I’m a sports analyst who lives and breathes NHL data, and I use AI to turn shots, travel, and goalie news into real win probabilities. This article breaks down the exact steps I take, from sourcing trusted stats to calibrating models, so you can forecast games, price moneylines, and manage risk with confidence.
Clean inputs are the absolute only way you are going to win games in this league. You have to use official schedules and play by play data while adding in expected goals, special teams form, rest, travel, and confirmed goalies. You also need to version your data to avoid leakage. You should start simple and then sharpen your approach. A baseline Elo or logistic model is a great place to start before you add Poisson or XGBoost models. You have to simulate overtime and shootouts and calibrate everything with Platt or isotonic scaling so your win odds actually map to reality. Validation is non negotiable. You need to validate like a pro using strict time split cross validation and walk forward testing. Score everything with log loss and Brier scores while checking your calibration bins. You have to stress test for goalie swaps, back to backs, and long road trips. Bet with discipline by converting probabilities to fair moneylines and staking with fractional Kelly criteria. Track your ROI after fees and watch for drift, never chase, and document every model run. Our edge comes from ATSwins. ATSwins is an AI powered sports prediction platform offering data driven picks, player props, betting splits, and profit tracking across the NFL, NBA, MLB, NHL, and NCAA. Free and paid plans give bettors insights and guides to make smarter, more informed decisions, and we apply these calibration, testing, and bankroll rules daily.
Building an NHL Prediction Algorithm That Trades Edges, Not Hype
Problem framing and success metrics
What the model should output
For ATSwins, an NHL prediction algorithm needs to turn raw hockey information into actionable outputs a bettor can actually use to make money. The core deliverables are not just vague feelings but hard numbers. You need game level win probabilities for each team both pregame and with an optional in game refresh. You need fair moneylines derived directly from those probabilities so you can compare them to the books. You need edges on totals, which means the over and under, and puckline, which is the spread, by specific price points. Finally, you need the expected value, or EV, and a recommended stake sizing given your specific bankroll constraints.
To keep things simple, you should standardize the base outputs per game. You want the probability that the Home team wins in regulation and the probability that the Away team wins in regulation. You also need the probability that the Home team wins by any method and the probability that the Away team wins by any method. You need to know the probability of the Total being greater than or equal to x goals for several bookmaker totals. Lastly, you need the probability that the Home team covers minus one and a half goals and the probability that the Away team covers plus one and a half goals, and vice versa. From these core numbers, you can derive pricing and find edges at the odds you are facing.
How we will measure success?
An algorithm without hard metrics is just noise and opinion. We keep a short list of model health and trading performance metrics that act as our source of truth. We look at Log loss on the binary outcome of a win or loss by any method. We look at the Brier score on the same target. We obsess over Calibration metrics like reliability plots and probability bins. For example, when we say a team has a forty to forty five percent chance of winning, they better win forty to forty five percent of the time. We track long run ROI after fees and vig, broken down by market type such as moneyline, totals, and puckline. We look at the coverage of confidence intervals on totals probabilities, and we monitor drawdown and turnover statistics.
We track these metrics by season and by rolling windows, such as thirty, sixty, and ninety days. Earlier research might not spell out every detail, so we lean on trusted public data and strict walk forward testing rather than hindsight tuning.
How to convert probabilities into betting prices?
There are a few quick formulas you will use constantly if you want to be sharp. To convert American odds to implied probability for negative odds A, the probability p equals the absolute value of A divided by the absolute value of A plus one hundred. For positive odds A, the probability p equals one hundred divided by A plus one hundred. To calculate the Fair moneyline from model probability p, if p is greater than zero point five, the Fair American odds equal negative one hundred times p divided by one minus p. If p is less than or equal to zero point five, the Fair American odds equal one hundred times one minus p divided by p.
To compute expected value at offered odds, the EV per one dollar stake equals the probability of winning times the payout when you win minus the probability of losing times the one dollar you lose. For American odds A, the payout when you win is the absolute value of A over one hundred if A is greater than zero, or one hundred over the absolute value of A if A is less than zero. Always include market fees and slippage in your mental math. For totals and puckline at minus one ten, you need a decent edge, often greater than three or four percentage points, just to clear the costs of doing business.
