31:T4ba,Plot.plot({ marginTop: 60, marginLeft: 12, width: width, height: 8 * Math.sqrt(width), x: { domain: [0, 90 * 60] }, // an hour an a half y: { grid: false, nice: false, ticks: false }, color: { legend: true }, marks: [ Plot.ruleX(goals, { x: "time", stroke: "splitType", strokeWidth: 3, strokeOpacity: 0.2 }), Plot.ruleX(mySplits, { x: "time", stroke: "splitType", strokeWidth: 3 }), Plot.rectY( times, Plot.binX( { y1: "count", thresholds: bins }, { x: "time", fill: "splitName", mixBlendMode: "multiply" } ) ), Plot.ruleY([0]), Plot.axisX({ tickFormat: msToTime, ticks: ticks }), Plot.axisX({ anchor: "top", //tickRotate: 90, //textAnchor: "end", tickFormat: (d) => msToTime(d), ticks: mySplits.map((d) => d.time) }) /*Plot.axisX({ anchor: "top", textAnchor: "end", tickRotate: 90, tickFormat: (d) => msToTime(d), ticks: goals.map((d) => d.time) }),*/ /*Plot.text(goals, { x: (d) => d.time, y: 90, text: (d) => msToTime(d.time), dy: 6, lineAnchor: "top" })*/ ] })32:T4f6,{ if (!person) { return md``; } else { return md`Split | Time | Percentile | ${focus.q}%-tile Time | Difference -------- | -------- | --------- | --------- | --------- Swim | ${msToTime(person.swim)} | ${percentile( filtered.map((d) => d.swim), person.swim, true )} | ${msToTime(goals[0].time)} | ${msToTime(person.swim - goals[0].time)} T1 | ${msToTime(person.t1)} | ${percentile( filtered.map((d) => d.t1), person.t1, true )} | ${msToTime(goals[3].time)} | ${msToTime(person.t1 - goals[3].time)} Bike | ${msToTime(person.bike)} | ${percentile( filtered.map((d) => d.bike), person.bike, true )} | ${msToTime(goals[1].time)} | ${msToTime(person.bike - goals[1].time)} T2 | ${msToTime(person.t2)} | ${percentile( filtered.map((d) => d.t2), person.t2, true )} | ${msToTime(goals[4].time)} | ${msToTime(person.t2 - goals[4].time)} Run | ${msToTime(person.run)} | ${percentile( filtered.map((d) => d.run), person.run, true )} | ${msToTime(goals[2].time)} | ${msToTime(person.run - goals[2].time)} Total | ${msToTime(person.full_course)} | ${percentile( filtered.map((d) => d.full_course), person.full_course, true )} | | `; } }33:T5a9,function simpleLinearRegression(x, y) { if (x.length !== y.length || x.length === 0) { throw new Error("x and y arrays must have the same non-zero length"); } const n = x.length; // Calculate means const xMean = x.reduce((sum, val) => sum + val, 0) / n; const yMean = y.reduce((sum, val) => sum + val, 0) / n; // Calculate slope and intercept using least squares let numerator = 0; let denominator = 0; for (let i = 0; i < n; i++) { const xDiff = x[i] - xMean; const yDiff = y[i] - yMean; numerator += xDiff * yDiff; denominator += xDiff * xDiff; } if (denominator === 0) { throw new Error("Cannot calculate regression: all x values are identical"); } const slope = numerator / denominator; const intercept = yMean - slope * xMean; // Calculate predicted values and residuals const predicted = x.map((xi) => slope * xi + intercept); const residuals = y.map((yi, i) => yi - predicted[i]); // Calculate R-squared const totalSumSquares = y.reduce( (sum, yi) => sum + Math.pow(yi - yMean, 2), 0 ); const residualSumSquares = residuals.reduce( (sum, residual) => sum + Math.pow(residual, 2), 0 ); const rSquared = 1 - residualSumSquares / totalSumSquares; return { slope, intercept, residuals, predicted, rSquared, // Helper method to get equation as string equation: `y = ${slope.toFixed(4)}x + ${intercept.toFixed(4)}` }; }34:Tcec,/** * Calculate race finish times with and without headwind * @param {number} distanceKm - Race distance in kilometers * @param {number} powerWatts - Constant power output in watts * @param {number} headwindKmh - Headwind speed in km/h (0 for no wind) * @param {number} riderMassKg - Rider + bike mass in kg (default 75kg) * @param {number} cda - Coefficient of drag × frontal area in m² (default 0.3) * @returns {Object} Race time analysis with and without headwind */ function raceTimeWithHeadwind( distanceKm, powerWatts, headwindKmh, riderMassKg = 75, cda = 0.3 ) { const airDensity = 1.225; // kg/m³ at sea level const crr = 0.005; // rolling resistance coefficient const g = 9.81; // gravity m/s² /** * Calculate speed for given power and headwind * Solves: Power = (air_resistance + rolling_resistance) × speed * Where air_resistance = 0.5 × ρ × CdA × (speed + headwind)² * And rolling_resistance = Crr × mass × g */ function speedForPower(powerW, headwindMs) { // Convert headwind from km/h to m/s const headwindSpeed = headwindMs / 3.6; // Rolling resistance force (constant) const rollingForce = crr * riderMassKg * g; // Solve the equation iteratively: powerW = (0.5 × ρ × CdA × (v + headwind)² + rollingForce) × v let speed = 10; // initial guess (m/s) for (let i = 0; i < 50; i++) { const relativeSpeed = speed + headwindSpeed; const airResistanceForce = 0.5 * airDensity * cda * relativeSpeed * relativeSpeed; const totalForce = airResistanceForce + rollingForce; const currentPower = totalForce * speed; // Newton's method adjustment // d/dv [totalForce × v] = totalForce + v × d/dv[airResistanceForce] // d/dv[0.5 × ρ × CdA × (v + headwind)²] = ρ × CdA × (v + headwind) const airResistanceDerivative = airDensity * cda * relativeSpeed; const derivative = totalForce + speed * airResistanceDerivative; const adjustment = (currentPower - powerW) / derivative; speed = Math.max(0.1, speed - adjustment); if (Math.abs(adjustment) < 0.001) break; } return speed; // m/s } // Calculate speeds const speedNoWind = speedForPower(powerWatts, 0); // m/s const speedWithHeadwind = speedForPower(powerWatts, headwindKmh); // m/s // Convert to km/h for display const speedNoWindKmh = speedNoWind * 3.6; const speedWithHeadwindKmh = speedWithHeadwind * 3.6; // Calculate race times (in hours) const timeNoWind = (distanceKm / speedNoWindKmh) * 3600; const timeWithHeadwind = (distanceKm / speedWithHeadwindKmh) * 3600; // Time difference const timeDifferenceSeconds = timeWithHeadwind - timeNoWind; const timeDifferenceMinutes = timeDifferenceSeconds / 60; const timeDifferenceHours = timeDifferenceMinutes / 60; return { distance: distanceKm, power: powerWatts, headwind: headwindKmh, speedNoWind: speedNoWindKmh, speedWithHeadwind: speedWithHeadwindKmh, speedLoss: speedNoWindKmh - speedWithHeadwindKmh, timeNoWind: timeNoWind, timeWithHeadwind: timeWithHeadwind, timeDifferenceMinutes: timeDifferenceMinutes, timeDifferenceSeconds: timeDifferenceSeconds, percentSlower: ((timeWithHeadwind - timeNoWind) / timeNoWind) * 100 }; }35:Te67,// https://open-meteo.com/en/docs/historical-weather-api?hourly=temperature_2m,apparent_temperature,relative_humidity_2m,wind_speed_10m,wind_speed_100m,cloud_cover,precipitation,dew_point_2m,weather_code,wind_direction_10m,wind_direction_100m,cloud_cover_low,cloud_cover_mid,cloud_cover_high,wet_bulb_temperature_2m,direct_radiation&timezone=America%2FChicago&latitude=41.85&longitude=-87.65&start_date=2016-08-01&temperature_unit=fahrenheit&end_date=2024-09-01#settings weatherPoints = { const medianTimes = d3.rollup( filteredAllYears, (v) => d3.median(v.map((d) => d.full_course)), (d) => d.year ); const medianTimesBike = d3.rollup( filteredAllYears, (v) => d3.median(v.map((d) => d.bike)), (d) => d.year ); const medianTimesRun = d3.rollup( filteredAllYears, (v) => d3.median(v.map((d) => d.run)), (d) => d.year ); const medianTimesSwim = d3.rollup( filteredAllYears, (v) => d3.median(v.map((d) => d.swim)), (d) => d.year ); const temps = { 2016: { time: "2016-08-28T10:00", temperature_2m: 75.8, apparent_temperature: 83.8, relative_humidity_2m: 88, wind_speed_10m: 4.3, cloud_cover: 89, precipitation: 0.0, wind_direction_10m: 66, direct_radiation: 264.0, wet_bulb_temperature_2m: 73.0, manual_apparent_temperature: 86.8 }, // aug 28, 2016 2017: { time: "2017-08-27T10:00", temperature_2m: 71.4, apparent_temperature: 68.2, relative_humidity_2m: 