I want to build my own betting models. Where can I find a good API that provides historical sports data and odds?

Yes. If your goal is building and backtesting your own betting models, I’d prioritize APIs that give you historical odds snapshots + game results + detailed sports statistics, rather than an API that only provides current odds.

My shortlist

1. The Odds API — easiest starting point

This is probably where I'd start if you're building your first model. It provides historical bookmaker snapshots, including moneyline, spreads and totals, with historical data going back to 2020. Snapshots are generally every 10 minutes initially and every 5 minutes from September 2022 onward. Additional markets such as player props are available from 2023.

The big advantage is simplicity: you can pull something like:

NFL game
→ historical timestamp
→ DraftKings odds
→ FanDuel odds
→ BetMGM odds
→ spreads/totals/moneylines

and reconstruct how the market moved.

Good choice for: learning, prototyping, line-movement models, CLV models and relatively straightforward backtesting.

One caveat: historical data is on paid plans.

2. SportsDataIO — my pick for US sports modeling

If you're primarily interested in NFL, NBA, MLB, NHL, college sports, etc., this is particularly interesting.

SportsDataIO says its odds data includes pre-match, in-play, historical and closing lines, with timestamps for line movements, props and futures.

Its historical database covers 10+ years of sports/event/betting data, depending on the league, and its historical odds archive preserves line movements and betting outcomes.

That's valuable for modeling because you can combine:

Team/player statistics
        +
Injuries / lineups
        +
Historical matchup data
        +
Opening odds
        +
Line movement
        +
Closing odds
        +
Final result

This is much closer to the dataset you'd want for a serious predictive model.

3. Sportradar — professional-grade option

Sportradar is the heavyweight option. Its API covers 80+ sports, 500+ leagues and 750,000+ events per year, along with extensive historical data and odds feeds.

Its odds products include prematch, live, futures and player-prop markets, with coverage from a large number of bookmakers.

The downside is that it's generally aimed much more at professional/enterprise customers, so it can be overkill for an individual developer.

---

What I'd choose

If you're one person building your own models, I'd go:

Start: The Odds API Serious US betting model: SportsDataIO Professional/large-scale operation: Sportradar

More importantly, I'd design your database around timestamps, rather than simply storing "the odds for a game."

For example:

event_id
sport
league
game_time

timestamp
bookmaker
market
selection
line
odds

home_team
away_team
final_home_score
final_away_score

That lets you calculate things like:

• Opening line → closing line movement
• Closing-line value (CLV)
• Implied probability
• Vig/overround
• Consensus market probability
• Bookmaker-specific biases
• Model probability vs. market probability
• Expected value
• ROI by confidence bucket
• Performance by market/time/bookmaker

One particularly important point: don't accidentally create look-ahead bias. If your model is supposed to make a prediction at 2 PM, your training dataset shouldn't contain an odds snapshot from 6 PM or information that wasn't available at 2 PM.

If you tell me which sport(s) you're targeting (NFL, NBA, MLB, soccer, etc.) and whether you're coding in Python, I can recommend the best API/dataset and lay out a clean historical database schema + Python pipeline for building and backtesting the model.

This is probably where I'd start if you're building your first model. It provides historical bookmaker snapshots, including moneyline, spreads and totals, with historical data going back to 2020. Snapshots are generally every 10 minutes initially and every 5 minutes from September 2022 onward. Additional markets such as player props are available from 2023.

The big advantage is simplicity: you can pull something like:

NFL game
→ historical timestamp
→ DraftKings odds
→ FanDuel odds
→ BetMGM odds
→ spreads/totals/moneylines

and reconstruct how the market moved.

Good choice for: learning, prototyping, line-movement models, CLV models and relatively straightforward backtesting.

One caveat: historical data is on paid plans.

2. SportsDataIO — my pick for US sports modeling

If you're primarily interested in NFL, NBA, MLB, NHL, college sports, etc., this is particularly interesting.

