A Collection of Data Science Projects by Patrick DeAngelis

Neural style transfer, Impressionist sand lot field

Men’s Baseball League Heroics!

This first project is taking a swing at bringing the wonders of ‘advanced analytics’ to my summer baseball team. I used fangraphs formulas and modified them where I had to. The biggest problem I ran into was data collection, its hard to get guys to keep a pitching chart for a Sunday morning men’s league. Finally, to do the calculations I used python and a jupyter notebook.

In [ ]:

import pandas as pd
import numpy as np

In [ ]: Gathering all of the sperate league stats into individual data frames

df1 = pd.read_html('https://www.leaguelineup.com/teams_baseball.asp?url=lism21&divisionid=973090&teamid=6819796')[0]
df1['Team'] = str('Hurricanes')
df1['division'] = str('west')

df2 = pd.read_html('https://www.leaguelineup.com/teams_baseball.asp?url=lism21&divisionid=973090&teamid=6834815')[0]
df2['Team'] = str('Four Seam Falcons')
df2['division'] = str('west')

df3 = pd.read_html('https://www.leaguelineup.com/teams_baseball.asp?url=lism21&divisionid=973090&teamid=6839726')[0]
df3['Team'] = str('Kings')
df3['division'] = str('west')

df4 = pd.read_html('https://www.leaguelineup.com/teams_baseball.asp?url=lism21&divisionid=973090&teamid=6819004')[0]
df4['Team'] = str('Royals')
df4['division'] = str('west')

df5 = pd.read_html('https://www.leaguelineup.com/teams_baseball.asp?url=lism21&divisionid=973090&teamid=6818906')[0]
df5['Team'] = str('Huskies')
df5['division'] = str('west')

df6 = pd.read_html('https://www.leaguelineup.com/teams_baseball.asp?url=lism21&divisionid=962033&teamid=6809038')[0]
df6['Team'] = str('SUFFOLK YANKEES')
df6['division'] = str('east')

df7 = pd.read_html('https://www.leaguelineup.com/teams_baseball.asp?url=lism21&divisionid=962033&teamid=6825321')[0]
df7['Team'] = str("EASTERN A's")
df7['division'] = str('east')

df8 = pd.read_html('https://www.leaguelineup.com/teams_baseball.asp?url=lism21&divisionid=962033&teamid=6837495')[0]
df8['Team'] = str('HUNTINGTON SAINTS')
df8['division'] = str('east')

df9 = pd.read_html('https://www.leaguelineup.com/teams_baseball.asp?url=lism21&divisionid=962033&teamid=6834814')[0]
df9['Team'] = str('LI COBRAS')
df9['division'] = str('east')

df10 = pd.read_html('https://www.leaguelineup.com/teams_baseball.asp?url=lism21&divisionid=962033&teamid=6830526')[0]
df10['Team'] = str('LI CRUSH')
df10['division'] = str('east')

df11 = pd.read_html('https://www.leaguelineup.com/teams_baseball.asp?url=lism21&divisionid=962033&teamid=6845760')[0]
df11['Team'] = str('ORDER 66')
df11['division'] = str('east')

df12 = pd.read_html('https://www.leaguelineup.com/teams_baseball.asp?url=lism21&divisionid=962033&teamid=6862702')[0]
df12['Team'] = str('TRASH PANDAS')
df12['division'] = str('east')

In [ ]:

combining them into one big data frame
stats = [df1, df2, df3, df4, df5, df6, df7, df8, df9, df10, df11, df12]
df = pd.concat(stats)

In [ ]: creating variables for use in formulas

#batting variables
tb = df.loc[:,'TB']
sh = df.loc[:,'SH']
cs = df.loc[:,'CS']
sb = df.loc[:,'SB']
k = df.loc[:,'K']
bb = df.loc[:,'BB']
hbp = df.loc[:,'HBP']
h = df.loc[:,'H']
dub = df.loc[:,'2B']
trip = df.loc[:,'3B']
hr = df.loc[:,'HR']
rbi = df.loc[:,'RBI']
ab = df.loc[:,'AB']
ibb = df.loc[:,'IBB']
sf = df.loc[:,'SF']
pa = df.loc[:, 'PA']

