Our challenge? Source data directly from the game servers, manage and transform it into a standardized format, and deliver it to our network of betting and media customers, all in real time.

So how is the GRID Data Platform preparing to handle millions of live data points per second, across multiple concurrent tier-one tournaments?

Building for Scale: When Global Esports Collide

Capturing data from multiple global tournaments isn’t simply about throughput; it’s about synchronization at scale. GRID’s data infrastructure is engineered to process millions of data points at once – one game of League of Legends Esports generates more than 1.2 million events that GRID captures live, all structured and distributed within milliseconds.

The platform’s architecture is built for redundancy, high availability, and automated scaling  meaning it can handle simultaneous tournament loads without latency spikes or loss of integrity. 

Whether it’s a triple-overtime CS2 final or a LoL Worlds semifinal running in parallel, GRID ensures that every kill, assist, and round result is captured, validated, and broadcast as structured data in real time.

Real-Time Reliability – Integrity by Design

In the esports betting ecosystem, data accuracy is everything. Even a fraction of a second delay or a missing event can translate to incorrect odds or unfair settlements. That’s why GRID’s systems are ingestion-first and verification-driven, ensuring each event is cross-validated against multiple checkpoints before leaving the platform.

By working directly with official tournament servers and rights holders, GRID guarantees that the data powering odds, visualizations, and broadcasts originates from the authentic in-game source — not scraped feeds or spectator views. This ensures:

• Integrity – Server-level data eliminates human error and manipulation risks.
• Security – Encrypted pipelines protect every packet from capture to distribution.
• Fairness – Partners can confidently price and settle markets knowing the data is authoritative.

Speed that Shapes Experiences

GRID’s live data distribution is built for sub-second latency, allowing sportsbooks to offer micro-markets such as “First Blood,” “Next Roshan,” or “Round Winner” with complete confidence. For media and broadcast partners, it means real-time visualizations, predictive insights, and automated storytelling all synchronized with the live action.

When two major tournaments happen at once, speed isn’t a luxury; it’s a necessity. GRID’s globally distributed architecture ensures data packets reach end users instantly, no matter where the match or bettor is located.

The Power of Official Data

In esports, there’s only one way to guarantee integrity, accuracy, and commercial sustainability: official data.

Unofficial or scraped data feeds often come with missing context, human latency, and no guarantee of compliance. Official data, however, is structured, verified, and rights-holder-authorized — the foundation that powers legitimate esports betting, media coverage, and fan engagement worldwide.

That’s why GRID’s partnerships with publishers and tournament organizers are critical. They ensure that every GRID-powered event in Chengdu, from LoL Worlds to IEM to BLAST  is backed by data you can trust.

GRID – Bet on data.

Three events. Millions of fans. One platform ensuring every moment, stat, and signal reaches the world in real time. The GRID Data Platform strives to define what real-time means for an industry built on milliseconds.

Because in Chengdu, when the world’s top teams take the stage, the data behind the game must perform just as flawlessly.If you are interested in getting access to official esports data reach out to data@grid.gg

The post Three Major Events in Chengdu, One Unified Data Powerhouse Online appeared first on grid.gg.

A-C

Bet Builder – A tool allowing bettors to create personalized bets by combining multiple markets within a single match.

Data Provider – A company that supplies real-time match data for odds calculation and market analysis (e.g., GRID).

D-G

Data Scraping – Refers to the methods used to source unofficial data such as OCR, Computer Vision or any other (sometimes manual) transcription of odds or data.

Delayed Esports Data – Live data with a noticeable delay from the action taking place in real time. This can be intentional (imposed by rights holders or distributors) or due to technological limitations.

Esports – Competitive, professionally organized video gaming events featuring teams or individuals in games like CS2, League of Legends, VALORANT, and Dota 2.

Esports Betting – Wagering on the outcome of professional esports matches, tournaments, or in-game events.

Esports Data – In-game data derived from competitive esports matches and competitions.

Esports Data Provider – A company that supplies real-time match data for odds calculation, market analysis, stats, predictions, and official data-powered products (e.g., GRID).

Esports Integrity Commission (ESIC) – A regulatory body focused on anti-corruption and match-fixing within esports.

Esports Predictions – Forecasting in-game outcomes or betting markets using historical data, statistics, and AI-driven models.

Esports Stats – Quantitative information about teams, players, or game performance used in analysis and betting markets.Esports-Specific Betting Markets – Unique bets available only in esports, such as “First Blood” or “Map Winner.”

