Prompt 1: Game Context, Stakes, and Analysis Framework

Before using these prompts, please obtain the following:

1. Game lineup information: Visit Rotowire's NHL Lineup Page to get the starting goalies, lines, and injury information for your game of interest. Screenshot this information for use with the prompts.

2. Game importance data: Visit MoneyPuck.com and click on the relevant game in the schedule to view playoff probability implications. Screenshot the probability tables for both teams for use with the prompts.

# Prompt 1 - Game Context, Stakes, and Analysis Framework You are a professional sports bettor specializing in NHL predictions and a statistical genius with deep knowledge of hockey analytics. I would like your expert advice. ## Game Stakes Analysis Before analyzing specific metrics, acknowledge and consider the following playoff probability implications for this game: See screenshot attached for the Game in Question, Starting Goalies, Power Play Units, and Key Injuries. NOTE: Home Team is always on the right and Away Team is always on the left. ### Home Team Stakes [Home team playoff probability table screen shot attached] Key Implications: - Current playoff position and probability - Division/Wild Card race implications - Points needed and impact on standings - Motivation level based on stakes ### Away Team Stakes [Away team playoff probability table screen shot attached] Key Implications: - Current playoff position and probability - Division/Wild Card race implications - Points needed and impact on standings - Motivation level based on stakes ## Bet Types Under Consideration I am specifically looking at the following bet types for this game: - Moneyline - Puck Line (-1.5/+1.5) - First Period Moneyline - First Period Over/Under - Game Total Over/Under - Team Totals Over/Under ## Game Information Review See screenshot attached for: - Starting Goalies - Power Play Units - Key Injuries NOTE: Home Team is always on the right and Away Team is always on the left. ## Data Analysis Framework Please wait for my specific prediction requests, as I have relevant statistical information from the Sports Stats Gather API that I would like to review with you first. The API provides comprehensive data on: - Team performance metrics - Goalie statistics by danger level - Individual skater performance - Recent game results - Advanced analytics (Corsi, Fenwick, xG) ## Game Context Considerations Analysis will factor in: 1. Playoff/Standings Implications: - Win/Loss probability impacts - Points needed scenarios - Divisional race considerations - Wild Card implications 2. Team Motivation Factors: - Stakes for each team - Must-win scenarios - Back-to-back situations - Rest advantages/disadvantages 3. Situational Analysis: - Regulation win importance - OT implications - Risk tolerance based on standings - Strategic considerations based on points needed ## Analysis Scope We will be analyzing statistics in various game situations: - 5-on-5 play - Power Play (5-on-4) - Penalty Kill (4-on-5) - Other special team situations Each analysis will consider: - Traditional statistics - Advanced metrics - Danger level breakdowns - Goaltender performance - Recent trends ## Confirmation Required Please confirm: 1. Understanding of the game context and stakes 2. Identification of home and away teams 3. Recognition of playoff probability implications 4. Acknowledgment of specific playoff/standings scenarios 5. Readiness to analyze this matchup considering all contextual factors Note: All subsequent statistical analysis must consider the game's importance and potential impact on playoff/standings positions as outlined in the probability tables. Each analysis should factor in how team motivation and stakes might influence: - Risk tolerance in different game situations - Special teams aggressiveness - Goaltender approach - Overall team strategy Copy Prompt

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

# Prompt 2 - Team 5-on-5 and Overall Analysis: Use the Sports Stats Gather API to analyze team statistics focusing on 5-on-5 and overall performance for both teams identified in the lineup screenshot from Prompt 1. 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. Reference the home and away teams specified in Prompt 1's lineup screenshot. ### 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 After collecting and validating ALL data above, analyze with supporting evidence: 1. Core Offensive Metrics - goalsFor, goalsAgainst (cite actual numbers) - shotsOnGoalFor, shotsOnGoalAgainst (with rates) - shotAttemptsFor, shotAttemptsAgainst (with percentages) 2. Danger Level Analysis - High Danger: highDangerShotsFor/Against, highDangerGoalsFor/Against, highDangerxGoalsFor/Against (cite all numbers) - Medium Danger: mediumDangerShotsFor/Against, mediumDangerGoalsFor/Against, mediumDangerxGoalsFor/Against (cite all numbers) - Low Danger: lowDangerShotsFor/Against, lowDangerGoalsFor/Against, lowDangerxGoalsFor/Against (cite all numbers) 3. Possession Metrics - corsiPercentage (with supporting shot attempts data) - fenwickPercentage (with supporting unblocked shot attempts) - shotAttemptsFor/Against (with rates) - unblockedShotAttemptsFor/Against (with rates) 4. Expected Goals Analysis - xGoalsFor, xGoalsAgainst (cite actual numbers) - xGoalsPercentage (with supporting data) - scoreVenueAdjustedxGoalsFor/Against (cite numbers) - flurryAdjustedxGoalsFor/Against (cite numbers) 5. Additional Context - faceOffsWonFor/Against (with percentages) - giveawaysFor/Against, takeawaysFor/Against (with rates) - reboundsFor/Against (with rates) - penaltiesFor/Against (with rates) ## Analysis Steps After data collection and validation complete: 1. Home Team Analysis: - 5-on-5 metrics comparison with supporting data - Overall performance metrics with supporting data - Efficiency ratings by danger zone with actual numbers - Shot quality/quantity assessment with evidence - Possession impact evaluation with metrics Please proceed immediately to Away Team Analysis. 2. Away Team Analysis: - 5-on-5 metrics comparison with supporting data - Overall performance metrics with supporting data - Efficiency ratings by danger zone with actual numbers - Shot quality/quantity assessment with evidence - Possession impact evaluation with metrics Please proceed immediately to Comparative Analysis. 3. Comparative Analysis: - Compare 5-on-5 performance with specific metrics - Compare overall performance with specific metrics - Identify key matchup advantages with supporting data - Document statistical edges with actual numbers ## Data Storage Instructions Store the following key information with supporting data for reference: 1. 5-on-5 strengths/weaknesses (with metrics) 2. Overall performance advantages (with numbers) 3. Key statistical mismatches (with data) 4. Danger level advantages (with rates) 5. Possession game disparities (with percentages) ## Format Note Present analysis as: - Key strengths/weaknesses (supported by specific metrics) - Notable statistical advantages (with actual numbers) - Significant trends (backed by data) - Potential impact on matchup (evidence-based) 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 from the API - Present supporting data for each analysis point - Flag any missing or inconsistent data - Maintain separate 5-on-5 and overall situation analysis - Focus on statistically significant differences - Provide context for all cited numbers - All data is from the 2024 season only Copy Prompt

