Browse Source

updates from weekend work.

master
Charles Reid 7 years ago
parent
commit
a901ece930
  1. 30
      PrintDF.py
  2. 86
      TeamWins.py
  3. 22
      TeamWinsMultiple.py
  4. 42
      Wins.py
  5. 481
      data/teamwins_hypothesis_test_df.csv
  6. BIN
      figs_wins/ALNL_multivariate_kde_1B_w_1973-2013.png
  7. BIN
      figs_wins/ALNL_multivariate_kde_2B_w_1973-2013.png
  8. BIN
      figs_wins/ALNL_multivariate_kde_3B_w_1973-2013.png
  9. BIN
      figs_wins/ALNL_multivariate_kde_AB_w_1973-2013.png
  10. BIN
      figs_wins/ALNL_multivariate_kde_BA_w_1973-2013.png
  11. BIN
      figs_wins/ALNL_multivariate_kde_BB_w_1973-2013.png
  12. BIN
      figs_wins/ALNL_multivariate_kde_DP_w_1973-2013.png
  13. BIN
      figs_wins/ALNL_multivariate_kde_Fld%_w_1973-2013.png
  14. BIN
      figs_wins/ALNL_multivariate_kde_HR_w_1973-2013.png
  15. BIN
      figs_wins/ALNL_multivariate_kde_H_w_1973-2013.png
  16. BIN
      figs_wins/ALNL_multivariate_kde_OPS_w_1973-2013.png
  17. BIN
      figs_wins/ALNL_multivariate_kde_RBI_w_1973-2013.png
  18. BIN
      figs_wins/ALNL_multivariate_kde_R_w_1973-2013.png
  19. BIN
      figs_wins/ALNL_multivariate_kde_RpG_w_1973-2013.png
  20. BIN
      figs_wins/ALNL_multivariate_kde_SLG_w_1973-2013.png
  21. BIN
      figs_wins/ALNL_multivariate_kde_SO_w_1973-2013.png
  22. BIN
      figs_wins/ALNL_qq_1B_w_linreg_1973-2013.png
  23. BIN
      figs_wins/ALNL_qq_2B_w_linreg_1973-2013.png
  24. BIN
      figs_wins/ALNL_qq_3B_w_linreg_1973-2013.png
  25. BIN
      figs_wins/ALNL_qq_AB_w_linreg_1973-2013.png
  26. BIN
      figs_wins/ALNL_qq_BA_w_linreg_1973-2013.png
  27. BIN
      figs_wins/ALNL_qq_BB_w_linreg_1973-2013.png
  28. BIN
      figs_wins/ALNL_qq_DP_w_linreg_1973-2013.png
  29. BIN
      figs_wins/ALNL_qq_Fld%_w_linreg_1973-2013.png
  30. BIN
      figs_wins/ALNL_qq_HR_w_linreg_1973-2013.png
  31. BIN
      figs_wins/ALNL_qq_H_w_linreg_1973-2013.png
  32. BIN
      figs_wins/ALNL_qq_HpG_w_linreg_1973-2013.png
  33. BIN
      figs_wins/ALNL_qq_OPS_w_linreg_1973-2013.png
  34. BIN
      figs_wins/ALNL_qq_RBI_w_linreg_1973-2013.png
  35. BIN
      figs_wins/ALNL_qq_R_w_linreg_1973-2013.png
  36. BIN
      figs_wins/ALNL_qq_RpG_w_linreg_1973-2013.png
  37. BIN
      figs_wins/ALNL_qq_SLG_w_linreg_1973-2013.png
  38. BIN
      figs_wins/ALNL_qq_SO_w_linreg_1973-2013.png

30
PrintDF.py

@ -0,0 +1,30 @@
from pandas import *
df = read_csv('data/teamwins_hypothesis_test_df.csv')
stats = unique(df['Stat'])
for stat in stats:
z = df[df['Stat']==stat]
stat_label = unique(df[df['Stat']==stat]['Stat Label'])[0]
print ""
print "{% codeblock %}"
print ""
print "Stat "+stat_label+":"
for zz in z.iterrows():
dat = zz[1]
print " For team "+dat['Team'].upper()+": Do we accept null hypothesis?",dat['Accept H0']," (t-test: T < t_threshold, %0.4g < %0.4g) "%(abs(dat['T Value']),dat['T Threshold'])
print ""
print " Teams accepting null hypothesis:",sum(z['Accept H0']==True)
print " Teams rejecting null hypothesis:",sum(z['Accept H0']==False)
print ""
print "{% endcodeblock %}"
print ""

86
TeamWins.py

@ -14,10 +14,17 @@ import statsmodels.api as sm
start_yr = 1973
end_yr = 2013
#keys = ['H','1B','2B','3B','BB','HR','AB','DP','SLG','SO','BA','OPS','R','RpG','RBI','Fld%']
#labels = ['Hits','Singles','Doubles','Triples','Walks','Home Runs','At Bats','Hit Into Doub Play','Slugging Pct','Strikeouts','Batting Avg','OBP+SP','Runs','Runs per Game','RBIs','Fielding Pct']
keys = ['BB']
labels = ['Walks']
keys = ['H','1B','2B','3B','BB','HR','AB','DP','SLG','SO','BA','OPS','R','RpG','RBI','Fld%']