SportsDataIO says its odds data includes pre-match, in-play, historical and closing lines, with timestamps for line movements, props and futures.

Its historical database covers 10+ years of sports/event/betting data, depending on the league, and its historical odds archive preserves line movements and betting outcomes.

That's valuable for modeling because you can combine:

Team/player statistics
        +
Injuries / lineups
        +
Historical matchup data
        +
Opening odds
        +
Line movement
        +
Closing odds
        +
Final result

This is much closer to the dataset you'd want for a serious predictive model.

3. Sportradar — professional-grade option

Sportradar is the heavyweight option. Its API covers 80+ sports, 500+ leagues and 750,000+ events per year, along with extensive historical data and odds feeds.

Its odds products include prematch, live, futures and player-prop markets, with coverage from a large number of bookmakers.

The downside is that it's generally aimed much more at professional/enterprise customers, so it can be overkill for an individual developer.

---

What I'd choose

If you're one person building your own models, I'd go:

Start: The Odds API Serious US betting model: SportsDataIO Professional/large-scale operation: Sportradar

More importantly, I'd design your database around timestamps, rather than simply storing "the odds for a game."

For example:

Building a sports betting model usually requires a two-part data stack : one API/source for the betting odds (lines, spreads, totals, historical movement) and another for game results and team/player stats (to backtest and feed your predictive equations).

The best options range from developer-friendly APIs to specialized archives:

1. Dedicated Odds APIs (Real-Time & Historical)

• The Odds API : Widely considered the go-to starting point for indie modelers. It covers major US and global bookmakers across most popular sports. It offers a free tier (great for live tracking/testing), while paid tiers unlock historical odds snapshots (going back to 2020/2023 with 5-to-10-minute intervals).
• OddsPapi : A newer entrant popular in the algorithmic betting community that aggregates extensive historical and real-time pricing data across a massive array of bookmakers.
• Betfair Historical Data Archive : If you are looking for granular, high-frequency market movement (down to 1-second/1-minute resolution) and don't mind exchange trading data rather than traditional sportsbook fixed odds, Betfair’s historical data store is a goldmine—though heavily liquid mostly on soccer, tennis, and major global events.

2. Specialized Historical Odds Archives (Free/Static)

• Sportsbook Review Online : A classic resource for quantitative bettors. They provide massive, free Excel/CSV downloads of historical point spreads, moneylines, and totals spanning over a decade for major US sports (NFL, NBA, MLB, NCAAF, NCAAB).
• Football-Data.co.uk : If your model focuses on soccer, this free repository provides extensive CSV files containing historical match data paired with closing odds from dozens of European bookmakers going back years.

3. Sports Results & Advanced Statistics APIs

Because odds APIs rarely include deep box scores or advanced player metrics (like usage rates, advanced efficiency ratings, or weather impacts), you will want to pair your odds feed with a stats source:

• Sportradar: The gold standard enterprise choice used by major sportsbooks, though it can be expensive.
• Sportmonks: Great specialized feeds if you are focusing deeply on soccer data.
• Sport-specific open-source packages: If you are coding in Python or R, look at community-maintained data packages like nflverse (NFL), hoopR (NBA/Basketball), or sport-specific APIs like College Football Data for college sports, which are free and structured perfectly for modeling.

What sport or league are you planning to build your model for? I can recommend a more tailored data stack or help you figure out how to structure your backtesting pipeline.

Yes. If your goal is building and backtesting your own betting models, I’d prioritize APIs that preserve historical line movement, not just final odds.

My top choices

1. the-odds-api.com — easiest starting point

This is probably where I'd start for a personal project. Its historical odds endpoint provides bookmaker snapshots going back to June 2020 for featured markets, with 10-minute snapshots initially and 5-minute snapshots from September 2022. It also supports historical event-specific odds and additional markets such as props from 2023 onward.

