Have you repeatedly tried to level up your NFL betting experience without success? Maybe you need an NFL betting model. While this may come as a shocker, a betting model does not predict outcomes in the strict sense of the word but uses statistical data to generate odds or probabilities it deems fair. You can use these projections to identify valuable opportunities based on what sportsbooks provide. Betting models are strictly data-driven, leaving no room for guesswork.
At NXTbets, we want to help you build an informed, profitable, and sustainable NFL betting career. In this guide, we will show you how to make an NFL betting model from scratch, identify the best data sources, calculate expected value, and manage your bankroll. This is a long-term project: you must focus on the process and recalibrate your model.
NFL betting models offer bettors an analytical advantage by offering pre-game projections. Our model will focus on common and highly volatile NFL betting markets, with an aim of helping bettors find value through a structured approach.
Scope
A simple NFL betting model (like ours) can project three betting markets. Core options include:
Spreads: It can predict the victory margin between two teams.
Totals: It can project the total points scored by both teams. Total bets are based on a particular threshold.
Moneyline: The model predicts the probability of both teams achieving an outright win.
Predictions can be extended to other markets, including player or team props. However, for a solid, profitable system, you should master the core markets above.
Granularly
We will focus on a team-level model. This means that instead of relying on full play/player-by-play/player simulation, our model will use team-wide stats and metrics. For refined projections, it will have a QB-sensitive adjustment to factor in quarterback performance, health, and situational contexts.
Limits
Limiting your expectations is crucial. Your NFL betting model cannot offer winning predictions every week, owing to the efficiency of NFL markets and constant line adjustment by sharp bettors. It is not a source of guaranteed projections, but:
A line shopping guide: It can help you identify games with a high value difference.
A discipline system: Instead of making emotional or impulsive decisions, your NFL betting model makes you rely on a proven, repeatable process.
Data You Need Before You Start
Betting models run on data. You need clean, reliable, and relevant datasets before you can begin predicting outcomes. Common categories include:
Historical Results & Lines
Past game results are crucial for building and testing datasets. You can choose final scores (including totals and moneyline) and closing spreads (a betting platform’s final odds just before the match kicks off).
Market Prices Live
You need live odds to compare your model’s projections to current market prices. You should also knowhow to remove the vig (the sportsbook’s commission) from the odds to establish the actual implied probability. This can help you identify positive expected value.
Efficiency Metrics
Box score stats like points and yards alone cannot help you fine-tune your projections. Good models use advanced, easily-adjustable efficiency metrics. Below are some.
Expected points added (EPA): This measures the impact of a team’s offensive or defensive play on the scores.
Pace/neutral pass rate: These focus on a team’s tendencies in different situations, for example, their pass rate on early downs in a close game.
Success rate: The number of games that meet a specific success threshold.
If you experience challenges accessing EPA or success rate data, you can use simpler metrics like yards per play.
Context Features
Besides efficiency metrics, comprehensive NFL betting models usually include situational features that influence game outcomes. These include:
Quarterback status: Noting the condition of QBs is essential. For example, indicate whether the starting QB has an injury.
Weather: Strong winds and high temperatures directly affect passes and scores.
Offensive line injuries: OL injuries can dramatically affect offensive and defensive play performances.
Travel/rest: Cross-country travel or a short week following a game night usually reflects on game outcomes.
Playground surface: For example, turf and grass have different injury risks and performance potential.
Time Cuts
Your model’s performance during testing should reflect its true predictive power. Therefore, you must restrict yourself to data that was available before the match. For example, you cannot include a team’s EPA from Week 11 when projecting a Week 10 game. These limits, known as time cuts, allow you to avoid “look-ahead” leakage when building and backtesting your model, which we shall discuss ahead.
For quality projections, your NFL betting model must logically progress from raw datasets to actionable probabilities. This needs several components, such as the following:
Rating Base
Your model will be founded on a modified Elo/team-strength rating system:
It separates offense and defense ratings, unlike a simple win/loss Elo, making it easy to identify teams with top offensive gameplay but weak defenses.
