Build a Sports Prediction Game with Live Video & RTC

Sports prediction games are one of the fastest-growing segments in interactive entertainment. The global fantasy sports and prediction gaming market is projected to reach USD 48.6 billion by 2030 according to Grand View Research, driven by mobile adoption and real-time engagement technology. Apps like Superbru, Monterosa's Interaction Cloud, and Golwiz have proven that fans will spend hours predicting outcomes — especially when they can watch the action live, see predictions from friends in real time, and celebrate correct calls with a community. The market opportunity sits at the intersection of live sports streaming, gamification, and social interaction.

This tutorial shows you how to build a sports prediction game from scratch — one where users watch live events via multi-angle video streams, submit predictions through real-time interactive overlays, see community predictions update live, and get instant result notifications when outcomes are decided. The architecture uses Tencent RTC (TRTC) as the communication backbone, leveraging its RTC and RTMP dual-mode streaming, Chat SDK for prediction messaging, and ultra-low latency infrastructure to create an experience where the prediction moment and the live moment are perfectly synchronized.

TL;DR

• Full RTC chain (100-300ms latency) is required for prediction game fairness — RTMP hybrid (300-500ms) allows cheating via faster data feeds
• Event-driven predictions tied to live match moments (goals, corners, penalties) create 10-20x more engagement points than pre-game picks only
• Chat SDK custom messages handle the entire prediction lifecycle: question broadcast, response collection, distribution updates, and settlement
• Anti-cheat validation requires server-side timestamp verification with stream latency offset to prevent post-event submission
• Voice rooms during live events increase prediction volume 2-3x compared to silent viewing

TL;DR

• Full RTC chain (100-300ms latency) is required for prediction game fairness — RTMP hybrid (300-500ms) allows cheating via faster data feeds
• Event-driven predictions tied to live match moments (goals, corners, penalties) create 10-20x more engagement points than pre-game picks only
• Chat SDK custom messages handle the entire prediction lifecycle: question broadcast, response collection, distribution updates, and settlement
• Anti-cheat validation requires server-side timestamp verification with stream latency offset to prevent post-event submission
• Voice rooms during live events increase prediction volume 2-3x compared to silent viewing

Why Real-Time Infrastructure Matters for Prediction Games

Most prediction games are boring. Users submit picks before the game, then check results hours later. The engagement window is tiny — a few seconds of input, then nothing until the game ends. According to Newzoo's Global Games Market report, interactive and social gaming features increase player session time by 2-3x compared to passive experiences. The prediction games that drive daily active usage solve this by making predictions a continuous, social, live experience:

• Live video keeps users in-app during the entire event
• In-play predictions create dozens of engagement moments per game (not just one pre-game pick)
• Social visibility — seeing what others predict — adds competitive pressure and FOMO
• Instant results — the moment a goal is scored or a play completes, predictions resolve immediately
• Community reaction — voice rooms and chat explode when predictions hit

The technical challenge: synchronizing video streams, prediction submissions, and result broadcasts within milliseconds. If the stream is delayed, users get spoiled by faster data feeds. If predictions close too late, users can cheat by watching the outcome first. Timing integrity requires ultra-low latency infrastructure.

TRTC's Interactive Game Console architecture solves this with end-to-end latency under 300ms for the full RTC chain, plus the RTMP+RTC dual-mode approach documented in the implementation guide for integrating existing broadcast infrastructure.

Architecture Overview

A sports prediction game has four main systems that must operate in tight synchronization:

┌──────────────────────────────────────────────────────────────────┐
│                        CLIENT APPLICATION                          │
│  ┌────────────┐  ┌─────────────────┐  ┌──────────────────────┐  │
│  │ Live Video │  │  Prediction UI   │  │  Community / Social  │  │
│  │  Player    │  │  (Submit/View)   │  │  (Chat + Voice)      │  │
│  └────────────┘  └─────────────────┘  └──────────────────────┘  │
├──────────────────────────────────────────────────────────────────┤
│                     REAL-TIME LAYER (TRTC)                         │
│  ┌────────────┐  ┌─────────────────┐  ┌──────────────────────┐  │
│  │ Live SDK   │  │    Chat SDK      │  │     RTC SDK          │  │
│  │ (Streams)  │  │  (Predictions)   │  │  (Voice Rooms)       │  │
│  └────────────┘  └─────────────────┘  └──────────────────────┘  │
├──────────────────────────────────────────────────────────────────┤
│                      GAME ENGINE (Backend)                         │
│  ┌──────────┐ ┌───────────┐ ┌──────────┐ ┌──────────────────┐  │
│  │Prediction│ │  Scoring   │ │ Event    │ │  Leaderboard     │  │
│  │ Manager  │ │  Engine    │ │ Source   │ │  Service         │  │
│  └──────────┘ └───────────┘ └──────────┘ └──────────────────┘  │
├──────────────────────────────────────────────────────────────────┤
│                      DATA LAYER                                    │
│  PostgreSQL │ Redis (Live State) │ TimescaleDB (Events) │ S3     │
└──────────────────────────────────────────────────────────────────┘

Key Integration Points

Start with the free Chat API — free forever — 1,000 MAU, no concurrency limits, push notifications included. | Result announcements | Chat SDK group messages | Instant settlement notifications | | Community discussion | RTC SDK voice rooms | Live commentary during events | | Prediction overlays | Chat SDK + Client rendering | Show community prediction distribution |

Part 1: Live Event Streaming — The Foundation

Users won't predict what they can't watch. The live stream is the anchor experience. Everything else — predictions, chat, leaderboards — overlays on top.

Dual-Mode Streaming Architecture

Following the architecture documented in TRTC's implementation guide, the system supports two streaming approaches:

Option A: RTMP Push + RTC Pull — For integrating existing broadcast feeds

• Camera/encoder pushes RTMP to TRTC
• Viewers pull via RTC protocol for low latency
• Latency: 300-500ms
• Lower development cost

Option B: Full RTC Chain — For maximum speed (recommended for prediction games)

• Camera SDK pushes directly via RTC
• Viewers subscribe via RTC
• Latency: 100-300ms
• Critical for prediction timing integrity

For a prediction game, Option B is strongly recommended. The 200ms difference between RTMP hybrid (300-500ms) and full RTC (100-300ms) directly impacts fairness — in fast-paced sports, 200ms is the difference between a legitimate prediction and a reaction to what already happened.

Setting Up the Live Stream (Broadcaster Side)

import TRTC from 'trtc-sdk-v5';

// Broadcaster: The event camera operator
class EventBroadcaster {
  constructor() {
    this.trtc = TRTC.create();
    this.isStreaming = false;
  }

  /**
   * Start broadcasting a sports event
   * @param {Object} config - Stream configuration
   * @param {number} config.roomId - Unique room for this event
   * @param {string} config.cameraAngle - 'main' | 'tactical' | 'closeup'
   */
  async startBroadcast(config) {
    const { roomId, cameraAngle = 'main' } = config;

    // Enter room as anchor (broadcaster)
    await this.trtc.enterRoom({
      sdkAppId: YOUR_SDK_APP_ID,
      userId: `broadcaster_${cameraAngle}`,
      userSig: await fetchUserSig(`broadcaster_${cameraAngle}`),
      roomId: roomId,
      scene: 'live',
      role: 'anchor'
    });

    // Configure video encoding for sports content
    const videoProfile = this.getVideoProfile(cameraAngle);

    await this.trtc.startLocalVideo({
      view: document.getElementById(`preview-${cameraAngle}`),
      option: videoProfile
    });

    // Start audio (commentary or ambient)
    await this.trtc.startLocalAudio({
      option: { profile: 'standard' }  // Higher quality for sports audio
    });

    this.isStreaming = true;
    console.log(`Broadcasting: ${cameraAngle} on room ${roomId}`);
  }

  getVideoProfile(angle) {
    const profiles = {
      main: {
        profile: '1080p',
        frameRate: 30,    // Sports needs smooth motion
        bitrate: 2500,
        mirror: false
      },
      tactical: {
        profile: '720p',   // Wide angle, less detail needed
        frameRate: 30,
        bitrate: 1500,
        mirror: false
      },
      closeup: {
        profile: '720p',
        frameRate: 30,
        bitrate: 1800,
        mirror: false
      }
    };
    return profiles[angle] || profiles.main;
  }

  async stopBroadcast() {
    await this.trtc.stopLocalVideo();
    await this.trtc.stopLocalAudio();
    await this.trtc.exitRoom();
    await this.trtc.destroy();
    this.isStreaming = false;
  }
}

Multi-Angle Viewer (Player Side)

// Player: Watch the event with angle switching
class EventViewer {
  constructor(containerId) {
    this.container = document.getElementById(containerId);
    this.streams = new Map(); // angle -> TRTC instance
    this.activeAngle = 'main';
  }

  /**
   * Connect to all available camera angles for an event
   * @param {Object} eventStreams - { main: roomId, tactical: roomId, closeup: roomId }
   */
  async connectToEvent(eventStreams) {
    for (const [angle, roomId] of Object.entries(eventStreams)) {
      const instance = TRTC.create();
      const viewElement = this.createStreamView(angle);

      await instance.enterRoom({
        sdkAppId: YOUR_SDK_APP_ID,
        userId: `viewer_${currentUser.id}_${angle}`,
        userSig: await fetchUserSig(`viewer_${currentUser.id}_${angle}`),
        roomId: roomId,
        scene: 'live',
        role: 'audience'
      });

      // Subscribe to broadcaster's video
      instance.on(TRTC.EVENT.REMOTE_VIDEO_AVAILABLE, async (event) => {
        await instance.startRemoteVideo({
          userId: event.userId,
          streamType: event.streamType,
          view: viewElement
        });
      });

      // Track first frame for loading state
      instance.on(TRTC.EVENT.FIRST_VIDEO_FRAME, () => {
        this.hideLoadingState(angle);
      });

      this.streams.set(angle, { instance, viewElement, roomId });
    }

    // Default: show main angle
    this.switchAngle('main');
  }

  createStreamView(angle) {
    const el = document.createElement('div');
    el.id = `stream-${angle}`;
    el.className = angle === this.activeAngle ? 'stream-active' : 'stream-hidden';
    el.style.cssText = 'width:100%;height:100%;position:absolute;top:0;left:0;';
    this.container.appendChild(el);
    return el;
  }