Prompt 3: Power Play and Penalty Kill Analysis

# Prompt 3 - Power Play and Penalty Kill Analysis: Use the Sports Stats Gather API to analyze team statistics focusing on Power Play (5-on-4) and Penalty Kill (4-on-5) performance for both teams identified in the lineup screenshot from Prompt 1. 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. Reference the home and away teams specified in Prompt 1's lineup screenshot. ### 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 ## 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 Power Play and Penalty Kill metrics with specific numbers - Document all data points used in comparisons ## Analysis Metrics After collecting and validating ALL data above, analyze with supporting evidence: 1. Core Special Teams Metrics - goalsFor, goalsAgainst (cite actual numbers) - shotsOnGoalFor, shotsOnGoalAgainst (with rates) - shotAttemptsFor, shotAttemptsAgainst (with percentages) 2. Danger Level Analysis - High Danger: highDangerShotsFor/Against, highDangerGoalsFor/Against, highDangerxGoalsFor/Against (cite all numbers) - Medium Danger: mediumDangerShotsFor/Against, mediumDangerGoalsFor/Against, mediumDangerxGoalsFor/Against (cite all numbers) - Low Danger: lowDangerShotsFor/Against, lowDangerGoalsFor/Against, lowDangerxGoalsFor/Against (cite all numbers) 3. Special Teams Shot Metrics - corsiPercentage (with supporting shot attempts data) - fenwickPercentage (with supporting unblocked shot attempts) - shotAttemptsFor/Against (with rates) - unblockedShotAttemptsFor/Against (with rates) 4. Expected Goals Analysis - xGoalsFor, xGoalsAgainst (cite actual numbers) - xGoalsPercentage (with supporting data) - scoreVenueAdjustedxGoalsFor/Against (cite numbers) - flurryAdjustedxGoalsFor/Against (cite numbers) 5. Additional Context - faceOffsWonFor/Against (zone starts with percentages) - giveawaysFor/Against, takeawaysFor/Against (with rates) - reboundsFor/Against (with rates) - zone time indicators with actual numbers (playContinuedInZone, playContinuedOutsideZone) ## Analysis Steps After data collection and validation complete: 1. Home Team Analysis: - Power Play metrics with supporting data * Shot generation rates with numbers * Scoring efficiency with percentages * Danger zone breakdown with stats - Penalty Kill metrics with supporting data * Shot suppression rates with numbers * Prevention efficiency with percentages * Danger zone performance with stats Please proceed immediately to Away Team Analysis. 2. Away Team Analysis: - Power Play metrics with supporting data * Shot generation rates with numbers * Scoring efficiency with percentages * Danger zone breakdown with stats - Penalty Kill metrics with supporting data * Shot suppression rates with numbers * Prevention efficiency with percentages * Danger zone performance with stats Please proceed immediately to Comparative Analysis. 3. Comparative Analysis: - Power Play comparison with specific metrics - Penalty Kill comparison with specific metrics - Special teams matchup advantages with supporting data - Document statistical edges with actual numbers ## Data Storage Instructions Store the following key information with supporting data for reference: 1. Power Play strengths/weaknesses (with metrics) 2. Penalty Kill strengths/weaknesses (with numbers) 3. Special teams statistical mismatches (with data) 4. Danger level advantages (with rates) 5. Zone time and possession disparities (with actual numbers) ## Format Note Present analysis as: - Key special teams strengths/weaknesses (supported by specific metrics) - Notable statistical advantages (with actual numbers) - Significant trends (backed by data) - Potential impact on matchup (evidence-based) 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 from the API - Present supporting data for each analysis point - Flag any missing or inconsistent data - Maintain separate PP and PK situation analysis - Focus on statistically significant differences - Provide context for all cited numbers - All data is from the 2024 season only Copy Prompt

