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NHL Prompts for Sports Stats Gather GPT

Model has been to inconsistent. We need to reconsider a few things. No data will be up to date.

Prompt 1: Game Context, Stakes, and Analysis Framework

Prompt 2: Team 5-on-5 and Overall Analysis

# Prompt 2 - Team 5-on-5 and Overall Analysis (2025 Model)
Use the Sports Stats Gather API to analyze team statistics focusing on 5-on-5 and overall performance for both teams. Note: All data is from the 2024 season only, no season filter is needed.

## Data Collection
Query ALL of the following first, then proceed with analysis. Do not wait for user confirmation.

### Home Team Queries
1. 5-on-5 data:
https://sportsstatsgather.com/api/nhl/data/teams?team=[Home Team]&situation=5on5

2. Overall data:
https://sportsstatsgather.com/api/nhl/data/teams?team=[Home Team]&situation=all

### Away Team Queries
1. 5-on-5 data:
https://sportsstatsgather.com/api/nhl/data/teams?team=[Away Team]&situation=5on5

2. Overall data:
https://sportsstatsgather.com/api/nhl/data/teams?team=[Away Team]&situation=all

## Data Validation Requirements
Before analysis:
- Confirm data retrieval for all four queries
- For each analysis point, cite specific data from API responses
- Present key metrics to support each conclusion
- Flag any missing or inconsistent data
- Support all trends with actual statistics
- Compare 5-on-5 vs overall performance with specific numbers
- Document all data points used in comparisons

## Analysis Metrics (2025 Model Weighted)

1. Scoring Chances Component (54% Weight)
- Shooting talent adjusted expected goals (cite actual numbers)
- Shot quality analysis by danger level
- Actual goals vs expected goals differential
- Territory and possession metrics
- Scoring chance creation rates
- Shot attempt generation patterns

2. Goaltending Impact Component (29% Weight)
- Team save percentage vs league average
- Goals against vs expected goals against
- High-danger save percentage
- Goalie pull timing and effectiveness
- Clean sheet preservation rates

3. Win Ability Component (17% Weight)
- Season win percentage (excluding OT/SO)
- Points percentage trends
- Regulation win rates
- Close game performance
- Late game effectiveness

4. Additional Context Metrics
- Home/away performance splits (+54% home advantage)
- Rest impact analysis (-4% back-to-back)
- Score-adjusted metrics
- Game state performance
- Strategic deployment patterns

## Analysis Steps
After data collection and validation complete:

1. Home Team Analysis:
- Component-weighted 5-on-5 metrics
- Overall performance metrics
- Goaltending impact assessment
- Win ability metrics
- Home ice performance patterns
Please proceed immediately to Away Team Analysis.

2. Away Team Analysis:
- Component-weighted 5-on-5 metrics
- Overall performance metrics
- Goaltending impact assessment
- Win ability metrics
- Road performance patterns
Please proceed immediately to Comparative Analysis.

3. Comparative Analysis:
- Component-by-component comparison
 * Scoring chances (54%)
 * Goaltending (29%)
 * Win ability (17%)
- Situational advantages
- Venue impact assessment
- Rest advantage impact

## Data Storage Instructions
Store the following key information with supporting data:
1. Scoring component advantages (54%)
2. Goaltending impact differentials (29%)
3. Win ability comparisons (17%)
4. Situational strengths/weaknesses
5. Venue and rest implications

## Format Note
Present analysis as:
- Component-weighted strengths/weaknesses
- Notable statistical advantages
- Significant trends
- Matchup implications
No need to show raw data, but cite specific numbers for all conclusions.

## Important Notes
- Only analyze data present in API responses
- Support all conclusions with specific metrics
- Present supporting data for each component
- Flag any missing or inconsistent data
- Maintain separate 5-on-5 and overall analysis
- Focus on statistically significant differences
- Provide context for all cited numbers
- All data is from the 2024 season only

Prompt 3: Power Play and Penalty Kill Analysis

# Prompt 3 - Power Play and Penalty Kill Analysis (2025 Model)
Use the Sports Stats Gather API to analyze team statistics focusing on Power Play (5-on-4) and Penalty Kill (4-on-5) performance. Note: All data is from the 2024 season only, no season filter is needed.