  /**
   * Switch between camera angles
   */
  switchAngle(newAngle) {
    if (!this.streams.has(newAngle)) return;

    // Hide current
    const current = this.streams.get(this.activeAngle);
    if (current) current.viewElement.className = 'stream-hidden';

    // Show new
    const next = this.streams.get(newAngle);
    next.viewElement.className = 'stream-active';
    this.activeAngle = newAngle;

    // Emit event for analytics
    trackEvent('angle_switched', { from: this.activeAngle, to: newAngle });
  }

  /**
   * Adaptive quality based on network
   */
  enableAdaptiveQuality() {
    for (const [angle, stream] of this.streams) {
      stream.instance.on(TRTC.EVENT.NETWORK_QUALITY, (event) => {
        if (event.downlinkNetworkQuality >= 4 && angle === this.activeAngle) {
          // Switch to small stream on active angle
          stream.instance.startRemoteVideo({
            userId: `broadcaster_${angle}`,
            streamType: TRTC.TYPE.STREAM_TYPE_SUB,
            view: stream.viewElement
          });
        }
      });
    }
  }

  async disconnect() {
    for (const [, stream] of this.streams) {
      await stream.instance.exitRoom();
      await stream.instance.destroy();
    }
    this.streams.clear();
  }
}

// Usage
const viewer = new EventViewer('video-container');
await viewer.connectToEvent({
  main: 300001,      // Main broadcast camera
  tactical: 300002,  // Wide tactical view
  closeup: 300003    // Player/action close-up
});
viewer.enableAdaptiveQuality();

Stream + Prediction Overlay Layout

<!-- HTML structure for the prediction game view -->
<div class="prediction-game-view">
  <!-- Live Stream Layer -->
  <div id='video-container' class="stream-layer">
    <!-- TRTC renders video here -->
  </div>

  <!-- Angle Switcher -->
  <div class="angle-controls">
    <button onclick="viewer.switchAngle('main')" class="active">Main</button>
    <button onclick="viewer.switchAngle('tactical')">Tactical</button>
    <button onclick="viewer.switchAngle('closeup')">Close-up</button>
  </div>

  <!-- Prediction Overlay (on top of stream) -->
  <div class="prediction-overlay" id='prediction-panel'>
    <!-- Dynamic prediction UI renders here -->
  </div>

  <!-- Live Stats Bar -->
  <div class="stats-bar">
    <span id='score-display'>0 - 0</span>
    <span id='time-display'>45:00</span>
    <span id='viewers-count'>12,847 watching</span>
  </div>

  <!-- Community Panel (collapsible sidebar) -->
  <div class="community-sidebar" id='sidebar'>
    <div class="tab-nav">
      <button data-tab="predictions">Predictions</button>
      <button data-tab="chat">Chat</button>
      <button data-tab="leaderboard">Rank</button>
    </div>
    <div id='predictions-feed' class="tab-content"></div>
    <div id='chat-feed' class="tab-content"></div>
    <div id='leaderboard' class="tab-content"></div>
  </div>
</div>

<style>
.prediction-game-view {
  position: relative;
  width: 100%;
  height: 100vh;
  background: #000;
  overflow: hidden;
}
.stream-layer { position: absolute; inset: 0; z-index: 1; }
.stream-active { display: block; width: 100%; height: 100%; }
.stream-hidden { display: none; }
.prediction-overlay {
  position: absolute;
  bottom: 80px;
  left: 50%;
  transform: translateX(-50%);
  z-index: 10;
  background: rgba(0,0,0,0.85);
  border-radius: 16px;
  padding: 20px;
  min-width: 360px;
}
.angle-controls {
  position: absolute;
  top: 16px;
  right: 16px;
  z-index: 10;
  display: flex;
  gap: 8px;
}
.angle-controls button {
  padding: 8px 16px;
  border-radius: 20px;
  border: 1px solid rgba(255,255,255,0.3);
  background: rgba(0,0,0,0.6);
  color: #fff;
  cursor: pointer;
}
.angle-controls button.active {
  background: #4CAF50;
  border-color: #4CAF50;
}
.stats-bar {
  position: absolute;
  top: 16px;
  left: 16px;
  z-index: 10;
  display: flex;
  gap: 20px;
  color: #fff;
  font-weight: bold;
}
.community-sidebar {
  position: absolute;
  top: 0;
  right: 0;
  width: 360px;
  height: 100%;
  background: rgba(15,15,25,0.95);
  z-index: 10;
  overflow-y: auto;
}
</style>

Part 2: Real-Time Prediction System

This is the core game mechanic. Users submit predictions, see community picks in real time, and get instant results.

Prediction Types

// Define prediction question types
const PREDICTION_TYPES = {
  // Pre-game predictions
  MATCH_WINNER: {
    type: 'single_choice',
    timing: 'pre_game',
    points: 10,
    template: (teams) => ({
      question: `Who will win: ${teams.home} vs ${teams.away}?`,
      options: [teams.home, 'Draw', teams.away]
    })
  },
  CORRECT_SCORE: {
    type: 'score_input',
    timing: 'pre_game',
    points: 25,
    template: (teams) => ({
      question: `Predict the final score: ${teams.home} vs ${teams.away}`,
      inputType: 'score_pair'
    })
  },
  FIRST_SCORER: {
    type: 'player_select',
    timing: 'pre_game',
    points: 15,
    template: (teams, players) => ({
      question: 'Who will score first?',
      options: players.map(p => ({ id: p.id, name: p.name, team: p.team }))
    })
  },

  // In-play predictions (the engagement multipliers)
  NEXT_GOAL: {
    type: 'single_choice',
    timing: 'in_play',
    lockWindow: 30000,  // 30 seconds to answer
    points: 5,
    template: (teams, context) => ({
      question: `Next goal scored by?`,
      options: [teams.home, teams.away, 'No more goals'],
      expiresAt: Date.now() + 30000
    })
  },
  NEXT_PLAY_RESULT: {
    type: 'single_choice',
    timing: 'in_play',
    lockWindow: 15000,
    points: 3,
    template: (context) => ({
      question: 'Result of next play?',
      options: ['Touchdown', 'First Down', 'Incomplete', 'Turnover'],
      expiresAt: Date.now() + 15000
    })
  },
  OVER_UNDER: {
    type: 'binary_choice',
    timing: 'in_play',
    lockWindow: 45000,
    points: 5,
    template: (stat, value) => ({
      question: `Total ${stat} Over/Under ${value}?`,
      options: ['Over', 'Under'],
      expiresAt: Date.now() + 45000
    })
  }
};

Prediction Engine (Backend)

// server/services/predictionEngine.js
class PredictionEngine {
  constructor(chatService, eventSource) {
    this.chat = chatService;
    this.eventSource = eventSource;
    this.activePredictions = new Map();
    this.results = new Map();
  }

  /**
   * Create and broadcast a new prediction question
   */
  async createPrediction(eventId, predictionConfig) {
    const prediction = {
      id: `pred_${eventId}_${Date.now()}`,
      eventId: eventId,
      question: predictionConfig.question,
      options: predictionConfig.options,
      type: predictionConfig.type,
      points: predictionConfig.points,
      createdAt: Date.now(),
      expiresAt: predictionConfig.expiresAt || null,
      status: 'open',       // open | locked | settled
      responses: {},         // userId -> choice
      distribution: {}       // option -> count
    };

    this.activePredictions.set(prediction.id, prediction);

    // Broadcast to all viewers via Chat SDK custom message
    await this.broadcastPrediction(eventId, prediction);

    // Schedule auto-lock if time-limited
    if (prediction.expiresAt) {
      setTimeout(() => this.lockPrediction(prediction.id), prediction.expiresAt - Date.now());
    }

    return prediction;
  }

  /**
   * Broadcast prediction to all viewers in the event room
   */
  async broadcastPrediction(eventId, prediction) {
    const message = {
      type: 'NEW_PREDICTION',
      prediction: {
        id: prediction.id,
        question: prediction.question,
        options: prediction.options,
        points: prediction.points,
        expiresAt: prediction.expiresAt,
        type: prediction.type
      }
    };

    // Send as high-priority group message
    await this.chat.sendToGroup(`event_${eventId}`, message, 'prediction_v1');
  }

  /**
   * Process a user's prediction submission
   */
  async submitPrediction(predictionId, userId, choice) {
    const prediction = this.activePredictions.get(predictionId);

    if (!prediction) throw new Error('PREDICTION_NOT_FOUND');
    if (prediction.status !== 'open') throw new Error('PREDICTION_LOCKED');
    if (prediction.expiresAt && Date.now() > prediction.expiresAt) throw new Error('PREDICTION_EXPIRED');
    if (prediction.responses[userId]) throw new Error('ALREADY_SUBMITTED');

    // Record the prediction
    prediction.responses[userId] = {
      choice: choice,
      timestamp: Date.now()
    };

    // Update distribution
    prediction.distribution[choice] = (prediction.distribution[choice] || 0) + 1;

    // Broadcast updated distribution to all viewers
    await this.broadcastDistribution(prediction);

    return { success: true, predictionId, choice };
  }

  /**
   * Show live prediction distribution (what % picked each option)
   */
  async broadcastDistribution(prediction) {
    const totalResponses = Object.keys(prediction.responses).length;
    const distribution = {};

    for (const [option, count] of Object.entries(prediction.distribution)) {
      distribution[option] = {
        count: count,
        percentage: totalResponses > 0 ? Math.round((count / totalResponses) * 100) : 0
      };
    }

    await this.chat.sendToGroup(`event_${prediction.eventId}`, {
      type: 'PREDICTION_UPDATE',
      predictionId: prediction.id,
      totalResponses: totalResponses,
      distribution: distribution
    }, 'prediction_update_v1');
  }

  /**
   * Lock prediction (no more submissions)
   */
  async lockPrediction(predictionId) {
    const prediction = this.activePredictions.get(predictionId);
    if (!prediction || prediction.status !== 'open') return;

    prediction.status = 'locked';

    await this.chat.sendToGroup(`event_${prediction.eventId}`, {
      type: 'PREDICTION_LOCKED',
      predictionId: prediction.id,
      totalResponses: Object.keys(prediction.responses).length
    }, 'prediction_v1');
  }

