Eventhacker

Pilot · Simulation · Venue Operations

Cologne Night Live Simulator

What happens when 300 guests arrive early, two people are missing at the bar, the cloakroom overflows or the headliner starts 45 minutes late?

The Cologne Night Live Simulator is intended to model event nights as a dynamic system. Instead of looking at isolated figures, it connects attendance, staffing, bar operations, technology, costs, waiting times, incidents and program flow on a shared timeline.

The goal is not to predict reality perfectly. The goal is to make possible bottlenecks, interactions and economic effects visible and comparable before the night happens.

Illustration of the Cologne Night Live Simulator with Cologne nightlife and cathedral
AI-generated illustration for visualizing the project idea.
PilotEarly development phase
CalibrationReal event data planned
No fake live dataIllustrative model description
Gamenet 2027Simulator plus game prototype

Two products, one foundation

The roadmap deliberately separates the operational simulator from the later game. They share a technical foundation, but they serve different users and should feel different in the interface.

Product 1

Night Live Simulator

A practical tool for clubs, venues and promoters. It focuses on planning, analysis, training, staffing, bar capacity, incidents, cost, revenue and scenario comparison.

  • Real event workflows
  • Calibration with pilot data
  • No game mechanics required
  • Operational decision support
Product 2

Night Live Game

A later playable game based on the simulation foundation. It may use stories, avatars, roles, decisions, consequences, atmosphere and multi-night story arcs.

  • Playable nights
  • Fictionalized people and venues
  • Story events and consequences
  • Economy, reputation and guest satisfaction

Shared Nightlife Simulation Engine

Simulator and game should not become two unrelated codebases. The shared engine owns the event-night model, repeatable simulation runs, logging and replay. Simulator and game can then use the same domain while keeping their own presentation.

Night Live Simulator

Planning, analysis, scenarios, training and venue operations.

Night Live Game

Story, gameplay, avatars, decisions and consequences.

Common domain objects

  • venue
  • event
  • timeline
  • guest_flow
  • staff
  • bar
  • incident
  • story_event
  • economy
  • scenario
  • simulation_run

Why simulate?

An event night consists of many dependent decisions. More guests do not automatically mean more profit. A full bar can become the bottleneck. Too little staff creates waiting times, too much staff increases costs. Program delays affect door, cloakroom, bar and technology at the same time.

These interactions are what the simulator is meant to make visible.

Questions the model should help explore

  • What happens when 30 percent of the guests arrive within 20 minutes?
  • When does the bar become the bottleneck?
  • How do waiting times change with one fewer service person?
  • What is the effect of a later program start?
  • How robust is the plan under unexpected incidents?
  • Which configuration creates less stress at a comparable result?

One shared timeline

Guests arrive, order, move through the venue, wait, leave again, staff switch roles and program items change the dynamic. The simulator should represent these processes over time so that not only final results, but also critical phases become visible.

20:00Doors open
21:00Arrival rises
23:30Peak entry
00:15Bar peak
01:00Headliner
03:30Second bar peak
05:00Last round
06:00Close

What gets simulated?

Guests

  • Expected attendance
  • Arrival distribution
  • Length of stay
  • Venue movement
  • Ordering behavior
  • Drop-off at long queues

Staff

  • Bar
  • Door
  • Cloakroom
  • Security
  • Technology
  • Runner roles

Bar and capacity

  • Stations
  • Service speed
  • Orders per interval
  • Average basket
  • Waiting times
  • Overload

Program and technology

  • Doors
  • Running order
  • DJ and live slots
  • Changeovers
  • Delays
  • Technical incidents

Economics

  • Entry
  • Bar revenue
  • Staffing
  • Technology
  • Artists
  • Fixed and variable costs

Incidents

  • Staff absence
  • Late artist
  • Technical problem
  • Demand variance
  • Capacity bottleneck
  • Additional security situation

Scenarios instead of gut feeling

The simulator should make multiple variants of the same night comparable. It should not only compare revenue and cost, but also waiting time, overloaded time windows, staff utilization, lost orders, peak capacity and resulting margin.

Scenario A
  • 450 guests
  • 4 bar staff
  • early peak
Scenario B
  • 450 guests
  • 5 bar staff
  • same demand
Scenario C
  • 520 guests
  • 5 bar staff
  • headliner +30 minutes
Maximum waiting time Overloaded windows Estimated lost orders
Planned

Monte Carlo model

An event night never runs exactly as planned. Later, the simulator should not only calculate one sequence, but run many slightly different variants of the same night.

A Monte Carlo model can vary arrival time, ordering behavior, length of stay or incidents within defined boundaries. A single forecast becomes a distribution of possible results.

Important

No artificial production values are shown here. Reliable ranges only make sense after calibration with real venue data.

Simulation becomes interesting through real nights

The model should be calibrated with real event data. The pilot must also work with small, incomplete data sets because not every venue has a fully digitized system.

  • Ticket and door counts
  • POS and bar revenue
  • Order timestamps
  • Shift plans
  • Program schedule
  • Documented incidents
  • Visitor counting
  • Sensor and monitoring data

Story material for the later game

Real nightlife stories can become structured, anonymizable story events. The roadmap does not force a fixed game mechanic yet. First, the material should be collected in a form that can later drive scenarios, choices and consequences.

  • Bollwerk team
  • promoters
  • bar
  • door
  • technology
  • security
  • artists
  • guest experiences

Example event structure

title: Headliner kommt zu spät
category: artist
trigger: 00:30
impact:
  guest_flow: high
  bar: medium
  staff: medium
  program: high
choices:
  - wait
  - move_support_act
  - extend_dj_set

Who is it for?

Clubs and venues

Test capacity, staffing and flow before one night starts.

Promoters

Compare scenarios before decisions become expensive.

Event technology

Understand effects of running order, changeovers and disruptions.

Research and education

Explore event operations as a dynamic system.

What the simulator is not

It should

  • make assumptions transparent
  • make scenarios comparable
  • show bottlenecks
  • support discussions with data

It should not

  • replace real safety planning
  • evaluate regulatory requirements
  • evaluate individual people
  • promise exact visitor reactions

Roadmap to Gamenet 2027

The target milestone is a presentable and playable build: a usable simulator, a playable vertical slice, at least one consistent venue, multiple night scenarios, anonymized story material and stable demo material for a public presentation.

Phase 1

Foundations / Simulator

  • Event-night data model
  • Timeline
  • Guests, staff and bar
  • Cost / revenue
  • Incidents and first scenarios
Phase 2

Pilot with real data

  • Collect real event data
  • Include Bollwerk as possible pilot venue
  • Gather stories and typical situations
  • Calibrate the model
  • Replay / timeline view
Phase 3

Simulation Engine

  • Stochastic parameters
  • Monte Carlo runs
  • Agents and entities
  • Detached API / engine
  • Seeds, logging and replay
Phase 4

Game Prototype

  • Playable vertical slice
  • One venue and one night
  • Roles and decisions
  • Story events
  • Avatars and simple economy
Phase 5

Gamenet 2027 Build

  • Usable simulator
  • Playable game prototype
  • Anonymized nightlife stories
  • Several night scenarios
  • Stable demo and presentation material

Pilot partners

Simulate a real night as a pilot?

For the next development phase we are looking for real event data and venues where the model can be tested against actual operations. Incomplete data is useful too: attendance, bar revenue, shift plan and a rough program schedule may already be enough for a first comparison.