I need to build a simple betting model. What's the best guide or software for a non-programmer?

If you’re a non-programmer, I’d strongly recommend starting with Google Sheets, not Python or specialized betting software. It’s transparent, easy to modify, and enough for a surprisingly useful first model.

My picks

• Best overall guide: underdogchance.com — probably the clearest starting point. It walks through a simple Elo-style team-rating → win probability → fair odds → EV model and includes basic backtesting.
• Best very-beginner video:

— specifically demonstrates building a model in Google Sheets with no programming.

• Best spreadsheet reference: underdogchance.com — explains probability, fair odds, EV and gives an actual spreadsheet example.
• Useful next step: bettinglab.co — once you understand the basics, learning to remove the sportsbook's vig is extremely useful.

What I'd build first

Don't start with a complicated AI model. Make a spreadsheet with just:

Historical games → team ratings → predicted probability → fair odds → sportsbook odds → expected value → bet/no bet

For example, your sheet could ultimately say:

Model probability: 56% Sportsbook implied probability: 52% Fair odds: -127 Available odds: +105 Positive EV: Yes Then backtest it on historical games that weren't used to build the ratings. That's much more important than adding dozens of statistics.

Software: Google Sheets → eventually Excel/Python if you outgrow it. Spreadsheet models are specifically well-suited to beginner Elo/rating systems, simple regression and EV calculations, while Python becomes useful when you need automation and large datasets.

If you tell me what sport and market you want to model (e.g. NBA spreads, NFL moneylines, MLB totals, soccer), I can lay out a very simple no-code Google Sheets model, including the exact columns and formulas, that you can build from scratch.

Building a sports betting model as a non-programmer is entirely doable. You don't need Python or R to find an edge; you just need a structured approach, clean data, and the right tool.

The best software options and guides for non-programmers break down into two main paths: the Spreadsheet Route (total control, free) and the No-Code AI Route (faster, automated analysis).

Phase 1: The Best Software Options

• Google Sheets / Microsoft Excel (The Gold Standard)
  • Why it's great: It gives you 100% ownership of your logic. You can pull free stats using functions like IMPORTHTML or IMPORTDATA in Google Sheets, or use simple arithmetic averages, VLOOKUP , and Poisson distributions to project scores or player props.
  • Best for: Learning how models work under the hood without paying a dime.
• Rithmm
  • Why it's great: It is specifically designed as a no-code AI sports betting app. It lets you build, customize, and tweak your own predictive models for leagues like the NFL, NBA, and college basketball by selecting factors and metrics you care about, without writing a single line of code.
  • Best for: Bettors who want AI-driven predictive analytics and custom modeling without managing a massive spreadsheet.
• Moddy.AI
  • Why it's great: A newer tool tailored for building sports models visually. It allows you to select from hundreds of statistics or prompt a lab partner to pull relevant data, then automatically tracks and backtests your custom model’s performance against live sportsbook odds.
  • Best for: Testing out complex statistical angles and seeing your ROI/win rate tracked automatically.

Phase 2: Recommended Guides & Frameworks

- Search YouTube for step-by-step visual tutorials like *"Build a Simple Betting Model (No Coding, No Paid Software)"* (such as the popular walkthroughs centered on Google Sheets using TeamRankings or Basketball-Reference data).
- **Core concept to learn:** How to scrape or paste basic opponent-adjusted stats, use `VLOOKUP` to match schedules to team ratings, and calculate expected point differentials or totals.[[1]](https://www.youtube.com/watch?v=gUdPpaGMqtk)

- If you want something more advanced than a spreadsheet but still don't want to code, you can use conversational AI tools (like Claude or ChatGPT).
- **How to use it:** Instead of writing code yourself, tell the AI: *"I want to build an Excel/Google Sheets template to predict NFL totals. What columns do I need, what formulas should I write in cell D2, and how do I structure the data?"* The AI writes the exact formulas for you to copy and paste.[[1]](https://www.youtube.com/watch?v=jhMsp5ZxHL4)

A Simple Formula to Start With

If you use a spreadsheet, don't try to build a complex multi-variable model on day one. Start with a basic Average-Based Matchup Model:

To help narrow down the best path for you, tell me:

• Which sport are you looking to model (e.g., NFL, NBA, MLB, Soccer)?
• Do you prefer a hands-on spreadsheet where you control everything, or a ready-made app/AI tool?

If you’re a non-programmer, I’d start with Google Sheets, not a dedicated betting-model platform. It’s cheap/free, transparent, and more than capable of building a simple model, calculating fair odds/EV, and backtesting a modest dataset.

