House edge: hold percentage and the cost of EGM gambling
Bibliographic record
Abstract
Price in commercial gambling is effectively the house edge of the game. For electronic gaming machines (EGMs), house edge is the hold percentage. The paper tracks changes in hold percentage for club and hotel EGM gambling in Australia. We use real gambling turnover and revenue data to show that hold generally falls over time, save for the State of Victoria between 1993 and 2009. In Victoria, hold fell during the roll-out phase of the sector, before rising steadily. We examine local level data, finding that hold varied considerably by gaming operator across the period, before converging. The unique owner/operator corporate duopoly that existed in Victoria is posed as a potential explanation for aggregate price changes. We then calculate estimates of the monetary value of changes in hold percentage. We find increased hold can lead to substantial monetary redistributions of gamblers' stakes toward the house and away from gamblers. Policy options to protect gamblers from the unfairness of undetectable price rises are discussed, including the possibility of a more tightly regulated hold percentage, a tax on windfall profits derived from raising hold, and tying game identities to particular hold percentages.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".