  /**
   * Settle prediction and award points
   */
  async settlePrediction(predictionId, correctAnswer) {
    const prediction = this.activePredictions.get(predictionId);
    if (!prediction) return;

    prediction.status = 'settled';
    prediction.correctAnswer = correctAnswer;

    // Calculate winners
    const winners = [];
    const losers = [];

    for (const [userId, response] of Object.entries(prediction.responses)) {
      if (response.choice === correctAnswer) {
        winners.push(userId);
        await this.awardPoints(userId, prediction.points);
      } else {
        losers.push(userId);
      }
    }

    // Broadcast result to all viewers
    await this.chat.sendToGroup(`event_${prediction.eventId}`, {
      type: 'PREDICTION_SETTLED',
      predictionId: prediction.id,
      correctAnswer: correctAnswer,
      winners: winners.length,
      losers: losers.length,
      totalPoints: prediction.points,
      distribution: prediction.distribution
    }, 'prediction_result_v1');

    this.activePredictions.delete(predictionId);
    this.results.set(predictionId, prediction);

    return { winners, losers };
  }

  async awardPoints(userId, points) {
    // Update leaderboard in Redis
    await redis.zincrby(`leaderboard:${this.currentEventId}`, points, userId);
    await redis.zincrby('leaderboard:global', points, userId);
  }
}

Client-Side Prediction UI

import TencentCloudChat from '@tencentcloud/chat';
import TIMUploadPlugin from 'tim-upload-plugin';

// Initialize Chat SDK for prediction messaging
const chat = TencentCloudChat.create({ SDKAppID: YOUR_SDK_APP_ID });
chat.registerPlugin({ 'tim-upload-plugin': TIMUploadPlugin });
chat.setLogLevel(1);

class PredictionUI {
  constructor(eventId) {
    this.eventId = eventId;
    this.groupId = `event_${eventId}`;
    this.currentPrediction = null;
    this.userPredictions = new Map();
  }

  async initialize(userId, userSig) {
    // Login to Chat SDK
    await chat.login({ userID: userId, userSig: userSig });

    // Join the event prediction group
    await chat.joinGroup({
      groupID: this.groupId,
      type: TencentCloudChat.TYPES.GRP_AVCHATROOM
    });

    // Listen for prediction events
    this.setupMessageListeners();
  }

  setupMessageListeners() {
    chat.on(TencentCloudChat.EVENT.MESSAGE_RECEIVED, (event) => {
      event.data.forEach(msg => {
        if (msg.type !== TencentCloudChat.TYPES.MSG_CUSTOM) return;

        const payload = JSON.parse(msg.payload.data);
        this.handlePredictionEvent(payload);
      });
    });
  }

  handlePredictionEvent(payload) {
    switch (payload.type) {
      case 'NEW_PREDICTION':
        this.showPredictionCard(payload.prediction);
        break;
      case 'PREDICTION_UPDATE':
        this.updateDistributionBar(payload);
        break;
      case 'PREDICTION_LOCKED':
        this.lockPredictionUI(payload.predictionId);
        break;
      case 'PREDICTION_SETTLED':
        this.showResult(payload);
        break;
    }
  }

  /**
   * Display a new prediction question to the user
   */
  showPredictionCard(prediction) {
    this.currentPrediction = prediction;
    const panel = document.getElementById('prediction-panel');

    let optionsHTML = '';
    if (prediction.type === 'single_choice' || prediction.type === 'binary_choice') {
      optionsHTML = prediction.options.map((option, idx) => `
        <button class="prediction-option" data-option="${option}" onclick="predictionUI.submit('${prediction.id}', '${option}')">
          <span class="option-label">${option}</span>
          <span class="option-percentage" id='pct-predictionid-idx'>--%</span>
          <div class="option-bar" id='bar-predictionid-idx' style="width: 0%"></div>
        </button>
      `).join('');
    } else if (prediction.type === 'score_input') {
      optionsHTML = `
        <div class="score-input-group">
          <input type="number" id='score-home' min="0" max="20" placeholder="0" />
          <span class="score-divider">-</span>
          <input type="number" id='score-away' min="0" max="20" placeholder="0" />
          <button onclick="predictionUI.submitScore('${prediction.id}')">Submit</button>
        </div>
      `;
    }

    panel.innerHTML = `
      <div class="prediction-card" id='card-predictionid'>
        <div class="prediction-header">
          <span class="prediction-points">🏆 ${prediction.points} pts</span>
          ${prediction.expiresAt ? `<span class="prediction-timer" id='timer-predictionid'></span>` : ''}
        </div>
        <h3 class="prediction-question">${prediction.question}</h3>
        <div class="prediction-options">${optionsHTML}</div>
        <div class="prediction-meta">
          <span id='response-count-predictionid'>0 predictions</span>
        </div>
      </div>
    `;

    // Start countdown if time-limited
    if (prediction.expiresAt) {
      this.startCountdown(prediction.id, prediction.expiresAt);
    }

    // Animate card entrance
    panel.classList.add('slide-in');
    setTimeout(() => panel.classList.remove('slide-in'), 500);
  }

  /**
   * Submit user's prediction
   */
  async submit(predictionId, choice) {
    if (this.userPredictions.has(predictionId)) {
      showToast('Already submitted!');
      return;
    }

    // Send prediction via custom message
    const message = chat.createCustomMessage({
      to: this.groupId,
      conversationType: TencentCloudChat.TYPES.CONV_GROUP,
      payload: {
        data: JSON.stringify({
          type: 'USER_PREDICTION',
          predictionId: predictionId,
          choice: choice,
          userId: currentUser.id,
          displayName: currentUser.name
        }),
        description: `${currentUser.name} predicted: ${choice}`,
        extension: 'prediction_submit_v1'
      }
    });

    await chat.sendMessage(message);

    // Mark as submitted locally
    this.userPredictions.set(predictionId, choice);

    // Update UI: highlight selected option
    document.querySelectorAll(`#card-${predictionId} .prediction-option`).forEach(btn => {
      btn.classList.remove('selected');
      if (btn.dataset.option === choice) {
        btn.classList.add('selected');
      }
      btn.disabled = true;
    });

    showToast(`Prediction submitted: ${choice}`);

    // Also notify server (for scoring)
    await fetch('/api/predictions/submit', {
      method: 'POST',
      headers: { 'Content-Type': 'application/json', 'Authorization': `Bearer ${authToken}` },
      body: JSON.stringify({ predictionId, choice })
    });
  }

  async submitScore(predictionId) {
    const home = document.getElementById('score-home').value;
    const away = document.getElementById('score-away').value;
    const choice = `${home}-${away}`;
    await this.submit(predictionId, choice);
  }

  /**
   * Update live distribution bars
   */
  updateDistributionBar(data) {
    const { predictionId, totalResponses, distribution } = data;

    document.getElementById(`response-count-${predictionId}`).textContent = 
      `${totalResponses} predictions`;

    Object.entries(distribution).forEach(([option, stats], idx) => {
      const pctEl = document.getElementById(`pct-${predictionId}-${idx}`);
      const barEl = document.getElementById(`bar-${predictionId}-${idx}`);
      if (pctEl) pctEl.textContent = `${stats.percentage}%`;
      if (barEl) barEl.style.width = `${stats.percentage}%`;
    });
  }

  /**
   * Show prediction result with animation
   */
  showResult(data) {
    const { predictionId, correctAnswer, winners, losers, totalPoints } = data;
    const userChoice = this.userPredictions.get(predictionId);
    const didWin = userChoice === correctAnswer;

    const card = document.getElementById(`card-${predictionId}`);
    if (!card) return;

    // Highlight correct answer
    card.querySelectorAll('.prediction-option').forEach(btn => {
      if (btn.dataset.option === correctAnswer) {
        btn.classList.add('correct');
      } else {
        btn.classList.add('incorrect');
      }
    });

    // Show result banner
    const resultBanner = document.createElement('div');
    resultBanner.className = `result-banner ${didWin ? 'win' : 'lose'}`;
    resultBanner.innerHTML = didWin
      ? `<span>✅ Correct! +${totalPoints} points</span>`
      : `<span>❌ Wrong! Answer: ${correctAnswer}</span>`;
    card.appendChild(resultBanner);

    // Trigger celebration animation if won
    if (didWin) {
      this.playCelebration();
    }

    // Auto-dismiss after 5 seconds
    setTimeout(() => {
      card.classList.add('fade-out');
      setTimeout(() => card.remove(), 500);
    }, 5000);
  }

  startCountdown(predictionId, expiresAt) {
    const timerEl = document.getElementById(`timer-${predictionId}`);
    const interval = setInterval(() => {
      const remaining = Math.max(0, expiresAt - Date.now());
      const seconds = Math.ceil(remaining / 1000);
      timerEl.textContent = `⏱️ ${seconds}s`;

      if (remaining <= 5000) timerEl.classList.add('urgent');
      if (remaining <= 0) {
        clearInterval(interval);
        timerEl.textContent = '🔒 Locked';
      }
    }, 100);
  }

  playCelebration() {
    // Confetti or points animation
    const particles = document.createElement('div');
    particles.className = 'celebration-particles';
    document.body.appendChild(particles);
    setTimeout(() => particles.remove(), 3000);
  }
}

// Initialize prediction UI
const predictionUI = new PredictionUI('match_12345');
await predictionUI.initialize(currentUser.id, currentUser.sig);

Prediction Styling

/* Prediction card animations and styling */
.prediction-card {
  background: linear-gradient(135deg, #1a1a2e 0%, #16213e 100%);
  border: 1px solid rgba(255,255,255,0.1);
  border-radius: 16px;
  padding: 24px;
  transition: all 0.3s ease;
}

.prediction-question {
  color: #fff;
  font-size: 18px;
  margin: 12px 0;
  text-align: center;
}

.prediction-option {
  position: relative;
  width: 100%;
  padding: 14px 20px;
  margin: 8px 0;
  border: 1px solid rgba(255,255,255,0.2);
  border-radius: 10px;
  background: rgba(255,255,255,0.05);
  color: #fff;
  cursor: pointer;
  display: flex;
  justify-content: space-between;
  align-items: center;
  overflow: hidden;
  transition: all 0.2s;
}

.prediction-option:hover:not(:disabled) {
  border-color: #4CAF50;
  background: rgba(76,175,80,0.1);
}

