I need to build a simple betting model. What's… | Parse
I need to build a simple betting model. What's the best guide or software for a non-programmer?
Data as of Sep 21, 2026 · Based on 323 AI responses from ChatGPT Search and Google AI Mode · See how Parse measures this
For a non-programmer, Rithmm is the best fit for building custom AI betting models via a simplified, no-code slider interface. If you prefer to learn the fundamentals by building your own model in a spreadsheet, OddsJam and Unabated provide foundational tutorials on betting math, while tools like the OddsJam Bet Tracker help monitor your results.
The best fit for non-programmers wanting to build custom AI betting models without writing code. It uses a beginner-friendly interface with sliders that allow you to adjust specific performance factors to inform predictions.
Best for those who want to learn how to build models manually in Excel or Google Sheets. It provides tutorials on foundational betting math, such as calculating implied probability, and a tracker for measuring model efficiency.
Underdog Chance | AI Sports Betting Analysis — 25 Years of Probability Modelshttps://www.underdogchance.com/betting-models-no-code/
6%
How to Build a Sports Betting Model in Excel | OddsJamhttps://oddsjam.com/betting-education/how-to-build-a-sports-betting-model-in-excel
3%
What is a Sports Betting Model? How to Build and Use a Sports Betting Model | OddsJamhttps://oddsjam.com/betting-education/betting-model
3%
How to Build an AI Sports Betting Model with Rithmmhttps://www.rithmm.com/post/how-to-build-an-ai-sports-betting-model-with-rithmm
2%
I want to start building models or atleast get a more analyzed ...https://www.reddit.com/r/algobetting/comments/1q13kbc/i_want_to_start_building_models_or_atleast_get_a/
2%
Rithmmhttps://www.rithmm.com/
2%
Sports Betting Models: Getting Started with a Simple Model (Basketball) | by David | Mediumhttps://medium.com/@dkatzman_3920/sports-betting-models-getting-started-with-a-simple-model-basketball-67ffaf4dba6a
1%
Sports Betting Models in Google Sheets: Practical Guide + Examples (2026)https://www.underdogchance.com/learn-to-bet/sports-betting-models/
1%
Building a Sports Betting Model: Tips, Tricks and Advice - VSiNhttps://vsin.com/betting-strategies/building-a-sports-betting-model-tips-tricks-and-advice/
1%
The Best Sports Betting AI Softwarehttps://www.rithmm.com/post/sports-betting-ai-software
1%
Question: I need to build a simple betting model. What's the best guide or software for a non-programmer?
For a non-programmer, start with Google Sheets rather than dedicated modeling software. It’s transparent, cheap, and enough for an Elo, regression, Poisson, or EV model.
Best beginner guide:Underdog Chance’s Google Sheets guide, which walks through probability, fair odds, EV, and a simple working model.
Best hands-on video:Kyle Kirms’ step-by-step NBA model in Excel/Sheets.
offers a no-code integration for sports data and odds.
If you don't know how to code, you have two great paths for building a betting model: No-code AI/analytics software that does the heavy lifting for you , or Spreadsheets (Excel/Google Sheets) where you control the logic using basic formulas.
Here is a breakdown of the best options for non-programmers:
1. Dedicated AI & Custom Model Software (Easiest)
If you want to build custom predictive models without wrestling with math or data feeds, specialized sports analytics tools are your best bet.
Rithmm : Widely considered the leading app for non-programmers looking to build custom sports betting models. It lets you select factors (like offense, defense, or specific player stats), tweak parameters visually, and instantly generates AI-driven predictions and positive Expected Value (+EV) spots for NFL, NBA, college sports, and player props.
OddsShopper : While not a "build-your-own-formula" model creator, it is an essential software tool for non-programmers. It reverse-engineers sharp market prices (de-vigging) to show you the real mathematical probability of an event, effectively serving as the output benchmark your model should aim to beat.
2. Microsoft Excel or Google Sheets (Most Customizable)
If you want to learn the underlying math and completely own your logic without paying for a niche subscription, spreadsheets are the gold standard.