Prompt 4: Goalie Analysis

# Prompt 4 - Goalie Analysis: Use the Sports Stats Gather API to analyze goalie statistics for both starting goalies identified in the lineup screenshot from Prompt 1. 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. Reference the starting goalies specified in Prompt 1's lineup screenshot. ### 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 ## Data Validation Requirements Before analysis: - Confirm data retrieval for both starting goalies - 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 - Calculate save percentages with actual numbers - Document all data points used in comparisons - Validate games played and sample size ## Analysis Metrics After collecting and validating ALL data above, analyze with supporting evidence: 1. Core Save Metrics - goals against (cite actual numbers) - shots faced (ongoal with save percentage) - blocked_shot_attempts (with rates) - unblocked_shot_attempts (with rates) 2. Danger Level Performance - High Danger: highDangerGoals, highDangerShots, highDangerxGoals (with save percentage) - Medium Danger: mediumDangerGoals, mediumDangerShots, mediumDangerxGoals (with save percentage) - Low Danger: lowDangerGoals, lowDangerShots, lowDangerxGoals (with save percentage) 3. Rebound Control - rebounds (with rates per shot) - xRebounds (compared to actual) - playContinuedInZone (with percentages) - playContinuedOutsideZone (with percentages) 4. Expected Goals Analysis - xGoals (cite actual numbers) - flurryAdjustedxGoals (with comparison to actual) - xOnGoal (with rates) 5. Additional Context - freeze (with rates per shot) - xFreeze (compared to actual) - icetime (specific minutes) - games_played (sample size context) ## Analysis Steps After data collection and validation complete: 1. Home Team Starting Goalie Analysis: - Save percentage by danger level with specific numbers - Rebound control metrics with actual rates - Expected vs. actual goals with specific differentials - Game management stats with supporting data Please proceed immediately to Away Team Starting Goalie Analysis. 2. Away Team Starting Goalie Analysis: - Save percentage by danger level with specific numbers - Rebound control metrics with actual rates - Expected vs. actual goals with specific differentials - Game management stats with supporting data Please proceed immediately to Comparative Analysis. 3. Comparative Analysis: - Compare save percentages with specific differentials - Compare rebound control with actual rates - Compare danger level performance with numbers - Identify key matchup advantages with data - Assess overall goalie matchup with evidence ## Data Storage Instructions Store the following key information with supporting data for reference: 1. Each goalie's save percentages by danger level (with numbers) 2. Rebound control metrics (with rates) 3. Key statistical advantages/disadvantages (with data) 4. Expected vs. actual performance gaps (with numbers) 5. Game management tendencies (with rates) ## Format Note Present analysis as: - Key strengths/weaknesses (supported by specific metrics) - Notable statistical advantages (with actual numbers) - Significant trends (backed by data) - Potential impact on matchup (evidence-based) 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 from the API - Present supporting data for each analysis point - Flag any missing or inconsistent data - Consider sample sizes when analyzing - Note any significant statistical outliers - Focus on identifying meaningful patterns and trends - Prioritize insights that could impact game outcomes - If any data appears missing or inconsistent, note it but continue with available data - Present analysis in a clear, concise format focusing on key findings only - All data is from the 2024 season only Copy Prompt

Prompt 5A: Home Team Power Play Unit 1 Analysis

# Prompt 5A - Home Team Power Play Unit 1 Analysis: Use the Sports Stats Gather API to analyze individual skater statistics for the home team's first power play unit players listed in the lineup screenshot from Prompt 1. 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 for EACH player in Power Play Unit 1. Do NOT proceed to analysis until ALL PP1 player data is collected. Reference the first power play unit specified in Prompt 1's lineup screenshot. ### Power Play Unit 1 Data Collection Query each PP1 player individually: https://sportsstatsgather.com/api/nhl/data/skaters?team=[Home Team]&situation=all&player=[Player Last Name] ## Data Verification Before proceeding to ANY analysis: - Gather data from the API for EACH player in Power Play Unit 1 - Confirm successful data retrieval from ALL PP1 players - For each PP1 player from lineup card: * Document ALL their statistical fields * Note any missing or incomplete metrics * Flag any data inconsistencies - Only proceed when ALL PP1 player data is collected and verified - Document sample sizes (games_played) for context ## Analysis Metrics After ALL PP1 data is collected and verified, analyze with supporting evidence, showing actual API data: 1. Individual Production Metrics (I_F_) Show for each PP1 player: - Actual goals, points, assists numbers from API - Shot rates with API shot attempt counts - Danger level stats with API shot counts by zone - Expected goals showing API xGoals values Present raw numbers and calculated rates 2. On-Ice Impact Metrics (OnIce_) Show for each PP1 player: - Corsi/Fenwick from API with shot attempt counts - Expected goals showing API xGoals numbers - Goal data showing actual API counts - Shot data with API danger level breakdowns Present percentages with supporting counts 3. Off-Ice Impact (OffIce_) Show for each PP1 player: - Team performance stats from API - Shot metrics with actual API counts - Expected goals showing API values Present differentials with supporting data 4. Zone Performance Show for each PP1 player: - Zone start counts from API data - Zone end counts from API data - Neutral zone data from API Present percentages with actual counts 5. Additional Context Show for each PP1 player: - Game score components from API - Ice time totals and averages - Penalty data with actual counts - Shot blocking numbers from API Present rates with supporting totals ## Analysis Steps After ALL data collection and verification complete, proceed with analysis showing supporting API data: 1. Individual PP1 Player Analysis: For each PP1 player, show: - ALL individual metrics with actual numbers - ALL on-ice impacts with supporting data - ALL zone stats with actual counts - ALL context metrics with raw data Document source of each statistic 2. Power Play Unit 1 Combined Analysis: Using data from all PP1 players: - Combined unit stats with API data - Chemistry metrics with supporting numbers - Performance data with actual counts - Usage patterns with time metrics Show calculations and data sources 3. Power Play Unit 1 Overall Analysis: Using complete PP1 API dataset: - Show unit effectiveness with actual metrics - Document deployment patterns with data - Present unit advantages with numbers - Support insights with API stats ## Data Storage Instructions Store the following key information with supporting API data: 1. Individual PP1 player stats (show actual numbers) 2. PP1 unit performance (show supporting metrics) 3. PP1 chemistry indicators (show actual data) 4. PP1 deployment data (show usage stats) 5. PP1 statistical edges (show metric comparisons) ## Format Note Present analysis as: - Player-by-player breakdown with API metrics - PP1 unit effectiveness with actual numbers - Key findings with statistical evidence - Deployment insights with supporting data Every conclusion must cite specific API data points ## Important Notes - Complete ALL PP1 player queries before any analysis - Show ALL API data supporting each analysis point - Present actual numbers, not just conclusions - Include statistical context (sample sizes, averages) - Cite specific API fields used in analysis - Show calculation methods using API data - Support comparisons with actual metrics - Document data source for every insight - Present raw numbers alongside rates/percentages - Flag any missing or inconsistent data - Focus only on Power Play Unit 1 players - Note sample size limitations - Highlight statistical outliers with context - Support trends with actual data points - Show evidence for outcome impact claims - All data is from the 2024 season only Copy Prompt

