Golf Handicap Formula Testing
Surface-Level Testing for Practical Insight
While not formal machine learning testing methods, the comparisons outlined here offer practical, surface-level ways to evaluate how different handicap formulas perform. For example, we might compare a simple approach like the “average of the last 10 scores” against the A.I.-powered formula across different conditions—such as home vs. away courses, specific handicap ranges, or by gender.
These comparisons help build confidence in the results, revealing which formulas perform best under certain circumstances. Although we won’t compare every formula in every possible scenario (that would take considerable time), our testing will be broad and thoughtful enough to provide meaningful, trustworthy insight into which systems are most accurate, fair, and adaptable.
Methods
Individual Golfer Lookback Method
The lookback method is a powerful and practical way to evaluate how well a golf handicap formula performs. It works by going back in time, using a golfer’s historical data up to a certain point to generate a handicap or score prediction, and then comparing that prediction to what the golfer actually shot in their next round—a round that already happened. By repeating this process across thousands of golfers and rounds, we can objectively measure a formula’s accuracy, consistency, and bias. The lookback method allows us to simulate real-world performance without needing to wait for future rounds, making it an ideal tool for testing and comparing traditional and A.I.-powered handicapping systems.
League Cross Play Method
The League Cross Play Method is a comprehensive way to evaluate the fairness and effectiveness of handicap formulas within a league setting. It takes a complete season’s worth of league scores and simulates every possible matchup, pitting each golfer against every other golfer across every round. This generates every combination of match play and stroke play results, allowing us to analyze how well a handicap formula levels the playing field.
By evaluating win/loss records, scoring differentials, and overall balance, the League Cross Play Method reveals whether a formula consistently gives all players an equal chance to compete—regardless of skill level, gender, or scoring trends. It’s a powerful tool for identifying bias, overcompensation, or imbalance in real-world league formats.
Wins/Losses/Ties & Bullseyes
This method directly compares two handicap or score prediction formulas by testing which one more accurately predicts a golfer’s next score. For each golfer, the system uses both formulas to predict an upcoming round (using historical data up to that point), then measures which prediction was closer to the actual score. Each round results in a win, loss, or tie for one formula over the other, and special recognition is given to “bullseyes”—exact predictions.
When comparing a predictive formula (like A.I.) to a potential-based formula (like WHS), this method also accounts for skewing, since potential-based systems are not designed to predict average performance. By analyzing large numbers of head-to-head outcomes, this approach provides a clear, objective view of which formula performs better under real-world conditions—and where certain systems may consistently fall short.
Statistics
When evaluating the performance of handicap and score prediction formulas, it's not enough to rely on feel or anecdotal results—statistics tell the real story. Key metrics like Mean (average error), Median (middle value of errors), Standard Deviation (variability of those errors), and Absolute Deviation (average distance from the true score, regardless of direction) help us understand not just how close predictions are, but how consistent and fair they are across different golfers and playing conditions.
For example:
- A low mean indicates the formula is accurate on average.
- A low median shows it works well for most players—not just a few.
- A low standard deviation means it’s reliable and doesn't fluctuate wildly.
- A low absolute deviation shows that it stays close to the target, round after round.
These comparisons bring clarity, objectivity, and confidence to the testing process—allowing us to move beyond opinion and focus on which formulas truly deliver the best experience for golfers.