Decision thresholds and staking logic
You should use a fractional Kelly approach for each bet, keyed to your specific edge and odds. You need to cap your stake size and set a per day risk limit so you do not blow up your account. Only bet when the edge exceeds a minimum threshold and the sample inputs are stable, especially around goalie confirmations.
A simple pregame workflow involves five steps. First, compute probabilities for ML, totals, and puckline. Second, convert those to fair prices and compare them to market odds. Third, screen bets using minimum edge thresholds and liquidity rules. Fourth, calculate your fractional Kelly stake. Fifth, log the bet with a timestamp, line, sportsbook, and model version.
A quick checklist before pricing any slate
Do we have the latest injury and goalie info? Are the lines and units plausible, meaning there are no missing scratches? Are travel and rest variables updated after last night’s games? Are we running with time aware features only so there is no leakage from postgame stats? Are the market odds captured with a timestamp and book ID? That is the absolute minimum to keep outputs consistent enough to compare week to week.
Data acquisition & feature engineering
Core sources
You need to use public data with a stable structure and clear documentation. You want NHL game, team, roster, schedule, and play by play data via official resources and APIs. You need season level and historical context, including past team performance, injuries, and goalie logs from comprehensive databases. You need five on five shot based metrics like xG, Corsi, and Fenwick, as well as special teams splits and score and venue adjusted measures from advanced analytics repositories. You should augment this with internal mapping tables for team names and player IDs to reduce downstream friction when lineups shift or franchises rebrand.
Ingestion and normalization steps
Pull the schedule and results daily and capture game IDs with standardized timestamps in Eastern Time and arenas. Ingest roster and line combos, confirming scratches and goalie starters as close to lock as possible. Load play by play data including shots, goals, and penalties, and build micro event features like xG per shot, zones, and rebound shots. Normalize team names and IDs across sources. Keep consistent season keys for joins and rolling features. Store snapshots with versioned timestamps to preserve exactly what you knew when you made a prediction.
A minimal relational schema would include games tables with IDs and dates, odds tables with market types and prices, teams tables with conference and division info, players tables with positions and handedness, lineups tables with roles and confirmation timestamps, play by play tables with event types and coordinates, and a feature table with generated values and timestamps.
Feature engineering that matters
Engineer features with a mindset of asking what we can actually know pregame. A non exhaustive but battle tested set includes strength metrics like rolling five on five xG for and against that is score adjusted. You want special teams data like power play xG per sixty and penalty kill xG against per sixty, looking at the recent ten games versus the season. You want shot share and quality share, along with finishing and save percentages with regression toward the league average to reduce noise.
For goalies and defense, you need a binary starting goalie confirmation and the projected starter identity. You need goalie quality metrics like GSAA or WAR like blended metrics and xG saved above expected with a rolling decay. You need defensive structure metrics like slot shots against, rebound chances, and rush chances allowed.
For game state and context, look at rest and back to back flags for zero, one, or two plus days of rest. Look at travel distance in the last forty eight to seventy two hours and road trip length. Look at time zone changes and fatigue from traveling east to west or vice versa. Look at rink effects to correct for shot recording biases.
Matchup features should include home ice advantage baselines by team and venue specific penalties called per game. You want faceoff strengths by zone and penalties drawn or taken per sixty. You want net front presence or expected tip and rebound rates versus the opponent’s defensive box outs.
Score effects and tempo are huge. Look at a team’s pace in tied versus trailing versus leading states. Look at possession tilt when protecting a lead and tendencies for pulling the goalie late in the game. For lines, injuries, and chemistry, track forward line combos and defensive pairings along with their average xG per sixty. Weight star player injuries and replacement level penalties. Cluster recent form with exponential decay so the last five games matter more than the last twenty. Use new line flags to capture uncertainty when lines just changed.
Opponent specific tendencies matter too. Look at head to head special teams mismatches and goalie rebound control versus the opponent’s net crash rates. Market aware variables can include closing line deltas on similar matchups historically, which is useful for caution but not for leakage. Also consider league schedule quirks like holidays or early starts.
For implementation, use exponential decay for rolling features where the weight equals lambda to the power of days ago. Choose a lambda between zero point eight five and zero point nine eight depending on how quickly you want form to decay. For rink effects, normalize shot counts and xG components by venue factors estimated over several seasons because some rinks record more attempts than others. Confirmed starter goalie features must default to projected starter until official confirmation. Freeze features at your prediction cutoff time to avoid leakage. Employ winsorization or robust scalers to tame outliers like crazy shooting percentage streaks.