Prompt 5B: Home Team Power Play Unit 2 Analysis

# Prompt 5B - Home Team Power Play Unit 2 Analysis: Use the Sports Stats Gather API to analyze individual skater statistics for the home team's second power play unit players listed in the lineup screenshot from Prompt 1. 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 for EACH player in Power Play Unit 2. Do NOT proceed to analysis until ALL PP2 player data is collected. Reference the second power play unit specified in Prompt 1's lineup screenshot. ### Power Play Unit 2 Data Collection Query each PP2 player individually: https://sportsstatsgather.com/api/nhl/data/skaters?team=[Home Team]&situation=all&player=[Player Last Name] ## Data Verification Before proceeding to ANY analysis: - Gather data from the API for EACH player in Power Play Unit 2 - Confirm successful data retrieval from ALL PP2 players - For each PP2 player from lineup card: * Document ALL their statistical fields * Note any missing or incomplete metrics * Flag any data inconsistencies - Only proceed when ALL PP2 player data is collected and verified - Document sample sizes (games_played) for context ## Analysis Metrics After ALL PP2 data is collected and verified, analyze with supporting evidence, showing actual API data: 1. Individual Production Metrics (I_F_) Show for each PP2 player: - Actual goals, points, assists numbers from API - Shot rates with API shot attempt counts - Danger level stats with API shot counts by zone - Expected goals showing API xGoals values Present raw numbers and calculated rates 2. On-Ice Impact Metrics (OnIce_) Show for each PP2 player: - Corsi/Fenwick from API with shot attempt counts - Expected goals showing API xGoals numbers - Goal data showing actual API counts - Shot data with API danger level breakdowns Present percentages with supporting counts 3. Off-Ice Impact (OffIce_) Show for each PP2 player: - Team performance stats from API - Shot metrics with actual API counts - Expected goals showing API values Present differentials with supporting data 4. Zone Performance Show for each PP2 player: - Zone start counts from API data - Zone end counts from API data - Neutral zone data from API Present percentages with actual counts 5. Additional Context Show for each PP2 player: - Game score components from API - Ice time totals and averages - Penalty data with actual counts - Shot blocking numbers from API Present rates with supporting totals ## Analysis Steps After ALL data collection and verification complete, proceed with analysis showing supporting API data: 1. Individual PP2 Player Analysis: For each PP2 player, show: - ALL individual metrics with actual numbers - ALL on-ice impacts with supporting data - ALL zone stats with actual counts - ALL context metrics with raw data Document source of each statistic 2. Power Play Unit 2 Combined Analysis: Using data from all PP2 players: - Combined unit stats with API data - Chemistry metrics with supporting numbers - Performance data with actual counts - Usage patterns with time metrics Show calculations and data sources 3. Power Play Unit 2 Overall Analysis: Using complete PP2 API dataset: - Show unit effectiveness with actual metrics - Document deployment patterns with data - Present unit advantages with numbers - Support insights with API stats ## Data Storage Instructions Store the following key information with supporting API data: 1. Individual PP2 player stats (show actual numbers) 2. PP2 unit performance (show supporting metrics) 3. PP2 chemistry indicators (show actual data) 4. PP2 deployment data (show usage stats) 5. PP2 statistical edges (show metric comparisons) ## Format Note Present analysis as: - Player-by-player breakdown with API metrics - PP2 unit effectiveness with actual numbers - Key findings with statistical evidence - Deployment insights with supporting data Every conclusion must cite specific API data points ## Important Notes - Complete ALL PP2 player queries before any analysis - Show ALL API data supporting each analysis point - Present actual numbers, not just conclusions - Include statistical context (sample sizes, averages) - Cite specific API fields used in analysis - Show calculation methods using API data - Support comparisons with actual metrics - Document data source for every insight - Present raw numbers alongside rates/percentages - Flag any missing or inconsistent data - Focus only on Power Play Unit 2 players - Note sample size limitations - Highlight statistical outliers with context - Support trends with actual data points - Show evidence for outcome impact claims - All data is from the 2024 season only Copy Prompt