.prediction-option.selected {
  border-color: #2196F3;
  background: rgba(33,150,243,0.2);
}

.prediction-option.correct {
  border-color: #4CAF50;
  background: rgba(76,175,80,0.3);
}

.prediction-option.incorrect {
  border-color: #f44336;
  background: rgba(244,67,54,0.1);
  opacity: 0.7;
}

.option-bar {
  position: absolute;
  left: 0;
  top: 0;
  height: 100%;
  background: rgba(33,150,243,0.15);
  transition: width 0.5s ease;
  z-index: 0;
}

.prediction-timer {
  color: #FF9800;
  font-weight: bold;
}

.prediction-timer.urgent {
  color: #f44336;
  animation: pulse 0.5s infinite;
}

@keyframes pulse {
  0%, 100% { opacity: 1; }
  50% { opacity: 0.5; }
}

.result-banner {
  margin-top: 16px;
  padding: 12px;
  border-radius: 8px;
  text-align: center;
  font-weight: bold;
  animation: slideDown 0.3s ease;
}

.result-banner.win {
  background: rgba(76,175,80,0.3);
  border: 1px solid #4CAF50;
  color: #4CAF50;
}

.result-banner.lose {
  background: rgba(244,67,54,0.2);
  border: 1px solid #f44336;
  color: #f44336;
}

.slide-in {
  animation: slideUp 0.4s ease;
}

@keyframes slideUp {
  from { transform: translateY(50px); opacity: 0; }
  to { transform: translateY(0); opacity: 1; }
}

.fade-out {
  animation: fadeOut 0.5s ease forwards;
}

@keyframes fadeOut {
  to { opacity: 0; transform: translateY(-20px); }
}

Part 3: Event-Driven Prediction Triggering

The best prediction games don't just ask questions on a timer — they react to game events. A corner kick triggers a "Who scores from this corner?" prediction. A red card triggers "Final score now?" In-play predictions tied to moments create intensity.

Match Event Integration

// server/services/matchEventProcessor.js
class MatchEventProcessor {
  constructor(predictionEngine, chatService) {
    this.predictionEngine = predictionEngine;
    this.chat = chatService;
    this.eventId = null;
  }

  /**
   * Connect to a live match data feed and trigger predictions based on events
   */
  connectToMatchFeed(eventId, feedUrl) {
    this.eventId = eventId;
    const eventSource = new EventSource(feedUrl);

    eventSource.onmessage = (event) => {
      const matchEvent = JSON.parse(event.data);
      this.processMatchEvent(matchEvent);
    };
  }

  async processMatchEvent(event) {
    switch (event.type) {
      case 'GOAL':
        await this.handleGoal(event);
        break;
      case 'CORNER_KICK':
        await this.triggerCornerPrediction(event);
        break;
      case 'PENALTY_AWARDED':
        await this.triggerPenaltyPrediction(event);
        break;
      case 'RED_CARD':
        await this.triggerScoreImpactPrediction(event);
        break;
      case 'HALFTIME':
        await this.triggerHalftimePredictions(event);
        break;
      case 'SUBSTITUTION':
        await this.triggerSubstitutionImpact(event);
        break;
      case 'INJURY_TIME':
        await this.triggerInjuryTimePrediction(event);
        break;
      case 'GAME_END':
        await this.settleAllOpenPredictions(event);
        break;
    }

    // Always broadcast the event for UI updates
    await this.broadcastMatchEvent(event);
  }

  async handleGoal(event) {
    // 1. Settle any "next goal" predictions
    await this.predictionEngine.settlePrediction(
      this.getActivePrediction('NEXT_GOAL'),
      event.scoringTeam
    );

    // 2. After goal celebration (10 sec), trigger new prediction
    setTimeout(async () => {
      await this.predictionEngine.createPrediction(this.eventId, {
        question: `Next goal scored by?`,
        options: [event.homeTeam, event.awayTeam, 'No more goals'],
        type: 'single_choice',
        points: 5,
        expiresAt: null  // Open until next goal or game end
      });
    }, 10000);

    // 3. Update the score and trigger total goals prediction if close to market
    const totalGoals = event.homeScore + event.awayScore;
    if (totalGoals === event.totalGoalsLine - 0.5 || totalGoals === event.totalGoalsLine + 0.5) {
      await this.predictionEngine.createPrediction(this.eventId, {
        question: `Total goals Over/Under ${event.totalGoalsLine}?`,
        options: ['Over', 'Under'],
        type: 'binary_choice',
        points: 5,
        expiresAt: Date.now() + 45000
      });
    }
  }

  async triggerCornerPrediction(event) {
    await this.predictionEngine.createPrediction(this.eventId, {
      question: `Will this corner kick result in a goal?`,
      options: ['Yes - Goal!', 'No goal'],
      type: 'binary_choice',
      points: 8,  // Higher points for harder predictions
      expiresAt: Date.now() + 15000  // 15 seconds to answer before the corner is taken
    });
  }

  async triggerPenaltyPrediction(event) {
    await this.predictionEngine.createPrediction(this.eventId, {
      question: `Will the penalty be scored?`,
      options: ['Goal!', 'Saved', 'Missed'],
      type: 'single_choice',
      points: 10,
      expiresAt: Date.now() + 20000
    });
  }

  async triggerHalftimePredictions(event) {
    // Multi-question burst during halftime
    const predictions = [
      {
        question: `Second half: who scores next?`,
        options: [event.homeTeam, event.awayTeam, 'No second half goals'],
        type: 'single_choice',
        points: 5,
        expiresAt: Date.now() + 900000  // 15 minutes (halftime duration)
      },
      {
        question: `Final score prediction?`,
        options: null,
        type: 'score_input',
        points: 25,
        expiresAt: Date.now() + 900000
      },
      {
        question: `Total corners in second half: Over/Under 4.5?`,
        options: ['Over', 'Under'],
        type: 'binary_choice',
        points: 5,
        expiresAt: Date.now() + 900000
      }
    ];

    for (const pred of predictions) {
      await this.predictionEngine.createPrediction(this.eventId, pred);
      await sleep(2000); // Stagger to avoid overwhelming users
    }
  }

  async triggerInjuryTimePrediction(event) {
    await this.predictionEngine.createPrediction(this.eventId, {
      question: `How many minutes of injury time?`,
      options: ['1-2 min', '3-4 min', '5+ min'],
      type: 'single_choice',
      points: 3,
      expiresAt: Date.now() + 10000
    });
  }

  async settleAllOpenPredictions(event) {
    // Settle all remaining open predictions with final match data
    const finalScore = `${event.homeScore}-${event.awayScore}`;
    const winner = event.homeScore > event.awayScore ? event.homeTeam
                 : event.awayScore > event.homeScore ? event.awayTeam
                 : 'Draw';

    // Settle match winner prediction
    const winnerPred = this.getActivePrediction('MATCH_WINNER');
    if (winnerPred) await this.predictionEngine.settlePrediction(winnerPred, winner);

    // Settle correct score prediction
    const scorePred = this.getActivePrediction('CORRECT_SCORE');
    if (scorePred) await this.predictionEngine.settlePrediction(scorePred, finalScore);

    // Broadcast final leaderboard
    await this.broadcastFinalLeaderboard();
  }

  async broadcastMatchEvent(event) {
    await this.chat.sendToGroup(`event_${this.eventId}`, {
      type: 'MATCH_EVENT',
      event: event
    }, 'match_event_v1');
  }

  getActivePrediction(type) {
    // Look up active prediction of given type
    for (const [id, pred] of this.predictionEngine.activePredictions) {
      if (pred.status === 'open' && pred.questionType === type) return id;
    }
    return null;
  }
}

Part 4: Voice Rooms for Live Commentary

While watching and predicting, users want to discuss. Voice rooms bring the stadium atmosphere.

Voice Room Integration

import TRTC from 'trtc-sdk-v5';

class PredictionVoiceRoom {
  constructor(eventId) {
    this.eventId = eventId;
    this.trtc = TRTC.create();
    this.roomId = this.generateRoomId(eventId);
    this.isSpeaking = false;
  }

  generateRoomId(eventId) {
    // Convert string event ID to numeric room ID for TRTC
    let hash = 0;
    for (let i = 0; i < eventId.length; i++) {
      hash = ((hash << 5) - hash) + eventId.charCodeAt(i);
      hash |= 0;
    }
    return Math.abs(hash) % 4294967294 + 1;
  }

  /**
   * Join the voice room as a listener
   */
  async joinAsListener() {
    await this.trtc.enterRoom({
      sdkAppId: YOUR_SDK_APP_ID,
      userId: currentUser.id,
      userSig: currentUser.sig,
      roomId: this.roomId,
      scene: 'live',
      role: 'audience'
    });

    // Listen for speakers
    this.trtc.on(TRTC.EVENT.REMOTE_AUDIO_AVAILABLE, (event) => {
      this.addSpeaker(event.userId);
    });

    this.trtc.on(TRTC.EVENT.REMOTE_AUDIO_UNAVAILABLE, (event) => {
      this.removeSpeaker(event.userId);
    });

    // Volume indicators
    this.trtc.enableAudioVolumeEvaluation({ interval: 200 });
    this.trtc.on(TRTC.EVENT.AUDIO_VOLUME, (event) => {
      event.result.forEach(({ userId, volume }) => {
        this.updateVolumeIndicator(userId, volume);
      });
    });

    console.log('Joined voice room as listener');
  }

  /**
   * Request to speak (raise hand)
   */
  async requestMic() {
    // Send request via Chat SDK signaling
    await chat.sendMessage(chat.createCustomMessage({
      to: `event_${this.eventId}`,
      conversationType: TencentCloudChat.TYPES.CONV_GROUP,
      payload: {
        data: JSON.stringify({
          type: 'MIC_REQUEST',
          userId: currentUser.id,
          displayName: currentUser.name
        }),
        description: `${currentUser.name} wants to speak`,
        extension: 'voice_room_signal'
      }
    }));
  }

  /**
   * Start speaking (after granted permission)
   */
  async startSpeaking() {
    await this.trtc.switchRole('anchor');
    await this.trtc.startLocalAudio({
      option: { profile: 'speech' }
    });
    this.isSpeaking = true;
  }

  /**
   * Stop speaking
   */
  async stopSpeaking() {
    await this.trtc.stopLocalAudio();
    await this.trtc.switchRole('audience');
    this.isSpeaking = false;
  }

  /**
   * Leave the voice room
   */
  async leave() {
    if (this.isSpeaking) await this.stopSpeaking();
    await this.trtc.exitRoom();
    await this.trtc.destroy();
  }

  addSpeaker(userId) {
    const speakersEl = document.getElementById('voice-speakers');
    const avatar = document.createElement('div');
    avatar.id = `speaker-${userId}`;
    avatar.className = 'speaker-avatar';
    avatar.innerHTML = `<span>${userId.charAt(0).toUpperCase()}</span>`;
    speakersEl.appendChild(avatar);
  }

  removeSpeaker(userId) {
    document.getElementById(`speaker-${userId}`)?.remove();
  }

  updateVolumeIndicator(userId, volume) {
    const avatar = document.getElementById(`speaker-${userId}`);
    if (avatar) {
      avatar.style.borderColor = volume > 15 ? '#4CAF50' : 'transparent';
      avatar.style.boxShadow = volume > 15 ? '0 0 8px rgba(76,175,80,0.5)' : 'none';
    }
  }
}

// Usage: Join voice room alongside prediction game
const voiceRoom = new PredictionVoiceRoom('match_12345');
await voiceRoom.joinAsListener();

Part 5: Result Broadcasting and Celebration

When a prediction resolves, the result needs to feel impactful — instant, visible, and celebratory for winners.

