Player Splits

In order to retrieve a player’s splits in a given season, you will first need to know the name of the player you are interested in. The spelling of the player’s name must exactly match its spelling on Pro Football Reference. You will also need to specify the season you are interested in, as well as if you want the stats as averages or sums.

Home-Road Splits

You can retrieve a player’s stats in home vs. road games either as averages or sums.

Averages

The following code will output Saquon Barkley’s stats in home vs. road games in the 2018 season as averages.

from pro_football_reference_web_scraper import player_splits as p

print(p.home_road(player = 'Saquon Barkley', position = 'RB', season = 2018, avg = True))

Output:

| game_location   |   games |   team_pts |   opp_pts |   rush_att |   rush_yds |   rush_td |   tgt |   rec_yds |   rec_td |
|:----------------|--------:|-----------:|----------:|-----------:|-----------:|----------:|------:|----------:|---------:|
| home            |       8 |     20.25  |     27.75 |     17     |     90.625 |     0.75  | 7.625 |    42.375 |    0.125 |
| away            |       8 |     25.875 |     23.75 |     15.625 |     72.75  |     0.625 | 7.5   |    47.75  |    0.375 |

Sums

The following code will output Saquon Barkley’s stats in home vs. road games in the 2018 season as sums.

from pro_football_reference_web_scraper import player_splits as p

print(p.home_road(player = 'Saquon Barkley', position = 'RB', season = 2018, avg = False))

Output:

| game_location   |   games |   team_pts |   opp_pts |   rush_att |   rush_yds |   rush_td |   tgt |   rec_yds |   rec_td |
|:----------------|--------:|-----------:|----------:|-----------:|-----------:|----------:|------:|----------:|---------:|
| home            |       8 |        162 |       222 |        136 |        725 |         6 |    61 |       339 |        1 |
| away            |       8 |        207 |       190 |        125 |        582 |         5 |    60 |       382 |        3 |

Win-Loss Splits

You can also retrieve a player’s stats in wins vs. losses either as averages or sums.

Averages

The following code will output Saquon Barkley’s stats in wins vs. losses in the 2018 season as averages.

from pro_football_reference_web_scraper import player_splits as p

print(p.win_loss(player = 'Saquon Barkley', position = 'RB', season = 2018, avg = True))

Output:

| result   |   games |   team_pts |   opp_pts |   rush_att |   rush_yds |   rush_td |   tgt |   rec_yds |   rec_td |
|:---------|--------:|-----------:|----------:|-----------:|-----------:|----------:|------:|----------:|---------:|
| W        |       5 |    32.4    |   24.6    |    20.4    |   117.2    |  0.8      |   4.4 |   25.2    | 0.2      |
| L        |      11 |    18.8182 |   26.2727 |    14.4545 |    65.5455 |  0.636364 |   9   |   54.0909 | 0.272727 |

Sums

The following code will output Saquon Barkley’s stats in wins vs. losses in the 2018 season as sums.

from pro_football_reference_web_scraper import player_splits as p

print(p.win_loss(player = 'Saquon Barkley', position = 'RB', season = 2018, avg = False))

Output:

| result   |   games |   team_pts |   opp_pts |   rush_att |   rush_yds |   rush_td |   tgt |   rec_yds |   rec_td |
|:---------|--------:|-----------:|----------:|-----------:|-----------:|----------:|------:|----------:|---------:|
| W        |       5 |        162 |       123 |        102 |        586 |         4 |    22 |       126 |        1 |
| L        |      11 |        207 |       289 |        159 |        721 |         7 |    99 |       595 |        3 |