You can see the earlier analysis here and here, which came to the same conclusion over the past five Federal elections, based on the two candidate preferred (TCP) counts for each seat by vote type.
If we look at the Coalition's TPP percentages (summed across all seats, for each vote type) it received:
Ordinary Votes: 49.952 % Absent Votes: 46.090 % Provisional Votes: 40.739 % Declaration Pre-Poll Votes: 51.580 % Postal Votes: 56.251 % Total Votes: 50.356 %
In the 2016 election, the Coalition lost the TPP count on election night (ordinary votes in the above list). But by the final count it had improved its position by 0.404 percentage points to win the final TPP count. The distribution of the Coalition's bias (compared with ordinary votes) across seats can be seen in the following charts.
The python code for the above analysis follows.
import pandas as pd import numpy as np from pandas import Series, DataFrame import matplotlib.pyplot as plt plt.style.use('../bin/markgraph.mplstyle') # --- get data e2016 = pd.read_csv('./Data/HouseTppByDivisionByVoteTypeDownload-20499.csv', header=1, index_col=None, quotechar='"',sep=',', na_values = ['na', '-', '.', '']) # --- some useful frames vote_types = ['OrdinaryVotes', 'AbsentVotes', 'ProvisionalVotes', 'DeclarationPrePollVotes', 'PostalVotes','TotalVotes'] vote_names = ['Ordinary Votes', 'Absent Votes', 'Provisional Votes', 'Pre-poll Declaration Votes', 'Postal Votes','Total Votes'] Coalition = 'Liberal/National Coalition' Labor = 'Australian Labor Party' Percent = 'Percentage' # --- now let's calculate and plot the comparisons votes = e2016.copy() year = 2016 print(year) # check vote sums Laborlist = (Labor + ' ' + pd.Series(vote_types[:-1])).tolist() Coalitionlist = (Coalition + ' ' + pd.Series(vote_types[:-1])).tolist() assert((votes[Laborlist + Coalitionlist].sum(axis=1) == votes['TotalVotes']).all()) # let's focus on Coalition TPP only (and we will recalculate) votes[Coalition + ' TotalVotes'] = votes[Coalitionlist].sum(axis=1) for x in vote_types[:-1]: total = votes[Coalition + ' ' + x] + votes[Labor + ' ' + x] votes[Coalition + ' ' + x + Percent] = votes[Coalition + ' '+ x] / total * 100.0 print(x + ': ', votes[Coalition + ' '+ x].sum() / total.sum() * 100.0, '%') votes[Coalition + ' TotalVotes' + Percent] = (votes[Coalition + ' TotalVotes'] / votes['TotalVotes'] * 100.0) print('Total Votes: ', votes[Coalition + ' TotalVotes'].sum() / votes['TotalVotes'].sum() * 100.0, '%') # and plot ... types = (Coalition + ' ' + pd.Series(vote_types) + Percent).tolist() ordinary = votes[Coalition + ' ' + 'OrdinaryVotes' + Percent] for type, name in zip(types[1:],vote_names[1:]): votes[type+'-Ordinary'] = votes[type] - ordinary ax = votes[type+'-Ordinary'].hist(bins=25) ax.set_title(str(year)+' Coalition TPP Bias in '+name+' cf Ordinary Votes') ax.set_xlabel('Coalition bias in TPP percentage points') ax.set_ylabel('Number of Seats') ax.axvline(0, color='#999999', linewidth=0.5) fig = ax.figure fig.tight_layout(pad=1) fig.text(0.99, 0.01, 'marktheballot.blogspot.com.au', ha='right', va='bottom', fontsize='x-small', fontstyle='italic', color='#999999') fig.savefig("./graphs/TCP3_Coalition_"+str(year)+'_hist_'+name+'-ordinary.png', dpi=125) plt.close()