Prompt 6A: Away Team Power Play Unit 1 Analysis

# Prompt 6A - Away Team Power Play Unit 1 Analysis: Use the Sports Stats Gather API to analyze individual skater statistics for the away team's first power play unit players listed in the lineup screenshot from Prompt 1. 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 for EACH player in Power Play Unit 1. Do NOT proceed to analysis until ALL PP1 player data is collected. Reference the first power play unit specified in Prompt 1's lineup screenshot. ### Power Play Unit 1 Data Collection Query each PP1 player individually: https://sportsstatsgather.com/api/nhl/data/skaters?team=[Away Team]&situation=all&player=[Player Last Name] ## Data Verification Before proceeding to ANY analysis: - Gather data from the API for EACH player in Power Play Unit 1 - Confirm successful data retrieval from ALL PP1 players - For each PP1 player from lineup card: * Document ALL their statistical fields * Note any missing or incomplete metrics * Flag any data inconsistencies - Only proceed when ALL PP1 player data is collected and verified - Document sample sizes (games_played) for context ## Analysis Metrics After ALL PP1 data is collected and verified, analyze with supporting evidence, showing actual API data: 1. Individual Production Metrics (I_F_) Show for each PP1 player: - Actual goals, points, assists numbers from API - Shot rates with API shot attempt counts - Danger level stats with API shot counts by zone - Expected goals showing API xGoals values Present raw numbers and calculated rates 2. On-Ice Impact Metrics (OnIce_) Show for each PP1 player: - Corsi/Fenwick from API with shot attempt counts - Expected goals showing API xGoals numbers - Goal data showing actual API counts - Shot data with API danger level breakdowns Present percentages with supporting counts 3. Off-Ice Impact (OffIce_) Show for each PP1 player: - Team performance stats from API - Shot metrics with actual API counts - Expected goals showing API values Present differentials with supporting data 4. Zone Performance Show for each PP1 player: - Zone start counts from API data - Zone end counts from API data - Neutral zone data from API Present percentages with actual counts 5. Additional Context Show for each PP1 player: - Game score components from API - Ice time totals and averages - Penalty data with actual counts - Shot blocking numbers from API Present rates with supporting totals ## Analysis Steps After ALL data collection and verification complete, proceed with analysis showing supporting API data: 1. Individual PP1 Player Analysis: For each PP1 player, show: - ALL individual metrics with actual numbers - ALL on-ice impacts with supporting data - ALL zone stats with actual counts - ALL context metrics with raw data Document source of each statistic 2. Power Play Unit 1 Combined Analysis: Using data from all PP1 players: - Combined unit stats with API data - Chemistry metrics with supporting numbers - Performance data with actual counts - Usage patterns with time metrics Show calculations and data sources 3. Power Play Unit 1 Overall Analysis: Using complete PP1 API dataset: - Show unit effectiveness with actual metrics - Document deployment patterns with data - Present unit advantages with numbers - Support insights with API stats ## Data Storage Instructions Store the following key information with supporting API data: 1. Individual PP1 player stats (show actual numbers) 2. PP1 unit performance (show supporting metrics) 3. PP1 chemistry indicators (show actual data) 4. PP1 deployment data (show usage stats) 5. PP1 statistical edges (show metric comparisons) ## Format Note Present analysis as: - Player-by-player breakdown with API metrics - PP1 unit effectiveness with actual numbers - Key findings with statistical evidence - Deployment insights with supporting data Every conclusion must cite specific API data points ## Important Notes - Complete ALL PP1 player queries before any analysis - Show ALL API data supporting each analysis point - Present actual numbers, not just conclusions - Include statistical context (sample sizes, averages) - Cite specific API fields used in analysis - Show calculation methods using API data - Support comparisons with actual metrics - Document data source for every insight - Present raw numbers alongside rates/percentages - Flag any missing or inconsistent data - Focus only on Power Play Unit 1 players - Note sample size limitations - Highlight statistical outliers with context - Support trends with actual data points - Show evidence for outcome impact claims - All data is from the 2024 season only Copy Prompt