53, wind_speed_10m: 16.7, cloud_cover: 99, precipitation: 0.0, wind_direction_10m: 186, direct_radiation: 238.0, wet_bulb_temperature_2m: 60.5, manual_apparent_temperature: 70.3 }, // aug 27, 2017 2018: { time: "2018-08-26T10:00", temperature_2m: 80.5, apparent_temperature: 88.4, relative_humidity_2m: 86, wind_speed_10m: 13.5, cloud_cover: 87, precipitation: 0.0, wind_direction_10m: 228, direct_radiation: 232.0, wet_bulb_temperature_2m: 77.0, manual_apparent_temperature: 90.7 }, // aug 26, 2018 2021: { time: "2021-08-29T10:00", temperature_2m: 79.7, apparent_temperature: 83.1, relative_humidity_2m: 79, wind_speed_10m: 22.9, cloud_cover: 91, precipitation: 0.0, wind_direction_10m: 208, direct_radiation: 199.0, wet_bulb_temperature_2m: 74.5, manual_apparent_temperature: 84.8 }, // aug 29, 2021 2022: { time: "2022-08-28T10:00", temperature_2m: 76.7, apparent_temperature: 78.3, relative_humidity_2m: 74, wind_speed_10m: 20.5, cloud_cover: 100, precipitation: 0.0, wind_direction_10m: 158, direct_radiation: 275.0, wet_bulb_temperature_2m: 70.6, manual_apparent_temperature: 80.6 }, // aug 28, 2022 2024: { time: "2024-08-25T10:00", temperature_2m: 83.2, apparent_temperature: 85.5, relative_humidity_2m: 58, wind_speed_10m: 16.0, cloud_cover: 1, precipitation: 0.0, wind_direction_10m: 203, direct_radiation: 422.0, wet_bulb_temperature_2m: 72.2, manual_apparent_temperature: 89.4 } // aug 25, 2024 }; return filterOptions.year.values.map((y) => ({ year: y, time: medianTimes.get(y), timeBike: medianTimesBike.get(y), timeRun: medianTimesRun.get(y), timeSwim: medianTimesSwim.get(y), temp: temps[y].wet_bulb_temperature_2m, windSpeed: temps[y].wind_speed_10m, wind: windAlongLSD( temps[y].wind_speed_10m, temps[y].wind_direction_10m, lsdDirection ) })); }36:Tc49,/** * Calculate quadratic regression and return residuals * @param {number[]} x - Independent variable values * @param {number[]} y - Dependent variable values * @returns {Object} Object containing coefficients, residuals, r-squared, equation text, and prediction function */ function quadraticRegression(x, y) { if (x.length !== y.length || x.length === 0) { throw new Error("x and y arrays must have the same non-zero length"); } const n = x.length; if (n < 3) { throw new Error("Quadratic regression requires at least 3 data points"); } // Set up the normal equations for quadratic regression: y = ax² + bx + c // We need to solve the system: // Σy = c*n + b*Σx + a*Σx² // Σxy = c*Σx + b*Σx² + a*Σx³ // Σx²y = c*Σx² + b*Σx³ + a*Σx⁴ let sumX = 0, sumX2 = 0, sumX3 = 0, sumX4 = 0; let sumY = 0, sumXY = 0, sumX2Y = 0; for (let i = 0; i < n; i++) { const xi = x[i]; const yi = y[i]; const xi2 = xi * xi; const xi3 = xi2 * xi; const xi4 = xi3 * xi; sumX += xi; sumX2 += xi2; sumX3 += xi3; sumX4 += xi4; sumY += yi; sumXY += xi * yi; sumX2Y += xi2 * yi; } // Matrix form: [A][coeffs] = [B] // where coeffs = [c, b, a] (constant, linear, quadratic) const A = [ [n, sumX, sumX2], [sumX, sumX2, sumX3], [sumX2, sumX3, sumX4] ]; const B = [sumY, sumXY, sumX2Y]; // Solve using Cramer's rule function determinant3x3(matrix) { const [[a, b, c], [d, e, f], [g, h, i]] = matrix; return a * (e * i - f * h) - b * (d * i - f * g) + c * (d * h - e * g); } const detA = determinant3x3(A); if (Math.abs(detA) < 1e-10) { throw new Error( "Cannot calculate quadratic regression: matrix is singular" ); } // Calculate coefficients using Cramer's rule const A_c = [ [B[0], A[0][1], A[0][2]], [B[1], A[1][1], A[1][2]], [B[2], A[2][1], A[2][2]] ]; const A_b = [ [A[0][0], B[0], A[0][2]], [A[1][0], B[1], A[1][2]], [A[2][0], B[2], A[2][2]] ]; const A_a = [ [A[0][0], A[0][1], B[0]], [A[1][0], A[1][1], B[1]], [A[2][0], A[2][1], B[2]] ]; const c = determinant3x3(A_c) / detA; // constant term const b = determinant3x3(A_b) / detA; // linear coefficient const a = determinant3x3(A_a) / detA; // quadratic coefficient // Calculate predicted values and residuals