labels = ['Hits','Singles','Doubles','Triples','Walks','Home Runs','At Bats','Hit Into Doub Play','Slugging Pct','Strikeouts','Batting Avg','OBP+SP','Runs','Runs per Game','RBIs','Fielding Pct']
#keys = ['BB']
#labels = ['Walks']
#keys = ['WOBA']
#labels = ['Weighted On Base Avg']
wins_key = 'Wpct'
wins_label = 'Win Pct'
def curve_fit_function(x,b0,b1):
return b0 + b1*x
@ -43,6 +50,9 @@ def load_data(cutoff_yr):
# Add runs per game column
df['RpG'] = df['R']/df['G']
# woba http://www.fangraphs.com/library/offense/woba/
df['WOBA'] = (0.7 * df['BB'] + 0.9 * df['1B'] + 1.2 * df['2B'] + 1.6 * df['3B'] + 2.1 * df['HR'])/( df['AB'] + df['BB'] )
return df
@ -56,9 +66,6 @@ def wins_teams_multivariate_kdes():
"""
cutoff_yrs = [[start_yr,end_yr]]
wins_key = 'Wpct'
wins_label = 'Win Pct'
print wins_label+": Multivariate KDEs"
for cutoff_yr in cutoff_yrs:
@ -70,28 +77,28 @@ def wins_teams_multivariate_kdes():
for key, label in zip(keys,labels):
print " Stat:",label
print " Stat:",label
lim_x = [min(all_df[wins_key].values),max(all_df[wins_key].values)]
lim_y = [min(all_df[key].values),max(all_df[key].values)]
lim_x = [min(all_df[key].values),max(all_df[key].values)]
lim_y = [min(all_df[wins_key].values),max(all_df[wins_key].values)]
for team in teams:
fig = plt.figure(figsize=(8,6))
ax = fig.add_subplot(111)
print " Team:",team
print " Team:",team
df = all_df[all_df['Team']==team]
mx = df[wins_key].values
my = df[key].values
mx = df[key].values
my = df[wins_key].values
xmin = lim_x[0]#mx.min()
xmax = lim_x[1]#mx.max()
ymin = lim_y[0]#my.min()
ymax = lim_y[1]#my.max()
X, Y = np.mgrid[xmin:xmax:200j, ymin:ymax:200j]
positions = np.vstack([X.ravel(), Y.ravel()])
values = np.vstack([mx, my])
@ -102,8 +109,8 @@ def wins_teams_multivariate_kdes():
extent=[xmin, xmax, ymin, ymax],
aspect='auto')
ax.plot(mx, my, 'k.', markersize=6)
ax.set_ylabel(label)
ax.set_xlabel(wins_label)
ax.set_xlabel(label)
ax.set_ylabel(wins_label)
ax.set_title(team.upper()+": "+label+"-"+wins_label+" Joint PDF, "+str(cutoff_yr[0])+"-"+str(cutoff_yr[1]))
ax.set_xlim([xmin, xmax])
@ -124,13 +131,12 @@ def wins_teams_regression_significance_test():
"""
cutoff_yrs = [[start_yr,end_yr]]
wins_key = 'Wpct'
wins_label = 'Win Pct'
print wins_label+": Regression Significance Test"
do_plots = True
test_df = DataFrame()
for cutoff_yr in cutoff_yrs:
print "Now on year",cutoff_yr
@ -142,8 +148,6 @@ def wins_teams_regression_significance_test():
print " Stat:",label
#lim_x = [min(all_df[wins_key].values),max(all_df[wins_key].values)]
#lim_y = [min(all_df[key].values),max(all_df[key].values)]
lim_x = [min(all_df[key].values),max(all_df[key].values)]
lim_y = [min(all_df[wins_key].values),max(all_df[wins_key].values)]
@ -158,10 +162,8 @@ def wins_teams_regression_significance_test():
df = all_df[all_df['Team']==team]
#mx = df[key].values
#my = df[wins_key].values
mx = df[wins_key].values
my = df[key].values
mx = df[key].values
my = df[wins_key].values
xmin = mx.min()
xmax = mx.max()
@ -209,18 +211,31 @@ def wins_teams_regression_significance_test():
Nparams = len(coeff)
dof = Nobs - Nparams - 1
t10, t05 = stats.t.isf(alpha,dof)
print " y = beta_0 + beta_1 x = %0.1f + %0.1f x"%(b0,b1)
print " MSE(beta_0) =", SE_b0
print " MSE(beta_1) =", SE_b1
print " T =",T
#print " y = beta_0 + beta_1 x = %0.2g + %0.2g x"%(b0,b1)
#print " MSE(beta_0) = %0.2g"%(SE_b0)
#print " MSE(beta_1) = %0.2g"%(SE_b1)
#print " T = %0.2g"%(T)
print " Null Hypothesis Accepted? (10%%) = %0.2f < %0.2f ="%(abs(T),t10), abs(T) < t10
print " Null Hypothesis Accepted? ( 5%%) = %0.2f < %2.4f ="%(abs(T),t05), abs(T) < t05
#print " Null Hypothesis Accepted? ( 5%%) = %0.2f < %0.2f ="%(abs(T),t05), abs(T) < t05
d = {'Team':team,
'Stat':key,
