1 | 0.87% |
| 76 | 1 | 0.87% |
| 52 | 1 | 0.87% |
| 58 | 1 | 0.87% |
| 43 | 1 | 0.87% |
| 50 | 1 | 0.87% |
| 100 | 1 | 0.87% |
| 90 | 1 | 0.87% |
| 77 | 1 | 0.87% |
Winners by State
Although entries are organized into five regions, ultimately they are based on the states in those regions. I believe these numbers are accurate. I prompted Gemini to look only in the "State" field of my cleaned-up dataset.
| State | Chocolate | Confections |
|---|---|---|
| Oregon | 18 | 42 |
| California | 20 | 38 |
| Missouri | 27 | 14 |
| North Carolina | 14 | 20 |
| New York | 20 | 6 |
| Massachusetts | 16 | 5 |
| Vermont | 2 | 17 |
| Colorado | 6 | 11 |
| Florida | 5 | 11 |
| Washington | 7 | 9 |
| Ohio | 11 | 2 |
| Hawaii | 10 | 2 |
| Arizona | 6 | 3 |
| Maine | 1 | 7 |
| Wisconsin | 0 | 8 |
| Georgia | 3 | 3 |
| Michigan | 4 | 2 |
| Pennsylvania | 3 | 2 |
| New Mexico | 4 | 0 |
| Virginia | 3 | 1 |
| New Jersey | 0 | 4 |
| Minnesota | 1 | 2 |
| Arkansas | 3 | 0 |
| Maryland | 3 | 0 |
| Texas | 2 | 1 |
| Utah | 3 | 0 |
| South Carolina | 0 | 2 |
| Tennessee | 0 | 2 |
| Alabama | 1 | 0 |
| New Hampshire | 1 | 0 |
| Louisiana | 1 | 0 |
| Wyoming | 0 | 1 |
Cocoa-Producing Country Mentions
I ran this question in two different queries, so the following numbers need to be checked. I suspect the reason is that in this count, the name “Ambanja” is associated with Madagascar (and “Semuliki” with Uganda) whereas in the other count that association was not made.
| Country | Count |
|---|---|
| Madagascar | 14 |
| Belize | 8 |
| Ecuador | 7 |
| Peru | 7 |
| Guatemala | 5 |
| Bolivia | 5 |
| Costa Rica | 4 |
| Dominican Republic | 3 |
| Haiti | 3 |
| Tanzania | 3 |
| Nicaragua | 2 |
| Venezuela | 2 |
| Colombia | 2 |
| Thailand | 1 |
| Honduras | 1 |
| Liberia | 1 |
| Jamaica | 1 |
| Ghana | 1 |
| Chile | 1 |
| Vietnam | 1 |
Exploring Capabilities
I prompted Gemini to look at the "Product Name" column in my data file to generate a list of names associated with farms and cocoa varietals by country name. I don’t know if this is complete (and needs to be checked), but it represents progress in the creation of a semantic network of associations that will make future exploration richer, more detailed and nuanced, and therefore more informative.
| Descriptor | Type | Country |
|---|---|---|
| Maya Mountain | Cacao Producer | Belize |
| Fazenda Camboa | Cacao Farm | Brazil |
| Zorzal | Bird Sanctuary and Cacao Farm | Dominican Republic |
| Hacienda Victoria | Cacao Farm | Ecuador |
| Cahabon | Cacao Origin / River | Guatemala |
| Lachua | Cacao Origin / Lake | Guatemala |
| Ambanja | Cacao Origin / Plantation | Madagascar |
| Akesson's Estate | Cacao Plantation | Madagascar |
| El Carmen | Cacao Farm | Nicaragua |
| O'Payo | Cacao Varietal | Nicaragua |
| Chuncho | Cacao Varietal | Peru |
| Maranon | Cacao Origin / Canyon | Peru |
| Kokoa Kamili | Cacao Fermentary | Tanzania |
| Semuliki Forest | National Park / Forest | Uganda |
| Ben Tre | Cacao Origin / Region | Vietnam |
In Closing, A Disclaimer ⋯ and a related podcast episode
I was asked by Suzie Wyshak and Seneca Klassen to help develop the criteria for what “Good” meant for the Chocolate category before the first awards season in 2011.
I was then asked to contribute to the criteria for what “Good” means in the Confections category.
I have never judged the Chocolate category, but I did judge the Confections category in 2014 and 2015.
I was involved in the vetting process when there was some concern that an entrant might be deliberately misrepresenting something about their supply chain. This is incredibly important because if a company lies, slips through, and is recognized, it calls into question the entire process.
TL;DR – the company in question did deliberately misrepresent crucial data in their entrant application and was disqualified.
TheChocolateWire
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Clay Gordon
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