How it works: You pull historical or current stats into a sheet, use basic formulas like average (Average), lookup (VLOOKUP or XLOOKUP), and simple arithmetic to project a team's score or win probability, and then compare that output to current sportsbook lines.
Guides to watch: There are great practical walkthroughs tailored for absolute beginners. For instance, check out this YouTube Guide to Building an NBA Betting Model in Google Sheets which uses free web data and basic cell references with zero programming required.
Recommendation on Where to Start
Choose Rithmm if you want plug-and-play customization where you can toggle factors and see results in minutes.
Choose Google Sheets if you want a free, hands-on learning experience where you can build a simple point-differential or average-scoring model from scratch.
To help narrow down the best path, tell me:
Which sport are you trying to model (NFL, NBA, soccer, etc.)?
Do you want to build your own formulas in Excel/Google Sheets , or would you prefer a ready-made app?
If you’re a non-programmer, I’d start with Google Sheets or Excel, not Python. A spreadsheet is enough to build a genuinely useful first model, and you can see every assumption and formula rather than relying on a black box.
What I’d use
Best beginner guide: the recent YouTube walkthrough “Build a Simple NBA Betting Model (No Coding, No Paid Data)”. It specifically builds the model in Google Sheets using free data and is aimed at people with no programming experience.
Best deeper resource:Statistical Sports Models in Excel by NBAstuffer. It's specifically designed to teach sports modeling through Excel without assuming a strong statistics/programming background, with examples covering several major sports.
Best spreadsheet building block: a no-vig/fair-odds calculator. Learning to convert bookmaker odds into implied probabilities, remove the vig, and compare those probabilities with your own estimate is a very useful foundation.
If you eventually want no-code automation: tools such as Rithmm are designed to create predictive models without programming, although I'd learn the spreadsheet fundamentals first so you understand what the software is actually doing.
The simple model I'd build first
Don't start with a complicated AI model. Make a sheet with:
Historical games/results
A handful of sensible predictors
Your estimated probability
Sportsbook odds
Implied probability
Expected value (EV)
Predicted vs. actual result
ROI and performance over a large sample
Then backtest it on games the model wasn't trained on. That's particularly important because a model can look fantastic on historical data simply because you've accidentally fitted it to that data.
If you tell me which sport and market you want to model (e.g. NBA spreads, NBA player props, NFL moneylines, MLB totals), I can point you to a specific beginner-friendly setup and lay out the spreadsheet columns/formulas step by step.
For a non-programmer, the absolute best tool to start with is Microsoft Excel or Google Sheets . You don't need a single line of code to build a functional probability or expected value (+EV) model; you just need a grasp of basic formulas (like VLOOKUP, AVERAGE , and IFERROR).
The best software, guides, and workflows to get you up and running include:
Google Sheets (with IMPORTHTML)
Why it’s great: It's free and allows you to pull live stats directly from public websites (like Basketball-Reference or TeamRankings) into your spreadsheet using formulas like IMPORTHTML.
How to use it: Follow no-code walkthroughs like the Build a Simple NBA Betting Model on YouTube , which teaches you how to map team stats, project scores, and compare them against sportsbook lines without writing code.
Excel-Based Templates and Communities
Why it’s great: If you want to skip building the architecture from scratch, platforms like ExcelBets offer pre-made frameworks, tutorials, and +EV subscription tools tailored specifically for spreadsheet-driven betting.
How to use it: Look for tutorials demonstrating a Step-by-Step Guide to Building a Sports Betting Model to see how to color-code dashboards, separate raw data from final outputs, and structure your sheets cleanly.
Books on Statistical Modeling for Non-Coders
Recommended Reading: Look for practical guides like Statistical Sports Models in Excel or audio/text resources focused on no-code automation and fundamental probability. They break down Poisson distributions and regression basics using standard spreadsheet math rather than Python or R.
To help narrow down the best specific approach, tell me:
Which sport are you trying to model (e.g., NFL, NBA, MLB, soccer)?
