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In this post we're going to continue the trend of doing stuff more on the NFL analytics side than fantasy football side of stuff.

I found this github repo that has next gen stats and passing locations, so what we're going to do in this post is visualize where QBs threw their completions, incompletions, interceptions and TDs in the 2019 season. Unfortanetly, data for 2020 is not yet available :( You can scroll down and check out our final visualizations for this post.

To start, let's import our standard libraries in Google Colab or your locally hosted jupyter notebook.

Next, let's laod the data source from the github repo and check out the results.

Let's inspect our DataFrame.

Here, we can see that we have data on each QB's pass locations, via the x_coord and y_coord columns. The x_coord tells us how east-west each throw went from the QBs frame of reference, and the y_coord tells us how north-south each throw went. Do note that these aren't yardlines, it's data telling us how far east-west and north-south a throw went relative to where the QB was on the field.

game_id team week.x name pass_type x_coord y_coord game_id.y season game_type week.y gameday weekday gametime away_team home_team away_score home_score home_result stadium location roof surface
1 2017091004 ARI 1 Carson Palmer COMPLETE 13.5 -2.8 2017_01_ARI_DET 2017 REG 1 2017-09-10 Sunday 13:00 ARI DET 23 35 12 Ford Field Home dome fieldturf
2 2017091004 ARI 1 Carson Palmer COMPLETE -13.7 -4.4 2017_01_ARI_DET 2017 REG 1 2017-09-10 Sunday 13:00 ARI DET 23 35 12 Ford Field Home dome fieldturf
3 2017091004 ARI 1 Carson Palmer COMPLETE 2.2 7.1 2017_01_ARI_DET 2017 REG 1 2017-09-10 Sunday 13:00 ARI DET 23 35 12 Ford Field Home dome fieldturf
4 2017091004 ARI 1 Carson Palmer COMPLETE 23.9 10.0 2017_01_ARI_DET 2017 REG 1 2017-09-10 Sunday 13:00 ARI DET 23 35 12 Ford Field Home dome fieldturf
5 2017091004 ARI 1 Carson Palmer COMPLETE -23.5 14.6 2017_01_ARI_DET 2017 REG 1 2017-09-10 Sunday 13:00 ARI DET 23 35 12 Ford Field Home dome fieldturf

And let's see how many seasons we have to work with using the Series method, unique.

Before we filter based off season, let's also check out the data type of this column (let's make sure it's not a string).

Awesome, so it's an integer. With that information, let's filter our DataFrame to only include 2019 data.

We're going to be writing a function to automate the process of visualization each player's pass_type. To start, though, let's grab Patrick Mahomes and visualize his complete passes.

game_id team week.x name pass_type x_coord y_coord game_id.y season game_type week.y gameday weekday gametime away_team home_team away_score home_score home_result stadium location roof surface
25293 2019090802 KC 1 Patrick Mahomes COMPLETE -15.1 5.9 2019_01_KC_JAX 2019 REG 1 2019-09-08 Sunday 13:00 KC JAX 40 26 -14 TIAA Bank Stadium Home outdoors grass
25294 2019090802 KC 1 Patrick Mahomes COMPLETE 8.8 7.9 2019_01_KC_JAX 2019 REG 1 2019-09-08 Sunday 13:00 KC JAX 40 26 -14 TIAA Bank Stadium Home outdoors grass
25295 2019090802 KC 1 Patrick Mahomes COMPLETE 21.1 5.7 2019_01_KC_JAX 2019 REG 1 2019-09-08 Sunday 13:00 KC JAX 40 26 -14 TIAA Bank Stadium Home outdoors grass
25296 2019090802 KC 1 Patrick Mahomes COMPLETE -5.6 0.6 2019_01_KC_JAX 2019 REG 1 2019-09-08 Sunday 13:00 KC JAX 40 26 -14 TIAA Bank Stadium Home outdoors grass
25297 2019090802 KC 1 Patrick Mahomes COMPLETE 13.8 22.3 2019_01_KC_JAX 2019 REG 1 2019-09-08 Sunday 13:00 KC JAX 40 26 -14 TIAA Bank Stadium Home outdoors grass

So the pass_type column here is going to be crucial. Let's inspect the unique values of this Series to see what we have to work with.

With that, let's just plot completions to start.

Pretty cool. With that, let's write a function to visualize completions along with incompletions, touchdowns, and interceptions and style each pass type accordingly.