'Stat Label':label,
'T Value': T,
'T Threshold': t10,
'SE' : SE_b1,
'Accept H0' : abs(T) < t10
}
test_df_item = DataFrame([d])
test_df = concat([test_df,test_df_item],ignore_index=True)
# Compute residuals
resid = my - (b0 + b1*mx)
if do_plots:
# Quantile-quantile and residual plots
ax1.set_title(team.upper()+": "+label+"-Wins Lin Reg\nQQ/Resid Plots "+str(cutoff_yr[0])+"-"+str(cutoff_yr[1]))
sm.qqplot(resid,ax=ax1)
@ -230,6 +245,7 @@ def wins_teams_regression_significance_test():
quants = osm[0]
sm.qqline(ax=ax1,line='s',x=quants,y=resid)
# Residual plot
ax2.plot(mx,np.abs(resid),'bo')
ax2.set_xlabel('Win Pct')
ax2.set_ylabel('Residual')
@ -239,12 +255,10 @@ def wins_teams_regression_significance_test():
plt.show()
plt.close('all')
# save stat test df
test_df.to_csv('data/teamwins_hypothesis_test_df.csv',index=False)
if __name__=="__main__":
wins_teams_multivariate_kdes()
#wins_teams_multivariate_kdes()
wins_teams_regression_significance_test()

22
TeamWinsMultiple.py

@ -55,12 +55,10 @@ def wins_teams_multiple_regression_significance_test():
wins_key = 'Wpct'
wins_label = 'Win Pct'
#keys = ['H','1B','2B','3B','BB','HR','AB','DP','SLG','SO','BA','OPS','R','RpG','RBI','Fld%']
#labels = ['Hits','Singles','Doubles','Triples','Walks','Home Runs','At Bats','Hit Into Doub Play','Slugging Pct','Strikeouts','Batting Avg','OBP+SP','Runs','Runs per Game','RBIs','Fielding Pct']
#keys = ['SLG','BA','OPS','RpG','RBI']
#labels = ['Slugging Pct','Batting Avg','OBP+SP','Runs per Game','RBIs']
keys = ['1BpG','2BpG','3BpG','HRpG']
labels = ['Singles','Doubles','Triples','Home Runs']
#keys = ['1BpG','2BpG','3BpG','HRpG']
#labels = ['Singles','Doubles','Triples','Home Runs']
keys = ['1BpG','3BpG','HRpG']
labels = ['Singles','Triples','Home Runs']
for cutoff_yr in cutoff_yrs:
@ -74,9 +72,9 @@ def wins_teams_multiple_regression_significance_test():
print " All Teams:"
mx = all_df[[key for key in keys]].values
# weight base hits by number of bases
for i in [0,1,2,3]:
mx[:,i] = (i+1)*mx[:,i]
## weight base hits by number of bases
#for i in [0,1,2,3]:
# mx[:,i] = (i+1)*mx[:,i]
my = all_df[wins_key].values
orig_keys = copy.deepcopy(keys)
@ -136,15 +134,17 @@ def wins_teams_multiple_regression_significance_test():
print "="*40
print "\n\n"
fig = plt.figure()
for i in range(4):
for i in range(3):
ip1 = i + 1
ax = fig.add_subplot(1,4,ip1)
ax.plot(mx[:,i],my,'bo')
ax.set_title(labels[i])
#plt.draw()
plt.draw()
plt.show()

42
Wins.py

@ -11,6 +11,16 @@ import statsmodels.api as sm
keys = ['H','1B','2B','3B','BB','HR','AB','DP','SLG','SO','BA','OPS','R','RpG','RBI','Fld%']
labels = ['Hits','Singles','Doubles','Triples','Walks','Home Runs','At Bats','Hit Into Doub Play','Slugging Pct','Strikeouts','Batting Avg','OBP+SP','Runs','Runs per Game','RBIs','Fielding Pct']
cutoff_yrs = [[1973,2013]]
wins_key = 'W'
wins_label = 'Wins'
def curve_fit_function(x,b0,b1):
return b0 + b1*x
@ -55,18 +65,6 @@ def wins_multivariate_kdes():
by plotting the multivariate KDE
(this is an eyeball-norm test)
"""
#keys = ['H','1B','2B','3B','BB','HR','AB','DP','SLG','SO','BA','OPS','R','RpG','RBI','Fld%']
#labels = ['Hits','Singles','Doubles','Triples','Walks','Home Runs','At Bats','Hit Into Doub Play','Slugging Pct','Strikeouts','Batting Avg','OBP+SP','Runs','Runs per Game','RBIs','Fielding Pct']
#cutoff_yrs = [[1970,2000],[2000,2013]]
keys = ['HpG']
labels = ['Hits']
cutoff_yrs = [[1973,2013]]
wins_key = 'Wpct'
wins_label = 'Win Pct'
print wins_label+": Multivariate KDEs"
@ -136,16 +134,6 @@ def wins_regression_significance_test():
and determine if the regression
coefficient is statistically significant
"""