The ratings update weekly based on game outcomes.
You also need an adjustable home-field advantage (HFA) rating to capture changing league-wide trends.
Predictive Features
To account for recent forms and situational contexts, the model needs advanced metrics.
To capture the team’s current performance, incorporate the rolling averages of advanced stats like success rate over the last 6-8 weeks.
Metrics will be opponent-adjusted to account for competition quality.
Integrate factors that can cause significant swings, like weather, rest/travel, and injury flags.
From Ratings to Projections
Next, convert ratings into quantifiable spreads and total projections.
Spreads: For spreads projections, compare both teams’ offensive and defensive ratings and add the HFA and relevant adjustments.
Totals: Unlike spreads, a simple rating difference is not needed for totals. Instead, we will map our team pace metrics and offensive/defensive ratings to historical final totals through linear regression.
Probability Layers
To identify the real value, you must look beyond the numbers (and instead focus on the probabilities)
Spreads and moneylines: To estimate each team’s win probability in spreads and moneylines, we will apply a logistic transform on the projected spread. This is better than a simple linear conversion, as the probability distribution is more accurate.
Totals: To calculate the probability of a game finishing over or under the projected total, we will rely on a Poisson-style or regression-based method.
Ensembling
Ensembling prevents a model from overreacting to small sample sizes or one-off events. It involves combining the model’s output with a market-anchored prior, such as the previous week’s closing line. This introduces an extra layer of intelligence while reducing overall variance.
By organizing your spreadsheet into dedicated tabs and following a logical workflow, you can build a powerful predictive tool from raw data. Use the following actionable guide to learn how to build an NFL betting model using Excel or Google Sheets:
Tabs to Create
Organization is crucial. To separate data, final picks, and calculations, your spreadsheet should have the following tabs:
Raw_results: This captures historical final scores and final odds.
Odds_live: This is where you will paste updated odds.
Ratings: Team strength ratings are calculated here. It is the model’s engine.
Projections: This field computes your model’s raw predictions.
Bankroll: This simple log allows you to track and manage your bankroll.
EV_Calc: Here, you will compare your projections to market odds, allowing you to assess value.
Picks_dashboard: You can enjoy a final, filtered view of the best betting opportunities here.
Import Your Data
Every clean model is founded on clean data. You can import your data through:
Copy/paste function: This is the simplest method you can use to import data. Copy historical scores and odds from reliable sports data websites and directly paste them into your spreadsheet.
Automation: Directly scrape data from relevant web resources if you are tech-savvy. For Google Sheets, use the IMPORTHTML or IMPORTXML functions. For Excel, use the Power Query function to access data sources and automate the cleaning process.
Regardless of your preferred method, use standardized team names, consistent headers, and standard date formats.
Build Rolling Features
The Features tab tracks teams’ current form and situational contexts. Therefore:
Use relevant functions to find the rolling advantages of key metrics like success rate or expected points added (EPA) over a set period.
Assign a binary flag to the affected teams for injuries. Use a lookup function.
You can also create a weather lookup table to account for changes in wind and temperature.
Compute Team Ratings
Your ratings tab houses your Elo or team strength ratings:
Give each team a baseline rating at the beginning of the season.
Use the Elo formula to update ratings after every game. This requires pitting the expected outcome against the actual outcome.
For enhanced responsiveness, use an adjusting K-factor.
Project Spreads/ Totals
Convert your ratings into predictions in the Projections tab. Spreads and totals have different formulas:
Spread Formula
To calculate the spreads projection, you need a formula that combines team ratings and contextual adjustments, such as:
You will have to create a formula that maps efficiency metrics and the historical pace to final totals. Use a helper table to store the regression’s coefficients. Here’s an effective formula:
F- function that uses linear regression for total scores.
Convert Odds to Implied Probabilities
Use the EV_Calc tab to compare your model with the market. You should know how to convert odds, calculate probability, and remove vig.
To Implied
Begin by converting American odds to decimal odds. Next, use the formula (1/Decimal odds) to calculate implied probability.