Real-Time Result Settlement

// server/services/resultBroadcaster.js
class ResultBroadcaster {
  constructor(chatService) {
    this.chat = chatService;
  }

  /**
   * Broadcast result to all viewers with rich formatting
   */
  async broadcastResult(eventId, result) {
    const {
      predictionId,
      correctAnswer,
      question,
      totalParticipants,
      winnersCount,
      topWinners  // Top 3 for spotlight
    } = result;

    // Message 1: The answer reveal
    await this.chat.sendToGroup(`event_${eventId}`, {
      type: 'PREDICTION_SETTLED',
      predictionId: predictionId,
      correctAnswer: correctAnswer,
      question: question,
      stats: {
        total: totalParticipants,
        correct: winnersCount,
        incorrect: totalParticipants - winnersCount,
        accuracy: Math.round((winnersCount / totalParticipants) * 100)
      }
    }, 'prediction_result_v1');

    // Message 2: Leaderboard update (after a brief delay for drama)
    setTimeout(async () => {
      await this.broadcastLeaderboardUpdate(eventId, topWinners);
    }, 2000);
  }

  async broadcastLeaderboardUpdate(eventId, topPlayers) {
    const leaderboard = await redis.zrevrange(
      `leaderboard:${eventId}`, 0, 9, 'WITHSCORES'
    );

    await this.chat.sendToGroup(`event_${eventId}`, {
      type: 'LEADERBOARD_UPDATE',
      rankings: leaderboard.map(([userId, score], idx) => ({
        rank: idx + 1,
        userId,
        score: parseInt(score),
        change: 0  // Calculate rank change from previous
      }))
    }, 'leaderboard_v1');
  }

  /**
   * End-of-event final results and rewards
   */
  async broadcastFinalResults(eventId) {
    const finalLeaderboard = await redis.zrevrange(
      `leaderboard:${eventId}`, 0, 99, 'WITHSCORES'
    );

    const totalPredictions = await redis.get(`total_predictions:${eventId}`);
    const totalParticipants = await redis.zcard(`leaderboard:${eventId}`);

    await this.chat.sendToGroup(`event_${eventId}`, {
      type: 'EVENT_FINAL_RESULTS',
      summary: {
        totalPredictions: parseInt(totalPredictions),
        totalParticipants: totalParticipants,
        topPerformer: finalLeaderboard[0],
        avgAccuracy: await this.calculateAvgAccuracy(eventId)
      },
      podium: finalLeaderboard.slice(0, 3).map(([userId, score], idx) => ({
        rank: idx + 1,
        userId,
        score: parseInt(score)
      })),
      fullLeaderboard: finalLeaderboard.map(([userId, score], idx) => ({
        rank: idx + 1,
        userId,
        score: parseInt(score)
      }))
    }, 'final_results_v1');
  }
}

Client-Side Result Handling

// Client: Handle results and update UI
class ResultHandler {
  handleSettlement(data) {
    const { predictionId, correctAnswer, stats } = data;
    const myChoice = predictionUI.userPredictions.get(predictionId);
    const didWin = myChoice === correctAnswer;

    // Update prediction card
    predictionUI.showResult(data);

    // Update score display
    this.updateMyScore(didWin ? data.totalPoints : 0);

    // Show result toast
    if (didWin) {
      this.showWinToast(data.totalPoints);
      this.triggerHapticFeedback('success');
    } else {
      this.showLoseToast(correctAnswer);
    }

    // Update accuracy tracker
    this.updateAccuracyDisplay();
  }

  handleLeaderboardUpdate(data) {
    const { rankings } = data;
    const leaderboardEl = document.getElementById('leaderboard');

    leaderboardEl.innerHTML = rankings.map(player => `
      <div class="leaderboard-row ${player.userId === currentUser.id ? 'highlight' : ''}">
        <span class="rank">#${player.rank}</span>
        <span class="name">${player.userId}</span>
        <span class="score">${player.score} pts</span>
      </div>
    `).join('');

    // Highlight current user's position
    const myRank = rankings.findIndex(p => p.userId === currentUser.id) + 1;
    if (myRank > 0) {
      document.getElementById('my-rank-display').textContent = `#${myRank}`;
    }
  }

  handleFinalResults(data) {
    const { summary, podium } = data;

    // Show full-screen results overlay
    const overlay = document.createElement('div');
    overlay.className = 'final-results-overlay';
    overlay.innerHTML = `
      <div class="results-modal">
        <h2>🏆 Event Complete!</h2>
        <div class="podium">
          ${podium.map((p, i) => `
            <div class="podium-place place-${i + 1}">
              <span class="medal">${['🥇', '🥈', '🥉'][i]}</span>
              <span class="name">${p.userId}</span>
              <span class="score">${p.score} pts</span>
            </div>
          `).join('')}
        </div>
        <div class="my-stats">
          <p>Your predictions: ${summary.totalPredictions}</p>
          <p>Accuracy: ${summary.avgAccuracy}%</p>
        </div>
        <button onclick="this.closest('.final-results-overlay').remove()">Close</button>
      </div>
    `;
    document.body.appendChild(overlay);
  }

  showWinToast(points) {
    showNotification({
      type: 'success',
      title: '✅ Correct!',
      message: `+${points} points`,
      duration: 3000
    });
  }

  showLoseToast(answer) {
    showNotification({
      type: 'error',
      title: '❌ Wrong',
      message: `Answer: ${answer}`,
      duration: 3000
    });
  }

  updateMyScore(additionalPoints) {
    const scoreEl = document.getElementById('my-score');
    const current = parseInt(scoreEl.textContent);
    const target = current + additionalPoints;

    // Animate score change
    this.animateValue(scoreEl, current, target, 500);
  }

  animateValue(element, start, end, duration) {
    const startTime = performance.now();
    const step = (timestamp) => {
      const progress = Math.min((timestamp - startTime) / duration, 1);
      const value = Math.floor(start + (end - start) * progress);
      element.textContent = value;
      if (progress < 1) requestAnimationFrame(step);
    };
    requestAnimationFrame(step);
  }
}

Part 6: RTMP + RTC Dual-Mode for Production Streaming

For production sports prediction games that integrate with existing broadcast infrastructure, the dual-mode approach from the TRTC architecture guide is essential.

RTMP Ingest + RTC Distribution

// Server: Generate RTMP push URLs for broadcast equipment
function generateRtmpIngestUrl(roomId, streamUserId) {
  const pushDomain = 'rtmp.rtc.qq.com';
  const streamKey = `${YOUR_SDK_APP_ID}_${roomId}_${streamUserId}_main`;
  const userSig = generateUserSig(streamUserId);

  return {
    rtmpUrl: `rtmp://${pushDomain}/push/${streamKey}?sdkappid=${YOUR_SDK_APP_ID}&userid=${streamUserId}&usersig=${encodeURIComponent(userSig)}`,
    streamKey: streamKey,
    roomId: roomId
  };
}

// Example: Set up broadcast for a Premier League match
const broadcastConfig = generateRtmpIngestUrl(400001, 'broadcast_epl_main');
console.log('Send this RTMP URL to your broadcast equipment:', broadcastConfig.rtmpUrl);

Why Dual-Mode Matters for Predictions

The RTMP+RTC approach gives you:

1. Compatibility: Works with existing OBS, vMix, broadcast encoders
2. Low latency for viewers: Even though input is RTMP, output to viewers is RTC (300-500ms)
3. Cost efficiency: RTMP encoding hardware is cheaper than custom RTC integration
4. Redundancy: If RTC pull fails, CDN fallback with HLS is available (higher latency but works)

// Client: Viewer connects via RTC (pulls the RTMP stream with low latency)
async function watchRtmpStreamViaRTC(roomId) {
  const viewer = TRTC.create();

  await viewer.enterRoom({
    sdkAppId: YOUR_SDK_APP_ID,
    userId: `pred_viewer_${currentUser.id}`,
    userSig: currentUser.sig,
    roomId: roomId,
    scene: 'live',
    role: 'audience'
  });

  viewer.on(TRTC.EVENT.REMOTE_VIDEO_AVAILABLE, async (event) => {
    // The RTMP stream appears as a remote user's video
    await viewer.startRemoteVideo({
      userId: event.userId,
      streamType: event.streamType,
      view: document.getElementById('live-stream')
    });
    console.log('Live stream connected via RTC');
  });

  // Monitor stream health
  viewer.on(TRTC.EVENT.NETWORK_QUALITY, (event) => {
    if (event.downlinkNetworkQuality >= 5) {
      // Fallback to CDN HLS if RTC fails
      showFallbackStream(roomId);
    }
  });

  return viewer;
}

// CDN fallback for degraded conditions
function showFallbackStream(roomId) {
  const hlsUrl = `https://your-cdn.com/live/stream_${roomId}.m3u8`;
  const fallbackPlayer = document.getElementById('fallback-player');
  fallbackPlayer.src = hlsUrl;
  fallbackPlayer.style.display = 'block';
  document.getElementById('live-stream').style.display = 'none';
  showWarning('Switched to standard quality due to network conditions. Predictions may be slightly delayed.');
}