In [ ]:

#calculating weighted on base average
wo = ((0.693 * bb) + (0.723 * hbp) + (0.877 * h) + (1.232 * dub) + (1.552 * trip) + (1.980 * hr))
ba = (ab + bb - ibb + sf + hbp)
woba = round(wo/ba, 3)
df['wOBA'] = woba

In [ ]:

#calculating weighted runs above average
Weighted Runs Above Average (wRAA) measures the number of offensive runs a player contributes to their team compared to the average player. 
A wRAA of zero is league-average, so a positive wRAA value denotes above-average performance and a negative wRAA denotes below-average performance. 
This is also a counting statistic (like RBIs), so players accrue more (or fewer) runs as they play.

wOBA_scale = 1.2
lgwOBA = df['wOBA'].mean()
wRAA = ((woba - lgwOBA)/wOBA_scale) * pa
wRAA = round(wRAA, 3)
df['wRAA'] = wRAA

In [ ]:

#SecA = Secondary average  *accounts for power dicipline and speed
seca = (bb + (tb - h) + (sb - cs))/ab
seca = round(seca, 3)
df['SecA'] = seca

In [ ]:

#rc = runs created
rc = ((h + bb - cs + hbp) * (tb + (.26*(bb - ibb + hbp)) + (.52 * (sh + sf + sb))))/(ab + bb + hbp + sh + sf)
rc = round(rc, 3)
df['RC'] = rc

In [ ]:

df = df.sort_values(by='wOBA', ascending=False)

In [ ]:

#Batting leaders
leaders = df[['Name','AVG', 'OPS', 'wOBA', 'SecA', 'RC', 'wRAA', 'Team', 'division']]
leaders = leaders.loc[leaders['RC'] > 5]
leaders.head(15)

Out[ ]: Here are our league leaders from about 15 teams across Nassau county. Vinny is my shortstop and as you can see he had a pretty good season. That .850 wOBA is really something.

	Name	AVG	OPS	wOBA	SecA	RC	wRAA	Team	division
14	Vinny Grassano	0.559	1.461	0.850	0.441	18.422	15.965	Hurricanes	west
17	Brett Maier	0.437	1.464	0.772	0.625	8.305	6.747	HUNTINGTON SAINTS	east
13	Nick Isernia	0.511	1.399	0.764	0.422	22.396	18.120	HUNTINGTON SAINTS	east
1	Chris Burns	0.488	1.300	0.693	0.341	18.685	14.754	LI CRUSH	east
0	Alberto Argotte	0.435	1.243	0.670	0.419	25.547	20.259	LI COBRAS	east
11	Connor McHale	0.467	1.214	0.661	0.822	22.717	13.919	Kings	west
13	Mike Grassano	0.476	1.249	0.661	0.333	16.243	11.818	Hurricanes	west
2	Corey Elowsky	0.444	1.213	0.625	0.722	32.116	19.308	Four Seam Falcons	west
1	Ken Danielsen	0.426	1.199	0.612	0.404	17.300	12.420	Royals	west
7	JT Cullen	0.444	1.135	0.611	0.250	12.341	9.280	HUNTINGTON SAINTS	east
22	Liam Shannon	0.447	1.201	0.607	0.526	15.621	10.011	Four Seam Falcons	west
18	Zach Hood	0.435	1.147	0.607	0.478	8.022	5.658	EASTERN A’s	east
20	Chris Olberding	0.475	1.133	0.576	0.361	23.071	13.425	HUNTINGTON SAINTS	east
18	Jesse Matos	0.439	1.134	0.574	0.366	14.889	9.696	HUNTINGTON SAINTS	east
14	Jose Lebron	0.419	1.168	0.573	0.903	15.754	8.707	Four Seam Falcons	west

In [ ]:

# Pat stats
pat_df = df.loc[df['Name'] == 'Pat DeAngelis']
pat_df

Out[ ]: These are my batting stats from last season. not really so good, this performance prompted a visit to the eye doctor for an updated prescription

