Seneca Golf: Turning Stats into Practice Priorities

After a round, I could review my golf statistics in GHIN. The harder question was what to do with them. With limited time to practice, I wanted to understand which part of my game deserved attention and why.

I built Seneca Golf to turn those statistics into a starting point for practice. The Game Improvement Finder compares a focused set of inputs with age and handicap benchmarks, ranks the relative gaps, and explains each improvement opportunity. I began as my own target user, which let me iterate quickly but leaves the experience to be validated with other golfers.

Outcome — A functioning prototype that puts Improvement Opportunities first, helping a golfer choose a practice focus and inspect the comparisons behind it.

Improvement Opportunities now lead the experience; Distance Benchmarks provide supporting context. Screens shown here document an earlier interface iteration and retain its former branding.

The Question Behind the Statistics

In my own review workflow, three questions remained unanswered:

• 1

  No benchmarks

  I could not compare my results with similar or lower-handicap golfers.

• 2

  No progression path

  I could not see the performance required to reach the next level.

• 3

  No prioritization

  I could not tell which gap deserved my limited practice time.

I could see my numbers, but I could not tell whether they were good, what better golfers were achieving, or what I should work on next.

My GHIN Round Review

GHIN showed what happened in the round, but not how the results compared, what better golfers achieved, or which gap mattered most.

Putting Improvement Opportunities First

The key design decision was to organize the experience around a practice decision. I moved Game Improvement ahead of Distance Benchmarks so golfers begin by comparing opportunities across their game, then explore the supporting detail.

Age and handicap establish the comparison group. Driving distance, fairways hit, greens in regulation, putts per round, and 2-putts-or-better supply the performance inputs. The results rank improvement opportunities by modeled percentile, with the largest relative gap presented first.

Recommendation Inputs

A focused set of inputs is enough to place a golfer in a cohort and compare each stat against that cohort’s average.

Recommendation Inputs

User Inputs

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Benchmark Comparison

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Relative Gaps

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Ranked Recommendation

Each opportunity pairs the comparison with a plain-language interpretation, giving the golfer a reason to consider that area for practice. The order reflects relative performance gaps, not measured strokes lost or a prediction of which change will produce the greatest scoring improvement.

Ranked Recommendation Result

1 2 3 4

Statistics → cohort comparison → ranked opportunities. The result offers a starting point for practice, supported by an explanation. Earlier interface iteration shown.

Ranked Recommendation Result

• 1

  Rank badge

  “Your #1 Opportunity” surfaces the single highest-priority gap first, not a full list to sort through.

• 2

  Plain-language interpretation

  What the percentile means and what to actually practice.

• 3

  You vs. Your Cohort

  The personal result placed directly next to the figure it’s being judged against.

• 4

  Relative gap badge

  The distance between the two, e.g. “-5% vs cohort.”

Distance Benchmarks as Supporting Context

The first version focused on distance: how does a result compare with golfers of a similar age and handicap? That benchmark view remains useful for exploring a specific part of the game. In the current experience, it supports the broader decision about what to practice, while the Game Improvement Finder is the entry point.

Benchmark View

1 2 3

The distance view provides context by club, age, and handicap, with estimated percentile bands. Earlier interface iteration shown.

Benchmark View

• 1

  Percentile range

  Estimated 10th, 50th, and 90th percentile lines put a distance result within a modeled range.

• 2

  Cohort comparison

  Filter by handicap group — 0–5, 6–15, 16–25, 26+ — instead of one blended average.

• 3

  Longer distances

  The upper band represents longer distances within the selected cohort; it does not establish overall playing ability.

Outcome, Trust, and Next Steps

Making the Basis of Each Recommendation Visible

Public golf-performance data is incomplete across age, handicap, club, and performance category. I combined published benchmarks with modeled estimates, then made the source, assumptions, and limitations visible so the output could be useful without appearing more certain than it was.

From Reviewing Results to Choosing a Focus