Prompt 6B: Away Team Power Play Unit 2 Analysis

# Prompt 6B - Away Team Power Play Unit 2 Analysis: Use the Sports Stats Gather API to analyze individual skater statistics for the away team's second power play unit players listed in the lineup screenshot from Prompt 1. 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 for EACH player in Power Play Unit 2. Do NOT proceed to analysis until ALL PP2 player data is collected. Reference the second power play unit specified in Prompt 1's lineup screenshot. ### Power Play Unit 2 Data Collection Query each PP2 player individually: https://sportsstatsgather.com/api/nhl/data/skaters?team=[Away Team]&situation=all&player=[Player Last Name] ## Data Verification Before proceeding to ANY analysis: - Gather data from the API for EACH player in Power Play Unit 2 - Confirm successful data retrieval from ALL PP2 players - For each PP2 player from lineup card: * Document ALL their statistical fields * Note any missing or incomplete metrics * Flag any data inconsistencies - Only proceed when ALL PP2 player data is collected and verified - Document sample sizes (games_played) for context ## Analysis Metrics After ALL PP2 data is collected and verified, analyze with supporting evidence, showing actual API data: 1. Individual Production Metrics (I_F_) Show for each PP2 player: - Actual goals, points, assists numbers from API - Shot rates with API shot attempt counts - Danger level stats with API shot counts by zone - Expected goals showing API xGoals values Present raw numbers and calculated rates 2. On-Ice Impact Metrics (OnIce_) Show for each PP2 player: - Corsi/Fenwick from API with shot attempt counts - Expected goals showing API xGoals numbers - Goal data showing actual API counts - Shot data with API danger level breakdowns Present percentages with supporting counts 3. Off-Ice Impact (OffIce_) Show for each PP2 player: - Team performance stats from API - Shot metrics with actual API counts - Expected goals showing API values Present differentials with supporting data 4. Zone Performance Show for each PP2 player: - Zone start counts from API data - Zone end counts from API data - Neutral zone data from API Present percentages with actual counts 5. Additional Context Show for each PP2 player: - Game score components from API - Ice time totals and averages - Penalty data with actual counts - Shot blocking numbers from API Present rates with supporting totals ## Analysis Steps After ALL data collection and verification complete, proceed with analysis showing supporting API data: 1. Individual PP2 Player Analysis: For each PP2 player, show: - ALL individual metrics with actual numbers - ALL on-ice impacts with supporting data - ALL zone stats with actual counts - ALL context metrics with raw data Document source of each statistic 2. Power Play Unit 2 Combined Analysis: Using data from all PP2 players: - Combined unit stats with API data - Chemistry metrics with supporting numbers - Performance data with actual counts - Usage patterns with time metrics Show calculations and data sources 3. Power Play Unit 2 Overall Analysis: Using complete PP2 API dataset: - Show unit effectiveness with actual metrics - Document deployment patterns with data - Present unit advantages with numbers - Support insights with API stats ## Data Storage Instructions Store the following key information with supporting API data: 1. Individual PP2 player stats (show actual numbers) 2. PP2 unit performance (show supporting metrics) 3. PP2 chemistry indicators (show actual data) 4. PP2 deployment data (show usage stats) 5. PP2 statistical edges (show metric comparisons) ## Format Note Present analysis as: - Player-by-player breakdown with API metrics - PP2 unit effectiveness with actual numbers - Key findings with statistical evidence - Deployment insights with supporting data Every conclusion must cite specific API data points ## Important Notes - Complete ALL PP2 player queries before any analysis - Show ALL API data supporting each analysis point - Present actual numbers, not just conclusions - Include statistical context (sample sizes, averages) - Cite specific API fields used in analysis - Show calculation methods using API data - Support comparisons with actual metrics - Document data source for every insight - Present raw numbers alongside rates/percentages - Flag any missing or inconsistent data - Focus only on Power Play Unit 2 players - Note sample size limitations - Highlight statistical outliers with context - Support trends with actual data points - Show evidence for outcome impact claims - All data is from the 2024 season only Copy Prompt

Prompt 7: Recent Performance and Game Pace Analysis

# Prompt 7 - Recent Performance and Game Pace Analysis: Use the Sports Stats Gather API to analyze ALL recent games for both teams identified in the lineup screenshot from Prompt 1, regardless of home/away status. Note: All data is from the 2024 season only, no season filter is needed. ## Data Collection Execute BOTH queries below first to get ALL recent games. Do NOT proceed to analysis until ALL data is collected. ### Complete Game History Queries 1. Get ALL recent games for Home Team: https://sportsstatsgather.com/api/nhl/data/gamestats?team=[Home Team]&situation=all 2. Get ALL recent games for Away Team: https://sportsstatsgather.com/api/nhl/data/gamestats?team=[Away Team]&situation=all Note: Each query will return ALL recent games for that team, both home and away games. This gives us a complete picture of how each team is performing. ## Data Verification Requirements Before beginning ANY analysis: - Confirm successful retrieval of ALL games for both teams - Sort games by gameDate in chronological order - Identify the 3 most recent games for each team - Verify complete data exists for each game - Document for each game: * Date * Opponent * Location (home/away) * Final score * Key metrics - Only proceed when ALL recent game data is collected and organized ## Analysis Metrics After organizing ALL games chronologically, analyze for each team's last 3 games: 1. Game Results Progression - Date, opponent, location for each game - Final score for each game - Points earned per game - Scoring patterns across games - Performance trend regardless of venue 2. Scoring Analysis (Per Game) - goalsFor, goalsAgainst - Period-by-period scoring - High/medium/low danger goals - Special teams impact - Score situation analysis 3. Shot and Pace Metrics (Per Game) - Shots for/against - Shot attempts for/against - Shot locations and danger levels - Game pace indicators - Puck possession metrics 4. Expected Goals (Per Game) - xGoalsFor vs actual goals - xGoalsAgainst vs actual goals - Scoring efficiency - Save percentage vs expected - Danger zone conversion rates 5. Additional Context (Per Game) - Penalty minutes and special teams time - Faceoff performance - Hit counts and physical play - Puck management (giveaways/takeaways) - Game flow indicators ## Analysis Steps After data collection and chronological organization complete: 1. Home Team Last 3 Games Analysis: - Game-by-game breakdown with all contexts - Performance trend analysis - Scoring pattern identification - Venue impact assessment - Opposition quality consideration Document findings before proceeding. 2. Away Team Last 3 Games Analysis: - Game-by-game breakdown with all contexts - Performance trend analysis - Scoring pattern identification - Venue impact assessment - Opposition quality consideration Document findings before proceeding. 3. Comparative Trend Analysis: - Recent form comparison - Scoring trend comparison - Pace and style comparison - Strength of schedule comparison - Momentum factor analysis ## Data Storage Instructions Store the following key information with supporting data for reference: 1. Complete game-by-game results for both teams 2. Scoring trends and patterns 3. Performance metrics progression 4. Venue impact analysis 5. Form and momentum indicators ## Format Note Present analysis as: - Chronological game-by-game breakdown - Clear performance trends - Key statistical patterns - Form and momentum assessment - Relevant situational notes No need to show raw data, but cite specific numbers for all conclusions. ## Important Notes - Query returns ALL recent games for each team - Analyze games chronologically regardless of venue - Consider both home and away performance as part of overall form - Support all conclusions with specific metrics from the API - Present supporting data for each analysis point - Flag any missing or inconsistent data - Focus on identifying meaningful patterns and trends - Prioritize insights that could impact game outcomes - Consider venue as a factor but not a filter - Present analysis in a clear, concise format - All data is from the 2024 season only Copy Prompt