Simple templates you can reuse
You should have a Feature dictionary which is a CSV with columns for feature name, source, update frequency, description, lag window, decay, and null policy. You need a Data quality checklist that goes from source checks to join rates to coverage by team and date to outlier flags and finally to an OK to model status. You need an Odds integration sheet which is a table aligning offered lines like ML, totals, and puckline with model probabilities and edge thresholds for bet selection.
Modeling approaches
Baseline: Elo + logistic as an anchor
Start with a transparent baseline. Maintain team Elo ratings with separate components for even strength and special teams. Add goalie adjustments so if the starter is above or below the team average, you shift the pregame rating. Include home ice advantage that varies by team and season. Convert Elo differences to win probabilities via a logistic transform. Calibrate probabilities to observed frequencies.
Update rules are simple. After each game, shift ratings based on pregame expectation and actual result. Optionally, update separate components for regulation versus OT and shootout performance. Why keep this? It is robust, easy to debug, and makes for a good benchmark when more complex models misbehave.
Poisson, Skellam, and bivariate goal models
For totals and puckline, model goals not just outcomes. Use a Poisson model per team to estimate attack and defense rates adjusted for opponent defenses, goalie, and venue. Use Skellam for goal differential because if goals are Poisson and independent, goal differential follows Skellam which is handy for puckline probabilities. Use Bivariate Poisson to introduce a correlation term to capture shared tempo or game state effects so the two teams’ goals aren’t fully independent.
Implementation details involve fitting log linear models for goal rates using features like xG, special teams strength, rest, and goalie adjustments. Use L1 or L2 regularization to prevent overfitting and keep parameters interpretable. Compute probabilities for zero to ten goals per team, then sum for the totals distribution.
To simulate OT and shootout, start with regulation probabilities from the goals model. Estimate the probability of OT from regulation tie probabilities. Assign OT and shootout win rates using league averages blended with team overtime performance and regressed goalie shootout save rates. Combine for the probability of winning by any method. Keep it simple early and refine if you see consistent bias around OT outcomes.
Gradient boosting with XGBoost for win probability
A tree based model typically outperforms linear baselines if you feed it solid features. Use XGBoost for binary classification of a home win by any method. Include interaction heavy features like PP versus PK mismatch, goalie versus shooter profiles, travel times rest interactions, and recent xG forms with decay. Tune class weights if class imbalance appears due to price filtered training sets, although raw NHL wins are close to balanced. Do not mix postgame or future information and train only with features known at prediction time.
For calibration, use scikit learn to calibrate model outputs with Platt scaling or isotonic regression on a holdout fold. Re check calibration by season and recalibrate when feature distributions shift. Apply SHAP values to understand which features move probabilities. This helps identify instability, like if a single goalie stat dominates too much.
Ensemble strategy
Blend diverse learners to improve stability. Take a weighted average of the Elo logistic baseline, the Poisson based win probability from goal simulations, and the XGBoost classifier output. Learn weights via time aware stacking, meaning you train weights only on prior seasons. Set guardrails so that when models disagree strongly, you reduce stake size or flag for human review.
Prevent data leakage (critical)
Freeze training rows to include only features available before puck drop. Guard against using closing odds or line moves as features for pregame predictions. Handle goalie confirmations carefully. If the starter isn’t confirmed by your cutoff time, use projected starter features and tag the prediction as uncertain.
Turning probabilities into edges on ML, totals, and puckline
For Moneyline, convert probability of winning to a fair price, compare to offered odds, and compute EV. For Totals, use the goals distribution to compute the probability of going Over K point five. Compare to minus one ten style prices and calculate the edge. For Puckline, use Skellam or bivariate Poisson to get the probability of Home minus one and a half and then price it.
A helpful routine involves taking each market and line, calculating the model probability, converting book odds to implied probability, finding the edge by subtracting implied probability from model probability, and keeping it if the EV is above threshold and probability calibration in that price region looks healthy.