My picks

• Best free starting guide: underdogchance.com — probably the closest match to what you’re asking for. It walks through probabilities, fair odds, EV, CLV, and a spreadsheet-based approach.
• Best beginner step-by-step example: landyourbets.com — particularly useful if you want to build something like an NBA totals model without coding.
• For a ready-made spreadsheet: underdogchance.com — useful if you'd rather study an existing model and modify it instead of starting with a blank sheet.
• For live odds/no-vig calculations: bettinglab.co — more advanced, but useful once your basic model works.
• If you absolutely don't want spreadsheets: moddy.ai is a no-code AI modeling platform specifically aimed at testing sports-betting theories.

What I'd actually build

Don't start with machine learning. Build a small probability model:

Team stats → projected outcome → probability → fair odds → compare with sportsbook odds → +EV bet

For example, for an NBA totals model you could initially use only:

• Team A offensive rating
• Team A defensive rating
• Team B offensive rating
• Team B defensive rating
• Pace

Then have Sheets produce a projected total and compare it with the sportsbook's total. That's enough to learn the important concepts without getting buried in statistics.

One important warning: don't judge the model by whether it wins its first 20 bets. Keep historical out-of-sample results and track things like ROI and closing-line value; short-term results can be extremely noisy.

If you tell me which sport and bet type you want to model (e.g. NFL spreads, NBA totals, MLB moneylines), I can give you a very simple Google Sheets model layout with the exact columns and formulas, designed for someone who doesn't know programming.

For a non-programmer, you don’t need complex software or coding languages like Python to start building a functional sports betting model . The best approach is using spreadsheet software combined with no-code data importation or specialized predictive apps.

The best tools, guides, and workflows to get started depend on how hands-on you want to be:

• Google Sheets or Microsoft Excel is the gold standard for non-programmers. You can build a robust power rating or point-spread model entirely through formulas (like VLOOKUP, AVERAGE , and IFERROR ). Google Sheets is especially useful because you can use functions like IMPORTHTML to pull free stats straight from public sports data websites.
• Rithmm is a dedicated AI sports betting app designed specifically for users who want custom predictive models without writing code. It lets you toggle stats, build analytical models for NFL, NBA, or college sports, and generates projections automatically without manual spreadsheet wrangling.
• YouTube No-Code Walkthroughs provide the best visual guides. Search for tutorials like “Build a Simple Betting Model (No Coding)” . Creators regularly walk through step-by-step setups on how to pull data into Google Sheets, map team schedules, and compare your custom projections against live sportsbook lines.
• Betstamp or Odds Screen Tools act as your companion for line shopping and tracking. Once your model spits out a number, you need a reliable place to compare those numbers against real-time odds to see if you actually have a positive expected value (+EV).

To help narrow down the best path forward, tell me:

• Which sport are you looking to model (e.g., NFL, NBA, MLB, soccer)?
• Do you prefer a fully manual spreadsheet you can customize, or an automated app/tool?

If you're a non-programmer, I would not start with Python or an expensive “AI betting model” service. Start with Google Sheets or Excel and build one small model end-to-end.

My top recommendation

Guide:

This is probably the best fit for you because it walks through a model in Google Sheets, including:

• collecting historical game data
• creating a basic Elo-style/team-rating model
• converting ratings into win probabilities
• calculating fair odds
• comparing your odds with sportsbook odds
• basic backtesting
• Brier score/log loss for determining whether the model is actually predictive

There's also a broader underdogchance.com that explains the concepts before you start building.

Software I'd use

betstamp.com is particularly useful alongside your model rather than as the model itself. It can track bets and analyze ROI/closing-line value across sports, leagues, bet types and sportsbooks.

For example, your first model could literally be:

Team stats → predicted probability → fair odds → sportsbook odds → expected value → bet/no bet

You don't need machine learning. A simple Elo/rating model or regression is plenty to learn the fundamentals. Current beginner guides also recommend starting with one sport/market rather than trying to model everything at once.

What I'd recommend for you

If you tell me which sport and market you want to bet (e.g. NFL spreads, NBA moneylines, MLB totals, player props), I can lay out a very simple Google Sheets model you can build without programming, including exactly what columns to create and the formulas to use.

For a non-programmer, you don’t need Python, databases, or complex math to get started . Decades before modern coding languages, sharp bettors built functional predictive models using spreadsheets—and today, you can leverage AI tools to do the heavy lifting without writing a single line of code.

The best software choices, guides, and workflows for non-programmers include:

1. The Gold Standard (Free & Flexible): Google Sheets or Microsoft Excel

Spreadsheets give you total transparency over your inputs, allowing you to learn what metrics actually drive value rather than trusting a black box.

