$ 1,000 + 100 No Limit Hold'em - WPT Prime Championship (Event #28)
NLHE Tournament at NagaWorld Integrated Resort
Entrants
1,484
Prize Pool
$750,000
1st Place Prize
$233,680
% of Field Paid
12.3%
| Player Tier | Cashed % | Full Field % | ROI % | Edge | Profit/Loss |
|---|---|---|---|---|---|
Recreational | 15.4% | 53.3% | -72.4% | 0.53x | -$820 |
Amateur | 13.7% | 16.3% | -57.4% | 0.82x | -$650 |
Semi-Pro | 8.8% | 7.5% | -45.3% | 1.05x | -$513 |
Professional | 15.9% | 9.0% | -31.2% | 1.32x | -$354 |
Elite Pro | 16.5% | 5.9% | -15.6% | 1.62x | -$177 |
World Class | 20.3% | 6.0% | -7.2% | 1.78x | -$82 |
Superstar | 9.3% | 2.2% | 1.0% | 1.94x | +$11 |
Understanding Our Field Analysis
Our field analysis uses a proprietary methodology that combines observed cashed players with series-level data to extrapolate the complete tournament field composition.
For this tournament, we have data on 12% of the field (players who cashed). We use this data, along with series trends and buy-in level characteristics, to model the likely skill distribution of the entire field.
Field Softness ranges from 0 to 1, with higher values indicating softer fields. Tournaments with softness above 0.6 offer excellent value, those between 0.5-0.6 are average, and those below 0.5 are considered tough fields.
Skill Gap measures the ratio between top and bottom quartile players, indicating how wide the skill disparity is within the field.
Our ROI Confidence rating of 63% indicates our level of certainty in the extrapolated metrics. Higher confidence comes from larger sample sizes and more complete data.
Tournament Comparison
Tournaments with buy-ins ±20% of $1,000
Comparison of all tournaments in this series
ROI Analysis
| Player Tier | ROI % | Edge | Buy-in | Expected Return |
|---|---|---|---|---|
| Superstar | 1.0% | 1.94x | $1,100 | $1,111 |
| World Class | -7.2% | 1.78x | $1,100 | $1,018 |
| Elite Pro | -15.6% | 1.62x | $1,100 | $923 |
| Professional | -31.2% | 1.32x | $1,100 | $746 |
| Semi-Pro | -45.3% | 1.05x | $1,100 | $587 |
| Amateur | -57.4% | 0.82x | $1,100 | $450 |
| Recreational | -72.4% | 0.53x | $1,100 | $280 |
These ROI projections are calculated using our extrapolated field model and analysis of payout structures. ROI is shown both as a dollar amount and as a percentage of buy-in.
Edge Multipliers quantify how much advantage each skill tier has over the average player in this specific tournament. A multiplier of 1.0 represents break-even against the average field, while values above 1.0 indicate an advantage.
Our system takes into consideration
- Field softness:The overall skill level of the tournament field.
- Payout Variance:To determine how shallow the tournament structure is. Ie: high variance in payout = less skill advantage
- Distribution of bad players:The most complex part. Mimics natural tournament psychology. Common edge cases: Many bad players then the edge multiplier is lower to mimic less aggresive isolation plays. A few bad players then all stronger players have a higher edge multiplier against weaker players to mimic scarcity. No bad players then super strong players have more edge versus medium strength players to mimic advanced play.
| Player Tier | % of Field | GPI Range | Edge | Expected ROI |
|---|---|---|---|---|
Superstar | 2.2% | 2,500+ | 1.94x | 1.0% ($11) |
World Class | 6.0% | 1,500-2,500 | 1.78x | -7.2% ($-82) |
Elite Pro | 5.9% | 1,000-1,500 | 1.62x | -15.6% ($-177) |
Professional | 9.0% | 650-1,000 | 1.32x | -31.2% ($-354) |
Semi-Pro | 7.5% | 400-650 | 1.05x | -45.3% ($-513) |
Amateur | 16.3% | 200-400 | 0.82x | -57.4% ($-650) |
Recreational | 53.3% | 0-200 | 0.53x | -72.4% ($-820) |
Tournament Structure
Prize Distribution
Structure Analysis
This tournament featured a top-heavy payout structure with 31.2% going to first place, significantly rewarding a final table finish.
The tournament paid the standard 12.3% of the field, creating a balanced risk/reward structure.
The payout variance of 1.01 indicates that this tournament has high variance, with results influenced significantly by luck alongside skill.
Tournament Results
| Place | Player | Country | Prize | GPI Points |
|---|---|---|---|---|
| 1st | Alexander Puchalski | - | 233,680 | 377.77 |
| 2nd | Khorshad Sagor | - | 150,000 | 326.57 |
| 3rd | Patrik Demus | - | 111,200 | 297.03 |
| 4th | Yannick Jobin | - | 82,700 | 276.36 |
| 5th | Yita Choong | - | 63,000 | 260.54 |
| 6th | Yuan Yu | - | 48,800 | 247.78 |
| 7th | Manuel Carvalho | - | 37,500 | 237.13 |
| 8th | Tommy Maguire | - | 29,200 | 228.03 |
| 9th | Xiao Liu | - | 22,900 | 220.10 |
| 10th | Nariman Yaghmai | - | 22,900 | 213.09 |
| 11th | Peter Kiem | - | 18,200 | 206.83 |
| 12th | Wonho Lee | - | 18,200 | 201.18 |
| 13th | Vyacheslav Shin | - | 14,600 | 196.05 |
| 14th | Armon Van Wijk | - | 14,600 | 191.35 |
| 15th | Remy Murcia | - | 11,800 | 187.03 |
| 16th | Micheal O'Neill | - | 11,800 | 183.03 |
| 17th | Chak Chan | - | 9,700 | 179.32 |
| 18th | Eduard Burd | - | 9,700 | 175.86 |
Advanced Metrics
Field Composition
Tournament Structure
Event Details
Metrics Explanation
- Extrapolated Field Softness
- Calculated using our proprietary model combining observed cashed players with series-level data to estimate the entire field composition.
- Buy-In Value Rating
- Field softness adjusted for buy-in level. Higher values represent better value for your investment.
- Payout Variance
- Measures correlation between player skill and final placement. Lower values indicate skill is more reliably rewarded.
- ROI Confidence
- Our confidence level in ROI projections, based on sample size, field completeness, and data quality.
- Field Completeness
- Percentage of the tournament field for which we have direct data, affecting model accuracy.
- Tier Edge Multipliers
- How much advantage each player tier has in this specific tournament structure and field composition.
Related Resources
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