Remove Vig
Add the implied probabilities. Divide each probability by the sum of implied probabilities to get the “fair” or no-vig probability.
EV Per Bet
Calculate the expected value formula to establish your long-run loss or profit per bet. You can use the following formula:
EV = (p_model * Payout) – ((1 – p_model) * Stake
Where:
p_model- outcome probability established by the user’s predictive model.
payout– potential return from a winning bet (including the stake).
stake– amount being wagered.
To calculate your edge, subtract the market’s no-vig probability from your model’s probability. The formula below applies:
(Model probability- Market probability)
Kelly & Staking
Staking is crucial for bankroll management. You can either use a Kelly fraction or a unit plan to make staking decisions.
Kelly Fraction
This method optimizes your bet size. To calculate your Kelly stake, use the formula f*=(bp- q)/b or [(Probability× Odds)-1]/ (odds-1). Most bettors prefer the more conservative Fractional Kelly, as it reduces variance and risks. In the first formula:
f*- Kelly Fraction, or the optimal percentage of your bankroll to wager on a particular outcome.
B- net odds from a winning bet.
P- model’s projected winning probability.
Q- oss probability.
Unit Plans
This involves using a consistent percentage of your bankroll on every bet, without considering the edge. It prevents high-variance swings.
Picks Dashboard
This is the command center:
You can filter your bets by week, injury flags, or minimum edge.
To color-code picks or display the model’s projected lines besides the sportsbook’s odds, use conditional formatting.
Backtesting & Validation (Trust, but Verify)
Consider your NFL betting model as a hypothesis whose scientific assessment requires backtesting. This process involves simulating your betting model on historical data to see how it would have performed. It allows you to establish whether your model is ready.
Walk-Forward Splits
For more accurate results, mimic real-time betting conditions when backtesting. The walk-forward validation is highly recommended for time series data like sports. Its rolling window saves you from testing your model on the entire historical dataset at once. It only requires you to:
Train: For example, build and calibrate your model using the NFL’s 2024 Week 1-8 data.
Test: Make projections and use the trained model to track performance for the out-of-sample data (Weeks 9-12).
Roll forward: Add the test data from weeks 9-12 to your training set for the next training period. You can then use the model to predict the outcomes of Weeks 13-16.
Metrics
Testing an NFL betting model needs more than a simple loss/win record. Include the following to assess its strengths and weaknesses accurately:
Return on investment: The profit divided by the total stake is perhaps the most crucial metric.
Closing line Value (CLV): The CLV is just as crucial as the final market closing line. It helps you track whether your bet price was better than the final market closing line.
Hit rate: The hit rate refers to the percentage of winning bets. Although a higher value may be appealing, profitable models rarely have high hit rates if higher odds accompany wins.
Calibration (decile reliability): This is a visualization that weighs your model’s projected probabilities against the actual win percentages. You can plot it easily by categorizing your bets into deciles (for example, all wagers with a 40-50% probability) and comparing if the actual win rate is within the 40-50% range.
Brier score & log loss: These scoring functions assess the accuracy of your probabilities. A lower score means your model’s probabilities are correctly calibrated. For example, if an event has a 75% winning chance according to your predictions, it should win about 75% of the time.
Significance & Drifts
Do not over-reli on small samples. A winning streak spanning 2-3 weeks may be luck. For better outcomes:
Prioritize confidence intervals.
Consider changes that can make your model’s performance drift over time, such as a key player’s injury or sudden changes in coaching philosophy.
Feature Discipline
You must scrutinize your model’s features:
What to remove: Drop those that cannot help predict outcomes from out-of-sample data.
Avoid/address multicollinearity: This occurs when two or more features are heavily correlated.
Seal leakages: Do not include data you wouldn’t have known at the time of the bet, such as a player’s injury status from a Sunday morning report in a Friday model projection.
Lastly, avoid overfitting, which is adding excess parameters or “knobs”. It makes your model excellent at predicting past data, but terrible at projecting upcoming games.
Live Updates and Light Automation
The accuracy of your model’s projection depends on the data you feed it. For better insights, keep it current through regular updates, odds refresh, and effective version control.