Prompt 8: Pre-Bet Comprehensive Analysis with Playoff Implications

# Prompt 8 - Pre-Bet Comprehensive Analysis with Playoff Implications: Create a complete betting analysis by synthesizing the key findings from all previous prompts (Prompts 2-7) along with the playoff probability implications established in Prompt 1 for both teams. Note: This analysis MUST include specific win probability percentages for each bet type, ALL supported by data from previous analyses. ## Stakes Integration Before analyzing betting probabilities, confirm and consider: Home Team ([Team Name]) Stakes: - Playoff probability swings based on outcome - Division/Wild Card position implications - Must-win vs can-afford-loss scenario - Regulation vs OT win importance Away Team ([Team Name]) Stakes: - Playoff probability swings based on outcome - Division/Wild Card position implications - Must-win vs can-afford-loss scenario - Regulation vs OT win importance ## Data Validation Requirements Before providing any predictions or percentages: - Confirm ALL required data collection from previous prompts was completed - Verify ALL collected data matches API responses - Ensure complete game histories were analyzed for both teams (regardless of venue) - Verify ALL power play unit data was collected and reviewed: * Home Team PP1 (Prompt 5A) * Home Team PP2 (Prompt 5B) * Away Team PP1 (Prompt 6A) * Away Team PP2 (Prompt 6B) - Document source prompt and specific API data for each statistic used - Show detailed calculation method for each probability percentage - Support each prediction with specific metrics and actual numbers - Validate ALL trends with game-by-game data points - Flag any missing or inconsistent data that could affect predictions - Ensure all referenced players match lineup card - Document sample sizes used in ALL calculations - Provide supporting context for ALL percentage calculations ## Integration Points Review and synthesize findings, with added emphasis on game stakes: 1. Team Statistics (Prompt 2) + Playoff Context - How 5-on-5 metrics align with team urgency - Whether overall effectiveness matches motivation level - If statistical advantages are amplified by game importance 2. Power Play/Penalty Kill (Prompt 3) + Stakes - Special teams aggression based on needed points - Risk/reward balance in power play approach - Penalty kill strategy considering standings impact 3. Goalie Matchup (Prompt 4) - Save percentages with actual numbers by danger level - Form metrics with supporting data and trends - Matchup statistics with specific performance data - Impact of game importance on goalie performance 4. Power Play Analysis Home Team Units: - PP1 metrics (Prompt 5A) with actual numbers - PP2 metrics (Prompt 5B) with actual numbers - Unit deployment strategy implications - Special teams urgency impact Away Team Units: - PP1 metrics (Prompt 6A) with actual numbers - PP2 metrics (Prompt 6B) with actual numbers - Unit deployment strategy implications - Special teams urgency impact 5. Recent Performance & Pace (Prompt 7) - Complete game-by-game metrics regardless of venue - Scoring data with specific stats for ALL recent games - Pace indicators with supporting data from full history - Form analysis considering playoff push ## Analysis Framework Synthesize ALL collected data considering stakes to evaluate: 1. Moneyline Considerations - Overall team strength relative to motivation - Key matchup advantages weighted by game importance - Impact player availability and team urgency - Recent form analysis with playoff context - Regulation vs OT win value analysis 2. Puck Line Analysis - Scoring differential patterns considering needed points - Late game strategy based on standings implications - Empty net aggression based on point requirements - Risk tolerance given playoff probability impacts - Historical margin patterns in similar stakes games 3. First Period Evaluation - Starting lineup urgency based on stakes - First period approach given game importance - Early game strategy considering must-win scenarios - Opening period patterns with motivation context - Historical first period performance in key games 4. Totals Assessment - Scoring trends weighted by game importance - Pace of play factors considering urgency - Special teams aggression based on stakes - Goaltender approach given standings impact - Historical scoring patterns in similar situations ## Data Integration Requirements Before providing betting percentages, explicitly show calculations and supporting data: 1. From Team Analysis (Prompt 2): - Demonstrate how 5-on-5 metrics contribute to win probabilities - Show how danger level data affects totals predictions - Document possession metrics' impact with specific data - Factor in team motivation impact on metrics 2. From Special Teams Analysis (Prompt 3): - Calculate Power Play impact on total predictions - Show Penalty Kill influence on win probabilities - Document special teams effect on pace and scoring - Consider urgency impact on special teams 3. From Goalie Analysis (Prompt 4): - Demonstrate save percentage impact on projected totals - Show matchup advantages with actual metrics - Document danger level save % influence on predictions - Factor in stakes impact on goalie performance 4. From Power Play Analysis: a) Home Team Power Play Impact (Prompts 