jldbc
Pitching / Batting Stats Mix-up
I've been having issues with batting_stats and pitching_stats returning unexpected data based on date parameters. Originally, pybaseball.pitching_stats(2021) was returning what would be expected from pybaseball.batting_stats(2021) (batting stats). This could originally be resolved by instead utilizing pybaseball.pitching_stats(2020, 2021) and filtering out 2020.
However, now pitching_stats is behaving normally, but batting_stats is returning pitching stats for any years beyond 2018. Any ideas why this is happening?
Bump... I am having a similar issue...
Batting stats and pitching stats are both returning the same frame
df = batting_stats(2017, ind=1) # get batting stats for season pitch = pitching_stats(2017, ind=1) pitch.equals(df) # outputs true
Which version of pybaseball are you using? I wasn't able to reproduce your example with 2.2.1 which is what pip grabs for me.
In [1]: from importlib.metadata import version
In [2]: import pybaseball
In [3]: version('pybaseball')
Out[3]: '2.2.1'
In [4]: batting = pybaseball.batting_stats(2017, ind=1)
In [5]: pitching = pybaseball.pitching_stats(2017, ind=1)
In [6]: batting.equals(pitching)
Out[6]: False
In [7]: batting.head()
Out[7]:
IDfg Season Name Team Age G AB PA H 1B 2B 3B HR R RBI BB ... Soft%+ Med%+ Hard%+ EV LA Barrels Barrel% maxEV HardHit HardHit% Events CStr% CSW% xBA xSLG xwOBA
1 15640 2017 Aaron Judge NYY 25 155 542 678 154 75 24 3 52 128 114 127 ... 61 88 140 94.9 15.8 84 0.249 121.1 186 0.550 338 0.157 0.290 0.281 0.651 0.441
6 5417 2017 Jose Altuve HOU 27 153 590 662 204 137 39 4 24 112 81 58 ... 103 107 87 86.1 9.7 29 0.057 109.1 143 0.280 511 0.161 0.233 0.271 0.445 0.340
9 15429 2017 Kris Bryant CHC 25 151 549 665 162 91 38 4 29 111 73 95 ... 80 106 102 87.0 17.0 38 0.089 113.0 158 0.370 427 0.138 0.239 0.248 0.478 0.364
4 4949 2017 Giancarlo Stanton MIA 27 159 597 692 168 77 32 0 59 123 132 85 ... 112 82 121 91.9 11.1 74 0.169 122.2 199 0.455 437 0.155 0.282 0.272 0.592 0.396
11 13510 2017 Jose Ramirez CLE 24 152 585 645 186 95 56 6 29 107 83 52 ... 90 100 105 88.2 15.0 24 0.046 108.3 180 0.345 521 0.177 0.231 0.294 0.478 0.353
[5 rows x 319 columns]
In [8]: pitching.head()
Out[8]:
IDfg Season Name Team Age W L WAR ERA G GS CG ShO SV BS IP TBF ... Pull%+ Cent%+ Oppo%+ Soft%+ Med%+ Hard%+ EV LA Barrels Barrel% maxEV HardHit HardHit% Events CStr% CSW% xERA
5 10603 2017 Chris Sale BOS 28 17 8 7.6 2.90 32 32 1 0 0 0 214.1 851 ... 95 102 105 100 105 92 86.8 15.0 27 0.055 112.7 149 0.303 492 0.183 0.332 2.49
0 2429 2017 Corey Kluber CLE 31 18 4 7.2 2.25 29 29 5 3 0 0 203.2 777 ... 96 104 100 132 95 90 85.3 10.5 25 0.053 111.6 137 0.291 471 0.190 0.346 2.47
2 3137 2017 Max Scherzer WSN 32 16 6 6.4 2.51 31 31 2 0 0 0 200.2 780 ... 108 93 97 100 110 84 86.5 19.1 23 0.052 112.0 130 0.291 446 0.159 0.314 2.43
3 10131 2017 Stephen Strasburg WSN 28 15 4 5.9 2.52 28 28 1 1 0 0 175.1 701 ... 89 108 105 110 104 87 87.3 10.8 24 0.054 113.5 138 0.312 443 0.175 0.305 2.66
7 15890 2017 Luis Severino NYY 23 14 6 5.6 2.98 31 31 0 0 0 0 193.1 783 ... 101 105 91 105 106 89 87.2 8.0 30 0.060 111.4 165 0.333 496 0.174 0.304 2.96
[5 rows x 334 columns]
Hi Tyler,
I have version 2.2.1 as well… perhaps my local environment is messed up (although I can’t see how). Even after reinstalling, I get the following:
input: from importlib.metadata import version from pybaseball import batting_stats, pitching_stats df = batting_stats(2017, ind=1) pitch = pitching_stats(2017, ind=1)
print(version('pybaseball'))
print(pitch.equals(df))
output: 2.2.1 True
Any more information I can provide? From: Tyler Burch @.> Sent: Monday, May 9, 2022 12:14 PM To: jldbc/pybaseball @.> Cc: Doddy-codes @.>; Comment @.> Subject: Re: [jldbc/pybaseball] Pitching / Batting Stats Mix-up (#221)
Which version of pybaseball are you using? I wasn't able to reproduce your example with 2.2.1 which is what pip grabs for me.
