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Update src/app/platform/page.tsx

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bender
2026-03-21 05:54:21 +00:00
parent 3123311991
commit cd1d3f4fc6
1 changed files with 24 additions and 64 deletions
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88
src/app/platform/page.tsx
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@@ -7,7 +7,7 @@ import ProductCardFour from '@/components/sections/product/ProductCardFour';
import FaqBase from '@/components/sections/faq/FaqBase';
import FooterCard from '@/components/sections/footer/FooterCard';
import Link from 'next/link';
import { Code, BarChart3, HelpCircle, Twitter, Linkedin, Github } from 'lucide-react';
import { Code, BarChart3, HelpCircle, Twitter, Linkedin, Github, Database, Globe, Activity, Trophy, Layers, GitBranch, Cpu, Brain } from 'lucide-react';
export default function PlatformPage() {
const navItems = [
@@ -52,40 +52,27 @@ export default function PlatformPage() {
tagAnimation="slide-up"
features={[
{
title: "Cricsheet Data Foundation",
description: "Comprehensive ball-by-ball data covering thousands of T20 matches across IPL, BBL, SA20, CPL, T20 Blast, and domestic competitions worldwide.",
bentoComponent: "orbiting-icons",
centerIcon: "Database",
title: "Cricsheet Data Foundation", description: "Comprehensive ball-by-ball data covering thousands of T20 matches across IPL, BBL, SA20, CPL, T20 Blast, and domestic competitions worldwide.", bentoComponent: "orbiting-icons", centerIcon: Database,
items: [
{ icon: "Globe", ring: 1 },
{ icon: "Activity", ring: 1 },
{ icon: "BarChart3", ring: 2 },
{ icon: "Zap", ring: 2 },
{ icon: "Target", ring: 3 },
{ icon: "Trophy", ring: 3 }
{ icon: Globe, ring: 1 },
{ icon: Activity, ring: 1 },
{ icon: BarChart3, ring: 2 },
{ icon: Code, ring: 2 },
{ icon: Trophy, ring: 3 },
{ icon: Trophy, ring: 3 }
]
},
{
title: "Feature Pipeline",
description: "Deep cricket domain knowledge transforms raw ball-by-ball data into meaningful features capturing context, phase dynamics, and player interactions.",
bentoComponent: "3d-task-list",
items: [
{ icon: "Layers", label: "Context Extraction", time: "Real-time" },
{ icon: "GitBranch", label: "Phase Analysis", time: "Streaming" },
{ icon: "Cpu", label: "Aggregation", time: "Batch" }
title: "Feature Pipeline", description: "Deep cricket domain knowledge transforms raw ball-by-ball data into meaningful features capturing context, phase dynamics, and player interactions.", bentoComponent: "3d-task-list", items: [
{ icon: Layers, label: "Context Extraction", time: "Real-time" },
{ icon: GitBranch, label: "Phase Analysis", time: "Streaming" },
{ icon: Cpu, label: "Aggregation", time: "Batch" }
]
},
{
title: "ML & Explainability",
description: "XGBoost ensemble models for prediction accuracy combined with SHAP for model explainability—every insight includes reasoning, not just numbers.",
bentoComponent: "marquee",
centerIcon: "Brain",
variant: "text",
texts: [
"XGBoost Ensembles",
"SHAP Explainability",
"Real-time Inference",
"Continuous Learning"
title: "ML & Explainability", description: "XGBoost ensemble models for prediction accuracy combined with SHAP for model explainability—every insight includes reasoning, not just numbers.", bentoComponent: "marquee", centerIcon: Brain,
variant: "text", texts: [
"XGBoost Ensembles", "SHAP Explainability", "Real-time Inference", "Continuous Learning"
]
}
]}
@@ -106,28 +93,13 @@ export default function PlatformPage() {
carouselMode="buttons"
products={[
{
id: "franchise",
name: "Franchise Strategy",
price: "Optimal Decisions",
variant: "Team Optimization",
imageSrc: "https://webuild-dev.s3.eu-north-1.amazonaws.com/users/user_3BF4j2bxRdT4nJ0q7EnEjwa2Qxd/a-franchise-scouting-scenario-showing-a--1774072043214-b70d5ada.png",
imageAlt: "Franchise strategy optimization"
id: "franchise", name: "Franchise Strategy", price: "Optimal Decisions", variant: "Team Optimization", imageSrc: "https://webuild-dev.s3.eu-north-1.amazonaws.com/users/user_3BF4j2bxRdT4nJ0q7EnEjwa2Qxd/a-franchise-scouting-scenario-showing-a--1774072043214-b70d5ada.png", imageAlt: "Franchise strategy optimization"
},
{
id: "analyst",
name: "Match Analysis",
price: "Real-Time Insights",
variant: "Broadcast Ready",
imageSrc: "https://webuild-dev.s3.eu-north-1.amazonaws.com/users/user_3BF4j2bxRdT4nJ0q7EnEjwa2Qxd/a-cricket-analyst-at-a-desk-with-multipl-1774072044293-8fa94f9f.png",
imageAlt: "Cricket analyst workspace"
id: "analyst", name: "Match Analysis", price: "Real-Time Insights", variant: "Broadcast Ready", imageSrc: "https://webuild-dev.s3.eu-north-1.amazonaws.com/users/user_3BF4j2bxRdT4nJ0q7EnEjwa2Qxd/a-cricket-analyst-at-a-desk-with-multipl-1774072044293-8fa94f9f.png", imageAlt: "Cricket analyst workspace"
},
{