## Data Collection
Query ALL of the following first, then proceed with analysis. Do not wait for user confirmation.

### Home Team Queries
1. Power Play data:
https://sportsstatsgather.com/api/nhl/data/teams?team=[Home Team]&situation=5on4

2. Penalty Kill data:
https://sportsstatsgather.com/api/nhl/data/teams?team=[Home Team]&situation=4on5

### Away Team Queries
1. Power Play data:
https://sportsstatsgather.com/api/nhl/data/teams?team=[Away Team]&situation=5on4

2. Penalty Kill data:
https://sportsstatsgather.com/api/nhl/data/teams?team=[Away Team]&situation=4on5

## Analysis Components (2025 Model Weighted)

1. Special Teams Scoring Chances (54% Weight)
- Shooting talent adjusted expected goals
- Shot quality by danger level
- Power Play shot generation rates
- Penalty Kill shot suppression
- Special teams territory control

2. Special Teams Goaltending (29% Weight)
- Penalty Kill save percentage
- Power Play goals against
- Short-handed goals for/against
- High-danger save percentage on PK
- Short-handed save percentage

3. Special Teams Win Ability (17% Weight)
- Power Play conversion rate
- Penalty Kill success rate
- Net special teams effectiveness
- Clutch situation performance
- Momentum impact analysis

## Analysis Metrics

1. Power Play Effectiveness
- Shooting talent adjusted goals
- Individual shooter success rates
- Unit chemistry metrics
- Zone entry success
- Formation effectiveness

2. Penalty Kill Performance
- Shot suppression rates
- Goaltending impact metrics
- Clearing efficiency
- Pressure resistance
- Short-handed threat level

3. Combined Special Teams
- Net effectiveness rating
- Situation management
- Personnel deployment
- Strategic adjustments
- Game state impact

## Data Storage Instructions
Store the following with supporting data:
1. Power Play strengths/weaknesses
2. Penalty Kill effectiveness metrics
3. Special teams mismatches
4. Goaltending impact ratings
5. Shooting talent advantages

## Important Notes
- Apply 2025 model component weights
- Focus on goaltending impact (29%)
- Consider shooting talent adjustments
- Support all conclusions with data
- Flag any inconsistencies

Prompt 4: Goalie Analysis

# Prompt 4 - Goalie Analysis (2025 Model - 29% Weight Component)
Use the Sports Stats Gather API to analyze goalie statistics for both starting goalies. Note: All data is from the 2024 season only, no season filter is needed.

## Data Collection
Query ALL of the following first, then proceed with analysis. Do NOT wait for user confirmation.

### Home Team Starting Goalie
Query:
https://sportsstatsgather.com/api/nhl/data/goalies?team=[Home Team]&position=G&situation=all

### Away Team Starting Goalie
Query:
https://sportsstatsgather.com/api/nhl/data/goalies?team=[Away Team]&position=G&situation=all

## Core Analysis Components (29% Model Weight)

1. Goals Saved Above Expected
- Per 60 minutes played rates
- Situation-adjusted metrics
- Recent trend analysis
- Home/away splits
- Score state impact

2. Save Percentage Analysis
- Overall save percentage
- High-danger save percentage
- Medium-danger save percentage
- Low-danger save percentage
- Special teams save percentage

3. Game Management
- Pull frequency metrics
- Recovery after pulls
- Early goal impact
- Late game performance
- Close game metrics

4. Shooting Talent Impact
- Performance vs high-skill shooters
- Rebound control metrics
- Second chance prevention
- Rush save percentage
- High-danger situation management

## Analysis Steps

1. Base Performance Metrics
- Goals against average
- Save percentage by danger
- Expected goals against
- Quality start percentage
- Consistency rating

2. Advanced Analytics
- Goals saved above expected
- Shot quality faced
- Rebound control
- Puck handling impact
- Zone exit contribution

3. Situational Analysis
- Home/away splits
- Score state performance
- Special teams impact
- Back-to-back metrics
- Rest impact factors

4. Comparative Edge Analysis
- Head-to-head advantage
- Style matchup impact
- Team system compatibility
- Workload management
- Support structure effectiveness

## Data Integration Requirements
Combine goalie analysis (29% weight) with:
- Scoring chances impact (54%)
- Team win ability (17%)
- Home ice advantage (+54%)
- Rest factor impact (-4%)

## Format Note
Present analysis as:
- Key strengths/weaknesses
- Statistical advantages
- Matchup implications
- Strategic considerations

## Important Notes
- Focus on 29% goaltending weight
- Consider shooter talent impact
- Document pull tendencies
- Support all conclusions with data
- Flag any inconsistencies
- Consider sample size context

Prompt 5A: Home Team Forwards Top Line Analysis

# Prompt 5A - Home Team Forwards Top Line Analysis (2025 Model)
Use the Sports Stats Gather API to analyze both individual skater statistics AND line combination data for the home team's first line forwards (LW-C-RW). Note: All data is from the 2024 season only, no season filter is needed.