Model comparison snapshot
The Elo plus logistic model is simple, stable, and transparent, but it has limited nuance and coarse player effects, making it best for baseline ML and sanity checks. The Poisson and Skellam models are natural for totals and puckline but rely on independence assumptions that can be wrong, making them best for totals, puckline, and regulation wins. The Bivariate Poisson handles correlation between team goals but has more parameters and is harder to fit, making it best for high tempo matchups. The XGBoost classifier offers high accuracy with rich features but needs calibration and risks leakage, making it best for ML pricing and market screening. The Ensemble blends strengths and improves robustness but adds complexity, making it best for final production probabilities.
Validation, deployment and monitoring
Walk-forward backtesting
Use chronological splits and never random splits. Train on seasons N minus three and N minus two, validate on N minus one, and test on N. Slide the window forward each season or monthly. Refit models only using data available prior to the test period. Report Log loss and Brier by season and by probability deciles. Report ROI after vig and fees on a fixed staking strategy. Run sensitivity analyses, like checking what happens if the starter goalie flips.
Calibration curves and bins
Bucket predicted probabilities into ten to twenty bins. Compare observed win rates to predicted within each bin. Track calibration drift monthly. Overconfident bins call for recalibration or feature scaling tweaks.
Goalie swap sensitivity
Before deployment, run a few systematic tests. Swap the starter with a league average goalie and re score each game. The change in win probability should be realistic, often a few percentage points depending on team quality. For elite goalies, verify that the modeled impact is larger but not absurd. Build an automated check so that if the probability swing after a goalie update exceeds a threshold without other feature changes, it flags for review.
Pregame vs live updates
Pregame is your base. If you want live, use the same backbone models with in game state features like current score, penalties, time remaining, and on ice skaters. Update only on stoppages or at the start of periods to control noise. Keep an in play calibration file because live markets can move fast and you need reliable latency. For ATSwins, pregame probabilities power picks and props, while live can focus on situational edges.
Bankroll controls (fractional Kelly)
Apply disciplined staking. Fractional Kelly equals f times Edge divided by Odds factor, where f is the Kelly fraction. Cap max stake per bet and per day. Reduce stakes when models disagree or in high variance markets. Maintain a rolling volatility estimate and scale risk in proportion to realized variance. For example, if the model win probability is fifty four percent at plus one hundred and the EV is zero point zero eight per dollar, at half Kelly for even odds, you stake about four percent of your bankroll, capped to your risk controls.
Alerting and model drift checks
Set up daily and weekly monitors. Check for data freshness alerts. Check prediction coverage. Check calibration drift. Check ROI and drawdown monitors by market. Check for outlier detection like sudden team level probability spikes. Check for odds ingestion lag warnings. When drift is detected, log the period, retrain windows, and impacted features. Prefer small corrective steps over wholesale changes unless something truly broke.
Versioned data and reproducible pipelines
Make your work reproducible. Version the raw inputs, the feature extraction code, and the trained model. Store prediction outputs with model version, features version, and odds snapshot ID. Use a DAG tool for orchestration or a simple time based runner early on. Keep a changelog so if a metric moves, you know why.
Step-by-step deployment plan
First, build data ingestion and normalization, then freeze daily snapshots. Second, engineer a minimal feature set and backtest Elo logistic as a baseline. Third, add Poisson for totals and puckline and validate distributions. Fourth, train XGBoost for ML and perform time based cross validation. Fifth, calibrate using scikit learn’s Platt or isotonic on prior seasons. Sixth, ensemble the three approaches. Seventh, run a full walk forward backtest with transaction costs. Eighth, implement fractional Kelly staking and risk caps. Ninth, ship to staging and run paper trading for at least two weeks. Tenth, move to production with monitoring.
Useful tools and references
You need the official league API docs for NHL data structures. You need trusted historical databases for context. You need repositories for advanced five on five metrics. You need scikit learn for calibration. You need XGBoost for gradient boosting. A minimal tech stack involves a relational DB, Python with pandas and numpy, orchestration via cron or Airflow, and dashboards for reporting.
Building features that map to bets
Moneyline pricing workflow
Compute the probability of a home win by any method. Convert to fair American odds and compare to the book. If the market is around even money, require an EV greater than two or three percent to fire. As prices widen, demand higher edges due to variance. Stake via fractional Kelly with caps.
Totals workflow
Use your goals model to produce a full goal distribution for the game. For each posted total, calculate probability of Over and Under. Convert minus one ten lines to implied probabilities. Compare to model probabilities and compute EV. If the distribution is overdispersed relative to Poisson, consider bivariate models.