Part 7: Mobile Implementation (React Native)

Prediction Game Screen

import React, { useState, useEffect, useCallback } from 'react';
import { View, Text, TouchableOpacity, ScrollView, Animated, StyleSheet } from 'react-native';
import { TRTCCloud, TRTCParams, TRTCAppScene, TRTCRoleType } from 'trtc-react-native';
import TencentCloudChat from '@tencentcloud/chat-sdk-react-native';

const PredictionGameScreen = ({ eventId, streamConfig }) => {
  const [currentPrediction, setCurrentPrediction] = useState(null);
  const [myScore, setMyScore] = useState(0);
  const [leaderboard, setLeaderboard] = useState([]);
  const [isStreaming, setIsStreaming] = useState(false);
  const [selectedChoice, setSelectedChoice] = useState(null);
  const [distribution, setDistribution] = useState({});
  
  const pulseAnim = new Animated.Value(1);
  const trtcCloud = TRTCCloud.sharedInstance();

  useEffect(() => {
    setupStream();
    setupChat();
    return () => cleanup();
  }, []);

  // Connect to live stream
  const setupStream = async () => {
    const params = new TRTCParams({
      sdkAppId: YOUR_SDK_APP_ID,
      userId: `viewer_${currentUser.id}`,
      userSig: currentUser.sig,
      roomId: streamConfig.mainRoomId,
      role: TRTCRoleType.TRTCRoleAudience
    });

    trtcCloud.enterRoom(params, TRTCAppScene.TRTCAppSceneLIVE);
    
    trtcCloud.on('onEnterRoom', (result) => {
      if (result > 0) setIsStreaming(true);
    });

    trtcCloud.on('onUserVideoAvailable', (userId, available) => {
      if (available) {
        trtcCloud.startRemoteView(userId, 0, 'stream-view');
      }
    });
  };

  // Connect to prediction chat
  const setupChat = async () => {
    await TencentCloudChat.login({
      userID: currentUser.id,
      userSig: currentUser.sig
    });

    await TencentCloudChat.joinGroup({ groupID: `event_${eventId}` });

    TencentCloudChat.on('onMessageReceived', (messages) => {
      messages.forEach(msg => {
        if (msg.type === 'TIMCustomElem') {
          const data = JSON.parse(msg.customElem.data);
          handlePredictionMessage(data);
        }
      });
    });
  };

  const handlePredictionMessage = useCallback((data) => {
    switch (data.type) {
      case 'NEW_PREDICTION':
        setCurrentPrediction(data.prediction);
        setSelectedChoice(null);
        startPulseAnimation();
        break;
      case 'PREDICTION_UPDATE':
        setDistribution(data.distribution);
        break;
      case 'PREDICTION_SETTLED':
        handleResult(data);
        break;
      case 'LEADERBOARD_UPDATE':
        setLeaderboard(data.rankings);
        break;
    }
  }, []);

  const handleResult = (data) => {
    const didWin = selectedChoice === data.correctAnswer;
    if (didWin) {
      setMyScore(prev => prev + data.totalPoints);
      // Haptic feedback
      Haptics.notificationAsync(Haptics.NotificationFeedbackType.Success);
    }
    // Clear prediction after showing result
    setTimeout(() => setCurrentPrediction(null), 5000);
  };

  const submitPrediction = async (choice) => {
    if (selectedChoice) return; // Already submitted
    setSelectedChoice(choice);

    // Send via Chat SDK
    await TencentCloudChat.sendCustomMessage({
      groupID: `event_${eventId}`,
      data: JSON.stringify({
        type: 'USER_PREDICTION',
        predictionId: currentPrediction.id,
        choice: choice,
        userId: currentUser.id
      })
    });

    // Notify server
    await fetch('/api/predictions/submit', {
      method: 'POST',
      headers: { 'Content-Type': 'application/json' },
      body: JSON.stringify({ predictionId: currentPrediction.id, choice })
    });
  };

  const startPulseAnimation = () => {
    Animated.loop(
      Animated.sequence([
        Animated.timing(pulseAnim, { toValue: 1.05, duration: 500, useNativeDriver: true }),
        Animated.timing(pulseAnim, { toValue: 1, duration: 500, useNativeDriver: true })
      ]),
      { iterations: 3 }
    ).start();
  };

  const cleanup = async () => {
    trtcCloud.exitRoom();
    await TencentCloudChat.quitGroup({ groupID: `event_${eventId}` });
  };

  return (
    <View style={styles.container}>
      {/* Live Stream */}
      <View style={styles.streamContainer} nativeid='stream-view' />

      {/* Score Display */}
      <View style={styles.scoreBar}>
        <Text style={styles.scoreText}>🏆 {myScore} pts</Text>
        <Text style={styles.viewerCount}>
          {isStreaming ? '🔴 LIVE' : '⏳ Connecting...'}
        </Text>
      </View>

      {/* Prediction Card */}
      {currentPrediction && (
        <Animated.View style={[styles.predictionCard, { transform: [{ scale: pulseAnim }] }]}>
          <Text style={styles.predictionPoints}>+{currentPrediction.points} pts</Text>
          <Text style={styles.predictionQuestion}>{currentPrediction.question}</Text>

          <View style={styles.optionsContainer}>
            {currentPrediction.options.map((option, idx) => (
              <TouchableOpacity
                key={idx}
                style={[
                  styles.optionButton,
                  selectedChoice === option && styles.optionSelected,
                  selectedChoice && selectedChoice !== option && styles.optionDisabled
                ]}
                onPress={() => submitPrediction(option)}
                disabled={!!selectedChoice}
              >
                <Text style={styles.optionText}>{option}</Text>
                {distribution[option] && (
                  <View style={[styles.distributionBar, { width: `${distribution[option].percentage}%` }]} />
                )}
                {distribution[option] && (
                  <Text style={styles.percentText}>{distribution[option].percentage}%</Text>
                )}
              </TouchableOpacity>
            ))}
          </View>
        </Animated.View>
      )}

      {/* Mini Leaderboard */}
      <View style={styles.miniLeaderboard}>
        {leaderboard.slice(0, 5).map((player, idx) => (
          <View key={player.userId} style={styles.leaderRow}>
            <Text style={styles.leaderRank}>#{idx + 1}</Text>
            <Text style={styles.leaderName}>{player.userId}</Text>
            <Text style={styles.leaderScore}>{player.score}</Text>
          </View>
        ))}
      </View>
    </View>
  );
};

const styles = StyleSheet.create({
  container: { flex: 1, backgroundColor: '#000' },
  streamContainer: { flex: 1 },
  scoreBar: {
    position: 'absolute', top: 50, left: 16, right: 16,
    flexDirection: 'row', justifyContent: 'space-between'
  },
  scoreText: { color: '#FFD700', fontSize: 18, fontWeight: 'bold' },
  viewerCount: { color: '#ff4444', fontSize: 14 },
  predictionCard: {
    position: 'absolute', bottom: 100, left: 16, right: 16,
    backgroundColor: 'rgba(20,20,40,0.95)', borderRadius: 16, padding: 20,
    borderWidth: 1, borderColor: 'rgba(255,255,255,0.1)'
  },
  predictionPoints: { color: '#FFD700', fontSize: 12, textAlign: 'right' },
  predictionQuestion: { color: '#fff', fontSize: 16, fontWeight: '600', marginVertical: 12, textAlign: 'center' },
  optionsContainer: { gap: 8 },
  optionButton: {
    padding: 14, borderRadius: 10, borderWidth: 1,
    borderColor: 'rgba(255,255,255,0.2)', backgroundColor: 'rgba(255,255,255,0.05)',
    position: 'relative', overflow: 'hidden'
  },
  optionSelected: { borderColor: '#4CAF50', backgroundColor: 'rgba(76,175,80,0.2)' },
  optionDisabled: { opacity: 0.5 },
  optionText: { color: '#fff', fontSize: 15, textAlign: 'center', zIndex: 1 },
  distributionBar: {
    position: 'absolute', left: 0, top: 0, bottom: 0,
    backgroundColor: 'rgba(33,150,243,0.15)'
  },
  percentText: { color: '#aaa', fontSize: 12, position: 'absolute', right: 12, top: 14 },
  miniLeaderboard: {
    position: 'absolute', top: 90, right: 16,
    backgroundColor: 'rgba(0,0,0,0.8)', borderRadius: 12, padding: 12, width: 180
  },
  leaderRow: { flexDirection: 'row', paddingVertical: 4 },
  leaderRank: { color: '#FFD700', width: 30, fontSize: 12 },
  leaderName: { color: '#fff', flex: 1, fontSize: 12 },
  leaderScore: { color: '#4CAF50', fontSize: 12 }
});

export default PredictionGameScreen;

Part 8: Backend Game Engine

Express API for Prediction Management

// server/routes/predictions.js
const express = require('express');
const router = express.Router();
const { PredictionEngine } = require('../services/predictionEngine');
const { ResultBroadcaster } = require('../services/resultBroadcaster');

// Submit a prediction
router.post('/submit', authenticate, async (req, res) => {
  const { predictionId, choice } = req.body;
  const userId = req.user.id;

  try {
    const result = await predictionEngine.submitPrediction(predictionId, userId, choice);
    res.json(result);
  } catch (error) {
    switch (error.message) {
      case 'PREDICTION_NOT_FOUND':
        res.status(404).json({ error: 'Prediction not found' });
        break;
      case 'PREDICTION_LOCKED':
        res.status(400).json({ error: 'Prediction is locked' });
        break;
      case 'PREDICTION_EXPIRED':
        res.status(400).json({ error: 'Time expired' });
        break;
      case 'ALREADY_SUBMITTED':
        res.status(409).json({ error: 'Already submitted' });
        break;
      default:
        res.status(500).json({ error: 'Internal error' });
    }
  }
});