Update Cadence
Below is an update schedule worth emulating:
Monday: Update your team ratings using the past week’s games. This is a critical update as it adjusts your entire betting model.
Friday/Saturday: For enhanced accuracy, conduct a targeted update on injuries and quarterback status on Fridays or Saturdays.. Final injury reports are released around this time, allowing you to tell who will play.
Sunday AM: The closing line indicates the most efficient market price. Compare your bet to the closing line value to assess whether you made a good bet.
Odds Refresh
You need a reliable live odds stream. You can manually copy them from reliable betting sites and paste them into your Odds_Live tab or use an automated approach. Here are your options:
Google Sheet functions: Although picky, Google Sheet functions like IMPORTXML and IMPORTHTML can pull data from web pages.
Sports betting APIs: Consider sports betting APIs for better outcomes. Many services have free or low-cost plans that can integrate directly into your spreadsheet, furnishing you with clean, real-time data.
In the correct format, consistently and accurately log when you pull the live odds data for easier line movement tracking.
Version Control
Archive your changes. You should:
Make a weekly duplicate of the entire sheet: This allows you to capture your model’s weekly performance results permanently
Keep a small change log on your main dashboard: Note down your tweaks, including home-field advantage (HFA) or K-Factor adjustments.
Archiving your performance and changes helps you identify your model’s strengths and weaknesses.
Market Strategy & Bankroll Integration
Having an NFL betting model is not enough. You need a solid betting strategy and disciplined bankroll management. Here are a few important considerations:
Line Shopping
Your betting model may give a -3.0 projection. However, sportsbooks offer different odds, some more profitable. Shopping for betting lines is, therefore, a good way to build a sustainable betting experience. Consider adding a simple “Odds Matrix” in your spreadsheet to compare relevant lines between major sportsbooks.
This strategy allows you to find the best bet price, even if it is only a few cents difference on the odds. This difference, accumulated over hundreds of predictions or bets, can be heavily profitable.
When to Bet
Bet timing is a crucial art. You can either bet early or later in the week:
Early in the week: Betting lines are softer and more vulnerable to market movements. You can lock in the best value if your model identifies a huge discrepancy.
Later in the week: As days go by, betting lines become more efficient as value decreases. On the bright side, you can access more information, such as weather updates and final injury reports.
The best strategy is to target key NFL numbers, particularly 3 and 7. These are usually the most common victory margins. For example, a bet at +3.5 instead of +2.5 or -6.5 instead of -7.5 would be valuable.
Staking
Your betting value is just as crucial as the NFL markets you bet on. You can either use the Kelly criterion or a flat unit plan to determine the right stake, which we have both discussed above. To avoid over-betting, use your platform’s in-built tools to limit your exposure per game or on special days like Sunday.
Compliance & Responsible Play
Your betting model should help you find value, not encourage recklessness. You need to stay disciplined, which requires consistent regulatory compliance and responsible betting.
Legal Use Only
To stay legally compliant:
Only wager in jurisdictions that permit and regulate sports betting.
Use licensed and regulated sportsbooks like DraftKings, BetMGM, and FanDuel.
Always consider your state’s sports betting laws and online gambling requirements before beginning your journey.
Understand and fulfill your tax obligations.
Player Protection
For sustainable results, use your platform’s built-in tools to stay in control of your betting experience. These include:
Deposit limits: Use deposit limits to regulate how much you can add to your account.
Bankroll limits: Some platforms allow you to cap how much you can spend on a single bet. Set a favorable amount.
Time limits: Reputable sites like Bet365 allow you to control how much time you stay logged in.
Self-exclusion: Set up a self-exclusion period if you need a break.
Betting should always be a form of entertainment, not a compulsion or liability.
Model Humility
Your model won’t always be perfect. To protect yourself and your bankroll, stick to your staking plans andavoid emotion-driven decisions. Do not let a single loss throw you into a frenzy. Use your bankroll wisely and learn to accept unfavorable outcomes.