## Data Collection (Complete ALL Queries First)
Execute ALL queries below first. Do NOT proceed to analysis until ALL data is collected.

### Individual Player Data Collection
Query each first line player individually. Ensure you use the idenified players for the game in question originally gathered from Prompt's 1 response:
https://sportsstatsgather.com/api/nhl/data/skaters?team=[Home Team]&situation=all&player=[Player Name]

### Line Combination Data Collection
Query using the center's name to get all line combination data. Ensure you use the idenified players for the game in question originally gathered from Prompt's 1 response:
https://sportsstatsgather.com/api/nhl/data/lines?&situation=5on5&name=[First Line Center's Last Name]&team=[Home Team]

## Component-Based Analysis (2025 Model)

1. Scoring Chances Analysis (54% Weight)
Individual Metrics:
- Shooting talent adjusted expected goals
- Actual vs expected goals differential
- Shot quality generation rates
- High-danger chance creation
- Conversion rates by danger level

Line Combination Metrics:
- Combined shooting talent impact
- Expected goals as a unit
- Chemistry-based conversion rates
- Territory control metrics
- Scoring pattern analysis

2. Support of Team Win Ability (17% Weight)
Individual Contribution:
- Points in team wins
- Critical goal scoring
- Late game performance
- Close game production

Line Unit Impact:
- Win/Loss record when together
- Score-state effectiveness
- Game-winning goal creation
- Momentum shift capability

3. Line Chemistry Metrics
Show for current line combination:
- Games played together
- Time on ice as a unit
- Goals For/Against when deployed
- Expected goals as a line
- Shot generation rates
- Scoring chance creation

4. Danger Level Analysis
Compare individual vs. line performance:
- High danger conversion rates
- Medium danger efficiency
- Low danger production
- Expected goals by level
- Shot quality metrics

5. Zone Performance
Individual and line unit metrics:
- Offensive zone time
- Entry success rates
- Cycle game effectiveness
- Rush attack statistics
- Territory control

## Matchup Considerations
1. Home Ice Impact (+54%):
- Home scoring rates
- Line matchup advantages
- Territory control at home
- Home deployment patterns

2. Rest Factor Analysis (-4% if B2B):
- Performance on rest
- Back-to-back impact
- Recovery patterns
- Energy management

## Important Notes
- Complete ALL queries before analysis
- Show ALL API data supporting analysis
- Present actual numbers, not just conclusions
- Include statistical context
- Focus only on first line forwards
- Highlight statistical outliers
- Support trends with data points
- Compare individual vs. line performance
- Document significant differences
- Note sample size differences
- All data is from 2024 season only

Prompt 5B: Home Team Defensive Top Pair Analysis

# Prompt 5B - Home Team Defensive Top Pair Analysis (2025 Model)
Use the Sports Stats Gather API to analyze both individual skater statistics AND defensive pair combination data for the home team's first defensive pair (LD-RD). Note: All data is from the 2024 season only, no season filter is needed.

## Data Collection (Complete ALL Queries First)
Execute ALL queries below first. Do NOT proceed to analysis until ALL data is collected.

### Individual Player Data Collection
Query each first pair defenseman individually. Ensure you use the idenified players for the game in question originally gathered from Prompt's 1 response:
https://sportsstatsgather.com/api/nhl/data/skaters?team=[Home Team]&situation=all&player=[Player Name]

### Defensive Pair Combination Data Collection
Query using one defenseman's name to get all pair combination data. Ensure you use the idenified players for the game in question originally gathered from Prompt's 1 response:
https://sportsstatsgather.com/api/nhl/data/lines?&situation=5on5&name=[First Pair RD Last Name]&team=[Home Team]

## Component-Based Analysis (2025 Model)

1. Shot/Chance Suppression (54% Weight)
Individual Metrics:
- Shot suppression rates
- Expected goals against
- High-danger chance prevention
- Gap control effectiveness
- Rush defense metrics

Pair Combination Metrics:
- Combined suppression rates
- Expected goals against as unit
- Territory control effectiveness
- Transition defense impact
- Zone exit success

