Age and Sex Analysis Of Microsoft USA MVPs

All text and code copyright (c) 2016 by Jamie Dixon. Used with permission.

Original post dated 2016-12-25 available at

By Jamie Dixon

A couple of weeks ago, this came across my Twitter

I participated in this hackathon (well, helped run the F# one). My response was:

I was surprised that I got into this exchange with a Microsoft PM:

That last comment by me was inspired by Mark Twain: “never wrestle with a pig. You just get dirty and the pig likes it.” But it did get me to thinking about the composition of the US MVPs. I did an analysis a couple of years ago of the photos of the Microsoft MVPs (found here and here) so it made sense to follow up on that code and see if I was wrong about my “middle age white guy” hypothesis. I could get the photos from the MVP site and pass them into the Microsoft Cognitive Services API for facial analysis for age/sex data. Using F# made the analysis a snap.

A nice thing about the Microsoft MVP website is that it is public and has photos of the MVPs. Here is one of the pages:

and when you look at the source of the page, each of those photos has a distinct uri:

I opened up Visual Studio and created a new F# project. I went into the script file and brought in the libraries to do some http requests. I then created a couple of functions to pull down the HTML of each of the 19 pages and put it into 1 big string:

let getPageContents(pageNumber:int) =
     let uri = new Uri("" + pageNumber.ToString())
     let request = WebRequest.Create(uri)
     request.Method <- "GET"
     let response = request.GetResponse()
     use stream = response.GetResponseStream()
     use reader = new StreamReader(stream)

let contents = 
    |> i -> getPageContents i)
    |> Seq.reduce(fun x y -> x + y)

(OT: Since I did a map..reduce on lines 12 and 13, does that mean I am working with “Big Data”?)

I then created a quick parser to find only the uris of the photos in all of the HTML.

let getUrisFromPageContents(pageContents:string) =
     let pattern = "/PublicProfile/Photo/\d+"
     let matchCollection = Regex.Matches(pageContents, pattern)
         |> Seq.cast 
         |> (m:Match) -> m.Value)
         |> v -> "" + v + "?language=en-us")
         |> Seq.toArray

let uris = getUrisFromPageContents contents

Sure enough, I got 684 uris for MVP photos. I then wrote another Web Request to pull down each of the photos and save them to disk:

let saveImage uri =
    use client = new WebClient()
    let id = Guid.NewGuid()
    let path = @"F:\Git\ChickenSoftware.ParseMvpPages.Solution\ChickenSoftware.ParseMvpPages\photos\" + id.ToString() + ".jpg"

|> Seq.iter saveImage

And I now have all 684 photos on disk.

I did not bring down the names of the MVPs – instead using a GUID to randomize the photos, but a name analysis would also be interesting. With the photos now local, I could then upload them to Microsoft Cognitive Services API to do facial analysis. You can read about the details of the API here. I created a third web request to pass the photo up and get the results from the API:

let getOxfordResults path =
    let queryString = HttpUtility.ParseQueryString(String.Empty)
    let uri = "" + queryString.ToString()
    let bytes = File.ReadAllBytes(path)
    let client = new HttpClient()
    let response = new HttpResponseMessage()
    let content = new ByteArrayContent(bytes)
    content.Headers.ContentType <- MediaTypeHeaderValue("application/octet-stream")
    let result = client.PostAsync(uri,content).Result
    match result.StatusCode with
    | HttpStatusCode.OK -> Some (result.Content.ReadAsStringAsync().Result)
    | _ -> None

Notice that I put a 5 second sleep into the call. This is because Microsoft throttles the requests to 20 per minute. Also, since some of the photos do not have a face, I used the F# option type. The results come back from the Microsoft Cognitive Services API as Json. To parse the results, I used the FSharp Json Type Provider:

type FaceInfo = JsonProvider<Sample="[{\"faceId\":\"83045097-daa1-4f1c-8669-ed012e9b5975\",\"faceRectangle\":{\"top\":187,\"left\":209,\"width\":214,\"height\":214},\"faceAttributes\":{\"gender\":\"male\",\"age\":42.8}}]">

let parseOxfordResuls results =
    match results with
    | Some r -> 
        let face = FaceInfo.Parse(r)
        match Seq.length face with
        | 0 -> None
        | _ -> let header = face |> Seq.head
    | None -> None

So now I can get estimated age and gender from Microsoft Cognitive Services API. I was disappointed that the API does not estimate race. I assume they have the technology but from a social-acceptance point of view, they don’t make it publically available. In any event, a look though their photos show that a majority are white people. In any event, I went ahead and ran this and went out to work on my sons stock car while the requests were spinning.

let results =
    let path = @"F:\Git\ChickenSoftware.ParseMvpPages.Solution\ChickenSoftware.ParseMvpPages\photos"
    |> f -> getOxfordResults f)
    |> r -> parseOxfordResuls r)

When I came back, I had a nice sequence of a tuple that contained ages and genders.

To analyze the data, I pulled in Math.NET. First, I took a look age:

Seq.length results  //684

let ages =
    |> Seq.filter(fun r -> r.IsSome)
    |> o -> fst o.Value)
    |> a -> float a)

let stats = new DescriptiveStatistics(ages)
let count = stats.Count
let largest = stats.Maximum
let smallest = stats.Minimum
let mean = stats.Mean
let median = Statistics.Median(ages)
let variance = stats.Variance
let standardDeviation = stats.StandardDeviation
let kurtosis = stats.Kurtosis
let skewness = stats.Skewness
let lowerQuartile = Statistics.LowerQuartile(ages)
let uppserQuartile = Statistics.UpperQuartile(ages)

Here are the results.

I got 620 valid photos of the 684 MVPs – so a 91% hit rate and I have enough observations to make the analysis statistically valid. It looks like Cognitive Services made at least 1 mistake with an age of 4.9 years –> perhaps someone was using a meme for their photo? In any event, the mean is estimated at 41.95 and the median is 40.95, so a slight skew left. (Note I mislabeled it on the screen shot above)

I then wanted to see the distribution of the ages so I brought in FSharp charting and ran a basic histogram:

open FSharp.Charting

let chart = Chart.Histogram(ages,Intervals=10.0)

So the ages look very Gaussian.

I then decided to look at gender:

let gender =
    |> Seq.filter(fun r -> r.IsSome)
    |> o -> snd o.Value)

    |> Seq.countBy(fun v -> v)
    |> (g,c) -> g, c, float c/float count)

With the results being:

So there are 12% females and 88% males. With an average age 42 years old and 88% male, “middle age white guy” seems like an appropriate label and I stand by my original tweet – we certainly have work to do in 2017.

You can find the gist here.

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