// Get leaderboard
router.get('/leaderboard/:eventId', async (req, res) => {
  const { eventId } = req.params;
  const { limit = 50 } = req.query;

  const rankings = await redis.zrevrange(
    `leaderboard:${eventId}`, 0, limit - 1, 'WITHSCORES'
  );

  const leaderboard = rankings.map(([userId, score], idx) => ({
    rank: idx + 1,
    userId,
    score: parseInt(score)
  }));

  res.json({ eventId, leaderboard });
});

// Get user's prediction history for an event
router.get('/history/:eventId/:userId', authenticate, async (req, res) => {
  const { eventId, userId } = req.params;

  const history = await db.predictions.findAll({
    where: { eventId, userId },
    order: [['createdAt', 'DESC']]
  });

  const stats = {
    total: history.length,
    correct: history.filter(p => p.isCorrect).length,
    points: history.reduce((sum, p) => sum + (p.pointsEarned || 0), 0),
    accuracy: history.length > 0
      ? Math.round((history.filter(p => p.isCorrect).length / history.length) * 100)
      : 0
  };

  res.json({ history, stats });
});

// Admin: Create a prediction (triggered by match events)
router.post('/create', adminAuth, async (req, res) => {
  const { eventId, question, options, type, points, expiresIn } = req.body;

  const prediction = await predictionEngine.createPrediction(eventId, {
    question,
    options,
    type,
    points,
    expiresAt: expiresIn ? Date.now() + expiresIn : null
  });

  res.json({ prediction });
});

// Admin: Settle a prediction
router.post('/settle', adminAuth, async (req, res) => {
  const { predictionId, correctAnswer } = req.body;

  const result = await predictionEngine.settlePrediction(predictionId, correctAnswer);
  await resultBroadcaster.broadcastResult(result.eventId, {
    predictionId,
    correctAnswer,
    question: result.question,
    totalParticipants: Object.keys(result.responses).length,
    winnersCount: result.winners.length,
    topWinners: result.winners.slice(0, 3)
  });

  res.json({ settled: true, winners: result.winners.length, losers: result.losers.length });
});

module.exports = router;

Database Schema

-- PostgreSQL schema for prediction game

CREATE TABLE events (
  id VARCHAR(64) PRIMARY KEY,
  name VARCHAR(255) NOT NULL,
  sport VARCHAR(50) NOT NULL,
  home_team VARCHAR(100),
  away_team VARCHAR(100),
  start_time TIMESTAMPTZ NOT NULL,
  end_time TIMESTAMPTZ,
  status VARCHAR(20) DEFAULT 'scheduled', -- scheduled | live | completed
  stream_room_id INTEGER,
  metadata JSONB DEFAULT '{}'
);

CREATE TABLE predictions (
  id VARCHAR(64) PRIMARY KEY,
  event_id VARCHAR(64) REFERENCES events(id),
  question TEXT NOT NULL,
  options JSONB NOT NULL,
  type VARCHAR(30) NOT NULL,
  points INTEGER DEFAULT 5,
  status VARCHAR(20) DEFAULT 'open', -- open | locked | settled
  correct_answer VARCHAR(255),
  created_at TIMESTAMPTZ DEFAULT NOW(),
  expires_at TIMESTAMPTZ,
  settled_at TIMESTAMPTZ
);

CREATE TABLE user_predictions (
  id SERIAL PRIMARY KEY,
  prediction_id VARCHAR(64) REFERENCES predictions(id),
  user_id VARCHAR(64) NOT NULL,
  choice VARCHAR(255) NOT NULL,
  is_correct BOOLEAN,
  points_earned INTEGER DEFAULT 0,
  submitted_at TIMESTAMPTZ DEFAULT NOW(),
  UNIQUE(prediction_id, user_id)
);

CREATE TABLE leaderboards (
  event_id VARCHAR(64) REFERENCES events(id),
  user_id VARCHAR(64) NOT NULL,
  total_points INTEGER DEFAULT 0,
  predictions_made INTEGER DEFAULT 0,
  predictions_correct INTEGER DEFAULT 0,
  accuracy DECIMAL(5,2) DEFAULT 0,
  PRIMARY KEY (event_id, user_id)
);

-- Indexes for performance
CREATE INDEX idx_predictions_event ON predictions(event_id, status);
CREATE INDEX idx_user_predictions_user ON user_predictions(user_id, prediction_id);
CREATE INDEX idx_leaderboards_points ON leaderboards(event_id, total_points DESC);

Part 9: Scaling for Major Events

When a World Cup final or Super Bowl is on, millions of users submit predictions simultaneously. Here's how to scale:

Message Queue for Prediction Spikes

// Use Redis streams for handling prediction submission spikes
class PredictionQueue {
  constructor(redisClient) {
    this.redis = redisClient;
    this.streamKey = 'predictions:incoming';
  }

  // Producer: Add prediction to queue (client-facing API)
  async enqueue(predictionId, userId, choice) {
    await this.redis.xadd(this.streamKey, '*',
      'predictionId', predictionId,
      'userId', userId,
      'choice', choice,
      'timestamp', Date.now().toString()
    );
  }

  // Consumer: Process predictions in batches
  async processQueue() {
    while (true) {
      const messages = await this.redis.xread(
        'COUNT', 100,        // Process 100 at a time
        'BLOCK', 1000,       // Wait 1s for new messages
        'STREAMS', this.streamKey, '$'
      );

      if (messages) {
        const entries = messages[0][1];
        await this.processBatch(entries);
      }
    }
  }

  async processBatch(entries) {
    const pipeline = this.redis.pipeline();

    for (const [id, fields] of entries) {
      const { predictionId, userId, choice } = Object.fromEntries(
        fields.reduce((acc, val, idx) => {
          if (idx % 2 === 0) acc.push([val, fields[idx + 1]]);
          return acc;
        }, [])
      );

      // Update distribution counter
      pipeline.hincrby(`pred:${predictionId}:dist`, choice, 1);
      // Record user's choice
      pipeline.hset(`pred:${predictionId}:responses`, userId, choice);
      // Acknowledge processed
      pipeline.xdel(this.streamKey, id);
    }

    await pipeline.exec();

    // Broadcast updated distribution every 100 predictions or 200ms
    // (batched to avoid overwhelming chat)
  }
}

Horizontal Scaling Architecture

┌──────────────────────────────────────────────────────────┐
│                    Load Balancer                           │
├──────────┬──────────┬──────────┬──────────┬─────────────┤
│ API      │ API      │ API      │ API      │ API         │
│ Server 1 │ Server 2 │ Server 3 │ Server 4 │ Server N    │
├──────────┴──────────┴──────────┴──────────┴─────────────┤
│                   Redis Cluster                            │
│  ┌─────────────┐  ┌──────────────┐  ┌────────────────┐  │
│  │ Predictions │  │ Leaderboards │  │ Rate Limiting  │  │
│  │ (Streams)   │  │ (Sorted Sets)│  │ (Per User)     │  │
│  └─────────────┘  └──────────────┘  └────────────────┘  │
├──────────────────────────────────────────────────────────┤
│                  TRTC Infrastructure                       │
│  ┌───────────────┐  ┌────────────────────────────────┐  │
│  │ Chat SDK      │  │ Live Streaming (Multi-Region)  │  │
│  │ (Global Edge) │  │ RTC Rooms                      │  │
│  └───────────────┘  └────────────────────────────────┘  │
└──────────────────────────────────────────────────────────┘

Part 10: Anti-Cheat and Fairness

Prediction Timing Integrity

// Ensure predictions are submitted BEFORE the outcome is known
class FairnessValidator {
  constructor(streamLatencyMs = 300) {
    this.streamLatency = streamLatencyMs; // TRTC stream delay
    this.eventTimestamps = new Map();     // When events actually occurred
  }

  /**
   * Validate that a prediction was submitted before the event occurred
   * Account for stream latency to prevent "fast feed" cheating
   */
  validatePredictionTiming(predictionSubmitTime, eventOccurrenceTime) {
    // The event occurred at eventOccurrenceTime on the field
    // The stream shows it at eventOccurrenceTime + streamLatency
    // The prediction must be submitted before eventOccurrenceTime
    // We add a small buffer (100ms) for network variance

    const deadline = eventOccurrenceTime - 100; // 100ms safety buffer

    if (predictionSubmitTime > deadline) {
      return {
        valid: false,
        reason: 'SUBMITTED_AFTER_EVENT',
        submittedAt: predictionSubmitTime,
        eventAt: eventOccurrenceTime,
        delta: predictionSubmitTime - eventOccurrenceTime
      };
    }

    return { valid: true };
  }

  /**
   * Detect automated/bot submissions
   */
  detectBotBehavior(userId, recentSubmissions) {
    // Check 1: Inhuman response times (< 500ms from question appearing)
    const avgResponseTime = recentSubmissions.reduce(
      (sum, s) => sum + s.responseTime, 0
    ) / recentSubmissions.length;

    if (avgResponseTime < 500) {
      return { flagged: true, reason: 'SUPERHUMAN_SPEED', avgMs: avgResponseTime };
    }

    // Check 2: Perfect accuracy over many predictions (statistically improbable)
    const accuracy = recentSubmissions.filter(s => s.correct).length / recentSubmissions.length;
    if (recentSubmissions.length > 20 && accuracy > 0.9) {
      return { flagged: true, reason: 'SUSPICIOUS_ACCURACY', accuracy };
    }

    // Check 3: Consistent pattern in timing (bot-like regularity)
    const timingVariance = this.calculateVariance(
      recentSubmissions.map(s => s.responseTime)
    );
    if (timingVariance < 50) { // Less than 50ms variance = likely automated
      return { flagged: true, reason: 'AUTOMATED_TIMING', variance: timingVariance };
    }

    return { flagged: false };
  }

  calculateVariance(values) {
    const mean = values.reduce((a, b) => a + b, 0) / values.length;
    return Math.sqrt(values.reduce((sum, v) => sum + Math.pow(v - mean, 2), 0) / values.length);
  }
}