• How to use it: Build a simple power-rating or average-scoring model (e.g., comparing a team's last 5 games or adjusted efficiency metrics against opponent allowances). Use functions like VLOOKUP to match schedules with stats.
• No-code data shortcuts: You can pull free historical data or schedules directly into spreadsheets using specialized add-ons, or visit public repositories like Football-Data.co.uk for ready-to-use CSV files.
• Recommended Guides: Look up structured video tutorials like Underdog Chance on YouTube , which specializes in step-by-step no-code modeling walkthroughs specifically for Google Sheets.

2. The AI-Assisted Route: Using ChatGPT or Claude as Your Co-Pilot

If you know what you want to test (e.g., "I think home underdogs in day games are undervalued" ), you can use conversational AI to build the mechanics for you.

• How to use it: Feed a generative AI tool a sample of raw CSV headers or pasted data tables from sites like Basketball Reference and prompt it: "Write out the exact Excel/Google Sheets formulas I need in these columns to project team totals based on past performance."
• Advantage: The AI acts as your personal programmer, translating your plain-English logic into working spreadsheet formulas or structured data layouts.

3. The Dedicated No-Code App: Rithmm

If you want quantitative-style projections and automated model building without managing a spreadsheet yourself, dedicated apps have emerged for everyday bettors.

• What it does: Rithmm lets you select custom factors, build AI-driven predictive models for games or player props, and automatically compares your custom projections against live market odds to spot edges.
• Best for: Bettors who want institutional-style analytics and edge detection via web or mobile apps without building the math from scratch.

To help narrow down the best starting point, tell me:

• Which sport or league are you trying to model (e.g., NFL, NBA, Soccer)?
• Do you prefer a free, hands-on spreadsheet approach or a plug-and-play app?

Building a sports or event betting model without knowing how to code is entirely possible. You don't need Python or R; you just need tools that can handle data organization, basic math, and probabilities.

Here are the best guides, frameworks, and software tools tailored for a non-programmer:

• Spreadsheets (Excel or Google Sheets) : The absolute gold standard for non-programmers. They allow you to build custom probability models, track bankrolls, and calculate expected value (EV) using built-in formulas without writing a single line of code. Google Sheets is especially great because it lets you pull live data using functions like IMPORTHTML or IMPORTXML.
• Unabated : A premier, user-friendly platform designed specifically for sports bettors. It offers built-in calculators, tools to find positive expected value (+EV), and tutorials that teach you how a sharp betting model works under the hood without needing technical skills. Check it out at Unabated.
• Pinnacle's Betting Resources : Pinnacle offers one of the best free educational hubs in the industry. Their articles break down how to convert betting odds into implied probabilities (𝑝=1 D e c i m a l O d d s ), how to calculate margin/vig, and how to build a basic closing-line value model using simple math concepts. Explore their guides at Pinnacle Betting Resources.
• Cinderblock / Action Network Tools : These platforms provide specialized calculators and data tracking tools that do the heavy lifting for you, helping you transition from casual tracking to algorithmic decision-making.
• The "Zen of Betting " & Substack Guides : There are several amateur modeling guides written by non-programmers on platforms like Substack that teach you how to scrape minimal data or manually input team power ratings to generate win probabilities using simple Elo or Poisson distribution templates.

To help narrow down the best approach, let me know:

• What sport or event are you looking to model?
• Do you prefer a fully visual software tool , or are you comfortable using Excel/Google Sheets?

If you’re a non-programmer, I’d start with Google Sheets or Excel, not Python. You can build a genuinely useful first model with spreadsheets, and several current guides specifically recommend that route for beginners.

My picks

1. Best overall guide: Underdog Chance

This is probably where I’d start. It walks through an Elo-style team-rating model → win probability → fair odds → EV → backtesting, without requiring programming.

2. Best if you want to learn Excel modeling: NBAstuffer's Statistical Sports Models in Excel

Older, but unusually well suited to beginners. It focuses on spreadsheet-based models and includes examples across NFL, NBA, NHL and soccer.

3. Best no-code software: Rithmm

If you don't actually want to build the spreadsheet yourself, Rithmm is worth investigating. It markets a no-code model builder that lets you create predictive models without programming.

4. Best DIY/no-code browser option: Shark Snip

It has a browser-based no-code model builder, backtesting, calibration tools and other betting analytics. This is more experimental/advanced than starting with a spreadsheet, though.

What I'd actually do

Don't start by buying an expensive "AI betting model." Build something tiny yourself first:

Sport → one market → 3–5 variables → probability → fair odds → compare with sportsbook → track results.