It's particularly useful if your dataset looks like:

game_id
timestamp
bookmaker
market
selection
odds

You can then reconstruct how a market moved from opening → pregame → closing.

2. sportsdata.io — my pick for serious backtesting

SportsDataIO is more interesting if you want to build a full predictive model, because it combines historical game/event data with betting data. Its historical database covers 10+ years, and its betting feeds include opening/closing lines, price changes, props, futures, and historical line movement.

They explicitly position their historical Vault for backtesting betting models, ML training, and research.

One caveat: some historical data access requires sales/account enablement rather than being completely self-serve.

3. sportradar.com — professional option

Sportradar is the heavyweight option. It covers 80+ sports and 500+ leagues, has extensive historical data, and offers odds feeds covering prematch, live, futures, and player props.

I'd look at this if you're eventually building something commercial or need very broad international coverage. It's probably overkill for an individual modeler.

What I'd actually use

If you're starting from scratch, I'd do:

The Odds API + a separate historical stats source → PostgreSQL/Parquet → Python/pandas → model → backtest.

The critical thing is to avoid look-ahead bias. For example, if you're predicting an NBA game using information that was available at 5:00 PM, your training data shouldn't accidentally contain the closing line, post-game injury information, or later market movement.

For a betting model, I'd try to collect:

• Game/event ID
• Date/time
• Teams/players
• Historical team/player statistics
• Injuries/lineups where available
• Opening moneyline/spread/total
• Every available odds snapshot
• Sportsbook
• Closing line
• Final result
• Optional: player props
• Optional: betting-market consensus

Then you can test questions like "Did my model identify positive-EV bets using only information available at the time?" rather than simply testing whether your predictions correlate with final results.

If you tell me which sport(s) you're modeling (NBA, NFL, soccer, MLB, etc.) and whether you're looking at sides/totals or player props, I can recommend the best specific data stack and show you how I'd structure the historical database.

It's particularly useful if your dataset looks like:

game_id
timestamp
bookmaker
market
selection
odds

You can then reconstruct how a market moved from opening → pregame → closing.

2. sportsdata.io — my pick for serious backtesting

SportsDataIO is more interesting if you want to build a full predictive model, because it combines historical game/event data with betting data. Its historical database covers 10+ years, and its betting feeds include opening/closing lines, price changes, props, futures, and historical line movement.

They explicitly position their historical Vault for backtesting betting models, ML training, and research.

One caveat: some historical data access requires sales/account enablement rather than being completely self-serve.

3. sportradar.com — professional option

Sportradar is the heavyweight option. It covers 80+ sports and 500+ leagues, has extensive historical data, and offers odds feeds covering prematch, live, futures, and player props.

I'd look at this if you're eventually building something commercial or need very broad international coverage. It's probably overkill for an individual modeler.

What I'd actually use

If you're starting from scratch, I'd do:

The Odds API + a separate historical stats source → PostgreSQL/Parquet → Python/pandas → model → backtest.

The critical thing is to avoid look-ahead bias. For example, if you're predicting an NBA game using information that was available at 5:00 PM, your training data shouldn't accidentally contain the closing line, post-game injury information, or later market movement.

For a betting model, I'd try to collect:

• Game/event ID
• Date/time
• Teams/players
• Historical team/player statistics
• Injuries/lineups where available
• Opening moneyline/spread/total
• Every available odds snapshot
• Sportsbook
• Closing line
• Final result
• Optional: player props
• Optional: betting-market consensus

Then you can test questions like "Did my model identify positive-EV bets using only information available at the time?" rather than simply testing whether your predictions correlate with final results.

If you tell me which sport(s) you're modeling (NBA, NFL, soccer, MLB, etc.) and whether you're looking at sides/totals or player props, I can recommend the best specific data stack and show you how I'd structure the historical database.