Conclusion: Beat the Closing Line with NXTbets
The secret to building a data-driven model is now in the open. It is this simple: use high-quality data and statistical metrics to create ratings, project lines, find your edge, compute the expected value, bet responsibly, and make result-based improvements. Your goal should not be to win, but to beat the closing line.
At NXTbets, we can help you elevate your betting journey. You can download and use our free NFL betting model template for smarter, more informed NFL betting decisions. Remember to alsosubscribe to our newsletterfor model tutorials, prompt market updates, and exciting NFL promotions.
Can I build a decent model using only a spreadsheet, or do I need a programming language?
You can create a profitable model using a simple spreadsheet. However, a programming language like Python or R may help with large datasets, complex models, or automation.
How should I handle weather and injury news that breaks after I bet?
You can adjust your model and, based on its projections, place a new bet (if you get favorable lines). However, refrain if the news causes extreme uncertainty. Avoiding high-variance situations that may challenge your model is recommended.
What edge threshold should I require before placing a wager?
Settle for a 2% positive edge value (+EV) or more, i.e., at the lowest, your model’s no-vig probability value should be 2% higher than the market’s implied no-vig probability. This is what translates into a value bet.
How often should I retrain or retune coefficients during the season?
While it’s necessary to update your data and team ratings weekly, you don’t have to retune your model’s coefficients frequently. To avoid overfitting (due to regular retraining), only adjust specific coefficients to correct any persistent bias.
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Build a Winning NFL Betting Model
Table of Contents
Have you repeatedly tried to level up your NFL betting experience without success? Maybe you need an NFL betting model. While this may come as a shocker, a betting model does not predict outcomes in the strict sense of the word but uses statistical data to generate odds or probabilities it deems fair. You can use these projections to identify valuable opportunities based on what sportsbooks provide. Betting models are strictly data-driven, leaving no room for guesswork.
At NXTbets, we want to help you build an informed, profitable, and sustainable NFL betting career. In this guide, we will show you how to make an NFL betting model from scratch, identify the best data sources, calculate expected value, and manage your bankroll. This is a long-term project: you must focus on the process and recalibrate your model.
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What This Model Aims to Predict
NFL betting models offer bettors an analytical advantage by offering pre-game projections. Our model will focus on common and highly volatile NFL betting markets, with an aim of helping bettors find value through a structured approach.
Scope
A simple NFL betting model (like ours) can project three betting markets. Core options include:
Predictions can be extended to other markets, including player or team props. However, for a solid, profitable system, you should master the core markets above.
Granularly
We will focus on a team-level model. This means that instead of relying on full play/player-by-play/player simulation, our model will use team-wide stats and metrics. For refined projections, it will have a QB-sensitive adjustment to factor in quarterback performance, health, and situational contexts.
Limits
Limiting your expectations is crucial. Your NFL betting model cannot offer winning predictions every week, owing to the efficiency of NFL markets and constant line adjustment by sharp bettors. It is not a source of guaranteed projections, but:
Data You Need Before You Start
Betting models run on data. You need clean, reliable, and relevant datasets before you can begin predicting outcomes. Common categories include:
Historical Results & Lines
Past game results are crucial for building and testing datasets. You can choose final scores (including totals and moneyline) and closing spreads (a betting platform’s final odds just before the match kicks off).
Market Prices Live
You need live odds to compare your model’s projections to current market prices. You should also know how to remove the vig (the sportsbook’s commission) from the odds to establish the actual implied probability. This can help you identify positive expected value.
Efficiency Metrics
Box score stats like points and yards alone cannot help you fine-tune your projections. Good models use advanced, easily-adjustable efficiency metrics. Below are some.
If you experience challenges accessing EPA or success rate data, you can use simpler metrics like yards per play.
Context Features
Besides efficiency metrics, comprehensive NFL betting models usually include situational features that influence game outcomes. These include:
Time Cuts
Your model’s performance during testing should reflect its true predictive power. Therefore, you must restrict yourself to data that was available before the match. For example, you cannot include a team’s EPA from Week 11 when projecting a Week 10 game. These limits, known as time cuts, allow you to avoid “look-ahead” leakage when building and backtesting your model, which we shall discuss ahead.