#keys = ['H','1B','2B','3B','BB','HR','AB','DP','SLG','SO','BA','OPS','R','RpG','RBI','Fld%']
#labels = ['Hits','Singles','Doubles','Triples','Walks','Home Runs','At Bats','Hit Into Doub Play','Slugging Pct','Strikeouts','Batting Avg','OBP+SP','Runs','Runs per Game','RBIs','Fielding Pct']
#cutoff_yrs = [[1970,2000],[2000,2013]]
keys = ['HpG']
labels = ['Hits']
cutoff_yrs = [[1973,2013]]
wins_key = 'Wpct'
wins_label = 'Win Pct'
print wins_label+": Univariate Linear Regression Significance Test"
@ -221,12 +209,12 @@ def wins_regression_significance_test():
dof = Nobs - Nparams - 1
t10, t05 = stats.t.isf(alpha,dof)
print " y = beta_0 + beta_1 x = %0.2g + %0.2g x"%(b0,b1)
print " MSE(beta_0) = %0.2g"%(SE_b0)
print " MSE(beta_1) = %0.2g"%(SE_b1)
print " T = %0.2g"%(T)
#print " y = beta_0 + beta_1 x = %0.2g + %0.2g x"%(b0,b1)
#print " MSE(beta_0) = %0.2g"%(SE_b0)
#print " MSE(beta_1) = %0.2g"%(SE_b1)
#print " T = %0.2g"%(T)
print " Null Hypothesis Accepted? (10%%) = %0.2f < %0.2f ="%(abs(T),t10), abs(T) < t10
print " Null Hypothesis Accepted? ( 5%%) = %0.2f < %0.2f ="%(abs(T),t05), abs(T) < t05
#print " Null Hypothesis Accepted? ( 5%%) = %0.2f < %0.2f ="%(abs(T),t05), abs(T) < t05
# Compute residuals
resid = my - (b0 + b1*mx)

481
data/teamwins_hypothesis_test_df.csv

@ -0,0 +1,481 @@
Accept H0,SE,Stat,Stat Label,T Threshold,T Value,Team
False,9.584692531184156e-05,H,Hits,1.30695158562383,3.445403934576238,ana
False,0.00031822899535203174,H,Hits,1.3562173340231973,1.7368412325068914,ari
False,0.00015881991521002345,H,Hits,1.30695158562383,2.8245472841351194,atl
True,0.00015794764457493327,H,Hits,1.30695158562383,0.2799021585634932,bal
False,0.00010799121176411983,H,Hits,1.30695158562383,1.3197768809105446,bos
False,0.00015280558355902735,H,Hits,1.30695158562383,2.839283418227601,chc
False,0.0001168041273200544,H,Hits,1.30695158562383,2.4955552804289383,chw
False,0.00014572923263724658,H,Hits,1.30695158562383,2.5566547300759463,cin
False,0.00012051452880150319,H,Hits,1.30695158562383,3.625447848990456,cle
True,0.00013732871850850149,H,Hits,1.3367571673273144,-0.16770064702084442,col
False,0.00012519054076052383,H,Hits,1.30695158562383,4.31865956480068,det
False,0.00022585991908476884,H,Hits,1.3367571673273144,1.4326712246671665,fla
False,0.00015502888895224898,H,Hits,1.30695158562383,2.7448751876494755,hou
True,0.0001581424337452018,H,Hits,1.30695158562383,0.8689689365299852,kcr
False,0.00011972801881523715,H,Hits,1.30695158562383,2.271721847253304,lad
False,0.00010892949554236983,H,Hits,1.30695158562383,4.564400913849085,mil
False,0.00014161441580441984,H,Hits,1.30695158562383,4.701018314172347,min
False,0.00015109348187898025,H,Hits,1.30695158562383,3.4789733320786764,nym
False,0.00010878327908870372,H,Hits,1.30695158562383,4.152433268742264,nyy
False,0.0001738231592655209,H,Hits,1.30695158562383,2.3657331505287993,oak
False,0.00012574553559476206,H,Hits,1.30695158562383,3.7178092017996103,phi
False,0.00014217370434444092,H,Hits,1.30695158562383,3.044917184495594,pit
False,0.0001535737805054233,H,Hits,1.30695158562383,1.6940258359457048,sdp
False,0.00011085361229979351,H,Hits,1.3094635471008582,4.30856015622041,sea
False,0.00011903935945942205,H,Hits,1.30695158562383,3.7663102229726397,sfg
False,0.00010327406743040688,H,Hits,1.30695158562383,2.641005293972084,stl
False,0.0002877461172117088,H,Hits,1.3562173340231973,-2.344518027644074,tbd
False,9.105120405423231e-05,H,Hits,1.30695158562383,2.3105689686005553,tex
False,0.00012701541108293146,H,Hits,1.3094635471008582,4.398432575165635,tor
False,0.00018246218341157864,H,Hits,1.30695158562383,2.219959086048691,wsn