const predicted = x.map((xi) => a * xi * xi + b * xi + c); const residuals = y.map((yi, i) => yi - predicted[i]); // Calculate R-squared const yMean = sumY / n; const totalSumSquares = y.reduce( (sum, yi) => sum + Math.pow(yi - yMean, 2), 0 ); const residualSumSquares = residuals.reduce( (sum, residual) => sum + residual * residual, 0 ); const rSquared = 1 - residualSumSquares / totalSumSquares; // Create prediction function const predict = (x) => a * x * x + b * x + c; return { coefficients: { a, b, c }, // y = ax² + bx + c predicted, residuals, rSquared, equation: `y = ${a.toFixed(6)}x² + ${b.toFixed(6)}x + ${c.toFixed(6)}`, predict // function to predict y given x }; }37:[{"id":"6ad79aa59890771c4acfb9dc0bb5d667aac48532f5dd587baf752b26193c2e79e3f20da461c2d513431706d5d71cac8a4f4187b4d43ecbac4437acdf46cb2be3","href":"https://static.observableusercontent.com/files/6ad79aa59890771c4acfb9dc0bb5d667aac48532f5dd587baf752b26193c2e79e3f20da461c2d513431706d5d71cac8a4f4187b4d43ecbac4437acdf46cb2be3","name":"chitri@1.tsv","type":"text/tab-separated-values","lastModified":1725036498334.797,"size":625483}] 2f:{"title":"Chicago Sprint Triathlon","cells":[{"id":0,"value":"# Chicago Sprint Triathlon","pinned":false,"hidden":false,"mode":"md"},{"id":990,"value":"I ran a sprint triathlon for the first time this year, and now I'm looking at the [finishing times](https://www.athlinks.com/event/30585/results/Event/1087072/Course/2499921/Division/2396971/Results) and being a nerd about it 🤓.","pinned":false,"hidden":false,"mode":"md"},{"id":1001,"value":"### Overall Times","pinned":false,"hidden":false,"mode":"md"},{"id":1006,"value":"How quickly do people typically finish? Explore the finishing times of different groups of people:","pinned":false,"hidden":false,"mode":"md"},{"id":566,"value":"viewof params = Inputs.form({\n year: Inputs.select(filterOptions.year.values, filterOptions.year.options),\n gender: Inputs.checkbox(\n filterOptions.gender.values,\n filterOptions.gender.options\n ),\n ageRange: interval(\n filterOptions.ageRange.values,\n filterOptions.ageRange.options\n )\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":818,"value":"quantiles = {\n const fc = filtered.map((d) => d.full_course);\n const lmh = [0.25, 0.75].map((q) => d3.quantile(fc, q));\n return md`In ${\n params.year\n }, people matching these criteria ↑ typically finished the race in between **${msToTime(\n Math.floor(lmh[0] / 60) * 60,\n true\n )}** and **${msToTime(\n Math.ceil(lmh[1] / 60) * 60\n )}**. Here's a chart showing how many triathletes finished with each time:`;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":645,"value":"Plot.plot({\n height: 150,\n width: width,\n\n x: {\n nice: true,\n label: \"Total Finishing Time\",\n domain: [60 * 60, 60 * 60 * 4]\n },\n y: { label: \"Number of triathletes\" },\n marks: [\n Plot.rectY(\n filtered,\n Plot.binX(\n {\n y1: \"count\",\n thresholds: _.range(100).map((d) => d * 5 * 60)\n },\n { x: \"full_course\" }\n )\n ),\n Plot.ruleY([0]),\n Plot.axisX({\n tickFormat: msToTime,\n ticks: _.range(3, 13).map((d) => d * 20 * 60)\n }),\n Plot.ruleX(quantiles)\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":900,"value":"I'm surprised to see such a big difference in times between 2016 and 2024. Could it be the weather? (it felt *hot* in 2024!)","pinned":false,"hidden":false,"mode":"md"},{"id":918,"value":"### The Weather","pinned":false,"hidden":false,"mode":"md"},{"id":976,"value":"Here are the median finishing times for each year the race has been run since 2016 (the swim was cancelled in 2019 and 2023 due to rough water, and in 2020 due to COVID), along with the apparent temperature (\"feels like\").","pinned":false,"hidden":false,"mode":"md"},{"id":941,"value":"Plot.plot({\n height: 150,\n y: {\n label: \"Median finish time\",\n tickFormat: (d) => msToTime(d, false),\n nice: true,\n grid: true\n },\n x: { label: \"Year\", tickFormat: d3.format(\"\") },\n marks: [\n Plot.line(weatherPoints, { x: \"year\", y: \"time\" }),\n Plot.dot(weatherPoints, {\n x: \"year\",\n y: \"time\",\n fill: \"black\",\n title: (d) => msToTime(d.time, true)\n })\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":944,"value":"Plot.plot({\n height: 150,\n x: { tickFormat: d3.format(\"\"), label: \"Year\" },\n y: { label: \"Apparent Temperature (ºF)\", nice: true, grid: true, ticks: 5 },\n marks: [\n Plot.line(weatherPoints, { x: \"year\", y: \"temp\" }),\n Plot.dot(weatherPoints, { x: \"year\", y: \"temp\", fill: \"black\" })\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1476,"value":"Plot.plot({\n height: 200,\n width: 300,\n x: { label: \"Apparent Temperature (ºF)\", tickFormat: (d) => `${d}º` },\n y: { nice: true, tickFormat: (d) => msToTime(d, false) },\n marks: [\n Plot.linearRegressionY(weatherPoints, {\n x: (d) => d.temp,\n y: \"time\",\n stroke: \"steelblue\",\n strokeWidth: 3\n }),\n Plot.text(weatherPoints, {\n x: (d) => d.temp,\n y: \"time\",\n fill: \"black\",\n stroke: \"white\",\n text: (d) => `${d.year}`\n })\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1711,"value":"regressionTemp = {\n return simpleLinearRegression(\n weatherPoints.map((d) => d.temp),\n weatherPoints.map((d) => d.time)\n );\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1817,"value":"regressionTempQuad = {\n return quadraticRegression(\n weatherPoints.map((d) => d.temp - 44),\n weatherPoints.map((d) => d.time)\n );\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":1821,"value":"msToTime(regressionTempQuad.predict(100), true)","pinned":true,"hidden":false,"mode":"ojs"},{"id":1838,"value":"{\n const data = _.range(50, 100).map((x) => ({\n temp: x,\n pred: regressionTempQuad.predict(x)\n }));\n return Plot.plot({\n height: 150,\n x: { tickFormat: d3.format(\"\"), label: \"Year\" },\n y: {\n label: \"Residual of bike time (controlling for temp)\",\n tickFormat: (d) => msToTime(d, false),\n nice: true,\n grid: true,\n ticks: 5\n },\n marks: [\n Plot.line(data, { x: \"temp\", y: \"pred\" }),\n Plot.dot(data, { x: \"temp\", y: \"pred\", fill: \"black\" })\n ]\n });\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":978,"value":"Some internet searching shows that [heat indeed slows down your pace](https://www.outsideonline.com/health/running/racing/race-strategy/how-much-does-heat-slow-your-race-pace/). I didn't know how big of an influence heat is! (though maybe I'm surprised the correlation isn't a little higher?).\n\nThe years I'm curious about are 2016 (faster) and 2024 (slower).","pinned":false,"hidden":false,"mode":"md"},{"id":1459,"value":"And here are the median finishing times for the bike portion only, along with the wind speed in the direction of LSD, which is where most of the bike portion is. Sometimes the wind at your back, other times in your face (but it hurts more than helps!)","pinned":false,"hidden":false,"mode":"md"},{"id":1451,"value":"Plot.plot({\n height: 150,\n y: {\n label: \"Median finish time (bike only)\",\n tickFormat: (d) => msToTime(d, true),\n nice: true,\n grid: true\n },\n x: { label: \"Year\", tickFormat: d3.format(\"\") },\n marks: [\n Plot.line(weatherPoints, { x: \"year\", y: \"timeBike\" }),\n Plot.dot(weatherPoints, { x: \"year\", y: \"timeBike\", fill: \"black\" })\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1694,"value":"Plot.plot({\n height: 200,\n width: 300,\n x: { label: \"Apparent Temperature (ºF)\" },\n y: { nice: true, tickFormat: (d) => msToTime(d, true) },\n marks: [\n Plot.linearRegressionY(weatherPoints, {\n x: \"temp\",\n y: \"timeBike\",\n stroke: \"steelblue\",\n strokeWidth: 3\n }),\n Plot.text(weatherPoints, {\n x: \"temp\",\n y: \"timeBike\",\n fill: \"black\",\n stroke: \"white\",\n text: (d) => `${d.year}`\n })\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1618,"value":"{\n const data = weatherPoints.map((d, i) => ({\n year: d.year,\n residual: regressionTempBike.residuals[i]\n }));\n return Plot.plot({\n height: 150,\n x: { tickFormat: d3.format(\"\"), label: \"Year\" },\n y: {\n label: \"Residual of bike time (controlling for temp)\",\n tickFormat: (d) => msToTime(d, true),\n\n nice: true,\n grid: true,\n ticks: 5\n },\n marks: [\n Plot.line(data, { x: \"year\", y: \"residual\" }),\n Plot.dot(data, { x: \"year\", y: \"residual\", fill: \"black\" })\n ]\n });\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1402,"value":"Plot.plot({\n height: 150,\n x: { tickFormat: d3.format(\"\"), label: \"Year\" },\n y: {\n label: \"Wind speed along direction (km/h)\",\n nice: true,\n grid: true,\n ticks: 5\n },\n marks: [\n Plot.line(weatherPoints, { x: \"year\", y: \"wind\" }),\n Plot.dot(weatherPoints, {\n x: \"year\",\n y: \"wind\",\n fill: \"black\",\n title: (d) => `${d.wind}`\n })\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1754,"value":"weatherPoints\n .map((d) => d.time - (d.temp - 70) * regressionTemp.slope)\n .map((d) => msToTime(d, false))","pinned":true,"hidden":false,"mode":"ojs"},{"id":1756,"value":"corrected = {\n const heatPenalties = weatherPoints.map((d) => (d.temp - 70.3) * 27);\n const windPenalties = weatherPoints.map((d) => windPenalty(d.wind));\n const penalties = heatPenalties.map((d, i) => d + windPenalties[i]);\n return weatherPoints.map((d, i) => ({\n year: d.year,\n time: d.time - penalties[i]\n }));\n //.map((d) => msToTime(d, true));\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":1799,"value":"Plot.plot({\n height: 150,\n y: {\n label: \"Median finish time\",\n tickFormat: (d) => msToTime(d, false),\n nice: true,\n grid: true,\n domain: [6000, 7000]\n },\n x: { label: \"Year\", tickFormat: d3.format(\"\") },\n marks: [\n Plot.line(corrected, { x: \"year\", y: \"time\" }),\n Plot.dot(corrected, {\n x: \"year\",\n y: \"time\",\n fill: \"black\",\n title: (d) => msToTime(d.time, true)\n })\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1440,"value":"// looks like it's about 15 degrees away from North by eyeballing on a map\nviewof lsdDirection = Inputs.range([-90, 90], {\n value: -15,\n step: 1,\n label: \"Direction of LSD (º clockwise from North)\"\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1631,"value":"{\n const data = weatherPoints.map((d, i) => ({\n year: d.year,\n wind: d.wind,\n residual: regressionTempBike.residuals[i],\n timeBike: d.timeBike\n }));\n return Plot.plot({\n height: 200,\n width: 300,\n x: {\n label: \"Wind speed along direction (km/h)\"\n },\n y: { nice: true, tickFormat: (d) => msToTime(d, true) },\n marks: [\n Plot.linearRegressionY(data, {\n x: \"wind\",\n y: \"residual\",\n stroke: \"steelblue\",\n strokeWidth: 3\n }),\n Plot.text(data, {\n x: \"wind\",\n y: \"residual\",\n fill: \"black\",\n stroke: \"white\",\n text: (d) => `${d.year}`\n })\n ]\n });\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1466,"value":"regressionTempBike = {\n return simpleLinearRegression(\n weatherPoints.map((d) => d.temp),\n weatherPoints.map((d) => d.timeBike)\n );\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1647,"value":"regressionWindResidual = {\n const data = weatherPoints.map((d, i) => ({\n year: d.year,\n wind: d.wind,\n residual: regressionTempBike.residuals[i],\n timeBike: d.timeBike\n }));\n return simpleLinearRegression(\n data.map((d) => d.wind),\n data.map((d) => d.residual)\n );\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":999,"value":"### Swim, Bike, Run","pinned":false,"hidden":false,"mode":"md"},{"id":1020,"value":"Now, how quickly do people typically finish each individual part of the race? Explore times of different groups of people:\n\n","pinned":false,"hidden":false,"mode":"md"},{"id":747,"value":"Inputs.bind(\n Inputs.form({\n year: Inputs.select(filterOptions.year.values, filterOptions.year.options),\n gender: Inputs.checkbox(\n