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RVCC greens performance analysis

Deacon dashboard readings, May 3 – Jul 17, 2026 · 11 weekly windows

How to read this: turf performance values (green speed, smoothness, trueness, firmness, soil moisture) are visual reads off the Deacon dashboard screenshots, not an export — trends and relationships are reliable, individual numbers are approximate. Weather values (air temp, dew point, humidity, wind) are exact daily station readings, not estimates — a genuine upgrade over the earlier chart-read temperatures. Firmness and soil moisture only have 3–5 weeks of readings out of 11; anywhere you pick one of those, watch the sample-size (n) warnings. Note: the section near the bottom titled "What this analysis could show once cut, roll & application data is logged" uses entirely made-up numbers for illustration — it is clearly marked and is not a finding about RVCC.

Weekly trends

"By day of week" uses real dots read directly from the Deacon screenshots, dated off the label under each point (afternoon duplicate readings dropped, morning kept). But treat it as an early signal, not a finding: Sunday and Saturday are structurally over-represented because every weekly screenshot starts on Sunday and ends on Saturday, so those two days are the most likely to have a plotted dot. Wednesday and Monday, sitting mid-week, often fall in a gap between readings — sample sizes (n) are shown on every bar so you can see how thin some days are.

Green speed (ft) — higher is faster
Smoothness & trueness (x10) — lower is better ball roll
Air temp (°F, weekly avg) & precip (in, weekly peak)
Firmness (in x100) & soil moisture (%) — sparse, see gaps
Dew point (°F) & humidity (%) — exact station data
Wind speed (mph) — exact station data

Explore any two metrics

Correlation matrix (weekly averages)

← scroll to see all metrics →

Covers the eight metrics with a value in every week: green speed, smoothness, trueness, air temp, precip, dew point, humidity, and wind. Air temp, dew point, humidity, and wind now come from an exact daily weather station record rather than the visually-read Deacon chart, so those four (and anything correlated with them) are on firmer ground than the turf metrics. Firmness and soil moisture are still left out — too few weeks (3–5 of 11) for a meaningful correlation.

Multi-variable patterns — when two drivers line up, where does a third land?

This goes past one-to-one correlation: pick two driver metrics and an outcome, and see the outcome's average in each of the four combinations (both high, both low, and the two mixes), plus a combined-effect regression across both drivers at once.

Predict course conditions

Type in a weather scenario — temperature, rainfall, humidity, dew point, wind — and this fits a regression against the 11 weeks of history to estimate what a metric would likely read under those conditions. Check or uncheck predictors to see how the estimate and its reliability change.

With only 11 weeks of history, every predictor you add uses up one of a very small number of degrees of freedom — start with one or two predictors for a steadier estimate, and treat anything with 3+ checked as a rough sketch, not a forecast. This will get meaningfully more trustworthy as more weeks of data come in.

What stands out

Data gap

Firmness and soil moisture aren't being tracked consistently. Speed, smoothness, and trueness get measured almost every week; the two metrics that most directly answer "are the greens getting too soft or too hard" are the ones going uncollected. This is the biggest thing standing between you and a real firmness benchmark for RVCC — a data-collection habit issue before it's an agronomy issue, worth raising directly with Jim.

Correlation

Smoothness and trueness move together strongly (r ≈ 0.80). Expected — both track ball-roll quality and both get knocked down by the same disruptive events (aeration, verticutting, heavy topdressing), then recover together.

Multi-variable

Heat and rain together explain about 43% of green speed's week-to-week swing (R² ≈ 0.43), and rain matters more than heat. In the combined-effect regression, an extra inch of peak weekly precip is associated with roughly a 0.35 ft drop in green speed, versus about a 0.04 ft drop per extra degree of air temp — rain is nearly 9x more influential on speed than heat over this stretch. In the quadrant view: weeks that were both hot and rainy averaged the slowest green speeds, while hot-but-dry weeks stayed closer to normal — heat alone isn't the driver, heat combined with rain is.

Correlation

Trueness is the metric most sensitive to rain (r ≈ -0.49) of the three ball-roll/speed metrics — a useful early-warning signal to watch in the days right after a storm, rather than waiting for the weekly average.