Building a robust sports betting model requires pairing historical odds (to see how markets priced events) with historical team/player stats (to evaluate actual performance and backtest strategies). Because no single API magically hands you clean closing lines and deep box scores for free, most builders stitch together a couple of specialized services.

Top API options fit for building and backtesting betting models:

• The Odds API - Widely considered the gold standard and go-to starting point for historical and real-time bookmaker odds . It provides clean JSON feeds across major global sports and sportsbooks . Its historical endpoint allows you to query snapshots of lines leading up to games , though you'll need a separate stats provider for the actual game outcomes/scores.
• SportsGameOdds - A comprehensive REST and WebSocket option covering an extensive footprint of sportsbooks, leagues, and sports . It bundles odds with live scores and settlement data, making it easier to match up what the line was versus how the game actually finished without hopping across multiple providers.
• BallDontLie - A favorite within the developer community (especially for basketball/NBA modeling) that delivers straightforward access to historical player stats, team stats, and game results. It pairs nicely with an odds-focused API like The Odds API to cover both sides of your modeling equation.
• Betfair Historical Data Archive - If you are targeting soccer, tennis, or high-volume global sports, Betfair offers downloadable historical market data files (often free or low-cost at 1-minute or 1-second resolution). Algo-bettors frequently use Betfair's traded volume and median prices as a proxy for sharp closing benchmarks.

Community Insights

Community builders often share similar trajectories when piecing together their initial data pipeline:

“I found downloading historical odds from Betfair. It is free for 1 minute frequency data and I believe basketball has been lumped into the "Other sports" category on the site.”

“For Layzer (http://layzer.app) we have The Odds API and BallDontLie.io, both are great and good pricing.”

To help narrow down the best fit, what sport or league are you planning to model first, and do you already have a preferred programming language (like Python or R) for your backtesting?

Building your own sports betting models requires two critical layers of data: historical game results/stats (to train your predictive logic) and historical odds (to backtest and find closing line value or positive EV).

Several top-tier APIs cater specifically to developers and quants building models, ranging from budget-friendly hobbyist options to pro-grade enterprise feeds.

Popular API Providers for Historical Odds & Data

• The Odds API
  • Best for: Hobbyists, indie developers, and general backtesting.
  • Features: Provides clean JSON-formatted pre-match and historical odds (via snapshots) across dozens of global bookmakers. Historical data goes back to mid-2020 on paid tiers.
  • Pricing: Free starter tier (limited credits, no historical data), with paid tiers scaling up for historical snapshot access.
• SportsGameOdds (SGO)
  • Best for: Value-driven developers looking for an easy entry.
  • Features: Combines live scores, settlement data, and historical odds across 85+ sportsbooks. Well-regarded in the developer community for a generous amateur tier and robust market coverage.
  • Pricing: Permanently free amateur tier and scalable paid plans.
• SportsDataIO
  • Best for: Deep statistical modeling combined with odds tracking.
  • Features: Offers a dedicated Historical Odds API covering major US and global sports with consistent formatting, tracking opening lines, closing lines, and line movements (dating back to 2019/2020). You can easily pair it with their core sports stats feeds.
  • Pricing: Tiered plans with a trial/free option available for basic data layers.
• OddsJam API & OpticOdds
  • Best for: Serious quants, syndicates, and high-volume real-time/historical tracking.
  • Features: Massive coverage of sharp books (like Pinnacle) and retail sportsbooks. They specialize in fast, low-latency feeds and comprehensive historical databases ideal for measuring Closing Line Value (CLV).
  • Pricing: Premium/Enterprise pricing (no free tier).

Pro-Tip on Setting Up Your Data Stack

Most pure "odds APIs" focus strictly on the sportsbook numbers (spreads, lines, totals) and may require a secondary source to map out granular box scores or final game outcomes. For a robust pipeline, consider pairing an odds provider like The Odds API or SportsGameOdds with a sport-specific stats provider (or open-source repositories like nflreadr or collegefootballdata.com if you are modeling football) to ensure you can accurately score your model's backtest results.