Do you want to pull live data automatically , or are you okay copy-pasting/entering stats manually?
If you’re a non-programmer building a simple betting model, I would not start with machine learning software. Start with a spreadsheet-based model, learn the math behind it, then move up if you need automation.
A good beginner path:
1. Start with Google Sheets or Excel (best first step)
Best for: a first NFL/NBA/MLB/soccer model, power ratings, expected value (EV), and tracking results.
You can build:
Team/player ratings
Win probability estimates
Fair odds
Edge vs. sportsbook lines
Bet tracking and ROI
Simple backtesting
A spreadsheet approach is commonly recommended for beginners because it avoids coding and makes it easier to see how assumptions affect predictions.
A simple first model might look like:
Collect past results
Create a rating for each team
Adjust for home field, injuries, rest, etc.
Convert ratings into a probability
Compare your probability to the market odds
Example:
Your model says Team A wins 55% of the time
Sportsbook odds imply 50%
You may have a potential edge
2. Beginner-friendly tools if you want less spreadsheet work
Akkio
Good if you want to upload a CSV and experiment with prediction models without coding. It’s a no-code predictive analytics platform aimed at non-technical users.
DataRobot
Powerful, but probably overkill for a first betting model. It automates many machine-learning steps, but it’s more suited to business/enterprise modeling than a hobby betting project.
Alteryx
Useful if your main challenge is cleaning and organizing large datasets rather than building the actual betting logic.
3. Guides I’d recommend learning from
For a non-programmer, look for guides covering:
Elo ratings
Logistic regression basics
Expected value betting
Backtesting
Closing line value (CLV)
A practical beginner guide is one that walks through building a basic probability model in Google Sheets, then testing whether it would have beaten historical lines.
My suggested learning path
Week 1:
Learn Excel/Sheets formulas
Build a simple Elo model
Week 2–3:
Add betting odds
Calculate implied probability
Track hypothetical bets
Month 2+:
Add variables: - injuries
pace
starting pitchers/QBs
weather
rest days
Only later:
Move to Python or AutoML tools
For a first project, I’d choose:
NFL: Elo + injury adjustments + market comparison
NBA: team ratings + pace + rest
MLB: starting pitcher + bullpen + park factors
Soccer: Poisson goal model
If you tell me which sport and bet type (moneyline, spread, totals, props, parlays), I can suggest a simple model structure and the exact spreadsheet columns to build.
Building a betting model as a non-programmer is entirely doable. You don’t need Python or R; you just need a structured approach, basic math, and the right tool to organize your data.
Phase 1: Choose Your Software
Google Sheets or Microsoft Excel (The Gold Standard)
Why it’s best: It's completely free, flexible, and allows you to own your data and formulas. Google Sheets is particularly powerful because it can pull live data from the web using functions like IMPORTHTML or IMPORTDATA.
Best for: Learning the core mechanics of expected value (EV), implementing simple point-differential averages (like Pythagorean expectation or simple net ratings), and tracking your bets.
No-Code / Predictive Analytics Platforms (The Shortcut)
Rithmm: A dedicated app built specifically for this. It allows you to toggle different stats, factors, and metrics to build custom predictive models for the NFL, NBA, and college sports without writing a single line of code.
Moddy.AI: An emerging platform designed to let users select or prompt specific statistics to build and publicly track sports models against live sportsbook odds.
Best for: People who want an automated interface that does the heavy statistical lifting and comparisons against live betting lines instantly.
Phase 2: Follow a Step-by-Step Guide
If you go the Google Sheets/Excel route, look for practical walkthroughs tailored to beginners:
YouTube Tutorials: Search for guides like "Build a Simple NBA Betting Model (No Coding, No Paid Data)" which walk you through pulling free data from sites like Basketball-Reference or TeamRankings using basic spreadsheet formulas like VLOOKUP.
The Unabated / Action Network Guides: These educational platforms offer great conceptual frameworks on how to convert basic stats into a power rating, and then convert that rating into a win probability or point spread.