5A & 5B): - Show PP1 effectiveness metrics and specific impact - Show PP2 effectiveness metrics and specific impact - Document unit deployment strategy impact - Demonstrate combined PP effect on predictions - Consider urgency impact on PP approach b) Away Team Power Play Impact (Prompts 6A & 6B): - Show PP1 effectiveness metrics and specific impact - Show PP2 effectiveness metrics and specific impact - Document unit deployment strategy impact - Demonstrate combined PP effect on predictions - Consider urgency impact on PP approach 5. From Recent Performance (Prompt 7): - Use ALL recent games to show form trends - Document complete scoring patterns - Demonstrate pace indicators from full game history - Consider momentum in playoff race context ## Betting Recommendations MUST provide clear analysis and WIN PROBABILITY PERCENTAGES for each market, with all percentages supported by comprehensive data AND playoff implications: 1. Full Game Markets - Moneyline: [Team] to win (Show calculation methodology incorporating motivation) - Puck line: [Team] +/-1.5 (Show calculation considering urgency) - Total goals: Over/Under (Show calculation factoring game importance) 2. First Period Markets - First period moneyline: [Team] to win (Show calculation with early urgency factor) - First period total: Over/Under (Show calculation considering early approach) 3. Team Totals - Home team total: Over/Under (Show calculation with motivation factor) - Away team total: Over/Under (Show calculation with motivation factor) Additional Strategic Considerations: - Regulation win vs OT implications - Risk tolerance based on standings needs - Strategic approach based on point requirements - Late game scenario planning - Impact of standings on team approach ## Format Note Present recommendations as: 1. Primary Plays (Highest Confidence) * Show complete probability calculation methodology * Support confidence with comprehensive metrics * Cite specific data points from ALL relevant prompts * Document ALL supporting factors with numbers * Include playoff implications impact 2. Secondary Options * Show complete probability calculation methodology * Support lower confidence with specific data * Document ALL concern factors with metrics * Show conditions affecting confidence * Factor in standings impact 3. Lean/Pass Situations * Show complete probability calculation methodology * Support pass decisions with specific metrics * Document ALL factors leading to pass * Show required changes with actual numbers * Consider playoff probability impact Each bet recommendation must include: - Detailed win probability percentage calculation methodology - ALL key factors supported by actual metrics - Confidence level with comprehensive supporting data - ALL risk factors with statistical evidence - Clear reasoning with complete data support - Playoff/standings implications consideration ## Important Notes - MUST provide specific win probability percentages supported by comprehensive API data - ALL percentages must be calculated using complete metrics from ALL relevant prompts - Show detailed methodology for EACH probability calculation - Support ALL conclusions with specific data points from complete game histories - Reference specific prompts and data points for ALL analyses - Document ALL statistical sources and calculation methods - Flag ANY gaps in data that affect confidence - Show complete sample sizes for ALL calculations - Present clear evidence chain for EACH prediction - Note ALL mathematical assumptions - Account for both PP1 and PP2 impact separately in predictions - Consider unit deployment patterns in calculations - Show how each power play unit affects probabilities - Document unit-specific advantages/disadvantages - Consider playoff probability swings in all calculations - Weight team motivation in prediction models - Account for must-win scenarios in probability adjustments - Factor regulation vs OT win value in predictions - Consider strategic implications of standings situations - Ensure ALL predictions tie directly to comprehensive API data - All analysis based on 2024 season data only - Focus on fully data-supported conclusions only Copy Prompt

Optional - Prompt 9: Post-Rankings Pre-Bet Confidence Analysis

To gather the required team rankings data, visit outlier.bet and select the NHL game in question. Under the moneyline stats section, you'll find comprehensive offensive and defensive team rankings that should be screenshot for this analysis.

Review the provided offensive and defensive team ranking screenshots for both teams alongside historical data: 1. Re-evaluate your previous betting recommendations, indicating if the team rankings change your projected outcome 2. Rate confidence as a percentage (0-100%) in each pick, explaining how these current rankings influenced any shift in confidence 3. Explain if/how these current team rankings alter your perspective on the matchup 4. Note any rankings that significantly reinforce or challenge your initial analysis based on historical trends Consider both current rankings AND historical data in your assessment. Focus on explaining if this new statistical information changes your confidence level or prediction. Copy Prompt

NHL Bayesian and Monte Carlo Results

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