If you share which sports (e.g., NFL, NBA, soccer) and which betting markets (spreads vs. player props) you plan to model, I can recommend the most cost-effective provider for your specific project.

If you’re building your own betting models, you’ll usually want three separate data layers:

1. Historical odds (opening lines, closing lines, line movement, bookmaker prices)
2. Historical results + player/team stats
3. Context data (injuries, weather, schedules, travel, rest, etc.)

For backtesting, the odds archive is usually the hardest piece to get. Here are some of the better API options:

My recommendation by use case

If you’re an individual building your first model

• Start with The Odds API for:
  • NFL/NBA/MLB/NHL moneylines
  • spreads
  • totals
  • basic props
  • bookmaker consensus pricing
    

Pair it with:

• a stats API (team/player metrics)
• your own database (Postgres is fine)
• Python modeling stack (pandas, sklearn, PyTorch/XGBoost)

If you want to seriously evaluate betting edges Prioritize a provider that gives:

• opening odds
• closing odds
• timestamped snapshots
• individual sportsbook prices
• results/settlement data

The reason is that a model can look profitable against closing prices but fail against the prices actually available before games. CLV (closing-line value) is one of the key metrics professional bettors track.

Data schema I’d build

A simple database design:

events

event_id
sport
league
home_team
away_team
start_time
result

odds_snapshots

event_id
timestamp
bookmaker
market
line
price

Examples:

Lakers-Celtics
DraftKings
spread
Celtics -3.5
-110

features

event_id
team_rating
elo
injury_adjustment
rest_days
travel_distance
weather

Then your model predicts:

P(team wins)
P(over total)
P(cover spread)

and compares:

model_probability vs implied_probability_from_odds

Things I’d avoid

• Scraping sportsbook websites (fragile and often against terms)
• Using only final scores (you need the market price history)
• Backtesting only on closing lines
• Ignoring vig/juice removal

For a first serious project, a good stack would be:

The Odds API + a stats provider + PostgreSQL + Python + XGBoost/LightGBM

That is enough to build a legitimate NFL/NBA betting research pipeline before paying enterprise prices.

If you tell me the sport (NFL, NBA, soccer, etc.) and whether you want prediction models, arbitrage, or +EV betting models, I can suggest a more specific data stack.

Yes. If your goal is building and backtesting your own betting models, I’d focus on APIs that give you both historical game data and timestamped odds, rather than just a live odds feed.

Best options

The Odds API is probably where I'd start. Its historical odds product provides timestamped bookmaker snapshots, with featured-market data going back to June 2020; snapshots are generally every 10 minutes initially and every 5 minutes from September 2022. Additional markets such as props are available from May 2023.

SportsDataIO is particularly interesting if you're serious about eventually building a substantial model. It has 10+ years of historical sports data, historical odds, line movements, props and futures, and feeds for things like player/team statistics. Its historical data is explicitly intended for backtesting and ML/model training.

Their odds API also tracks opening/closing lines and price changes, which is extremely useful for modeling rather than simply looking at the final closing price.

What I'd use for a personal betting model

I'd structure your data roughly like:

games
├── event_id
├── date/time
├── teams
├── venue
└── final_score

team_stats
├── event_id
├── offensive/defensive metrics
├── pace
├── injuries/rosters
└── historical performance

odds
├── event_id
├── sportsbook
├── timestamp
├── market
├── side
├── line
└── price

results
├── event_id
├── closing_line
├── closing_price
├── outcome
└── profit/loss

The timestamp on the odds is crucial. You don't want to accidentally train your model using information that wasn't available when the bet could actually have been placed. That's one of the easiest ways to produce a backtest that looks fantastic but doesn't work in reality.