Phase 3: The Blueprint for a Simple Spreadsheet Model
If you're starting from scratch in Google Sheets, keep your first iteration basic:
The Database: Create a tab for team stats (e.g., points scored per game, points allowed per game, adjusted for home/away splits).
The Schedule: Pull in upcoming matchups for the day.
The Projection: Use a simple average formula—take the away team's average offensive output, match it against the home team's defensive allowance, and vice versa, to spit out a projected final score.
The Comparison: Put the current sportsbook odds next to your projection. If your model differs from the sportsbook line by a significant margin (your "edge"), that highlights a potential value bet.
To help narrow down the best path, tell me:
Which sport are you trying to model (NFL, NBA, Soccer, etc.)?
Do you prefer a fully hands-on spreadsheet you can customize, or an automated app that does the math for you?
If you're a non-programmer, I’d start with Google Sheets, not Python or a complicated AI betting platform. A simple spreadsheet model is enough to learn the core concepts: probability, fair odds, expected value, and backtesting.
My picks
Best way to learn:Google Sheets + a simple Elo model. It's transparent, easy to modify, and you can see exactly why the model produces a number.
Best if you want something ready-made:Underdog Chance has downloadable Google Sheets models for NFL, NBA, MLB, NHL and other markets.
Best no-code software:SharpLab AI is designed specifically around building and tracking betting models without coding or spreadsheets.
Best for learning the fundamentals: this 2026 step-by-step guide is a reasonable starting point because it walks through spreadsheets, data, backtesting and eventually Python.
What I'd actually do
Don't start with machine learning. Build this:
Historical data → team rating → predicted probability → fair odds → compare with sportsbook odds → record result
For example, for an NFL spread model, you could initially use:
Team strength
Home-field advantage
Recent performance
Injuries
Rest
Market spread
Then have the spreadsheet calculate your projected spread and whether the sportsbook price represents positive expected value.
The important part is backtesting. Don't judge the model by whether yesterday's picks won. Track predictions and prices before games happen, and evaluate calibration, ROI and closing-line value over a substantial sample.
If you tell me which sport/market you want to model (NFL spreads, NBA totals, MLB moneylines, player props, etc.), I can give you a very simple Google Sheets model you can build in about 30 minutes, including the exact columns and formulas.
For a non-programmer, you don’t need complex Python scripts or expensive enterprise software to get started. The absolute best approach is using spreadsheet software combined with free public data , or trying out emerging no-code AI model builders designed specifically for sports bettors.
The Best Software Options
Google Sheets or Microsoft Excel (The Gold Standard)
Why: Almost all beginner-to-intermediate betting models are built here. They handle basic arithmetic, averages, and conditional formatting easily. Google Sheets is especially useful because you can pull live data from web pages using formulas like IMPORTHTML or IMPORTXML.
Best for: Complete control over your logic without writing a single line of code.
Rithmm
Why: A dedicated predictive-analytics app built specifically for sports bettors who want custom models without coding. It allows you to select factors, adjust player or team metrics, and build strategies for NFL, NBA, MLB, and college sports.
Best for: A plug-and-play app interface that does the heavy lifting for you.
Moddy.AI
Why: A newer no-code platform where you can use a conversational interface or manual selectors to pull from massive statistical databases, build a model, and automatically backtest it against sportsbook odds.
Best for: Testing out advanced stats and AI guidance without a programming background.
How to Build Your First Simple Spreadsheet Model
If you go the Google Sheets/Excel route, follow this straightforward framework:
Gather Free Data: Use sites like Basketball Reference, Pro Football Reference , or TeamRankings which offer clean, copy-pasteable or scrapable tables for scoring averages, pace, and efficiency.
Create a Master Schedule/Matchup Tab: Set up columns for the Date, Away Team , and Home Team.
Use VLOOKUP or XLOOKUP: Build a reference table of each team's offensive and defensive efficiency ratings. Use lookup formulas so that when you input a game, the spreadsheet automatically pulls each team's stats into the calculation row.