id: "scout",
name: "Talent Discovery",
price: "Hidden Gems Found",
variant: "Global Coverage",
imageSrc: "https://webuild-dev.s3.eu-north-1.amazonaws.com/users/user_3BF4j2bxRdT4nJ0q7EnEjwa2Qxd/a-sophisticated-player-profiling-interfa-1774072044221-0c247c5b.png",
imageAlt: "Player profiling system"
id: "scout", name: "Talent Discovery", price: "Hidden Gems Found", variant: "Global Coverage", imageSrc: "https://webuild-dev.s3.eu-north-1.amazonaws.com/users/user_3BF4j2bxRdT4nJ0q7EnEjwa2Qxd/a-sophisticated-player-profiling-interfa-1774072044221-0c247c5b.png", imageAlt: "Player profiling system"
}
]}
ariaLabel="CricIntel use cases"
@@ -147,34 +119,22 @@ export default function PlatformPage() {
tagAnimation="slide-up"
faqs={[
{
id: "faq-1",
title: "What data sources does the platform use?",
content: "CricIntel integrates comprehensive ball-by-ball data from Cricsheet, covering all major T20 leagues (IPL, BBL, SA20, CPL, T20 Blast) and domestic competitions. Our data pipeline processes thousands of matches in real-time, extracting context-aware features for accurate predictions."
id: "faq-1", title: "What data sources does the platform use?", content: "CricIntel integrates comprehensive ball-by-ball data from Cricsheet, covering all major T20 leagues (IPL, BBL, SA20, CPL, T20 Blast) and domestic competitions. Our data pipeline processes thousands of matches in real-time, extracting context-aware features for accurate predictions."
},
{
id: "faq-2",
title: "How does the Player Intelligence module work?",
content: "The Player Intelligence module uses unsupervised machine learning to cluster players by playing style, calculates context-aware performance scores accounting for opposition and match conditions, and models career trajectories. This enables franchises to identify undervalued talent and find similar player profiles globally."
id: "faq-2", title: "How does the Player Intelligence module work?", content: "The Player Intelligence module uses unsupervised machine learning to cluster players by playing style, calculates context-aware performance scores accounting for opposition and match conditions, and models career trajectories. This enables franchises to identify undervalued talent and find similar player profiles globally."
},
{
id: "faq-3",
title: "Can I integrate CricIntel with my existing systems?",
content: "Yes. CricIntel provides APIs for data export, custom dashboards, and integration with third-party analytics tools. Our platform architecture is modular, allowing you to use specific modules independently or as a complete solution."
id: "faq-3", title: "Can I integrate CricIntel with my existing systems?", content: "Yes. CricIntel provides APIs for data export, custom dashboards, and integration with third-party analytics tools. Our platform architecture is modular, allowing you to use specific modules independently or as a complete solution."
},
{
id: "faq-4",
title: "What's included in the Team Strategy module?",
content: "The Team Strategy module includes optimal batting order construction using ML algorithms, best XI selection against specific opponents, and IPL auction value estimation. All recommendations are backed by historical data analysis and predictive modeling."
id: "faq-4", title: "What's included in the Team Strategy module?", content: "The Team Strategy module includes optimal batting order construction using ML algorithms, best XI selection against specific opponents, and IPL auction value estimation. All recommendations are backed by historical data analysis and predictive modeling."
},
{
id: "faq-5",
title: "How is prediction accuracy validated?",
content: "CricIntel uses rigorous backtesting on historical data and cross-validation techniques. Our XGBoost ensemble models achieve 99.2% accuracy, validated against actual match outcomes. SHAP explainability ensures every prediction is interpretable and trustworthy."
id: "faq-5", title: "How is prediction accuracy validated?", content: "CricIntel uses rigorous backtesting on historical data and cross-validation techniques. Our XGBoost ensemble models achieve 99.2% accuracy, validated against actual match outcomes. SHAP explainability ensures every prediction is interpretable and trustworthy."
},
{
id: "faq-6",
title: "What's the cost structure?",
content: "CricIntel offers flexible pricing based on usage patterns and features required. Contact our sales team for enterprise quotes, trial access, or custom solutions tailored to your organization's needs."
id: "faq-6", title: "What's the cost structure?", content: "CricIntel offers flexible pricing based on usage patterns and features required. Contact our sales team for enterprise quotes, trial access, or custom solutions tailored to your organization's needs."
}
]}
faqsAnimation="slide-up"