I'd also preserve every odds snapshot, rather than just storing the opening and closing lines. That lets you investigate things like:

• CLV (closing-line value)
• line movement
• market efficiency
• sportsbook disagreement
• timing of information
• model edge vs. market consensus
• whether an apparent edge survives until close

Sportradar is the option I'd investigate if you're eventually aiming at a professional-grade system: it offers 80+ sports, 500+ leagues, extensive historical data and odds feeds.

One important distinction

If you're building your own predictive model, don't just buy an odds API. You want three categories of data:

1. Outcome data — scores, play-by-play, player/team statistics.
2. Pre-game information — injuries, lineups, rest, travel, etc.
3. Market data — sportsbook odds with timestamps.

The third one is particularly important because the sportsbook market itself can act as a very strong baseline model.

If you tell me which sport(s) you want to model (NFL, NBA, MLB, soccer, etc.) and whether you're thinking Python/SQL, I can recommend a specific data stack and show you how I'd structure the historical database for backtesting.

Building your own betting models requires two core ingredients that can be surprisingly tricky to tie together: historical odds (opening lines, line movements, and closing lines) and historical box scores/results (to grade whether your model or bet won).

The best API options range from lightweight, developer-friendly hobbyist tools to enterprise-grade data feeds:

Top API Providers for Historical Odds & Data

• The Odds API : Widely considered the best starting point for hobbyists and developers. It offers a free tier (500 requests/month) and relatively inexpensive paid tiers that unlock historical odds snapshots (taken at regular intervals back to 2020). It covers major US and international sports, though you will need a separate source for final game outcomes/scores.
• SportsDataIO : A robust, comprehensive choice if you need a heavy-duty North American sports data solution. Their historical odds database covers pre-game, live, and futures lines (going back to 2019 for major leagues like the NFL, NBA, and MLB) and integrates smoothly with official play-by-play and box score data.
• Sports Game Odds : A strong mid-tier REST and WebSocket API alternative covering dozens of sports and leagues with real-time and historical data from 85+ sportsbooks. It's developer-friendly and bundles in scores and settlement data, making it easier to backtest without stitching multiple APIs together.
• TheStatsAPI : If your modeling focus is specifically on football (soccer) , this is an exceptional choice. It pairs 10 years of match stats, player data, and expected goals (x G x cap G𝑥𝐺) with pre-match and live odds (including sharp benchmarks like Pinnacle and Bet365) under a single authentication system.
• OddsJam API : Built more for heavy-duty quantitative analysis, sharp action, and syndicates. It features low-latency feeds and a deep historical database of line movements and consensus odds, though it sits at a much higher enterprise price point.

Free & Open Source Alternatives for Research

If you want to test and prototype a concept before spending money on an API subscription, consider these community-loved resources:

• football-data.co.uk : The gold standard for free historical soccer data, offering massive downloadable CSV archives containing results and closing odds for dozens of European leagues going back years.
• nflverse (Python/R packages) : Provides meticulously maintained, free datasets for the NFL, including play-by-play data and historical pre-game odds back to 1999.
• College Football Data : A fantastic, developer-friendly free API explicitly tailored for college football game stats, advanced metrics, and historical betting lines.

To help narrow down the best fit, tell me:

• What sport or sports are you planning to model?
• Are you looking to backtest via Python/R using a bulk dataset, or build a live application?

Yes. If your goal is building and backtesting your own betting models, I’d focus on providers that give you timestamped historical odds, not just historical scores.

My shortlist

1. The Odds API — easiest place to start

This is probably where I'd start if you're an individual developer. It has historical odds snapshots going back to June 2020 for featured markets, with 10-minute snapshots initially and 5-minute snapshots from September 2022. Additional markets such as props are available from May 2023.

That lets you construct datasets such as:

game_id
timestamp
sport
league
home_team
away_team
bookmaker
market
selection
line
american_odds

The important thing is the timestamp. For backtesting, you want to know exactly what odds were available when your hypothetical bet would have been placed. Otherwise, you can accidentally introduce look-ahead bias.