Fantasy Esports – Formats where users create virtual teams based on real-world performance metrics.

First Blood – A popular betting market referring to the first kill in a match, common in MOBAs like League of Legends and Dota 2.

Game Server – The most reliable source of live esports data, only accessible through direct, official partnership with rights holders or super rights holders.

H–M

In-Play Betting (Live Betting) – Wagering on events occurring during a live match.

In-Game Data – Data sourced directly from the game, referring to actions within the game itself—player position, K/D/A, economy status, etc.

Integrity Protocols – Rules and monitoring systems designed to prevent cheating, match-fixing, and insider betting.

Live Esports Data – Data sourced directly from the game taking place and not subject to any delay compared to real-time.

Map Winner – A common market in esports where bettors predict the winner of a specific map within a best-of series.

Match-Fixing – Illicit manipulation of match outcomes for financial gain.

Micro-Betting – Wagering on small, discrete in-game events.

N–S

Official Esports Data – Data officially sanctioned by the rights holder.

Odds Provider – Firms that set betting odds, for best results, rely on an official data source.

Parlay Bets – A type of accumulator bet where all selected outcomes must be correct for a payout, often offering higher odds.

Pre-Match Betting – Wagering that occurs before a match begins, often using more static, data-derived odds.

Purple Screen of Death – A technical failure in broadcasting or data feeds, typically indicating a critical stream or feed disruption.

Real-Time Esports Data – Immediate, non-delayed data captured directly from live esports gameplay.

Regulated Esports Betting – Betting conducted through government-approved and licensed platforms.

Rights Holder (RH) – The entity holding the in-game data legal rights, usually a game developer or an appointed tournament organizer.

Roster – The list of players who make up a professional esports team for a given competition.

Sanctioned Event – An esports event officially endorsed by a rights holder.

Skin Gambling – Betting with virtual in-game items, typically outside regulated frameworks.

Streaming Feed – The live video broadcast of an esports match, often used for entertainment but not reliable as a data source.

Super Rights Holder – The creator of the in-game data IP—game developer or publisher. They control both the competitions and the game (the sport itself).Synthetic Play / Synthetic Gaming – Staged gaming events designed solely for betting.

T–Z

Trader Portal – A user interface provided to sportsbooks for setting, adjusting, and managing esports betting markets in real time.

Unofficial Data – Data sourced without sanctioned access from the rights holder, typically delayed, less granular, and less reliable.

Unsanctioned Event – Competitions organized without official rights holder endorsement.

Virtual Esports – AI-driven, digitally simulated sports events with outcomes distinct from live competition.

Wagering Handle – The total amount of money accepted in bets by a sportsbook over a given period.

Bookmark this glossary for future reference as the esports betting ecosystem continues to evolve—and follow GRID for more insights, case studies, and data-powered analysis on the future of competitive gaming and betting.

The post Esports Betting Glossary (Updated for 2025) appeared first on grid.gg.

Maintaining high stability and consistency is especially important in GRID’s management of real-time data, as issues can be difficult to detect and undo in time for delivery. In many cases, the original data will simply not be available for a redo, so any misstep the live data takes can be critical.

To maintain a high level of test coverage we write our Go tests utilizing the built-in testing package, as it is very rich in features and it makes our tests easy to write.

In this article we’ll share some of our practices for Go testing that may help one write more manageable and reliable tests, as well as a healthier code base.

Use the built-in testing package

To start with, we need to use the provided built-in testing package. To add a unit test to a function for example, add a testing file to the same package as the function to be tested. By convention, the unit tests’ file should have the same name as your functions followed by _test (e.g. foo_test.go for a file called foo.go).

For a unit test to be recognized as such, the name of the test function should start with a prefix “Test” and should take *testing.T as its only parameter. By convention, the tests are usually named after the function that they’re testing (e.g. TestFoo for a function called Foo).

