From a Black Hole to a Hole-in-One: How a Performance Evaluation of a Golf Course Can Lead to a Good Scorecard for Both Players and Facility Managers
Bibliographic record
Abstract
HOW A PERFORMANCE EVALUATION OF A GOLF COURSE CAN LEAD TO A GOOD SCORECARD FOR BOTH PLAYERS AND FACILITY MANAGERS. EXECUTIVE SUMMARY While interest in golf has been growing over the years, many golf courses have been losing money. This article describes a spreadsheet-based performance evaluation system for a Texas golf course. A profitlinked, productivity measurement model is used on multiperiod data to analyze performance, isolate problem areas, and recommend measures to revitalize the course. Tiger Woods and Michelle Wie, the teenage phenom, are golf superstars. Thanks in part to their fame (and to the fame of some of their predecessors, such as Arnold Palmer and Jack Nicklaus), golf has been growing in popularity over the years. Since the 1950s, public courses have shown the most growth and currently have the largest market share with 46% of the golf market.1 Statistics show, however, that the industry is overbuilt and that half of the golf courses do not make money.2 In fact, cities around the country are reporting financial problems with their golf courses.3 If the municipal golf courses keep on losing money year after year, taxpayers are not going to be happy. The financial viability of golf courses is essential for producing many more Woodses and Wies of the future. That is why I used a productivity measurement model to help identify the problem areas in a typical municipal golf course, collected the financial data, developed a spreadsheet-based performance evaluation system, analyzed the results, identified any problems, and developed possible solutions. PERFORMANCE MEASUREMENT MODELS Profit margins and productivity are the two most important performance indicators for CEOs in their strategic decision making, according to Industry Week's 27th annual survey.4 Performance measurement has gained some importance in recent years because of the balanced scorecard created by Robert Kaplan and David Norton.5 The scorecard does not attempt to link productivity to profitability, and it uses both financial and nonfinancial measures, but, according to a 1998 survey of U.S. and Canadian companies, financial measures are given more importance and used most often.6 The significant problems facing many golf courses now seem to be financial; therefore, I will focus on financial performance. The objective of performance evaluation is to identify the problem areas and their root causes so that management can take corrective action to improve the situation. Profit-linked, totalfactor productivity measurement models are more suitable for organization-level performance measurement. Their advantage lies in the fact that they link productivity to profitability.7 Although Ebony Hills Golf Course, the subject of this study, is not a for-profit organization, it generates revenues. So a model such as the APC model is appropriate for its performance measurement. The APC model was developed in 1980 at the American Productivity and Quality Center (formerly called The American Productivity Center-APC).8 The terms total-factor and multi-factor are sometimes used interchangeably. When all factors of production are not used in the model, becomes a measurement model.9 The APC model is attractive to the business community because it uses readily available accounting data and provides performance results in dollars as opposed to abstract indexes. In the APC model, data from two periods are compared simultaneously. A first-period performance is used as the standard against which the performances of other future periods are measured. To gauge firm performance over time, [w]hat matters...is not the absolute magnitude in any area, but the trend...that the measurements will give...no matter how crude and approximate the individual readings are by themselves.10 This model also can be easily implemented in popular spreadsheet software such as Microsoft Excel and can facilitate easy creation of graphs that are useful for trend analysis. …
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.013 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.014 | 0.012 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.009 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".