Build Your Own Totals Model in Excel: Downloadable Template and Walkthrough

Build Your Own Totals Model in Excel: Downloadable Template and Walkthrough

Hook: Tired of juggling five tabs, three sportsbooks, and a dozen metrics to decide whether to play the over or under? You need a fast, transparent totals projection you control. This guide gives you a downloadable Excel template and a step-by-step walkthrough to build a practical totals model, run simple simulations, and compare your projection to market totals — all in under an hour.

Why build a DIY totals model in 2026?

Late-2025 and early-2026 trends changed the totals landscape: sportsbooks expanded live/in-play markets, more operators publish granular closing data/APIs, and model-driven sharp action moves lines faster than ever. Building your own model means you can:

• React faster to line movement and in-play opportunities.
• Test hypotheses (pace changes, weather, injuries) with the same inputs you care about.
• Maintain an audit trail for your decisions — crucial for repeatable edge.

What the downloadable Excel file includes

We provide a ready-to-use workbook to get you started. Download the template here: Download Template (Excel). The workbook contains:

• Inputs sheet — sample box scores-derived metrics for teams (pace, offensive rating, defensive rating, recent form, home/away splits).
• QuickSim sheet — a simple Monte Carlo totals simulator for high-scoring sports (NBA, NFL totals approximations using normal noise).
• PoissonSoccer sheet — a Poisson-based simulation for low-scoring sports (soccer, hockey; adjustable lambda per team).
• Market sheet — space to paste live and closing totals/lines from multiple sportsbooks for quick comparison.
• Validation sheet — historical sample data and backtest visualizations (sample formulas and charts).
• Readme — step-by-step notes and which Excel functions we used (LET, XLOOKUP, SEQUENCE, NORM.INV, RANDARRAY, FILTER).

Before we start: model scope and expectations

Keep in mind this template is intentionally simple and transparent. It’s a foundation — not a black-box machine-learning system. The goal is to produce defensible totals projections quickly so you can spot value against market totals and test adjustments. If you want to incorporate advanced techniques (ensemble methods, neural nets, third-party APIs), this workbook is a launchpad, not the final product.

Step-by-step build: from raw inputs to projected total

1) Collect core data inputs

Open the Inputs sheet. You need to populate basic, reliable metrics. Minimal set:

• Team offensive points per 100 possessions (or per game).
• Team defensive points allowed per 100 possessions.
• Pace (possessions per game) or league-average possessions.
• Home/Away adjustments (points differential when at home vs away).
• Recent form modifier (last 5 games offensive/defensive change).
• Weather / Surface / Special adjustments for outdoor sports.

Why these? They’re easy to source from box scores, public APIs, and they directly impact scoring. The template includes sample rows for 10 teams so you can see how we structure the sheet.

2) Create a simple expected-score engine

We use a normalized approach: expected team score = team offensive metric adjusted by opponent defensive metric, pace, and context modifiers.

In Excel terms (QuickSim), the formula looks like this (example using named cells):

=LET(
    TeamOff, XLOOKUP(HomeTeam, Inputs!Team, Inputs!Offense),
    OppDef, XLOOKUP(AwayTeam, Inputs!Team, Inputs!Defense),
    PaceAdj, (XLOOKUP(HomeTeam,PaceRange) + LeaguePace)/2,
    BaseScore, TeamOff * (OppDef / LeagueDef) * PaceAdj / LeaguePace,
    HomeAdj, XLOOKUP(HomeTeam, Inputs!HomeAdj),
    BaseScore * (1 + HomeAdj + RecentForm)
)

Don’t worry if LET and XLOOKUP are new — the workbook includes equivalent formulas for older Excel versions (INDEX/MATCH).

3) Choose a distribution for simulation

Choice depends on the sport:

• NBA / High-scoring sports: Use a normal approximation for total points. Per-team scoring variability tends to be well-approximated by a normal distribution at the game level.
• NFL / Moderate variance: Normal can work, or use a discretized distribution calibrated from sample residuals.
• Soccer / Hockey / Low scoring: Use Poisson processes for goal counts (the Poisson assumption fits discrete scoring events better).

