[Shark69]

Very interesting functionality. I am doing a bit of research on the data that comes out and the most useful thing I have achieved is the following that I explain with an example:

{
    "timer.model": {
        "version": 3,
        "totalTime": 35191528,
        "totalWords": 131355,
        "totalPercent": 1.0758328462887201,
        "averageCalculator": {
            "samples1": [],
            "samples2": [],
            "normalDistributions": [
                {
                    "count": 466,
                    "sum": 102178.8613209594,
                    "sumOfSquares": 23794574.149871096
                },
                {
                    "count": 17,
                    "sum": 9752.422438256466,
                    "sumOfSquares": 5781216.939200989
                }
            ],
            "outliers": [
                [
                    52.47694415740492
                ],
                [
                    833.0194263574235
                ],
                [
                    389.67816758480717,
                    395.84946569614374,
                    416.29497472494796
                ]
            ]
        }
    },
    "fpr": {
        "position": "997716",
        "time": null,
        "timeZoneOffset": null,
        "country": "",
        "device": ""
    },
    "book.info.store": {
        "numberOfWords": 167321,
        "percentOfBook": 1.3649912331969611
    },
    "page.history.store": [
        {
            "position": "77096",
            "time": "2021-04-18T23:56:15.675000"
        },
        {
            "position": "169022",
            "time": "2021-04-21T17:47:22.352000"
        },
        {
            "position": "836132",
            "time": "2021-05-04T00:19:54.125000"
        }
    ],
    "lpr": {
        "position": "997716",
        "time": "2021-05-07T21:49:36.114000"
    },
    "whisperstore.migration.status": [
        false,
        false
    ]
}

totalTime is the total read time in milliseconds.
Once approximately fifteen samples have been made (each sample is taken when the page is turned), statistics come out. Under normalDistributions comes out count which refers to the samples taken. sum refers to the sum of words per minute. With which if we divide sum / count we get the average reading in words per minute. In the example, 102 178/466 = 219
outliers are the samples that deviate from the mean. This usually happens at the beginning of the book when we skip a lot of useless words before the actual beginning of the book.

Of the various normalDistributions, the first is composed of normal page reads in which after a few tests, values of pages are entered which have been read at a speed of between 0 and 800 words per minute approximately. The second consists of readings with more than 800 words per minute. This happens when we go back and forth in the book just to look at something that we may have forgotten. This is not a normal reading and gives some anomalous data of words per minute with which they go to the other collection so that they do not distort the real data.

If the book is left open without locking, it counts the reading speed until it locks. Therefore, to make the calculation better, when we are not reading, it is best to lock the device. If we are sleepy and fall asleep, too bad ... that counts as slow reading (and it's true, ha ha ha)