2. Support of Goaltending (29% Weight)
Individual Contribution:
- Shot quality against reduction
- Rebound prevention
- Screen mitigation
- Clear sight line maintenance
- Recovery support

Pair Unit Impact:
- Combined shot quality against
- Joint rebound control
- Slot protection effectiveness
- Cross-ice pass prevention
- Goalie sight line management

3. Contribution to Win Ability (17% Weight)
Individual Impact:
- Performance in wins
- Close game metrics
- Late game effectiveness
- Critical situation play

Pair Effectiveness:
- Win/Loss record together
- Score-state performance
- Game-saving plays
- Pressure situation handling

4. Additional Metrics
Defensive Zone Control:
- Exit success rates
- Possession retention
- Breakout effectiveness
- Neutral zone control
- Pinch success rates

Matchup Performance:
- vs Top Lines
- vs Speed teams
- vs Cycle teams
- vs Different styles
- Adjustment capability

## Home Ice Considerations (+54%)
1. Venue Impact Analysis:
- Home deployment patterns
- Matchup control advantage
- Territory defense at home
- Home pair chemistry

2. Rest Factor Impact (-4% if B2B):
- Performance when rested
- Back-to-back impact
- Recovery patterns
- Ice time management

## Important Notes
- Complete ALL queries before analysis
- Show ALL API data supporting analysis
- Present actual numbers, not just conclusions
- Include statistical context
- Focus only on top defensive pair
- Highlight statistical outliers
- Support trends with data points
- Compare individual vs. pair performance
- Document significant differences
- Note sample size differences
- All data is from 2024 season only

Prompt 6A: Away Team Forwards Top Line Analysis

# Prompt 6A - Away Team Forwards Top Line Analysis (2025 Model)
Use the Sports Stats Gather API to analyze both individual skater statistics AND line combination data for the away team's first line forwards (LW-C-RW). Note: All data is from the 2024 season only, no season filter is needed.

## Data Collection (Complete ALL Queries First)
Execute ALL queries below first. Do NOT proceed to analysis until ALL data is collected.

### Individual Player Data Collection
Query each first line player individually:
https://sportsstatsgather.com/api/nhl/data/skaters?team=[Away Team]&situation=all&player=[Player Name]

### Line Combination Data Collection
Query using the center's name to get all line combination data:
https://sportsstatsgather.com/api/nhl/data/lines?&situation=5on5&name=[First Line Center's Last Name]&team=[Away Team]

## Component-Based Analysis (2025 Model)

1. Scoring Chances Analysis (54% Weight)
Individual Road Metrics:
- Shooting talent adjusted expected goals
- Road vs home shooting percentage
- Away game shot generation
- High-danger creation on road
- Conversion rates by venue

Line Combination Road Metrics:
- Combined road scoring rates
- Expected goals as road unit
- Away game chemistry patterns
- Road territory control
- Scoring consistency away

2. Support of Team Win Ability (17% Weight)
Individual Road Contribution:
- Points in road wins
- Away game critical goals
- Road game-winners
- Clutch road performance

Line Unit Road Impact:
- Road Win/Loss record together
- Away game effectiveness
- Road momentum creation
- Late game road performance

3. Line Chemistry Road Metrics
Show for current line combination:
- Road games played together
- Away game time on ice
- Goals For/Against when away
- Road expected goals as line
- Away game shot generation
- Road scoring chance creation

4. Away Game Danger Level Analysis
Compare individual vs. line performance:
- Road high danger conversion
- Away medium danger efficiency
- Road low danger production
- Expected goals by venue
- Road shot quality metrics

5. Road Zone Performance
Individual and line unit metrics:
- Away zone time
- Road entry success
- Away cycle effectiveness
- Road rush statistics
- Away territory control

## Road Game Considerations
1. Venue Impact Analysis (-54% road disadvantage):
- Road scoring patterns
- Away matchup challenges
- Territory control on road
- Away deployment adaptations

2. Rest Factor Analysis (-4% if B2B):
- Road game rest impact
- Travel recovery patterns
- Back-to-back performance
- Energy management away

## Important Notes
- Complete ALL queries before analysis
- Show ALL API data supporting analysis
- Present actual numbers, not just conclusions
- Include statistical context
- Focus only on first line forwards
- Highlight road performance patterns
- Support trends with data points
- Compare home vs road performance
- Document significant differences
- Note sample size differences
- All data is from 2024 season only

Prompt 6B: Away Team First Defensive Pair Analysis

# Prompt 6B - Away Team First Defensive Pair Analysis (2025 Model)
Use the Sports Stats Gather API to analyze both individual skater statistics AND defensive pair combination data for the away team's first defensive pair (LD-RD). Note: All data is from the 2024 season only, no season filter is needed.