False,0.00017266591475312922,1B,Singles,1.30695158562383,1.437458329632823,ana
True,0.00039475893834929854,1B,Singles,1.3562173340231973,1.0824000824059634,ari
True,0.00028664851248781463,1B,Singles,1.30695158562383,-0.5779535052193777,atl
True,0.00021662608249922442,1B,Singles,1.30695158562383,0.6798773606587458,bal
True,0.00013354252363538487,1B,Singles,1.30695158562383,-1.2891940119637748,bos
True,0.00014738939291710754,1B,Singles,1.30695158562383,0.833387664159335,chc
True,0.00014529114495277147,1B,Singles,1.30695158562383,-0.507094811344496,chw
False,0.00016919620786165796,1B,Singles,1.30695158562383,2.161856547932475,cin
True,0.0001674572991503963,1B,Singles,1.30695158562383,0.13643951828337286,cle
True,0.00016770071505168386,1B,Singles,1.3367571673273144,-0.49401658998018105,col
False,0.00018798894155805235,1B,Singles,1.30695158562383,2.8901841267490003,det
True,0.00027791862104206987,1B,Singles,1.3367571673273144,0.07078072234078131,fla
True,0.00020373975535568537,1B,Singles,1.30695158562383,0.7930847264599113,hou
True,0.00017736789519777008,1B,Singles,1.30695158562383,1.2978191887384636,kcr
False,0.0001539925935860148,1B,Singles,1.30695158562383,1.8568926825051528,lad
False,0.0001314098834585802,1B,Singles,1.30695158562383,1.5899068207149876,mil
False,0.00015224243615505887,1B,Singles,1.30695158562383,1.71955911234912,min
True,0.0002285631353670777,1B,Singles,1.30695158562383,0.43145606130578,nym
True,0.0001674023034996366,1B,Singles,1.30695158562383,0.6143593931653252,nyy
True,0.00024769397422724575,1B,Singles,1.30695158562383,0.36605469865154416,oak
False,0.0001704222913239804,1B,Singles,1.30695158562383,1.6371485830315062,phi
False,0.00015117142289286232,1B,Singles,1.30695158562383,3.331324538058116,pit
True,0.0001663567675764854,1B,Singles,1.30695158562383,0.16812632342985173,sdp
False,0.00019395351144700561,1B,Singles,1.3094635471008582,1.7541565269632051,sea
True,0.00018932373492249515,1B,Singles,1.30695158562383,-0.19654366240780957,sfg
True,0.000130439252358973,1B,Singles,1.30695158562383,0.47044202546612157,stl
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True,5.201155943838336e-05,SO,Strikeouts,1.30695158562383,0.31582776079900277,bos
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True,9.524378492592647e-05,SO,Strikeouts,1.3367571673273144,0.4187702340180029,col
True,9.4998545291622e-05,SO,Strikeouts,1.30695158562383,-0.9189144375892259,det
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True,7.307555334742974e-05,SO,Strikeouts,1.30695158562383,-0.9673812099480014,hou
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True,7.271548306160611e-05,SO,Strikeouts,1.30695158562383,-0.9157833483759656,lad
False,4.862908194148951e-05,SO,Strikeouts,1.30695158562383,-1.6386502192476358,mil
True,8.167033299357736e-05,SO,Strikeouts,1.30695158562383,-0.5451265413971444,min
True,0.00011062584401267966,SO,Strikeouts,1.30695158562383,0.4612954420844502,nym
True,6.080404220100196e-05,SO,Strikeouts,1.30695158562383,1.13074893118044,nyy
True,9.374763579178431e-05,SO,Strikeouts,1.30695158562383,0.08898041082197387,oak
True,7.913498815918026e-05,SO,Strikeouts,1.30695158562383,-1.0647467369892294,phi
False,7.035449859609025e-05,SO,Strikeouts,1.30695158562383,-3.6598674401621087,pit
True,5.94113202536523e-05,SO,Strikeouts,1.30695158562383,-0.1927099020201208,sdp
True,8.719959049228848e-05,SO,Strikeouts,1.3094635471008582,1.2992064866836472,sea
True,9.618687639986182e-05,SO,Strikeouts,1.30695158562383,1.3035455337561461,sfg
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True,5.393811022290493e-05,SO,Strikeouts,1.30695158562383,0.9480237405215846,tex