Here’s an example of a simple unit test of a function Foo:

To run one’s tests, use the go test tool (append ./... to run all packages in your current folder):

func Test_Foo(t *testing.T) {
 var tests = []struct {
  name string
  input string
  want  string
 }{
  {"yellow should return color", "yellow", "color"},
  {"banana should return fruit", "banana", "fruit"},
  {"duck should return animal", "duck", "animal"},
 }
 for _, test := range tests {
  t.Run(test.name, func(t *testing.T) {
   actual := Foo(test.input)
   if actual != test.want {
    t.Errorf("got %s, want %s", actual, test.want)
   }
  })
 }
}

And here’s an example of some tests structured using nested t.Run functions:

func Test_MyStuct(t *testing.T) {
 t.Run("ParseString", func(t *testing.T) {
  t.Run("should return true on valid string", func(t *testing.T) {
   ...
  }
  t.Run("should return false on invalid string", func(t *testing.T) {
   ...
  }
 }
 t.Run("ParseInt", func(t *testing.T) {
  t.Run("should return true on valid integer", func(t *testing.T) {
   ...
  }
  t.Run("should return false on invalid integer", func(t *testing.T) {
   ...
  }
 }
}

Subtests called by the same testing function will, generally, run in series, and will run through all subtests even if an earlier subtest has failed. In the cases where one would prefer the tests to run in parallel there’s instead the t.Parallel method. t.Parallel is called the same way as t.Run, but all subtests of a single test function that is called using t.Parallel run at the same time. For completely isolated tests, using t.Parallel instead of t.Run is a good practice as it allows the testing framework to better utilize its available resources.

Use the assert library

In addition to the built-in testing framework it is also highly recommended to use the github.com/stretchr/testify/assert library. Using this library, one can make tests simpler and easier to read, mainly by simplifying complex checks for expected results. By simply passing the expected and actual values to a one-line function, the library will manage and log the checking of the result as well as marking the full test as failed on failure.

Here’s an example of how one might use assert.Equal function in a test:

func Test_MyFunction(t *testing.T) { 
  result := MyFunction() expected := "some expected value" 
  assert.Equal(t, expected, result, "they should be equal") 
}

The library also provides many other useful functions, such as assert.True, assert.Nil,  assert.Error,  assert.ElementsMatch,  assert.WithinDuration, and many others.

On failure the assert functions will generally let the test continue, allowing for multiple assert failures to be recorded in a single run of a test. In the cases where one would rather let the tests stop on failure there’s an accompanying library github.com/stretchr/testify/require, which performs the exact same checks as the assert library, but it instead stops the test on the first assert failure.

Run coverage tests

To check one’s tests’ coverage and generate coverage reports, the go test command offers the -cover flag. When one runs tests with the -cover flag, Go will collect coverage data for the given code and generate a report that shows how much of the code was covered by the tests.

Here’s an example of running a coverage test on a simple project:

$ go test -cover ./...
ok      MyProject 0.003s  coverage: 50.0% of statements

This runs all tests found in the given directory and responds with the percentage of code covered by the tests.

Just getting the percentage is not always enough, as it can be hard to figure out what part of one’s code is lacking in coverage. To get more detailed information out of the coverage test there’s the -coverprofile flag. This flag allows one to specify a file to which the detailed coverage profile will be written. Using this profile one can generate a coverage report using the go tool cover command.

Here’s an example of a coverage report being generated, in this case an html report:

$ go test -cover -coverprofile=coverage.profile
ok      MyProject 0.003s  coverage: 50.0% of statements
$ go tool cover -html=coverage.profileUsing the coverage reports can more easily identify areas of one’s code that are not covered by tests in a visual and customizable way.

Check for race conditions in your tests

A common cause for flaky and unreliable tests is race conditions. These are cases where parallelized operations behave inconsistently depending on the condition in which they’re run. For example, when run on a local machine or later run in a pipeline. In Go, these most often occur when working with goroutines, and they can be quite difficult to catch while implementing tests.

To help detect instances of race conditions the go test command offers the -race flag. The flag adds data race detection for the tests. When one runs tests with this flag the testing tool will detect and notify if any data race occurs. If a data race is detected, it will report an error and provide information about the location of the race.

Here’s an example of a basic race condition, and how the testing tool detects it:

func Test_MyFunction(t *testing.T) {
 expected := 4
 actual := expected
 go func() {
  actual = 5
 }()
 if expected != actual {
  t.Fail()
 }
}

The tests that have race conditions are treated as failing tests and the logs show the location of the affected data and the goroutines that are involved.

We hope these tips and best practices will help you along the way as you build your code, making your tests consistent and reliable, and saving you time in the long run.

Make sure to follow GRID on LinkedIn and Twitter for news and updates on esports and game data- and keep an eye out for future articles from the GRID Engineering Team for help in building your esports applications!

The post Testing in Go: Best Practices and Tips appeared first on grid.gg.