Core Modelling Approach (Concepts You’ll Implement)
For quality projections, your NFL betting model must logically progress from raw datasets to actionable probabilities. This needs several components, such as the following:
Rating Base
Your model will be founded on a modified Elo/team-strength rating system:
You also need an adjustable home-field advantage (HFA) rating to capture changing league-wide trends.
Predictive Features
To account for recent forms and situational contexts, the model needs advanced metrics.
From Ratings to Projections
Next, convert ratings into quantifiable spreads and total projections.
Probability Layers
To identify the real value, you must look beyond the numbers (and instead focus on the probabilities)
Ensembling
Ensembling prevents a model from overreacting to small sample sizes or one-off events. It involves combining the model’s output with a market-anchored prior, such as the previous week’s closing line. This introduces an extra layer of intelligence while reducing overall variance.
Step-by-Step Spreadsheet Build (Download CTA Included)
By organizing your spreadsheet into dedicated tabs and following a logical workflow, you can build a powerful predictive tool from raw data. Use the following actionable guide to learn how to build an NFL betting model using Excel or Google Sheets:
Tabs to Create
Organization is crucial. To separate data, final picks, and calculations, your spreadsheet should have the following tabs:
Import Your Data
Every clean model is founded on clean data. You can import your data through:
Regardless of your preferred method, use standardized team names, consistent headers, and standard date formats.
Build Rolling Features
The Features tab tracks teams’ current form and situational contexts. Therefore:
You can also create a weather lookup table to account for changes in wind and temperature.
Compute Team Ratings
Your ratings tab houses your Elo or team strength ratings:
For enhanced responsiveness, use an adjusting K-factor.
Project Spreads/ Totals
Convert your ratings into predictions in the Projections tab. Spreads and totals have different formulas:
Spread Formula
To calculate the spreads projection, you need a formula that combines team ratings and contextual adjustments, such as:
ProjSpread = (OffStr_home – DefStr_away) – (OffStr_away – DefStr_home) + HFA + Adj_weather + Adj_QB
Where:
Total Formula
You will have to create a formula that maps efficiency metrics and the historical pace to final totals. Use a helper table to store the regression’s coefficients. Here’s an effective formula:
ProjTotal = f(Pace_home, Pace_away, OffStr, DefStr, weather)
Where:
Convert Odds to Implied Probabilities
Use the EV_Calc tab to compare your model with the market. You should know how to convert odds, calculate probability, and remove vig.
To Implied
Begin by converting American odds to decimal odds. Next, use the formula (1/Decimal odds) to calculate implied probability.
Remove Vig
Add the implied probabilities. Divide each probability by the sum of implied probabilities to get the “fair” or no-vig probability.
EV Per Bet
Calculate the expected value formula to establish your long-run loss or profit per bet. You can use the following formula:
EV = (p_model * Payout) – ((1 – p_model) * Stake
Where:
To calculate your edge, subtract the market’s no-vig probability from your model’s probability. The formula below applies:
(Model probability- Market probability)
Kelly & Staking
Staking is crucial for bankroll management. You can either use a Kelly fraction or a unit plan to make staking decisions.
Kelly Fraction
This method optimizes your bet size. To calculate your Kelly stake, use the formula f*=(bp- q)/b or [(Probability× Odds)-1]/ (odds-1). Most bettors prefer the more conservative Fractional Kelly, as it reduces variance and risks. In the first formula:
Unit Plans
This involves using a consistent percentage of your bankroll on every bet, without considering the edge. It prevents high-variance swings.
Picks Dashboard
This is the command center:
Backtesting & Validation (Trust, but Verify)
Consider your NFL betting model as a hypothesis whose scientific assessment requires backtesting. This process involves simulating your betting model on historical data to see how it would have performed. It allows you to establish whether your model is ready.