True,8.18196293378085e-05,SO,Strikeouts,1.3094635471008582,1.1679024968508402,tor
True,7.410828623935751e-05,SO,Strikeouts,1.30695158562383,-0.8169753543947321,wsn
False,0.6645688672967675,BA,Batting Avg,1.30695158562383,3.591743785933609,ana
False,2.167457504761147,BA,Batting Avg,1.3562173340231973,2.453763325608341,ari
False,1.0712596302198545,BA,Batting Avg,1.30695158562383,4.077751986723472,atl
True,1.2037470273503783,BA,Batting Avg,1.30695158562383,0.7701125622966051,bal
False,0.7781217641381092,BA,Batting Avg,1.30695158562383,2.881345000850664,bos
False,1.0205236586414255,BA,Batting Avg,1.30695158562383,4.1770921002474966,chc
False,0.8015393813596197,BA,Batting Avg,1.30695158562383,2.0587498662700416,chw
False,0.9578484707256149,BA,Batting Avg,1.30695158562383,3.7599295434184916,cin
False,0.6971908698693167,BA,Batting Avg,1.30695158562383,5.438302515565456,cle
True,1.032243057459623,BA,Batting Avg,1.3367571673273144,0.5465590826120905,col
False,0.9576573271067033,BA,Batting Avg,1.30695158562383,4.83146931745735,det
False,1.5461664905242836,BA,Batting Avg,1.3367571673273144,2.525863041531313,fla
False,0.9301478390960568,BA,Batting Avg,1.30695158562383,3.155611950755871,hou
False,1.2392279997411997,BA,Batting Avg,1.30695158562383,1.3969606401266155,kcr
False,0.8727279047837734,BA,Batting Avg,1.30695158562383,2.5015472069495783,lad
False,0.7975505227285801,BA,Batting Avg,1.30695158562383,4.697698379195869,mil
False,1.0133958474857514,BA,Batting Avg,1.30695158562383,4.023062413976428,min
False,1.0394951525175995,BA,Batting Avg,1.30695158562383,3.4777951797033846,nym
False,0.7749961556609045,BA,Batting Avg,1.30695158562383,4.481242351924887,nyy
False,1.3422600305033368,BA,Batting Avg,1.30695158562383,2.468905257636488,oak
False,0.9223803988092547,BA,Batting Avg,1.30695158562383,3.7366747653888646,phi
False,1.1307734379225032,BA,Batting Avg,1.30695158562383,3.3580852738613025,pit
False,1.0241370476090703,BA,Batting Avg,1.30695158562383,1.8762336029605933,sdp
False,0.7205913207454183,BA,Batting Avg,1.3094635471008582,5.013904487102085,sea
False,0.8157092820280226,BA,Batting Avg,1.30695158562383,4.4253436338636405,sfg
False,0.8274572450516756,BA,Batting Avg,1.30695158562383,3.041897394339577,stl
False,2.128828883115894,BA,Batting Avg,1.3562173340231973,-1.93297437384639,tbd
False,0.6699724572169621,BA,Batting Avg,1.30695158562383,3.079202467472821,tex
False,0.9867671029632544,BA,Batting Avg,1.3094635471008582,3.9019852235634525,tor
False,1.3879800002280813,BA,Batting Avg,1.30695158562383,2.2604149999217817,wsn
False,0.19333185103849948,OPS,OBP+SP,1.30695158562383,3.631031619815086,ana
False,0.5953798702923778,OPS,OBP+SP,1.3562173340231973,4.112282515146569,ari
False,0.24506374347988136,OPS,OBP+SP,1.30695158562383,5.382925168390353,atl
True,0.3584915568695938,OPS,OBP+SP,1.30695158562383,1.1615491727048322,bal
False,0.16533128101114963,OPS,OBP+SP,1.30695158562383,4.505613147288313,bos
False,0.26338188728696915,OPS,OBP+SP,1.30695158562383,3.32188231855049,chc
False,0.18200097659000386,OPS,OBP+SP,1.30695158562383,3.7246316486891913,chw
False,0.28906256090107385,OPS,OBP+SP,1.30695158562383,2.4491269465024375,cin
False,0.1575663514325794,OPS,OBP+SP,1.30695158562383,6.478830689998397,cle
False,0.33400919708721666,OPS,OBP+SP,1.3367571673273144,1.8174801126352649,col
False,0.28433154625064844,OPS,OBP+SP,1.30695158562383,4.852396617016509,det
False,0.3780053656573547,OPS,OBP+SP,1.3367571673273144,3.2448955645105504,fla
False,0.20435942992770695,OPS,OBP+SP,1.30695158562383,2.763527300419274,hou
True,0.4870526079042082,OPS,OBP+SP,1.30695158562383,0.928859966079957,kcr
False,0.2222295695173399,OPS,OBP+SP,1.30695158562383,2.150971052618654,lad