For example, for an NBA spread model:

Team strength + home court + recent performance + injuries → predicted margin → probability of covering → fair spread

Then test it against hundreds of historical games before trusting it. A recent beginner guide recommends starting with one sport, a handful of variables, and a substantial historical sample rather than immediately building something complicated.

If you tell me which sport/market you want to model (e.g. NFL spreads, NBA moneylines, MLB totals, player props), I can recommend the simplest specific model + data source + spreadsheet structure for a non-programmer.

If you're a non-programmer, I’d start with Google Sheets, not a specialized betting platform or Python. You can build a surprisingly useful first model with formulas alone, and it forces you to understand what the model is actually doing. Current beginner guides also recommend Sheets/Excel as the best starting point before moving to code.

My top picks

1. Best overall guide: Underdog Chance

   • Their How to Build Your First Sports Betting Model walks through an Elo-style model → probability → fair odds → EV → backtesting.
   • This is probably the closest match to what you're asking for.
   • They also have a Google Sheets video series that starts with basic spreadsheet skills and progresses through odds, EV, Kelly, and regression.

2. Best truly no-code walkthrough: Weekday Wagers

   • A recent tutorial builds a simple NBA model entirely in Google Sheets with free data, explicitly without Python or programming.
   • It's deliberately basic, which is actually a good thing for your first model.

3. Best if you want a book/template approach

   • Statistical Sports Models in Excel is specifically aimed at beginners and has been recommended by bettors looking for Excel-based modeling resources.

What I'd actually build first

Don't start with machine learning. Build a single-market model:

Inputs → Projection → Probability → Fair Odds → Market Odds → Edge → Bet/No Bet

For example, for an NBA game:

• Team offensive rating
• Team defensive rating
• Pace
• Home/away
• Recent performance
• Your projected total
• Sportsbook total
• Estimated probability of Over/Under
• Expected value
• Result

Then track every prediction, including bets you didn't make. After a few hundred observations, you can determine whether your model is actually calibrated rather than just looking good in a small backtest.

Software recommendation

One important distinction: a betting model isn't necessarily a "betting system." The model estimates probabilities; the betting rules determine when your estimated edge is large enough to wager. Keeping those separate makes the model much easier to test.

If you tell me what sport and market you want to model (e.g. NFL spreads, NBA totals, MLB moneylines, player props), I can point you to the simplest modeling approach and lay out exactly what your first Google Sheet should contain.

Building a sports betting model as a non-programmer is entirely doable. You don't need Python or R; you just need structured thinking, historical data, and spreadsheet software.

Recommended Tools & Software

• Microsoft Excel / Google Sheets : The absolute gold standard for non-programmers. Google Sheets is fantastic if you want free, built-in web-scraping functions like =IMPORTHTML() or =IMPORTDATA() to pull live odds and stats. Excel is powerful if you're dealing with massive CSV files of historical data.
• Octoparse / ParseHub : Free or freemium visual web scrapers. If you need to pull data from stats sites that Google Sheets can't easily grab, these tools let you point-and-click to download data into a spreadsheet without writing a single line of code.
• Oddsjam / Unabated : While these are paid tools, platforms like Unabated offer built-in calculators, expected value (EV) tools, and simulators that do the heavy mathematical lifting for you if you want to bypass building the formulas yourself.

Step-by-Step Guides & Resources

• Pinnacle Betting Articles : Pinnacle has one of the best free educational blogs in the industry. Search for their beginner guides on "how to build a predictive model" or "closing line value." They explain the math (like converting odds to probabilities) in plain English.
• Plus EV Analytics Guides : Sites like Unabated Learning Center provide free articles and video walkthroughs on how to price bets, calculate implied probability, and construct basic edge-finding spreadsheets.
• YouTube Creators : Channels that focus on sports analytics for beginners (such as Unabated Sports or independent math/sports creators) frequently post step-by-step tutorials on setting up a basic closing-line or Poisson distribution model in Google Sheets.

Core Concepts to Master First

1. Implied Probability : Learn how to convert American or decimal odds into a true percentage so you know what probability the sportsbook expects. (e.g., -110 means a 52.38% implied probability).
2. Data Collection : Pick one sport and a small set of variables (e.g., NFL team yards per play or NBA team offensive rating). Don't try to track 50 variables on day one.
3. The Baseline Comparison : Your model isn't finished until you test it against current market odds. If your model's prediction matches the sportsbook's odds, you don't have an edge yet; you need to find where your variables disagree with—and outperform—the market.

To help narrow down the best approach, tell me:

• What sport are you planning to model (NFL, NBA, soccer, etc.)?
• Do you already have a source of data , or do you need help finding where to download stats?