## Data Collection (Complete ALL Queries First)
Execute ALL queries below first. Do NOT proceed to analysis until ALL data is collected.

### Individual Player Data Collection
Query each first pair defenseman individually:
https://sportsstatsgather.com/api/nhl/data/skaters?team=[Away Team]&situation=all&player=[Player Name]

### Defensive Pair Combination Data Collection
Query using one defenseman's name to get all pair combination data:
https://sportsstatsgather.com/api/nhl/data/lines?&situation=5on5&name=[First Pair RD Last Name]&team=[Away Team]

## Component-Based Analysis (2025 Model)

1. Shot/Chance Suppression (54% Weight)
Individual Road Metrics:
- Away game suppression rates
- Road expected goals against
- Road high-danger prevention
- Away gap control
- Road rush defense

Pair Road Combination Metrics:
- Combined road suppression
- Away expected goals against
- Road territory defense
- Away transition impact
- Road zone exit success

2. Support of Goaltending (29% Weight)
Individual Road Contribution:
- Away shot quality against
- Road rebound prevention
- Away screen management
- Road sight line maintenance
- Away recovery support

Pair Road Unit Impact:
- Combined road shot prevention
- Away rebound control
- Road slot protection
- Away pass prevention
- Road goalie support

3. Contribution to Win Ability (17% Weight)
Individual Road Impact:
- Away game performance
- Road close game metrics
- Away late game play
- Road pressure situations

Pair Road Effectiveness:
- Away Win/Loss record
- Road score-state play
- Away game-saving plays
- Road pressure handling

4. Additional Road Metrics
Defensive Zone Control:
- Away exit success
- Road possession metrics
- Away breakout patterns
- Road neutral zone play
- Away pinch success

Matchup Performance:
- vs Home top lines
- vs Speed teams away
- vs Cycle teams away
- Road style adaptations
- Away game adjustments

## Road Game Analysis (-54% venue impact)
1. Away Game Patterns:
- Road deployment challenges
- Matchup disadvantages
- Away territory defense
- Road pair chemistry

2. Rest Factor Impact (-4% if B2B):
- Road game rest impact
- Travel recovery patterns
- Back-to-back performance
- Road ice time management

## Important Notes
- Complete ALL queries before analysis
- Show ALL API data supporting analysis
- Present actual numbers, not just conclusions
- Include statistical context
- Focus only on top defensive pair
- Highlight road performance patterns
- Support trends with data points
- Compare home vs road performance
- Document significant differences
- Note sample size differences
- All data is from 2024 season only

Prompt 7: Recent Performance and Game Pace Analysis

# Prompt 7 - Recent Performance and Game Pace Analysis (2025 Model)
Use the Sports Stats Gather API to analyze recent 3 games for both teams. Note: All data is from the 2024 season only, no season filter is needed. Use gameDate in your API query results to identify the date of the game. ONLY CONSIDER THE MOST 3 RECENT GAMES FROM TODAY'S DATE.

## Data Collection 
Execute BOTH queries below first. Do NOT proceed until ALL data is collected.

1. Home Team Recent Games:
https://sportsstatsgather.com/api/nhl/data/gamestats?team=[Home Team]&situation=all

2. Away Team Recent Games:
https://sportsstatsgather.com/api/nhl/data/gamestats?team=[Away Team]&situation=all

## Component-Based Analysis (2025 Model)

1. Scoring Chances Trends (54% Weight)
For each recent game:
- Shooting talent adjusted expected goals
- Shot quality by danger level
- Scoring chance generation
- Territory control metrics
- Shot attempt patterns

2. Goaltending Performance (29% Weight)
For each recent game:
- Goals saved above expected
- Save percentage by danger
- Pull timing and impact
- Clean sheet preservation
- Recovery performance

3. Win Ability Patterns (17% Weight)
For each recent game:
- Regulation/OT/SO outcomes
- Close game performance
- Lead protection metrics
- Comeback capability
- Game control indicators

## Game Context Analysis
1. Venue Impact Assessment
- Home performance (+54%)
- Road performance
- Venue-specific patterns
- Territory control by venue