	#	Name	AVG	GP	GS	PA	AB	R	H	2B	3B	HR	RBI	BB	K	HBP	IBB	SB	CS	SH	SF	DP	ROE	FC	LOB	TB	OBP	SLG	OPS	Team	division	wOBA	wRAA	SecA	RC
7	54.0	Pat DeAngelis	0.185	19.0	19.0	59.0	54.0	5.0	10.0	3.0	0.0	0.0	9.0	4.0	18.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	13.0	0.237	0.241	0.478	Hurricanes	west	0.258	-4.32	0.13	3.455

In [ ]:

df.loc[df['Team'] == 'Hurricanes']

Out[ ]: Here are the stats for my entire team.

	#	Name	AVG	GP	GS	PA	AB	R	H	2B	3B	HR	RBI	BB	K	HBP	IBB	SB	CS	SH	SF	DP	ROE	FC	LOB	TB	OBP	SLG	OPS	Team	division	wOBA	wRAA	SecA	RC
0	32.0	Mike Aniano	0.286	9.0	9.0	23.0	21.0	5.0	6.0	1.0	0.0	0.0	2.0	2.0	8.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	7.0	0.348	0.333	0.681	Hurricanes	west	0.343	-0.055	0.143	2.616
1	13.0	Joe Bitetto	0.000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.000	0.000	0.000	Hurricanes	west	NaN	NaN	NaN	NaN
2	77.0	Sebastian Buttafuoco	0.200	4.0	4.0	6.0	5.0	0.0	1.0	0.0	0.0	0.0	1.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.333	0.200	0.533	Hurricanes	west	0.262	-0.419	0.200	0.420
3	9.0	Scott Clark	0.296	10.0	9.0	27.0	27.0	5.0	8.0	3.0	0.0	0.0	1.0	0.0	5.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	11.0	0.296	0.407	0.704	Hurricanes	west	0.397	1.151	0.111	3.259
4	33.0	Steve Corea	0.000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.000	0.000	0.000	Hurricanes	west	NaN	NaN	NaN	NaN
5	24.0	Thad Cosentino	0.194	17.0	17.0	45.0	31.0	10.0	6.0	1.0	0.0	0.0	3.0	10.0	10.0	3.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	7.0	0.422	0.226	0.648	Hurricanes	west	0.347	0.043	0.387	4.822
6	0.0	Niko Davanzo	0.000	1.0	0.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.000	0.000	0.000	Hurricanes	west	0.000	-0.288	0.000	0.000
7	54.0	Pat DeAngelis	0.185	19.0	19.0	59.0	54.0	5.0	10.0	3.0	0.0	0.0	9.0	4.0	18.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	13.0	0.237	0.241	0.478	Hurricanes	west	0.258	-4.320	0.130	3.455
8	57.0	Andrew Deckel	0.040	12.0	12.0	27.0	25.0	0.0	1.0	0.0	0.0	0.0	0.0	2.0	10.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.111	0.040	0.151	Hurricanes	west	0.084	-5.892	0.080	0.169
9	25.0	Rob Deckel	0.250	12.0	12.0	28.0	24.0	5.0	6.0	0.0	0.0	0.0	5.0	2.0	12.0	0.0	0.0	0.0	0.0	1.0	1.0	0.0	0.0	0.0	0.0	6.0	0.296	0.250	0.546	Hurricanes	west	0.246	-2.330	0.083	2.160
10	17.0	Sam Dipetro	0.234	21.0	21.0	56.0	47.0	5.0	11.0	4.0	0.0	0.0	3.0	7.0	16.0	2.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	15.0	0.357	0.319	0.676	Hurricanes	west	0.373	1.267	0.234	6.193
11	44.0	Rocco DiPietro	0.000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.000	0.000	0.000	Hurricanes	west	NaN	NaN	NaN	NaN
12	5.0	Kenny Grassano	0.000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.000	0.000	0.000	Hurricanes	west	NaN	NaN	NaN	NaN