Walk-Forward Splits
For more accurate results, mimic real-time betting conditions when backtesting. The walk-forward validation is highly recommended for time series data like sports. Its rolling window saves you from testing your model on the entire historical dataset at once. It only requires you to:
Metrics
Testing an NFL betting model needs more than a simple loss/win record. Include the following to assess its strengths and weaknesses accurately:
Significance & Drifts
Do not over-reli on small samples. A winning streak spanning 2-3 weeks may be luck. For better outcomes:
Feature Discipline
You must scrutinize your model’s features:
Lastly, avoid overfitting, which is adding excess parameters or “knobs”. It makes your model excellent at predicting past data, but terrible at projecting upcoming games.
Live Updates and Light Automation
The accuracy of your model’s projection depends on the data you feed it. For better insights, keep it current through regular updates, odds refresh, and effective version control.
Update Cadence
Below is an update schedule worth emulating:
Odds Refresh
You need a reliable live odds stream. You can manually copy them from reliable betting sites and paste them into your Odds_Live tab or use an automated approach. Here are your options:
In the correct format, consistently and accurately log when you pull the live odds data for easier line movement tracking.
Version Control
Archive your changes. You should:
Archiving your performance and changes helps you identify your model’s strengths and weaknesses.
Market Strategy & Bankroll Integration
Having an NFL betting model is not enough. You need a solid betting strategy and disciplined bankroll management. Here are a few important considerations:
Line Shopping
Your betting model may give a -3.0 projection. However, sportsbooks offer different odds, some more profitable. Shopping for betting lines is, therefore, a good way to build a sustainable betting experience. Consider adding a simple “Odds Matrix” in your spreadsheet to compare relevant lines between major sportsbooks.
This strategy allows you to find the best bet price, even if it is only a few cents difference on the odds. This difference, accumulated over hundreds of predictions or bets, can be heavily profitable.
When to Bet
Bet timing is a crucial art. You can either bet early or later in the week:
The best strategy is to target key NFL numbers, particularly 3 and 7. These are usually the most common victory margins. For example, a bet at +3.5 instead of +2.5 or -6.5 instead of -7.5 would be valuable.
Staking
Your betting value is just as crucial as the NFL markets you bet on. You can either use the Kelly criterion or a flat unit plan to determine the right stake, which we have both discussed above. To avoid over-betting, use your platform’s in-built tools to limit your exposure per game or on special days like Sunday.
Compliance & Responsible Play
Your betting model should help you find value, not encourage recklessness. You need to stay disciplined, which requires consistent regulatory compliance and responsible betting.
Legal Use Only
To stay legally compliant:
Player Protection
For sustainable results, use your platform’s built-in tools to stay in control of your betting experience. These include:
Betting should always be a form of entertainment, not a compulsion or liability.
Model Humility
Your model won’t always be perfect. To protect yourself and your bankroll, stick to your staking plans and avoid emotion-driven decisions. Do not let a single loss throw you into a frenzy. Use your bankroll wisely and learn to accept unfavorable outcomes.
Conclusion: Beat the Closing Line with NXTbets
The secret to building a data-driven model is now in the open. It is this simple: use high-quality data and statistical metrics to create ratings, project lines, find your edge, compute the expected value, bet responsibly, and make result-based improvements. Your goal should not be to win, but to beat the closing line.
At NXTbets, we can help you elevate your betting journey. You can download and use our free NFL betting model template for smarter, more informed NFL betting decisions. Remember to also subscribe to our newsletter for model tutorials, prompt market updates, and exciting NFL promotions.
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Frequently Asked Questions (FAQs)
You can create a profitable model using a simple spreadsheet. However, a programming language like Python or R may help with large datasets, complex models, or automation.
You can adjust your model and, based on its projections, place a new bet (if you get favorable lines). However, refrain if the news causes extreme uncertainty. Avoiding high-variance situations that may challenge your model is recommended.
Settle for a 2% positive edge value (+EV) or more, i.e., at the lowest, your model’s no-vig probability value should be 2% higher than the market’s implied no-vig probability. This is what translates into a value bet.
While it’s necessary to update your data and team ratings weekly, you don’t have to retune your model’s coefficients frequently. To avoid overfitting (due to regular retraining), only adjust specific coefficients to correct any persistent bias.
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