filterOptions.gender.values,\n filterOptions.gender.options\n ),\n ageRange: interval(\n filterOptions.ageRange.values,\n filterOptions.ageRange.options\n )\n }),\n viewof params\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":183,"value":"$31","pinned":false,"hidden":false,"mode":"ojs"},{"id":1111,"value":"Use this to highlight a particular person (by bib number), and compare to a percentile within this group.","pinned":false,"hidden":false,"mode":"md"},{"id":1093,"value":"viewof focus = Inputs.form({\n bibNumber: Inputs.text({ label: \"Bib Number\", value: \"2385\" }),\n q: Inputs.range([0, 100], {\n step: 1,\n value: 50,\n label: \"Percentile\"\n })\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1141,"value":"$32","pinned":false,"hidden":false,"mode":"ojs"},{"id":1041,"value":"### Pacing","pinned":false,"hidden":false,"mode":"md"},{"id":1320,"value":"Given that I was really busted after the bike and did relatively worse on the run, it seems like I should have kept more energy for the run. Do people who are better tend to pace themselves better? Here's a chart of the ratio of the time that the second half of the bike course took compared to the first: higher values mean that the second part was relatively slower than the first.","pinned":false,"hidden":false,"mode":"md"},{"id":1054,"value":"Plot.plot({\n grid: true,\n y: { label: \"Extent slower on second part of bike (ratio of times)\" },\n x: { label: \"Time on full course\", tickFormat: msToTime },\n marks: [\n Plot.dot(withAll, {\n x: \"full_course\",\n y: \"bike_ratio\"\n }),\n Plot.linearRegressionY(withAll, {\n x: \"full_course\",\n y: \"bike_ratio\",\n stroke: \"red\"\n }),\n Plot.dot(\n withAll.filter((d) => d.bib === Number(focus.bibNumber)),\n {\n x: \"full_course\",\n y: \"bike_ratio\",\n stroke: \"red\",\n r: 6\n }\n )\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1323,"value":"It does seem like people who do better save more energy for the second part of the bike.","pinned":false,"hidden":false,"mode":"md"},{"id":1327,"value":"---\n
\n
\n
\n
\n
","pinned":false,"hidden":false,"mode":"md"},{"id":1044,"value":"withAll = {\n const withAll = chitri.filter(\n (d) =>\n d.year === 2024 &&\n d.full_course &&\n d.swim &&\n d.t1 &&\n d.bike &&\n d.t2 &&\n d.run &&\n d.start_to_bike_split &&\n d.start_to_run_split\n );\n withAll.forEach((d, i) => {\n d.rank = i + 1;\n d.h1_bike_time = d.start_to_bike_split - (d.t1 + d.swim);\n d.h2_bike_time = d.bike - d.h1_bike_time;\n d.h1_run_time = d.start_to_run_split - (d.t2 + d.bike + d.t1 + d.swim);\n d.h2_run_time = d.run - d.h1_run_time;\n d.bike_ratio = d.h2_bike_time / d.h1_bike_time;\n d.run_ratio = d.h2_run_time / d.h1_run_time;\n });\n return withAll;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":333,"value":"filtered = chitri.filter(\n (d) =>\n params.gender.includes(d.gender) &&\n d.year === params.year &&\n d.age >= params.ageRange[0] &&\n d.age <= params.ageRange[1]\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":1406,"value":"filteredAllYears = chitri.filter(\n (d) =>\n params.gender.includes(d.gender) &&\n d.age >= params.ageRange[0] &&\n d.age <= params.ageRange[1]\n)","pinned":false,"hidden":false,"mode":"ojs"},{"id":168,"value":"times = {\n const times = [];\n [\"swim\", \"t1\", \"bike\", \"t2\", \"run\"].forEach((splitName) => {\n filtered.forEach((d) => {\n times.push({\n splitName,\n time: d[splitName]\n });\n });\n });\n return times;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1119,"value":"person = chitri.filter(\n (d) => d.bib === Number(focus.bibNumber) && d.year === params.year\n)[0]","pinned":false,"hidden":false,"mode":"ojs"},{"id":239,"value":"mySplits = {\n if (!person) {\n return [];\n }\n return [\"swim\", \"t1\", \"bike\", \"t2\", \"run\"]\n .map((splitName) => ({\n time: person[splitName] ? person[splitName] : undefined,\n splitType: splitName\n }))\n .filter((d) => d);\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":104,"value":"function getSplits(data, splitName) {\n return data.map((d) => d[splitName]).filter((d) => d);\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":365,"value":"goals = {\n return [\"swim\", \"bike\", \"run\", \"t1\", \"t2\"].map((splitType) => ({\n time: d3.quantile(getSplits(filtered, splitType), focus.q / 100),\n splitType\n }));\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":68,"value":"bins = _.range(1000).map((d) => d * 1 * 60)","pinned":false,"hidden":false,"mode":"ojs"},{"id":79,"value":"ticks = {\n if (width > 650) {\n return _.range(1000).map((d) => d * 5 * 60);\n } else {\n return _.range(1000).map((d) => d * 10 * 60);\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":1463,"value":"$33","pinned":false,"hidden":false,"mode":"ojs"},{"id":1729,"value":"function windPenalty(windSpeed) {\n const p = 100;\n const raceDistance = 24.14;\n const weight = 80;\n const out = raceTimeWithHeadwind(raceDistance / 2, p, windSpeed, weight);\n const back = raceTimeWithHeadwind(raceDistance / 2, p, -windSpeed, weight);\n return out.timeDifferenceSeconds + back.timeDifferenceSeconds;\n}","pinned":true,"hidden":false,"mode":"ojs"},{"id":1727,"value":"$34","pinned":true,"hidden":false,"mode":"ojs"},{"id":1355,"value":"function windAlongLSD(speed, direction, lsdDirection = 25) {\n const windDir = direction - lsdDirection;\n const windDirRad = windDir * (Math.PI / 180);\n return Math.abs(Math.cos(windDirRad)) * speed;\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":920,"value":"$35","pinned":false,"hidden":false,"mode":"ojs"},{"id":36,"value":"function msToTime(duration, includeSeconds = true) {\n let prefix = \"\";\n if (duration < 0) {\n duration = -duration;\n prefix = \"-\";\n }\n let seconds = Math.floor(duration % 60),\n minutes = Math.floor((duration / 60) % 60),\n hours = Math.floor((duration / (60 * 60)) % 24);\n\n minutes = minutes < 10 && hours ? \"0\" + minutes : minutes;\n seconds = seconds < 10 ? \"0\" + seconds : seconds;\n\n if (hours) {\n if (includeSeconds) {\n return prefix + hours + \":\" + minutes + \":\" + seconds;\n } else {\n return prefix + hours + \":\" + minutes;\n }\n } else {\n if (includeSeconds) {\n return prefix + minutes + \":\" + seconds;\n } else {\n return prefix + minutes;\n }\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":346,"value":"function percentile(arr, val, format = false) {\n let count = 0;\n arr.forEach((v) => {\n if (v < val) {\n count++;\n } else if (v == val) {\n count += 0.5;\n }\n });\n let result = (100 * count) / arr.length;\n if (format) {\n return d3.format(\".0%\")(result / 100);\n } else {\n return result;\n }\n}","pinned":false,"hidden":false,"mode":"ojs"},{"id":435,"value":"Plot.plot({\n y: {\n grid: true,\n domain: [90 * 60, 180 * 60]\n },\n marks: [\n Plot.boxY(chitri, {\n x: (d) => Math.floor(d.age / 5) * 5,\n y: (d) => d.full_course\n }),\n Plot.axisY({ tickFormat: (d) => msToTime(d, false), ticks })\n ]\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":290,"value":"import { interval } from \"@mootari/range-slider\"","pinned":true,"hidden":false,"mode":"ojs"},{"id":554,"value":"chitri = await FileAttachment(\"chitri@1.tsv\").tsv({typed: \"auto\"})","pinned":true,"hidden":false,"mode":"ojs"},{"id":778,"value":"filterOptions = ({\n year: {\n values: [2016, 2017, 2018, 2021, 2022, 2024],\n options: {\n label: \"Race Year\",\n format: d3.format(\"\"),\n value: 2024\n }\n },\n gender: {\n values: [\"M\", \"F\", \"NB\", \"NOT SPECIFIED\"],\n options: {\n label: \"Gender\",\n value: [\"M\", \"F\", \"NB\", \"NOT SPECIFIED\"]\n }\n },\n ageRange: {\n values: [10, 90],\n options: {\n step: 1,\n value: [10, 90],\n label: \"Age Range\"\n }\n }\n})","pinned":false,"hidden":false,"mode":"ojs"},{"id":1815,"value":"$36","pinned":true,"hidden":false,"mode":"ojs"}],"files":"$W37","stdlib":"1","titling":"auto"}