4) Implement Monte Carlo in Excel (QuickSim)

We simulate many games to produce probabilities for Over/Under. The QuickSim sheet uses NORM.INV(RAND(), mean, sd) to simulate team scores for high-scoring sports.

Example flow in Excel:

1. Compute expected points for home and away teams (meanHome, meanAway).
2. Estimate standard deviation (sdHome, sdAway) from historical residuals — sheet has sample calculation: =STDEV.P(residuals).
3. Simulate N games using RANDARRAY (Excel 365):

=LET(
    SimN, 10000,
    HomeSims, NORM.INV(RANDARRAY(SimN), meanHome, sdHome),
    AwaySims, NORM.INV(RANDARRAY(SimN), meanAway, sdAway),
    TotalSims, HomeSims + AwaySims,
    OverProb, COUNTIF(TotalSims, ">" & MarketTotal)/SimN,
    OverProb
)

If you don’t have Excel 365, the template also contains a column-based Monte Carlo that uses simple RAND() and fills down for 5,000 rows.

5) Implement Poisson simulation for soccer/hockey

For low-scoring sports, use Poisson to simulate discrete goal counts. Excel has POISSON.DIST (or POISSON in older builds). The template computes lambdas (expected goals) for each team and uses inverse sampling:

=LET(
    LambdaHome, ExpectedGoalsHome,
    U, RAND(),
    k, 0,
    CDF, POISSON.DIST(k, LambdaHome, TRUE),
    WHILE(CDF < U, k=k+1; CDF = POISSON.DIST(k, LambdaHome, TRUE)),
    k
)

The workbook implements this with vectorized helper columns so you can run 10,000 Poisson simulations without macros — useful when modelling soccer or hockey games where discrete counts matter.

Calibration: make your model match reality

Calibration separates a toy model from a usable one. Use the Validation sheet to align your expected totals with observed totals over a training set (ideally last 200-500 games). Steps:

• Compute residuals = ObservedTotal - ModelExpectedTotal.
• Check the mean residual — if it’s not near zero, apply a bias correction (simple additive constant).
• Compute sd of residuals — use this as your simulation sd or adjust with a volatility multiplier.
• Perform a small rolling calibration (last 30 games) to capture recent meta shifts (rule changes, roster moves).

In 2026, the most successful sharps frequently calibrate to the closing market rather than raw observed totals. Closing totals embed aggregated market wisdom and late-breaking news; you can use them as a target to minimize closing residuals — many shops treat the closing book as their primary calibration target (see coverage on how fast markets move and where to source closing data: closing data/APIs).

Advanced adjustments you can add quickly

After you’re comfortable with the baseline, add these practical adjustments (all included as toggles in the template):

• Pace shift: Use opponent-adjusted possessions if a team faces an outlier defense/pace opponent.
• Injury impact: Simple lineup replacement method: scale offensive/defensive rates by expected minutes lost.
• Weather / Venue: Add a multiplicative factor for severe wind, rain, or nonstandard surfaces.
• Correlation: In some sports, team scores are correlated (fast tempo increases both teams' scores). Implement correlation by simulating paired normal variables with a correlation coefficient (use CHOL decomposition through simple math or use the correlated noise technique in the workbook).
• Market implied total: Back out market-implied team scoring using bookmaker implied team totals and use those as inputs for another model layer.

Validation & live monitoring

Validation is continuous. The template includes a compact backtest routine:

• Run simulated probabilities on historical games (out-of-sample) and calculate Brier score or log loss for over/under predictions.
• Track model vs market closing edge: Average (ModelProb - MarketImpliedProb) for closed opportunities.
• Visualize residuals and calibration curve — the workbook provides sample charts you can copy.

Pair the backtest with lightweight live monitoring for your pipelines so you get alerts when your input feeds or calibration stats drift beyond thresholds.

Simulation basics — explained plainly

Understanding what you simulate matters. Here are quick rules for decision-making:

• Simulate totals, not margins: Totals directly map to over/under markets and remove winner-side variance.
• 96%+ confidence intervals are wide: Simulated distributions typically show fat tails — trust probabilities rather than point estimates.
• More sims = smoother probs: 5,000–20,000 Monte Carlo runs are a good tradeoff between accuracy and speed in Excel 365. Use RANDARRAY to keep it fast.