Puckline workflow
Compute the probability of Home winning by two or more using Skellam or simulations. Compare to puckline prices. Mind empty net effects by adjusting late game scoring probabilities.
Handling uncertainty
If the goalie is not confirmed, add an uncertainty penalty to stake size or raise the edge threshold. If line rushes change after the morning skate, recompute special teams matchups. If there is large travel and a back to back, increase variance estimates and consider smaller stakes.
Time-based cross-validation and class imbalance
Time-aware splits
Split by date so the future never informs the past. Use expanding windows where you train on past seasons and validate on the next block. Keep a last season holdout untouched until final scoring.
Class imbalance tweaks
NHL ML targets are near fifty fifty. But after filtering for edges or price ranges, you can create imbalance. Use class weights or stratified sampling in training. Evaluate by log loss and calibration more than accuracy.
SHAP and feature attribution
Why attribution matters
You want to know why the model likes a side, not just that it does. SHAP values help debug. For example, knowing that goalie quality plus rest advantage plus PP mismatch contributed plus four percentage points is valuable.
Practical SHAP usage
Compute SHAP on validation sets periodically. Monitor if certain features become overly dominant which could signal leakage. Communicate to users with simple summaries like edge driven by PP vs PK mismatch.
Simulation of OT and shootout
Simple starting approach
Use the regulation goals model to get the probability of a tie at sixty minutes. Assign OT win rate based on league average. Assign shootout win rate using regressed goalie metrics plus a league prior. Combine probabilities conservatively. Shootouts are noisy, so do not let a small sample shootout metric swing probabilities too much.
Practical templates to speed up builds
Feature register template (sample fields)
You should track feature name, description, source table, lag in games or days, decay lambda, null policy like median or last valid, data type, expected range, owner, and last updated.
Betting report template (per slate)
Your report should include Game ID, matchup, start time, model probability of home win, fair ML, best market ML, EV, stake, probability of Over six point five, fair price vs market, EV, stake, probability of Home minus one point five, EV, stake, notes on goalies and injuries, model version, and data snapshot ID.
Daily QA checklist
Check if data ingestion succeeded. Check if roster coverage is complete. Check goalie confirmations for a high percentage of games. Check feature null rates. Check odds capture. Check calibration on the last seven days within the expected band.
Using ATSwins outputs in practice
Pregame picks and player props
ATSwins aggregates picks, props, betting splits, and profit tracking. Your NHL algorithm feeds the core probabilities that connect to sides and totals by flagging when the current price deviates from fair. It connects to props by linking expected team scoring to player shots. It connects to splits by pressure testing edges against market consensus. You can explore the broader platform and how it surfaces model driven plays at ATSwins.
Transparency for users
Attach a few key drivers to every pick. Show historical performance by market and by team. Offer bankroll tools so users can pick a risk level.
Updating cadence
Do a morning pass for projected starters and travel. Do a midday refresh after skate news. Do a final pregame run at confirmation time. Do optional in game updates if needed.
Common pitfalls and how to avoid them
Avoid leakage from postgame stats by locking features. Avoid overfitting to rare events like shootouts by using priors. Avoid ignoring variance by sizing bets correctly. Avoid miscalibration at extremes by recalibrating. Avoid overreacting to micro sample trends by trusting decay. Avoid disconnected totals and ML models by keeping them coherent.
Extensions worth testing
Test player level on ice xG models. Test contextual finishing talent. Test penalty differential models. Test rink bias adjustments. Test travel fatigue modeled with circadian directionality. Test a meta model that conditions on market closers as an external target.
Sensible defaults for a first production version
For data, use nightly refresh from official APIs and daily CSV pulls from public databases. For features, use the top forty signals like xG, PP PK, goalie quality, and rest. For models, use a baseline Elo logistic, Poisson goals with simple OT extension, and XGBoost classifier for ML with isotonic calibration. For ensemble, use a weighted blend tuned on past seasons. For validation, use rolling origin. For betting, use fractional Kelly. For monitoring, use daily calibration bins.
How-to recap: from idea to edges?
Step 1: Acquire and clean data
Pull schedule, rosters, and play by play data. Merge historical stats and team metrics. Normalize IDs and store snapshots.
