Retrieve statistical metrics including minimum, maximum, and standard deviation for all auction rounds

STATISTICAL ANALYSIS

Round Performance 2

Advanced statistical metrics with minimum, maximum, and standard deviation data for comprehensive auction round analysis.

Advanced Analytics

This query provides statistical distribution metrics essential for variance analysis, outlier detection, and data quality monitoring.

Overview

The getRoundPerform2 query retrieves advanced statistical metrics for each auction round, including minimum value, maximum value, and standard deviation. These metrics are crucial for understanding data distribution, identifying anomalies, and performing sophisticated analytics.

GraphQL Schema

query {
  getRoundPerform2: [RoundPerform2]
}

Return Type

RoundPerform2 Object

roundNumberInt!

The unique identifier for the auction round. Sequential numbering starting from 1.

minFloat!

The minimum value observed in the round. Useful for identifying lower bounds and potential outliers.

maxFloat!

The maximum value observed in the round. Useful for identifying upper bounds and peak performance.

stdFloat!

Standard deviation measuring data dispersion. Higher values indicate greater variability in round performance.

Example Usage

Query Request

query GetStatisticalMetrics {
  getRoundPerform2 {
    roundNumber
    min
    max
    std
  }
}

Successful Response

{
  "data": {
    "getRoundPerform2": [
      {
        "roundNumber": 1,
        "min": 0.01,
        "max": 0.12,
        "std": 0.025
      },
      {
        "roundNumber": 2,
        "min": 0.03,
        "max": 0.18,
        "std": 0.032
      },
      {
        "roundNumber": 3,
        "min": 0.05,
        "max": 0.25,
        "std": 0.045
      },
      {
        "roundNumber": 4,
        "min": 0.08,
        "max": 0.35,
        "std": 0.058
      },
      {
        "roundNumber": 5,
        "min": 0.12,
        "max": 0.50,
        "std": 0.072
      }
    ]
  }
}

Statistical Insights

The example demonstrates increasing standard deviation over rounds, indicating growing volatility and wider price ranges as the auction progresses.

Chart Integration Examples

Statistical Visualization Techniques

📊

Box Plot / Candlestick Chart

Visualize min, max, and standard deviation using box plots to show statistical distribution and identify outliers.

Recharts CandlestickD3.js Box PlotPlotly Box

📈

Range Area Chart

Display min-max range as shaded areas to visualize the spread and volatility across rounds.

Recharts AreaChartChart.js FillApexCharts Range

🎯

Standard Deviation Line Chart

Plot standard deviation progression to track volatility trends and data consistency over time.

Chart.js LineRecharts LineChart

📉

Error Bars Chart

Add error bars to show variance and confidence intervals based on standard deviation values.

Recharts ErrorBarPlotly Error Bars

Implementation Examples

React + Recharts Range Chart

import { AreaChart, Area, XAxis, YAxis, CartesianGrid, Tooltip, Legend } from 'recharts';
import { useQuery, gql } from '@apollo/client';

const GET_ROUND_PERFORM_2 = gql`
  query GetStatisticalMetrics {
    getRoundPerform2 {
      roundNumber
      min
      max
      std
    }
  }
`;

function StatisticalRangeChart() {
  const { data, loading } = useQuery(GET_ROUND_PERFORM_2);

  if (loading) return <div>Loading statistical data...</div>;

  // Transform data to show range
  const chartData = data.getRoundPerform2.map(round => ({
    round: `Round ${round.roundNumber}`,
    range: [round.min, round.max],
    min: round.min,
    max: round.max,
    std: round.std
  }));

  return (
    <AreaChart width={800} height={400} data={chartData}>
      <CartesianGrid strokeDasharray="3 3" />
      <XAxis dataKey="round" />
      <YAxis label={{ value: 'Value Range', angle: -90, position: 'insideLeft' }} />
      <Tooltip />
      <Legend />
      <Area
        type="monotone"
        dataKey="max"
        stackId="1"
        stroke="#f59e0b"
        fill="#fef3c7"
        name="Maximum"
      />
      <Area
        type="monotone"
        dataKey="min"
        stackId="2"
        stroke="#d97706"
        fill="#fbbf24"
        name="Minimum"
      />
    </AreaChart>
  );
}

Standard Deviation Volatility Chart

import { Line } from 'react-chartjs-2';

function VolatilityChart({ data }) {
  const chartData = {
    labels: data.getRoundPerform2.map(r => `Round ${r.roundNumber}`),
    datasets: [
      {
        label: 'Standard Deviation (Volatility)',
        data: data.getRoundPerform2.map(r => r.std),
        borderColor: 'rgb(245, 158, 11)',
        backgroundColor: 'rgba(245, 158, 11, 0.1)',
        fill: true,
        tension: 0.4,
        pointRadius: 5,
        pointHoverRadius: 7
      }
    ]
  };

  const options = {
    responsive: true,
    plugins: {
      title: {
        display: true,
        text: 'Round Volatility Trend (Standard Deviation)'
      },
      tooltip: {
        callbacks: {
          label: (context) => {
            return `Std Dev: ${context.parsed.y.toFixed(4)}`;
          }
        }
      }
    },
    scales: {
      y: {
        beginAtZero: true,
        title: {
          display: true,
          text: 'Standard Deviation'
        }
      }
    }
  };

  return <Line data={chartData} options={options} />;
}

Statistical Analysis Use Cases

🔍

Outlier Detection

Use min/max values and standard deviation to identify unusual bidding patterns or anomalies requiring investigation

📊

Volatility Analysis

Track standard deviation trends to understand market stability and predict periods of high or low variability

🎯

Quality Monitoring

Monitor data quality by detecting rounds with abnormally high standard deviation or unrealistic min/max ranges

📈

Risk Assessment

Evaluate investment risk based on price variance and volatility metrics for informed decision making

Statistical Concepts Explained

Understanding the Metrics

📉

Minimum Value

The lowest observed value in the dataset. Important for:

Establishing price floors or lower bounds
Identifying potential data entry errors
Understanding best-case scenario for buyers

📈

Maximum Value

The highest observed value in the dataset. Important for:

Establishing price ceilings or upper bounds
Identifying peak performance or outliers
Understanding worst-case scenario for buyers

📊

Standard Deviation (σ)

Measures how spread out values are from the mean. Important for:

Assessing data consistency and reliability
Quantifying volatility and risk levels
Comparing variability across different rounds

Low std (<0.03) = Stable, predictable behavior
High std (>0.07) = Volatile, unpredictable behavior

Advanced Analytics Tips

📊 Range Calculation

Calculate range: range = max - min to understand value spread

🎯 Coefficient of Variation

Normalize volatility: CV = (std / mean) × 100 for relative comparison

🔔 Anomaly Detection

Flag outliers using: value > mean + (2 × std) for two-sigma rule

📈 Trend Analysis

Track how standard deviation changes over rounds to predict increasing or decreasing market stability

Best Practices

⚡ Combine with Other Metrics

Use alongside RoundPerform1 data (price/volume) for comprehensive analytics and correlation analysis

🎨 Visual Clarity

Use color gradients to represent volatility levels - green for low std, yellow for medium, red for high

📊 Context Matters

Always display statistical metrics with context - what the values represent and their significance

🔍 Data Validation

Verify that min ≤ max and std ≥ 0 to catch potential API errors or data corruption

Get Round Perform 2