False,0.2381414073232705,OPS,OBP+SP,1.30695158562383,3.7877946640209825,mil
False,0.3163813045131713,OPS,OBP+SP,1.30695158562383,4.235042812891304,min
False,0.2354619892224984,OPS,OBP+SP,1.30695158562383,5.052428450268966,nym
False,0.16651402691862907,OPS,OBP+SP,1.30695158562383,5.78723690926745,nyy
False,0.29034016695702036,OPS,OBP+SP,1.30695158562383,3.011704116332147,oak
False,0.23830544885072516,OPS,OBP+SP,1.30695158562383,3.5773800506107882,phi
False,0.44746659043547965,OPS,OBP+SP,1.30695158562383,2.2901251916301995,pit
False,0.28586863248101757,OPS,OBP+SP,1.30695158562383,1.9077003228475578,sdp
False,0.18215220750654282,OPS,OBP+SP,1.3094635471008582,5.5871771270985855,sea
False,0.17685251424808038,OPS,OBP+SP,1.30695158562383,5.124308038480866,sfg
False,0.17511999953783297,OPS,OBP+SP,1.30695158562383,3.6632414329677343,stl
True,0.9292136881836696,OPS,OBP+SP,1.3562173340231973,1.1805629618907385,tbd
False,0.16958750569856026,OPS,OBP+SP,1.30695158562383,2.7433625620104416,tex
False,0.24855267239527312,OPS,OBP+SP,1.3094635471008582,4.803069729909913,tor
True,0.3642974457321888,OPS,OBP+SP,1.30695158562383,1.0828966054464857,wsn
False,9.815338549253472e-05,R,Runs,1.30695158562383,4.527776887080148,ana
False,0.0002012474482450273,R,Runs,1.3562173340231973,4.690161886186438,ari
False,0.00013462899486506316,R,Runs,1.30695158562383,4.9666423002190685,atl
False,0.00016251766973090993,R,Runs,1.30695158562383,1.8317329393873663,bal
False,8.131479212637369e-05,R,Runs,1.30695158562383,4.36804372852863,bos
False,0.000138491510879786,R,Runs,1.30695158562383,4.094659547315852,chc
False,8.025202466930867e-05,R,Runs,1.30695158562383,4.693087857981065,chw
False,0.00013695581486041544,R,Runs,1.30695158562383,3.2477267546785704,cin
False,8.693749545173881e-05,R,Runs,1.30695158562383,5.342367040857697,cle
False,0.00014720947880300672,R,Runs,1.3367571673273144,1.5176539418947548,col
False,0.00012105474836531982,R,Runs,1.30695158562383,5.138785342017461,det
False,0.00020323548109993372,R,Runs,1.3367571673273144,3.062069214930251,fla
False,0.00010563111922948359,R,Runs,1.30695158562383,3.005338530113243,hou
True,0.00018555285723025717,R,Runs,1.30695158562383,1.0298398340787878,kcr
False,0.00010917344476133351,R,Runs,1.30695158562383,2.7726819419914,lad
False,0.0001071117868573769,R,Runs,1.30695158562383,4.881255011661208,mil
False,0.00014456148915012218,R,Runs,1.30695158562383,4.327049626777327,min
False,0.00011331913670142879,R,Runs,1.30695158562383,5.9898526840796285,nym
False,7.430858376481425e-05,R,Runs,1.30695158562383,6.859405391155341,nyy
False,0.00013502265179014524,R,Runs,1.30695158562383,3.5106195578466908,oak
False,0.00010827629937776615,R,Runs,1.30695158562383,4.479902022280332,phi
False,0.0001876909516753687,R,Runs,1.30695158562383,3.46774143425468,pit
False,0.00014019756440976342,R,Runs,1.30695158562383,2.5330002442585697,sdp
False,8.144190500510023e-05,R,Runs,1.3094635471008582,6.326916028333758,sea
False,9.493705989435727e-05,R,Runs,1.30695158562383,4.4818617213813905,sfg
False,8.367412427416146e-05,R,Runs,1.30695158562383,5.033047058038527,stl
False,0.000385690552537647,R,Runs,1.3562173340231973,2.1517575848787684,tbd
False,8.096031074912711e-05,R,Runs,1.30695158562383,2.9174789770116587,tex
False,0.0001133131935128778,R,Runs,1.3094635471008582,5.420313832092462,tor
False,0.00019160024413381442,R,Runs,1.30695158562383,2.5709974795386255,wsn
False,0.015484839877081748,RpG,Runs per Game,1.30695158562383,4.338606250674649,ana
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False,0.02144318901989682,RpG,Runs per Game,1.30695158562383,5.406262055520443,atl