Rate Limiting Predictions

// Prevent rapid-fire prediction manipulation
const predictionRateLimiter = {
  limits: {
    perPrediction: 1,          // Only 1 submission per prediction (enforced)
    perMinute: 10,             // Max 10 predictions per minute
    perEvent: 100              // Max 100 predictions per event
  },

  async checkLimit(userId, predictionId) {
    const key = `ratelimit:${userId}`;
    const minute = Math.floor(Date.now() / 60000);

    const [perMin, perEvent] = await redis.pipeline()
      .get(`${key}:min:${minute}`)
      .get(`${key}:event:${predictionId.split('_')[1]}`)
      .exec();

    if (parseInt(perMin?.[1] || 0) >= this.limits.perMinute) {
      throw new Error('RATE_LIMIT_MINUTE');
    }

    if (parseInt(perEvent?.[1] || 0) >= this.limits.perEvent) {
      throw new Error('RATE_LIMIT_EVENT');
    }

    // Increment counters
    await redis.pipeline()
      .incr(`${key}:min:${minute}`)
      .expire(`${key}:min:${minute}`, 120)
      .incr(`${key}:event:${predictionId.split('_')[1]}`)
      .exec();
  }
};

Part 11: Analytics and Engagement Optimization

Key Metrics

// Track prediction game engagement metrics
const analyticsEvents = {
  // Engagement funnel
  'prediction_shown': { eventId, predictionId, question },
  'prediction_submitted': { eventId, predictionId, choice, responseTimeMs },
  'prediction_result_viewed': { eventId, predictionId, correct, points },

  // Stream engagement
  'stream_joined': { eventId, angle },
  'angle_switched': { eventId, fromAngle, toAngle },
  'stream_watch_duration': { eventId, durationMs },

  // Social
  'voice_room_joined': { eventId, role },
  'voice_room_duration': { eventId, durationMs, spoke: boolean },
  'chat_message_sent': { eventId, messageType },

  // Retention
  'session_start': { eventId },
  'session_end': { eventId, totalDurationMs, predictionsCount, pointsEarned },
  'return_for_next_event': { daysSinceLastEvent }
};

// Calculate key engagement KPIs
class PredictionAnalytics {
  async getEngagementMetrics(eventId) {
    return {
      // Prediction engagement rate
      predictionRate: await this.calcPredictionRate(eventId),
      // Average response time
      avgResponseTime: await this.calcAvgResponseTime(eventId),
      // Completion rate (% who stay for all predictions)
      completionRate: await this.calcCompletionRate(eventId),
      // Social interaction rate
      socialRate: await this.calcSocialRate(eventId),
      // Revenue correlation
      retentionCorrelation: await this.calcRetentionCorrelation(eventId)
    };
  }

  async calcPredictionRate(eventId) {
    const viewers = await redis.get(`viewers:${eventId}`);
    const predictors = await redis.scard(`predictors:${eventId}`);
    return (predictors / parseInt(viewers)) * 100; // Target: > 40%
  }
}

Part 12: Deployment Checklist

Launch Readiness

Infrastructure:

•  TRTC production SDK credentials configured
•  Live streaming tested with production broadcast equipment
•  Chat SDK groups pre-created for upcoming events
•  Redis cluster sized for expected concurrent predictions (benchmark: 100K/sec)
•  CDN fallback configured for stream degradation
•  Cloud recording enabled (compliance)

Game Logic:

•  Prediction creation API tested with all question types
•  Settlement logic verified with edge cases (ties, cancellations)
•  Leaderboard scoring verified (no negative points, proper tiebreaking)
•  Anti-cheat validators active
•  Rate limiting tested under load

Client:

•  Multi-angle stream switching smooth (< 500ms transition)
•  Prediction cards render within 200ms of receiving message
•  Result animations work on low-end devices
•  Network degradation gracefully handled (offline mode for predictions)
•  Push notifications for prediction results when app backgrounded

Scale:

•  Load tested with 50,000 concurrent viewers + 10,000 concurrent predictions/min
•  Message batching prevents chat flooding during peak moments
•  Leaderboard updates batched to once per 2 seconds (not per prediction)

Key Takeaways

Building a sports prediction game that actually retains users requires more than a form and a database. With over 200 million users globally engaging in fantasy sports and prediction platforms per Statista's digital market insights, competition for attention is fierce. The technical foundation comes down to three synchronized systems:

Live Streaming — TRTC's Live SDK and RTC provide the ultra-low latency (< 300ms) required for fair in-play predictions. The RTMP+RTC dual-mode from the architecture guide enables integration with existing broadcast infrastructure while maintaining the sub-second delivery needed for timing integrity.

Real-Time Predictions — TRTC's Chat SDK handles the prediction distribution layer — custom messages broadcast questions, collect responses, and settle results instantly across all connected clients. No need to build WebSocket infrastructure from scratch.

Community Interaction — Voice rooms via RTC and text chat via Chat SDK create the social layer that transforms a solo prediction game into a multiplayer experience. Users who discuss predictions with friends place 2-3x more predictions per event.

The Interactive Game Console solution provides the architectural reference for sports prediction games specifically — covering the exact scenarios of multi-angle live video + real-time interaction + low-latency result delivery that prediction games demand.

Start with a single sport and a single prediction type (match winner). Get the live stream + prediction + result cycle working smoothly. Then expand to in-play predictions, multiple sports, voice rooms, and social features. Each layer builds engagement on top of the working foundation.

Further Reading

• Interactive Game Console Solution — Sports prediction game architecture reference with RTC + RTMP dual-mode
• TRTC Live Streaming Product — Live SDK capabilities for event broadcasting
• TRTC Real-Time Communication — RTC SDK for voice rooms and ultra-low latency
• Chat SDK Product — Custom messaging for predictions, results, and community features
• RTMP + RTC Implementation Guide — Technical reference for dual-mode streaming architecture

Speed Up Integration with MCP

The @tencentcloud/sdk-mcp server connects your AI coding assistant directly to TRTC SDK documentation:

{
  "mcpServers": {
    "tencentcloud-sdk-mcp": {
      "command": "npx",
      "args": ["-y", "@tencentcloud/sdk-mcp"],
      "env": {
        "SDKAPPID": "YOUR_SDKAPPID",
        "SECRETKEY": "YOUR_SECRET_KEY"
      }
    }
  }
}

Try: "Set up multi-camera live streaming for a sports prediction game with real-time audience interaction" — your assistant generates the complete TRTC integration.

Frequently Asked Questions

How is a sports prediction game different from sports betting?

Prediction games are free-to-play or use virtual currency, removing gambling license requirements in most jurisdictions. Players earn points for correct predictions rather than monetary payouts. This makes them accessible to wider audiences while still driving engagement through competition and leaderboards.

What latency is needed for fair in-play predictions?

Full RTC chain (100-300ms) is strongly recommended. The 200ms difference between RTMP hybrid and full RTC directly impacts fairness — in fast-paced sports, 200ms is enough to react to an already-visible outcome rather than genuinely predict it.

How do you prevent cheating in prediction games?

Server-side timestamp validation ensures predictions are submitted before events occur (with stream latency offset). Bot detection flags inhuman response times (<500ms), statistically improbable accuracy (>90% over 20+ predictions), and automated timing patterns (variance <50ms).

How many predictions should a game offer per match?

Start with 1 pre-game prediction (match winner), then add 10-20 in-play predictions triggered by match events (goals, corners, penalties, halftime). Users who engage with in-play predictions have 2-3x higher session duration than those who only predict pre-game.

What tech stack is needed to build a prediction game?

Frontend (React/React Native), TRTC Live SDK (streaming), TRTC Chat SDK (prediction messaging), Redis (leaderboards and real-time state), PostgreSQL (persistent data), and a match event data feed. The TRTC components handle the hardest parts: low-latency video and real-time message delivery.

Can prediction games scale for World Cup or Super Bowl events?

Yes. Use Redis Streams for prediction submission queues (handles 100K+ per second), batch distribution updates to avoid chat flooding, and leverage TRTC's global edge network for stream delivery to millions. Pre-size infrastructure based on expected concurrent users.

How do voice rooms improve prediction game engagement?

Users who discuss predictions with friends in voice rooms place 2-3x more predictions per event. Voice creates stadium atmosphere, social pressure to participate, and real-time celebration/commiseration that keeps users in-app for the entire event duration.

What monetization models work for prediction games?

Common models include premium prediction packs (exclusive in-play questions), cosmetic items (avatars, celebration animations), ad-supported free tier, and branded tournaments with prize pools. The engagement data also has value for sports media and broadcasting partners.

Frequently Asked Questions

How is a sports prediction game different from sports betting?

Prediction games are free-to-play or use virtual currency, removing gambling license requirements in most jurisdictions. Players earn points for correct predictions rather than monetary payouts. This makes them accessible to wider audiences while still driving engagement through competition and leaderboards.

What latency is needed for fair in-play predictions?

Full RTC chain (100-300ms) is strongly recommended. The 200ms difference between RTMP hybrid and full RTC directly impacts fairness — in fast-paced sports, 200ms is enough to react to an already-visible outcome rather than genuinely predict it.

How do you prevent cheating in prediction games?

Server-side timestamp validation ensures predictions are submitted before events occur (with stream latency offset). Bot detection flags inhuman response times (<500ms), statistically improbable accuracy (>90% over 20+ predictions), and automated timing patterns (variance <50ms).

How many predictions should a game offer per match?

Start with 1 pre-game prediction (match winner), then add 10-20 in-play predictions triggered by match events (goals, corners, penalties, halftime). Users who engage with in-play predictions have 2-3x higher session duration than those who only predict pre-game.

What tech stack is needed to build a prediction game?

Frontend (React/React Native), TRTC Live SDK (streaming), TRTC Chat SDK (prediction messaging), Redis (leaderboards and real-time state), PostgreSQL (persistent data), and a match event data feed. The TRTC components handle the hardest parts: low-latency video and real-time message delivery.

Can prediction games scale for World Cup or Super Bowl events?

Yes. Use Redis Streams for prediction submission queues (handles 100K+ per second), batch distribution updates to avoid chat flooding, and leverage TRTC's global edge network for stream delivery to millions. Pre-size infrastructure based on expected concurrent users.

How do voice rooms improve prediction game engagement?

Users who discuss predictions with friends in voice rooms place 2-3x more predictions per event. Voice creates stadium atmosphere, social pressure to participate, and real-time celebration/commiseration that keeps users in-app for the entire event duration.

What monetization models work for prediction games?

Common models include premium prediction packs (exclusive in-play questions), cosmetic items (avatars, celebration animations), ad-supported free tier, and branded tournaments with prize pools. The engagement data also has value for sports media and broadcasting partners.