In [1]: from importlib.metadata import version
In [2]: import pybaseball
In [3]: version('pybaseball')
Out[3]: '2.2.1'
In [4]: batting = pybaseball.batting_stats(2017, ind=1)
In [5]: pitching = pybaseball.pitching_stats(2017, ind=1)
In [6]: batting.equals(pitching)
Out[6]: False
In [7]: batting.head()
Out[7]:
IDfg Season Name Team Age G AB PA H 1B 2B 3B HR R RBI BB ... Soft%+ Med%+ Hard%+ EV LA Barrels Barrel% maxEV HardHit HardHit% Events CStr% CSW% xBA xSLG xwOBA
1 15640 2017 Aaron Judge NYY 25 155 542 678 154 75 24 3 52 128 114 127 ... 61 88 140 94.9 15.8 84 0.249 121.1 186 0.550 338 0.157 0.290 0.281 0.651 0.441
6 5417 2017 Jose Altuve HOU 27 153 590 662 204 137 39 4 24 112 81 58 ... 103 107 87 86.1 9.7 29 0.057 109.1 143 0.280 511 0.161 0.233 0.271 0.445 0.340
9 15429 2017 Kris Bryant CHC 25 151 549 665 162 91 38 4 29 111 73 95 ... 80 106 102 87.0 17.0 38 0.089 113.0 158 0.370 427 0.138 0.239 0.248 0.478 0.364
4 4949 2017 Giancarlo Stanton MIA 27 159 597 692 168 77 32 0 59 123 132 85 ... 112 82 121 91.9 11.1 74 0.169 122.2 199 0.455 437 0.155 0.282 0.272 0.592 0.396
11 13510 2017 Jose Ramirez CLE 24 152 585 645 186 95 56 6 29 107 83 52 ... 90 100 105 88.2 15.0 24 0.046 108.3 180 0.345 521 0.177 0.231 0.294 0.478 0.353
[5 rows x 319 columns]
In [8]: pitching.head()
Out[8]:
IDfg Season Name Team Age W L WAR ERA G GS CG ShO SV BS IP TBF ... Pull%+ Cent%+ Oppo%+ Soft%+ Med%+ Hard%+ EV LA Barrels Barrel% maxEV HardHit HardHit% Events CStr% CSW% xERA
5 10603 2017 Chris Sale BOS 28 17 8 7.6 2.90 32 32 1 0 0 0 214.1 851 ... 95 102 105 100 105 92 86.8 15.0 27 0.055 112.7 149 0.303 492 0.183 0.332 2.49
0 2429 2017 Corey Kluber CLE 31 18 4 7.2 2.25 29 29 5 3 0 0 203.2 777 ... 96 104 100 132 95 90 85.3 10.5 25 0.053 111.6 137 0.291 471 0.190 0.346 2.47
2 3137 2017 Max Scherzer WSN 32 16 6 6.4 2.51 31 31 2 0 0 0 200.2 780 ... 108 93 97 100 110 84 86.5 19.1 23 0.052 112.0 130 0.291 446 0.159 0.314 2.43
3 10131 2017 Stephen Strasburg WSN 28 15 4 5.9 2.52 28 28 1 1 0 0 175.1 701 ... 89 108 105 110 104 87 87.3 10.8 24 0.054 113.5 138 0.312 443 0.175 0.305 2.66
7 15890 2017 Luis Severino NYY 23 14 6 5.6 2.98 31 31 0 0 0 0 193.1 783 ... 101 105 91 105 106 89 87.2 8.0 30 0.060 111.4 165 0.333 496 0.174 0.304 2.96
[5 rows x 334 columns]
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