False,0.02637525502882498,RpG,Runs per Game,1.30695158562383,1.917087712036727,bal
False,0.012491507304050513,RpG,Runs per Game,1.30695158562383,4.835655761152289,bos
False,0.021699361898996322,RpG,Runs per Game,1.30695158562383,4.223653360253775,chc
False,0.013349271078161572,RpG,Runs per Game,1.30695158562383,4.296634820486198,chw
False,0.021270087140419957,RpG,Runs per Game,1.30695158562383,3.5328770520618678,cin
False,0.01255341358150465,RpG,Runs per Game,1.30695158562383,6.371935002155336,cle
False,0.022932887762077383,RpG,Runs per Game,1.3367571673273144,1.951157705860133,col
False,0.020419719644326845,RpG,Runs per Game,1.30695158562383,5.008628382038359,det
False,0.03279031033297357,RpG,Runs per Game,1.3367571673273144,2.938636417420847,fla
False,0.01660677888497236,RpG,Runs per Game,1.30695158562383,2.971439352706021,hou
True,0.030885780254737157,RpG,Runs per Game,1.30695158562383,1.0519294704752051,kcr
False,0.01782265225740639,RpG,Runs per Game,1.30695158562383,2.828903800683967,lad
False,0.017711861721380454,RpG,Runs per Game,1.30695158562383,4.477950991936447,mil
False,0.024745830235131043,RpG,Runs per Game,1.30695158562383,3.598422009353945,min
False,0.018470132401608677,RpG,Runs per Game,1.30695158562383,5.921159746626327,nym
False,0.012265832812307574,RpG,Runs per Game,1.30695158562383,6.801750120514107,nyy
False,0.02196483646826081,RpG,Runs per Game,1.30695158562383,3.2799260091808136,oak
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False,0.03164551834287443,RpG,Runs per Game,1.30695158562383,3.2010414426929064,pit
False,0.02232489870484168,RpG,Runs per Game,1.30695158562383,2.487830199419751,sdp
False,0.0127967210244798,RpG,Runs per Game,1.3094635471008582,6.344434556260111,sea
False,0.01568128872675579,RpG,Runs per Game,1.30695158562383,4.320241335671798,sfg
False,0.01433387647219143,RpG,Runs per Game,1.30695158562383,4.770287730899537,stl
False,0.06317664409209753,RpG,Runs per Game,1.3562173340231973,2.1089684777378515,tbd
False,0.013265987656434487,RpG,Runs per Game,1.30695158562383,3.0001896263717613,tex
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False,0.03166813313137669,RpG,Runs per Game,1.30695158562383,2.5188242154118576,wsn
False,9.720594537272272e-05,RBI,RBIs,1.30695158562383,4.581032927547805,ana
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False,0.00016569650495911064,RBI,RBIs,1.30695158562383,1.5370747180951239,bal
False,8.158584059833488e-05,RBI,RBIs,1.30695158562383,4.268642249172758,bos
False,0.00013751205944862322,RBI,RBIs,1.30695158562383,3.9429810509346463,chc
False,7.83901142725003e-05,RBI,RBIs,1.30695158562383,4.7901903922099605,chw
False,0.00014077838392007782,RBI,RBIs,1.30695158562383,2.733674814601657,cin
False,8.729411354798997e-05,RBI,RBIs,1.30695158562383,5.444836307132685,cle
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True,0.00019684889688669654,RBI,RBIs,1.30695158562383,0.9950749361574094,kcr
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False,0.00010865539792635328,RBI,RBIs,1.30695158562383,4.730441359991239,mil
False,0.00015225741032013243,RBI,RBIs,1.30695158562383,4.4097216484479365,min
False,0.00011633282406325211,RBI,RBIs,1.30695158562383,5.748431261492726,nym
False,7.567598203202415e-05,RBI,RBIs,1.30695158562383,6.555011099101986,nyy
False,0.0001355122195236454,RBI,RBIs,1.30695158562383,3.4007611271488773,oak
False,0.00011262470006969816,RBI,RBIs,1.30695158562383,4.403381252539692,phi
False,0.0002115300231224537,RBI,RBIs,1.30695158562383,2.8318016170265174,pit
False,0.0001407790507726638,RBI,RBIs,1.30695158562383,2.294504512137081,sdp
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