Green Thumb Data: Building a Simple Spreadsheet to Predict Harvests Like Fantasy Football Managers

Green Thumb Data: Build a simple harvest-forecast spreadsheet inspired by FPL managers

Struggling to predict whether your balcony basil or microgreen tray will produce enough for dinner? You’re not alone. Urban gardeners face limited space, variable light, and patchy feedback. In 2026 the fastest path to consistent harvests isn’t a magic soil mix — it’s good data and repeatable processes. This hands-on guide shows you how to track garden metrics, score each variable like a Fantasy Premier League stat sheet, and forecast yields with a straightforward spreadsheet workflow.

Why this matters now (2026 context)

By late 2025 and into 2026, more gardeners are using cheap sensors, low-power IoT, and community-shared datasets to squeeze predictable production out of small spaces. Advances in accessible PAR meters, open-source sensor firmware, and spreadsheet-friendly APIs let you combine manual observations with live feeds. This tutorial focuses on a low-barrier method: no Python required, no paid AI — just a spreadsheet you can adapt, share, and scale with community-sourced weights.

Quick overview — what you’ll end up with

• A practical spreadsheet template that turns raw garden metrics into weekly scores
• A simple FPL-style scoring model that ranks plants/plots by predicted yield
• Basic forecasting techniques (moving averages, linear regression) to produce a harvest forecast
• Automation tips: connecting sensors and mobile data entry to keep your sheet current

Step 1 — Decide the metrics you’ll track

Pick variables that are easy to measure and proven to affect yield. Start small — add more as you get comfortable. Below are the core metrics for most indoor and balcony setups.

• Light Hours — daily or weekly hours of usable light (sunlight or grow-light PAR estimate)
• Watering — volume (mL) per watering or a simple frequency count
• Fertilization — N-P-K dose or mg/L; date of last feed
• Temperature — daily average or min/max
• Humidity — average daily %
• Soil/Media EC (ppm) or pH — when available
• Pest/Disease flags — 0/1 or severity score
• Plant Stage — seedling, vegetative, flowering, harvest
• Outcome — harvest weight (g), harvest date, taste rating

Why these metrics?

These cover light, water, nutrients and environmental stress — the primary determinants of yield. Even if your initial measurements are rough (e.g., “2 hand-watering events this week”), the spreadsheet will learn patterns and give useful forecasts.

Step 2 — Structure the spreadsheet

Use Google Sheets or Excel. Create three sheets/tabs:

1. Raw Data — one row per planting/week per pot/plant
2. Scores — normalized metric scores and weighted totals
3. Forecasts & Dashboard — charts, rankings, and harvest predictions

Raw Data tab — suggested columns

• Date (week start)
• Plot/Container ID
• Crop/Variety
• Light Hours (h)
• Water Volume (mL)
• Fertilizer mg/L or dose
• Temp Avg (°C)
• Humidity (%)
• EC/PPM (optional)
• Pest Flag (0/1) or Severity (0-5)
• Stage
• Harvest Weight (g) — fill after harvest

Scores tab — turn raw values into 0–10 points

Inspired by FPL stat aggregation, we convert each metric into a simple 0–10 score so we can sum and rank. Example approach:

• Normalize each metric to a 0–1 range: normalized = (value - min) / (max - min)
• Scale to 0–10: score = normalized * 10
• Apply direction where lower is better (e.g., pest severity): score = (1 - normalized) * 10

Sample normalization ranges (adjust for your crop)

• Light Hours: 0–18 (0 = dark, 18 = full sun + supplemental light)
• Water Volume (weekly): 0–2000 mL (depends on container)
• Fertilizer: 0–2000 ppm (or your local recommended max)
• Temp Avg: 10–30°C (10 minimum, 30 high)
• Humidity: 30–90%

Example formula (Google Sheets / Excel)

Suppose Light Hours in Raw Data!D2 and we want a 0-10 Light Score in Scores!B2.

Normalized formula (in plain text):

=IF(Raw!D2="","", (MIN(MAX(Raw!D2,0),18) - 0) / (18 - 0) * 10)

Adjust min/max for specific crops. Repeat for each metric.

Step 3 — Weighting: build your FPL-style points model

In Fantasy leagues, different events get different points. For plants, assign relative importance. Example weights (sum to 1):

• Light Score: 0.30
• Water Score: 0.20
• Fertilizer Score: 0.15
• Temperature Score: 0.10
• Humidity Score: 0.05
• EC/pH Score: 0.05
• Pest Penalty: -0.15 (negative weight)

Then compute a single Weekly Performance Index as a weighted sum:

Performance = SUM(metric_score * weight)

Why weights matter

Different crops respond differently. Microgreens prioritize light and seed vigor, tomatoes care more about fertilization and heat. Start with expert defaults, then tune weights using your community dataset or the spreadsheet’s historical correlations.

Step 4 — Translate performance into a harvest forecast

Now convert weekly performance into an expected yield. We’ll keep it simple with two approaches you can do in a spreadsheet:

Approach A — Momentum & moving-average projection (best for short crops)

1. Compute a 2–4 week moving average of the Performance index.
2. Use a simple multiplier based on crop days to maturity. For example, if your moving average is 7/10 for basil in week 3 of a 6-week cycle, assume final yield = current harvest rate * (remaining weeks / elapsed weeks) * (moving average / 10).

Easy formula sketch:

=CurrentHarvestRate * (TotalWeeks / WeeksElapsed) * (MovingAvg/10)

Approach B — Linear regression from historical data (better as you collect seasons of data)

If you have past plantings with weekly performance and final harvest weights, use linear regression to predict final weight from the accumulated performance score.