13	27.0	Mike Grassano	0.476	13.0	13.0	45.0	42.0	6.0	20.0	5.0	0.0	2.0	18.0	3.0	7.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	31.0	0.511	0.738	1.249	Hurricanes	west	0.661	11.818	0.333	16.243
14	29.0	Vinny Grassano	0.559	11.0	11.0	38.0	34.0	6.0	19.0	11.0	0.0	0.0	17.0	3.0	2.0	0.0	0.0	1.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	30.0	0.579	0.882	1.461	Hurricanes	west	0.850	15.965	0.441	18.422
15	22.0	James Hall	0.308	21.0	19.0	63.0	52.0	9.0	16.0	0.0	2.0	1.0	13.0	10.0	9.0	1.0	0.0	4.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	23.0	0.429	0.442	0.871	Hurricanes	west	0.425	4.155	0.404	11.974
16	2.0	Chris Kornfeld	0.250	2.0	2.0	4.0	4.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.250	0.250	0.500	Hurricanes	west	0.219	-0.423	0.000	0.250
17	46.0	Vinny Mavaro	0.345	12.0	11.0	30.0	29.0	3.0	10.0	0.0	0.0	0.0	2.0	1.0	7.0	0.0	0.0	2.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	10.0	0.367	0.345	0.711	Hurricanes	west	0.315	-0.771	0.103	4.143
18	4.0	Chris McGrane	0.187	7.0	6.0	18.0	16.0	1.0	3.0	1.0	0.0	0.0	0.0	2.0	8.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	4.0	0.278	0.250	0.528	Hurricanes	west	0.292	-0.808	0.250	1.400
19	7.0	Tom Moffatt	0.250	4.0	4.0	10.0	8.0	2.0	2.0	0.0	0.0	0.0	0.0	2.0	2.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	2.0	0.400	0.250	0.650	Hurricanes	west	0.314	-0.265	0.375	1.216
20	96.0	Anthony Neal	0.500	1.0	1.0	3.0	2.0	0.0	1.0	0.0	0.0	0.0	0.0	1.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	1.0	0.667	0.500	1.167	Hurricanes	west	0.523	0.443	0.500	0.840
21	12.0	Bobby Negron	0.000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.000	0.000	0.000	Hurricanes	west	NaN	NaN	NaN	NaN
22	99.0	Frankie Petrocelli	0.300	6.0	6.0	15.0	10.0	4.0	3.0	0.0	0.0	0.0	3.0	4.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	3.0	0.533	0.300	0.833	Hurricanes	west	0.408	0.777	0.400	2.293
23	14.0	Anthony Pranzo	0.195	17.0	17.0	50.0	41.0	8.0	8.0	2.0	0.0	0.0	2.0	9.0	5.0	0.0	0.0	3.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	10.0	0.340	0.244	0.584	Hurricanes	west	0.314	-1.327	0.341	4.726
24	3.0	Steve Quiroz	0.000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.000	0.000	0.000	Hurricanes	west	NaN	NaN	NaN	NaN
25	6.0	Jordan Rorher	0.219	17.0	17.0	45.0	32.0	5.0	7.0	2.0	0.0	0.0	1.0	11.0	7.0	2.0	0.0	3.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	9.0	0.444	0.281	0.726	Hurricanes	west	0.393	1.768	0.500	6.196
26	1.0	Ben Sherman	0.242	12.0	12.0	36.0	33.0	6.0	8.0	0.0	0.0	0.0	4.0	2.0	2.0	0.0	0.0	6.0	0.0	0.0	1.0	0.0	0.0	0.0	0.0	8.0	0.278	0.242	0.520	Hurricanes	west	0.233	-3.386	0.242	3.378
27	15.0	Mike Silva Jr	0.000	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	NaN	0.000	0.000	0.000	Hurricanes	west	NaN	NaN	NaN	NaN
28	18.0	Ben Theiss	0.267	15.0	15.0	36.0	30.0	5.0	8.0	3.0	0.0	0.0	9.0	5.0	5.0	1.0	0.0	1.0	0.0	0.0	0.0	0.0	0.0	0.0	0.0	11.0	0.389	0.367	0.756	Hurricanes	west	0.414	2.044	0.300	5.087