Forecasting Football Fever

Forecasting Football Fever

Forecasting Football Fever: Exploring Seasonal Datasets in Deephaven.The hype seems to reach an unbearable level by the time the playoffs roll around.

From September to January every year, football takes over America. Games dominate TV Sunday and Monday nights, and my brother tears his hair out each week over his consistently underperforming fantasy teams. The hype seems to reach an unbearable level by the time the playoffs roll around.

But is there a way to measure and forecast that hype? I decided to use one of my favorite NFL players, Peyton Manning, in order to explore seasonality in Deephaven’s Jupyter Notebooks. Using a dataset of Manning’s Wikipedia search frequencies taken over an 8 year period from 2008 to 2016, my goal was to break down how football hype evolved throughout the season.

To do this, I decided to take two approaches to analyzing seasonality. The first was the traditional ARIMA model, and the second was the newer Fbprophet library. I would use both these methods to fit, predict, and validate models to see which was better at understanding NFL hype.

OUR DATA

We can plot our data in Deephaven with the following code:

manning_table = createTableFromData({'Date':df.ds,'Search Frequency':df.y},\  
        columns=None, convertUnknownToString=False)\
       .update('Date=convertDateTime(Date+`T16:30 NY`)')

    manning_plot = Plot.plot("Search Frequency", manning_table, 'Date','Search_Frequency')\
       .chartTitle("Peyton Manning Search Frequency")\
       .xLabel('Date')\
       .yLabel('Search Frequency')\
       .figure

    display(ChartWidget(manning_plot))
view raw
manning1.py hosted with ❤ by GitHub

At a top-level glance, our data is log-transformed Wikipedia page views for Peyton Manning taken each day for about 8 years. The data appears to exhibit some strong seasonal trends that we can look into.

data-science seasonality football jupyter-notebook data analysis

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