2. Rest Factor Analysis
- Back-to-back impact (-4%)
- Days rest influence
- Travel recovery patterns
- Energy management

## Required Analysis (Last 3 Games Each Team)

1. Component Weight Distribution
- Scoring chances effectiveness
- Goaltending impact
- Win ability demonstration
- Combined weighted performance

2. Game Flow Patterns
- Period-by-period performance
- Score state adaptation
- Line combination effectiveness
- Momentum management

3. Situational Effectiveness
- Leading/trailing performance
- Close game management
- Late game execution
- Special teams timing

## Important Notes
- Emphasize current trends over historicals
- Support all analysis with data
- Document venue impacts clearly
- Consider rest/travel factors
- All data from 2024 season only

## Data Storage Instructions
Store the following key information with supporting data for reference:
1. Complete game-by-game results for both teams for the last 3 games
2. Scoring trends and patterns
3. Performance metrics progression
4. Venue impact analysis
5. Form and momentum indicators
6. Identify whether a home or away game

## Format Note
Present analysis as:
- Chronological game-by-game breakdown
- Clear performance trends
- Key statistical patterns
- Form and momentum assessment
- Relevant situational notes
- List if game was home or away game
No need to show raw data, but cite specific numbers for all conclusions.

Prompt 8: Pre-Bet Comprehensive Analysis with Line Matchups and Player Props

# Prompt 8 - Pre-Bet Comprehensive Analysis (2025 Model)

## 2025 Model Framework
All analysis must follow these exact weightings:

1. Primary Components:
- Scoring Chances: 54% of total consideration
- Goaltending Impact: 29% of total consideration
- Win Ability: 17% of total consideration

2. Additive Factors:
- Home Ice: +54% probability adjustment
- Back-to-Back: -4% probability adjustment

## Game Context
Home Team: [Home Team Identified in Prompt 1]
Away Team: [Away Team Identified in Prompt 1]
Venue: [Venue Location Identified in Prompt 1]
Rest Situation: [B2B status for each team]

## Component-Based Analysis

1. Scoring Chances (54%) Integration:
- 5v5 metrics from Prompt 2
- Special teams data from Prompt 3
- First line impact (Prompts 5A/6A)
- Top pair influence (Prompts 5B/6B)
- Recent trends from Prompt 7

2. Goaltending Impact (29%) Integration:
- Core metrics from Prompt 4
- Special teams performance
- Defensive support analysis
- Recent form assessment
- Situational effectiveness

3. Win Ability (17%) Integration:
- Season performance patterns
- Recent game outcomes
- Close game metrics
- Momentum indicators
- Clutch situation history

## Market Analysis Framework

1. Moneyline Considerations
A. Base Component Calculation:
- Scoring Chances (54%): [Detailed metrics]
- Goaltending (29%): [Detailed metrics]
- Win Ability (17%): [Detailed metrics]

B. Adjustments:
- Home Ice (+54% if applicable)
- Rest (-4% if B2B)
- Final ML Probability: [Show calculation]

2. Puck Line Analysis
A. Scoring Differential Probability:
- Component-based spread likelihood
- Goal distribution patterns
- Empty net factors
- Final PL Probability: [Show calculation]

3. Totals Analysis
A. Expected Goals Calculation:
- Shooting talent adjusted metrics
- Goaltending impact assessment
- Pace indicators
- Final Total Probability: [Show calculation]

4. Period Betting
A. First Period Probability:
- Component-weighted start trends
- First line effectiveness
- Goalie first period metrics
- Final P1 Probability: [Show calculation]

5. Player Props
A. Shooter Analysis:
- Individual talent metrics
- Line combination impact
- Matchup advantages
- Final Prop Probability: [Show calculation]

## Required Calculations
For EACH bet type:
1. Base Probability:
- Show component calculations
- Weight each factor properly
- Document all metrics used

2. Adjustments:
- Apply venue impact
- Consider rest factors
- Account for injuries
- Show final numbers

3. Confidence Rating:
- Data support level
- Sample size assessment
- Trend reliability
- Final confidence score

## Final Output Format
For each bet type, provide:
1. Calculated Probability
2. Component Breakdown:
   - Scoring (54%)
   - Goaltending (29%)
   - Win Ability (17%)
3. Adjustments:
   - Home/Away (+/-54%)
   - Rest Impact (-4% if B2B)
4. Final Recommendation
5. Confidence Level (%)

## Important Notes
- All probabilities must show calculation method
- Each component must be weighted correctly
- Venue and rest impacts must be specified
- All conclusions require data support
- Focus on 2024 season data only

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