Watch item

The mid-July week (Jul 12–17) is your best-documented week — the only one with all five metrics measured, including both firmness and soil moisture. Worth using as a template for what a fully-logged week looks like.

Data wishlist — what we could learn with more

This analysis is limited by two gaps: some metrics aren't measured every week, and none of the weeks note what maintenance actually happened. Closing either would unlock real answers, not just better-looking charts.

If measured every week

Consistent firmness & soil moisture, by green. A real firmness benchmark per green instead of one club-wide number; the actual moisture-to-firmness curve for these greens (how many points of soil moisture it takes to move firmness from "suitable" into "firm"); a drought-stress threshold specific to RVCC; and a check on whether irrigation programming is delivering what's intended.

If cutting & rolling were logged

Dates for mowing height/frequency and rolling would let this tool tell a maintenance-driven dip from a weather-driven one — right now a bad smoothness/trueness week could be aeration or could be rain, with no way to separate them. Logged dates would also reveal actual recovery time after aeration/verticutting, rolling's isolated effect on speed and smoothness (separate from mowing changes), and whether a height-of-cut change produces a lasting gain or a short-term bump.

If applications were logged

Product, date, and rate for fertilizer, PGR, and fungicide applications would show whether PGR timing matches the actual speed decay pattern rather than a calendar schedule, whether a firmness dip after a feed is a growth flush rather than weather, and whether fungicide timing lines up with the humidity/temperature conditions that predict disease pressure.

The biggest unlock

A weather-adjusted baseline. With consistent metrics and a maintenance log together, this could separate what the weather did to the greens from what the maintenance program did — which is really the question the committee is trying to answer. None of this needs new software: a shared log (date, practice, product/height/setting, which greens) alongside the existing Deacon exports would be enough to start answering these questions within a season.

For the committee

⚠ HYPOTHETICAL EXAMPLE SECTION — every number and chart below is made up for illustration. None of it is RVCC data or a real finding about our greens or our maintenance program.

What this analysis could show once cut, roll & application data is logged

The charts below use invented numbers to illustrate the kind of question this analysis could answer for RVCC once mowing, rolling, and application dates are logged alongside the Deacon readings. The shapes and values are constructed for illustration only — they are not based on RVCC's greens, staff, or performance in any way. The purpose is to show the board what becomes possible, not to report a result.

EXAMPLE 1 · MOWING FREQUENCY
Illustrative: green speed by mowing frequency (made-up data)
What this would let you ask: if real data showed green speed essentially flat between 4 and 5 cuts a week — as in this made-up example — that's a legitimate basis to ask whether the fifth weekly cut is earning its keep, or whether that crew time could shift to a project that's currently underfunded. This is exactly the kind of trade-off decision a small club watching every dollar should be able to make with data instead of guessing — and it cuts both ways: if the real data instead showed a real drop-off at 4 cuts, that would be a clear, defensible case for keeping the fifth day.
EXAMPLE 2 · ROLLING FREQUENCY
Illustrative: smoothness by weekly rolling count (made-up data)
What this would let you ask: in this made-up example, a third weekly roll adds almost nothing beyond what two rolls already deliver. If that pattern held in real data, it would tell you the point of diminishing returns for rolling — useful for staffing decisions in a normal week, and just as useful for knowing when it is worth adding an extra roll ahead of a big event.
EXAMPLE 3 · PGR APPLICATION TIMING
Illustrative: green speed decay after a PGR application (made-up data)
What this would let you ask: if real application dates were logged next to real speed readings, you could see exactly how many days a PGR application holds before speed starts to fade — in this made-up example, around day 18. That turns "we reapply every 3 weeks" from a calendar habit into a decision backed by this course's own data, which can mean fewer applications (saving product cost) or better-timed ones (better playing conditions), depending what the real curve looks like.

The honest version of this pitch: RVCC doesn't have this data yet. The real, factual case for building it is already in the Data wishlist and Top findings sections above — firmness and soil moisture are only measured 3–5 weeks out of 11, and no week has any record of what maintenance actually happened. That gap, not any invented chart, is the strongest reason to ask for consistent logging.