In Google Sheets/Excel you can use SLOPE and INTERCEPT:

=SLOPE(HistoricalHarvestWeights, HistoricalAccumulatedPerformance)

Then predict:

=INTERCEPT + SLOPE * CurrentAccumulatedPerformance

This approach improves over time as you add more rows of real harvests.

Putting it together: sample pipeline

1. Each week: enter raw metrics.
2. Sheet computes normalized scores and Performance.
3. Dashboard shows ranks (highest Performance first).
4. Forecast tab uses moving average or regression to output harvest_weight_predicted.
5. After harvest: enter actual weight. The regression updates and improves future forecasts.

Step 5 — Ranking like an FPL manager

Create a live leaderboard of your pots/plots using the Performance metric. Add percentile or rank functions:

Example (Google Sheets): =RANK.EQ(PerformanceCell, PerformanceRange, 0)

Use conditional formatting to color-code the top performers and the underperformers. This gives a quick planning view: move lights, change feed schedules, or prioritize pest checks for low-ranked containers.

Weekly squad selection analogy

Think of your active pots as a fantasy squad for the coming week: rotate limited sunlight or the best soil into top-ranked containers to maximize overall yield — the same trade-offs FPL managers make balancing form and fixtures.

Step 6 — Automation & sensor integration (2026-friendly tips)

By 2026, many gardeners have low-cost tools to auto-fill parts of the Raw Data tab. Here are practical integrations:

• Smart light meters (PAR) can write daily totals to a Google Sheet via IFTTT or a microcontroller and webhooks.
• WiFi soil moisture sensors can post readings to a sheet using an intermediary like Home Assistant or a simple REST webhook.
• Use a phone form (Google Forms) for quick manual weekly entries — great for watering volume and pest flags.
• Apps Script (Google Sheets) or Power Query (Excel) can fetch weather data (sun hours, temp) for your coordinates to fill environmental metrics automatically.

Start by auto-filling one metric (light or temp). That reduces friction and improves the forecast power quickly.

Tuning, validation and trusting your model

Important: early forecasts will be noisy. Use these checks:

• Backtest — apply your current model to past seasons to see how close predictions were.
• Cross-validate — hold out some harvests from regression training and test forecast accuracy.
• Adjust weights — run simple correlations between each metric score and final harvest weight. Increase weights for metrics with stronger correlation.
• Avoid overfitting — too many weights for only a few harvests leads to fragile forecasts. Keep it simple until you have 20+ harvest records.

Troubleshooting common issues

Problem: Missing data

Solution: Use last-observation-carried-forward for environmental metrics and treat missing pest flags as 0 until inspected. Alternatively, set a data-completeness score and discount forecasts when coverage is poor.

Problem: Sensor drift or bad readings

Solution: Include a sensor-check column (manual quick reading) each month. Compare to a handheld meter. If drift is detected, add a correction factor or reset the historical data for that sensor period.

Problem: Unexpected pest outbreaks or heat waves

Solution: Add event flags (e.g., heatwave = 1) and treat them as strong negative modifiers. In short time windows, manual overrides are valid — then use those events to teach the model via regression.

Experience & case study: balcony basil league (example)

Case: a renter in a small 1-bed flat ran six 6-week basil cycles through 2025–26. They tracked light hours (phone photo log), two weekly water events, and a single feed at week 2. Using our scoring (weights tuned toward light and water), the spreadsheet predicted harvest weight within 12% on average by week 4. Key learnings:

• Light variation (east window vs. south window) was the dominant factor — weight correlated 0.72 with cumulative light score.
• Minor pests caused the largest single-source variance but were easy to flag and penalize in the model.
• Community-shared weight data for the same variety improved predictions once merged (consent-based) in early 2026.

"Turning my basil into a mini FPL table made decisions less emotional — I reallocated lights and increased ventilation based on ranks, and harvests became reliable." — A community gardener, Jan 2026

Advanced strategies (for when you’re ready)

• Use weighted moving averages that prioritize recent weeks (exponential smoothing) to capture momentum.
• Segment by crop type and build crop-specific weights and regression models.
• Explore Google Sheets add-ons that provide basic ML predictions or connect to low-code ML platforms to get non-linear models without coding.
• Share anonymized data within a community to build more robust regressions — more samples = less noise.

Ethics, privacy and data sharing (short note)

If you connect sensors or share templates, be mindful of location privacy and consent. Community datasets are powerful but should be anonymized and used with permission. In 2026 community gardens and data co-ops are emerging; consider joining a local collective to pool knowledge responsibly. Also watch product quality alerts for inputs and botanicals if you source feeds or amendments — see guidance on product quality and recalls.

Actionable checklist to get started today

1. Create a Google Sheet with Raw Data, Scores, Forecasts tabs.
2. Pick 5 metrics (light, water, fertilizer, temp, pest) and start weekly entries.
3. Implement the normalization formulas and a simple weighted Performance score.
4. Rank pots weekly and take one action on the lowest-ranked item (move, change water, inspect for pests).
5. Record final harvest weight and update your regression every 3 cycles.

Final thoughts — where this approach leads in 2026

As low-cost sensors and community data become mainstream, even city dwellers can run small-scale, reliable production systems — especially for fast-turn crops like microgreens, herbs and salad leaves. A simple FPL-style spreadsheet is a great first step: it teaches discipline in data collection, surfaces problems early, and produces surprisingly accurate harvest forecasts within a few cycles.

Takeaway

Start simple: track the variables you can measure consistently, convert them into normalized points, weight them by importance, and use moving averages or regression to forecast yields. Tune using actual harvests, and scale up by adding sensors and community data once your process is stable.

Call to action

Ready to try it? Build your first sheet this weekend and share a screenshot in our community forum for feedback. If you want a starter template and a short video walkthrough, join the grown.live community showcase — post your results with the tag #GreenThumbData and we’ll feature the best setups each month.

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