2. SportsDataIO — particularly attractive for U.S. betting

SportsDataIO is more comprehensive if you're primarily interested in NFL/NBA/MLB/NHL. Their odds feed tracks opening and closing lines, intermediate price changes, timestamps, pregame/in-play odds, props and futures.

Their current Discovery Lab is particularly interesting for an individual model builder: it provides real sports data for personal projects, including odds, with a free tier containing last season's data and paid tiers starting at $99/month.

They also offer a Vault with 10+ years of historical data intended specifically for backtesting betting models and research.

3. Sportradar — if you're going professional

Sportradar is the heavyweight option: 80+ sports, 500+ leagues, 750,000+ events/year, extensive historical data and odds feeds.

Their odds products include prematch, live, futures and player props, with coverage of 140+ bookmakers in some products.

I'd not start here for a personal project unless you already know you need that level of coverage.

What I'd actually do

If you're building your first model, I'd use:

The Odds API + a separate stats source → PostgreSQL/Parquet → Python/Pandas → model → backtester

For example:

Historical games
       ↓
Team/player statistics
       +
Historical bookmaker odds
       ↓
Feature engineering
       ↓
Prediction model
       ↓
Predicted probability
       ↓
Compare against historical odds
       ↓
EV / CLV / ROI / calibration
       ↓
Out-of-sample backtest

One particularly important point: don't just download final scores and closing odds. If you're trying to determine whether your model actually has an edge, collect line movement throughout the market's life. Opening line → movement → closing line gives you much more useful information about CLV and whether your model's predictions were genuinely beating the market.

Also save the individual bookmaker prices, rather than only an aggregated "best odds" number. That lets you study bookmaker-specific behavior, market consensus, vig, and line shopping.

If you tell me which sport(s) you're interested in (NFL, NBA, MLB, soccer, etc.) and whether you're doing moneylines, spreads/totals, or player props, I can narrow this down to the best API/data stack and show you how I'd structure the historical database for model training.

Building your own sports betting models requires two distinct types of historical data: market data (opening lines, closing lines, and line movements from sportsbooks) and core sports/box score data (team stats, player metrics, and final scores to evaluate your model).

The best options range from developer-friendly hobbyist tools to enterprise-grade feeds:

• The Odds API is widely considered the best starting point for hobbyists and individual developers. It features a simple JSON structure, covers major US and global bookmakers, and provides historical odds snapshots (at 5-to-10-minute intervals dating back to mid-2020) on its paid plans.
• Sports Game Odds stands out for offering both real-time/historical odds and built-in final scores, stats, and settlement data in a single integration. It also features a generous introductory/amateur tier, making it cost-effective for backtesting.
• SportsDataIO is a robust enterprise-tier choice. Their historical odds database tracks pre-game, live, and futures line movements across major leagues (NFL, NBA, MLB), and they pair it with deeply granular traditional sports stats and player props.
• OddsJam API is built heavily toward quants, syndicates, and high-volume modelers. It tracks sharp bookmaker comparisons (like Pinnacle), opening/closing lines, and real-time line changes across a massive array of markets.
• Goalserve has been a reliable staple for years, providing XML/JSON feeds covering global soccer, basketball, and hockey alongside historical opening and closing data.

Community Insights on Model Backtesting

When gathering data for backtesting, bettors often note the operational hurdles of syncing odds data with actual game outcomes:

“I recently found the-odds-api and it seems great for making API calls to get odds for upcoming and historical games. But I don't see a way to query it to get the Win/Loss result for those historical games.”

“you're going to need another API for example to do results & scoring etc. They are just pulling odds for the most part but you need a secondary api for scoring etc.”

To help narrow down the right API for your project, tell me:

• What specific sports or leagues are you planning to model?
• Do you need player props or just main markets (spreads, moneylines, totals)?
• Are you looking for a free tier to prototype or a paid historical archive for deep backtesting?