Practical examples from the workbook

Two short examples from the template — both available to run instantly:

Example A — NBA game QuickSim

• Inputs: Home team offensive rating (113.5), Away team defensive rating (109.2), Pace 100.5
• Expected totals: Home 115, Away 109 => ModelExpectedTotal = 224
• Sim: N = 10,000, sd estimated from residuals = 12.3
• Result: Prob(Over 223.5) = 0.58 (58%). If market total is 226.5, you have ~edge.

Example B — Soccer Poisson

• Inputs: Home expected goals 1.5, Away expected goals 1.1
• Sim: 10,000 Poisson samplings => Distribution of total goals
• Result: Prob(Over 2.5 goals) = 0.42. If market shows 2.5 at -110, check implied prob (~52.4%) and skip — no value.

Common pitfalls and how to avoid them

• Overfitting to small samples: Don’t create dozens of knobs for one team. Regularize by keeping adjustments simple and testing out-of-sample.
• Ignoring market information: The closing market is data too. If the market consistently disagrees with you, reassess inputs first before doubling down.
• Not updating sd estimates: Volatility regimes change — recalc sd on rolling windows.
• Forgetting correlation: Treating team scores as independent can bias total distributions, especially in basketball where pace pushes both teams up.

Tip: In 2026, using a lightweight Excel model that ingests a sportsbook API for live totals + your simulations is often more actionable than a complex offline model that updates weekly.

How to compare your projection to sportsbooks (fast)

1. Paste live totals into the Market sheet (we provide columns for 6 books).
2. Compute each book's implied probability: For totals, translate juice to probability only for calibration (we show a conversion formula in the Readme).
3. Identify divergence flags: ModelTotal is greater than MarketTotal by more than your threshold (e.g., 1.5 points for NBA, 0.4 goals for soccer).
4. Apply stake sizing: Use Kelly fraction on your edge after accounting for estimation error (template shows a conservative Kelly variant).

Extending the Excel model (next-level add-ons)

Once the baseline works, these 2026-friendly upgrades are high-impact:

• API automation: Use Power Query to pull box scores and market totals from APIs (many shops provide CSV/JSON endpoints now).
• Ensemble approach: Combine QuickSim and Poisson outputs with weighted averaging based on sport and historical calibration performance.
• Small ML layer: Use small ML layer like XGBoost or a lightweight regression to learn residuals; keep the predictions interpretable by applying ML only to the residual correction term.
• Dashboarding: Publish a live sheet for the next 24 hours of games with model vs market spreads so you can scan opportunities quickly — many teams apply rapid content techniques to surface next-day lists (rapid edge publishing).

Responsible model use and bankroll rules

Even the best models are probabilistic. Follow these guardrails:

• Set a maximum stake per bet (2–3% of bankroll) regardless of edge.
• Record every bet and outcome (the template contains a Bet Log sheet).
• Periodically retrain and don’t chase losses by increasing variance in your input parameters without evidence.

Download and get started

Get the template and sample data to follow along: Download Template (Excel). The file includes annotated cells so you can step through each formula and see where to plug in your data sources.

Actionable takeaways

• Start simple: Build the baseline QuickSim and Poisson sheets first — you’ll get actionable over/under signals within hours.
• Calibrate to closing totals: In 2026 markets move faster; aligning to closing lines reduces surprise variance.
• Use simulations, not point estimates: Probabilities beat gut feelings for over/under decisions.
• Automate data feeds: Add Power Query/API pulls once your model logic is stable to save manual work.

Final notes — future directions

Looking forward in 2026, expect tighter in-play markets, more public closing datasets, and more accessible APIs. A nimble Excel-based totals model that ingests live feeds and re-simulates quickly will remain a competitive edge for bettors and fantasy players who want transparency and speed without building a full data science stack.

Call to action

Ready to stop guessing and start simulating? Download the Excel template, open the Readme tab, and run the QuickSim on a live game tonight. Join our Totals Lab newsletter to get weekly calibration tips, updated sample data, and community-shared tweaks from other users in 2026.

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