Step 2: Build features
Build core team strength, special teams splits, and goalie metrics with decay. Add rest, back to back, travel, and rink effects. Add lineups and injuries. Add score effects.
Step 3: Train models
Fit an Elo baseline. Fit a Poisson model for goals. Fit an XGBoost classifier for ML. Calibrate everything.
Step 4: Ensemble and simulate
Blend models. Simulate regulation plus OT and shootout. Price markets and screen for edges.
Step 5: Validate with walk-forward tests
Use chronological splits. Check calibration curves and goalie sensitivity.
Step 6: Deploy and monitor
Automate ingestion and training. Add bankroll controls. Recalibrate regularly.
Step 7: Communicate clearly to users
Show edges with fair prices. Provide feature attributions. Track profits in a clean dashboard.
Quick reference links
Check official league API docs. Check historical stats databases. Check advanced metrics repositories. Check scikit learn docs. Check XGBoost docs.
Conclusion
We covered how to turn NHL data into fair odds using smart features, calibration, and strict walk forward testing. The big takeaways are that clean inputs matter most, probabilities must be calibrated, and bankroll rules protect edge. Start simple, test, then iterate. For faster results, our expertise at ATSwins is an AI powered sports prediction platform offering data driven picks, player props, betting splits, and profit tracking across NFL, NBA, MLB, NHL, and NCAA. Free and paid plans help you make smarter decisions.
Frequently Asked Questions (FAQs)
What is an NHL prediction algorithm, in plain terms, and how does it turn stats into win probabilities?
An NHL prediction algorithm is a model that takes team and player data, then estimates each team’s chance to win. In practice, you feed it inputs like expected goals, special teams, goalie quality, rest and travel, and recent form. The model, often logistic regression or gradient boosted trees via tools like scikit learn or XGBoost, outputs a probability for each side. From there, you convert probabilities to fair moneylines, check edges versus the market, and optionally simulate game totals or pucklines. The final step is calibration, making sure a sixty percent edge actually wins about sixty percent over time, not just on paper.
What data do I need to build an NHL prediction algorithm that holds up night after night?
Use reliable, structured sources and keep them consistent. You need core game, team, and player data from official league APIs. You need historical records and schedules from comprehensive databases. You need shot quality and five on five rates like xG from advanced analytics repositories. Blend those with context like confirmed goalies, back to back flags, travel distance, injuries, and special teams form. Many pros also include exponential decay recent form so yesterday counts more than October. Keep timestamps clean so your NHL prediction algorithm never sees tomorrow’s info today.
How should I validate an NHL prediction algorithm so I don’t fool myself?
Two keys are time aware testing and proper scoring. Do walk forward splits by date. Train on the past, predict the future, roll forward. Score the NHL prediction algorithm with log loss and Brier score, then check calibration reliability plots. You can use scikit learn’s calibration tools for Platt or isotonic methods. Also, segment by situations like backup goalie starts, long road trips, or heavy favorites so you see where the model shines and where it struggles. Finally, track ROI after realistic fees and slippage because paper edges don’t always survive the real world.
Can an NHL prediction algorithm help with live betting & bankroll, or just pregame?
Both. A pregame NHL prediction algorithm sets baseline odds. Live, you can update inputs like goalie pulls, penalties, shot quality, and fatigue to refresh probabilities as the game state changes. If you can’t update live models, set rules like avoiding thin in play edges without confirmation. For bankroll, stick to fractional Kelly rather than full Kelly to limit risk. Even a small edge can be volatile; unit sizing and pacing matter. If you’re new, consider flat staking until your NHL prediction algorithm proves itself. For background on Kelly math, see the Kelly criterion, then adapt it cautiously.
How does ATSwins.ai fit with my NHL prediction algorithm and workflow?
ATSwins.ai complements your NHL prediction algorithm by adding real world betting context. ATSwins.ai is an AI powered sports prediction platform offering data driven picks, player props, betting splits, and profit tracking across NFL, NBA, MLB, NHL, and NCAA. Free and paid plans give bettors insights and guides to make smarter, more informed decisions. Use it to sanity check your edges against market moves, review player prop trends, and log results to see what your NHL prediction algorithm does best and where it needs tweaks. You will get clearer feedback loops, better discipline, and fewer blind spots. Check it out at ATSwins.ai.
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