Project the Score/Outcome: Create a simple formula. For example, estimate a team's expected points by taking the Away Team's average points scored and averaging it with the Home Team's average points allowed.
Compare to Market Lines: Add a column for the current sportsbook spread or total, and subtract it from your model’s projection to highlight potential value discrepancies.
If you want to dive deeper, let me know:
Which sport you want to model (NFL, NBA, MLB, etc.)
Whether you prefer a ready-made app or a step-by-step guide to setting up a Google Sheet
I can help you map out the exact formulas or metrics to start tracking.
If you’re a non-programmer, I’d start with Google Sheets rather than betting-model software. It lets you understand the model instead of buying a black box, and current guides specifically recommend Sheets/Excel for first models.
My top picks
Best overall guide: Underdog Chance
Their beginner guide walks through an Elo-style model → win probability → fair odds → EV → backtesting.
That’s exactly the progression I’d recommend for a first model.
Don't start with a complicated machine-learning model. Build this:
Inputs
Team rating
Home/away
Recent performance
A handful of meaningful stats
Sportsbook odds
↓
Model
Estimate probability of Team A winning
↓
Outputs
Model probability: 56%
Market implied probability: 52%
Fair odds: -127
Available odds: +105
Expected value: positive
Bet/no-bet flag
Then track every prediction and backtest it on historical games before risking money. The beginner guides emphasize validation/backtesting because a model can look great while simply being overfit to past results.
One particularly useful addition is no-vig probability—removing the sportsbook's margin before comparing your model to the market.
If you tell me which sport/market you want to model (NFL spread, NBA moneyline, MLB totals, player props, etc.), I can lay out a very simple Google Sheets model, cell by cell, with the formulas, so you don't need to know programming.
Building a sports betting model as a non-programmer is entirely doable. You don't need Python or R; you just need a good grasp of spreadsheet logic, a data source, and a way to compare your numbers against sportsbook lines.
The best tools, software, and approaches for a non-programmer are detailed below:
The Best Software: Microsoft Excel or Google Sheets
Spreadsheets are the gold standard for non-programmer models. They allow you to build custom rating systems, expected value (+EV) calculators, and bankroll management trackers visually.
Google Sheets: Excellent if you want to pull free web data dynamically using formulas like IMPORTHTML or IMPORTDATA . It's also free and accessible anywhere.
Microsoft Excel: Great for heavier data sets, advanced pivot tables, and cleaner offline performance.
How to Structure a Simple Model (The Workflow)
You don’t need to predict final scores down to the decimal on day one. A simple, effective entry-level model relies on power ratings or averages:
Gather Data: Pull basic stats (e.g., points per game, opponent points allowed) from free public sites like TeamRankings or Basketball/Pro-Football Reference.
Match and Normalize: Use spreadsheet functions like VLOOKUP or XLOOKUP to match team names across your schedule sheet and your stats sheet.
Make Projections: Average a team's offensive rating against the opposing team's defensive allowance to project an expected score or margin.
Compare (Line Shopping): Compare your projected spread or total against current sportsbook odds to find discrepancies (positive expected value, or +EV).
Pre-Built Resources & Guides
If you'd rather look at an existing template or follow a step-by-step visual tutorial rather than starting from a blank grid:
YouTube Tutorials: Creators like Gameday Math and various sports analytics channels offer free walkthroughs on YouTube showing how to set up basic NBA, NHL, or NFL models in Google Sheets using simple formulas and free data.
Community Templates: Platforms like Etsy or specialized sports betting blogging sites frequently sell or share plug-and-play Excel/Google Sheets expected-value and dynamic odds dashboards if you prefer a pre-formatted starting point.
If you'd like, let me know:
Which sport you want to model (NFL, NBA, MLB, soccer, etc.)
What type of bet you're targeting (spreads, totals, or moneylines)
I can give you a step-by-step formula breakdown to set up your first sheet.
Underdog Chance’s Google Sheets guide, which walks through probability, fair odds, EV, and a simple working model.
Kyle Kirms’ step-by-step NBA model in Excel/Sheets.
